diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..d079320fc0089792fda7151a5caa51a08cb62721 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +trackio/assets/badge.png filter=lfs diff=lfs merge=lfs -text +trackio/assets/trackio_logo_old.png filter=lfs diff=lfs merge=lfs -text diff --git a/trackio/.DS_Store b/trackio/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..4c7acc58dab4e35c5d2974a040a6041b65b12296 Binary files /dev/null and b/trackio/.DS_Store differ diff --git a/trackio/CHANGELOG.md b/trackio/CHANGELOG.md new file mode 100644 index 0000000000000000000000000000000000000000..cb3b2fecc60d2231ab74048b40486b2378f45dfe --- /dev/null +++ b/trackio/CHANGELOG.md @@ -0,0 +1,242 @@ +# trackio + +## 0.23.0 + +### Features + +- [#494](https://github.com/gradio-app/trackio/pull/494) [`e8a897d`](https://github.com/gradio-app/trackio/commit/e8a897d2266d9b2558f72d768b0b21f4d0a8781b) - Add a settings/CLI page to Trackio. Thanks @abidlabs! +- [#481](https://github.com/gradio-app/trackio/pull/481) [`882647e`](https://github.com/gradio-app/trackio/commit/882647ec1599cf04500d03b5ca75ddc2733682e2) - Add multi-GPU system metrics support. Thanks @Saba9! +- [#485](https://github.com/gradio-app/trackio/pull/485) [`46a3cc3`](https://github.com/gradio-app/trackio/commit/46a3cc3758719e171417612efee102a487e71ebd) - Fix/remove flaky E2E space tests. Thanks @abidlabs! +- [#501](https://github.com/gradio-app/trackio/pull/501) [`06ea885`](https://github.com/gradio-app/trackio/commit/06ea8852f5e40ab3f1cf629a0a01af5c17f847a1) - Fix SQLite corruption on bucket-mounted Spaces. Thanks @abidlabs! +- [#496](https://github.com/gradio-app/trackio/pull/496) [`af23d74`](https://github.com/gradio-app/trackio/commit/af23d74438b146c4a3512ace15ea984656e943ed) - Prevent trackio errors from crashing the user's training loop. Thanks @abidlabs! + +## 0.22.0 + +### Features + +- [#484](https://github.com/gradio-app/trackio/pull/484) [`cc05ada`](https://github.com/gradio-app/trackio/commit/cc05ada8e89773f3a894af99b801ef680f64418f) - Fix duplicate columns in parquet export. Thanks @abidlabs! +- [#487](https://github.com/gradio-app/trackio/pull/487) [`853f764`](https://github.com/gradio-app/trackio/commit/853f7646a70d12633afaa4f69db86425aa665413) - Relax `PIL` dependency and remove `plotly` as it's no longer used. Thanks @abidlabs! + +## 0.21.2 + +### Features + +- [#482](https://github.com/gradio-app/trackio/pull/482) [`f62180a`](https://github.com/gradio-app/trackio/commit/f62180a0218bc99a259d5ca110a0384a6cae11c8) - Use server-side bucket copy when freezing Spaces. Thanks @abidlabs! + +## 0.21.1 + +### Features + +- [#475](https://github.com/gradio-app/trackio/pull/475) [`fcb476c`](https://github.com/gradio-app/trackio/commit/fcb476cd37a40923e9679aaf966f41d582a878a8) - Tweaks. Thanks @abidlabs! +- [#477](https://github.com/gradio-app/trackio/pull/477) [`7d52dfd`](https://github.com/gradio-app/trackio/commit/7d52dfdce5b6eff6a34501a6d5a620220663cf09) - Fix `.sync()` and add `.freeze()` as a separate methods. Thanks @abidlabs! + +## 0.21.0 + +### Features + +- [#467](https://github.com/gradio-app/trackio/pull/467) [`f357deb`](https://github.com/gradio-app/trackio/commit/f357debf78957e4c1f2b901bee4f77cf397298b4) - Allow logged metrics as x-axis choices. Thanks @abidlabs! +- [#474](https://github.com/gradio-app/trackio/pull/474) [`655673d`](https://github.com/gradio-app/trackio/commit/655673d4c6b7c8b7ee8f87f2589f2dbbc3d2ef91) - Fix file descriptor leak from `sqlite3.connect`. Thanks @abidlabs! +- [#470](https://github.com/gradio-app/trackio/pull/470) [`bea8c9d`](https://github.com/gradio-app/trackio/commit/bea8c9dcae0b59d071b6c779c97ee525c9bbf6e7) - Restores tooltips to line plots and fixes the call to uses TTL instead of OAuth. Thanks @abidlabs! +- [#471](https://github.com/gradio-app/trackio/pull/471) [`246fce0`](https://github.com/gradio-app/trackio/commit/246fce0a01619e1c2c538c67b3e460883334d500) - Deprecate dataset backend in favor of buckets. Thanks @abidlabs! +- [#465](https://github.com/gradio-app/trackio/pull/465) [`3e11174`](https://github.com/gradio-app/trackio/commit/3e1117438bb8168b802245a33059affa558ae519) - Use HF buckets as backend. Thanks @abidlabs! +- [#469](https://github.com/gradio-app/trackio/pull/469) [`915d170`](https://github.com/gradio-app/trackio/commit/915d17045133172b59195acfdcc70709229668aa) - Make static Spaces work with Buckets and also allow conversion from Gradio SDK to Static Spaces. Thanks @abidlabs! + +## 0.20.2 + +### Features + +- [#464](https://github.com/gradio-app/trackio/pull/464) [`c89ebb3`](https://github.com/gradio-app/trackio/commit/c89ebb3b50f695bc7f16cbc6f46dce86f79a01e9) - Improve rendering of curves. Thanks @abidlabs! +- [#462](https://github.com/gradio-app/trackio/pull/462) [`9160b78`](https://github.com/gradio-app/trackio/commit/9160b78ff6f258f0b87a4f34a24e7d0b5dfbf2fb) - Refactor plot title to display only the metric name without the path. Thanks @qgallouedec! + +## 0.20.1 + +### Features + +- [#454](https://github.com/gradio-app/trackio/pull/454) [`22881db`](https://github.com/gradio-app/trackio/commit/22881dbbbb6b81197a00a19853771007093d61e4) - Bar chart single point. Thanks @abidlabs! +- [#455](https://github.com/gradio-app/trackio/pull/455) [`f8db51a`](https://github.com/gradio-app/trackio/commit/f8db51a20ca61ef703f3f2c2ee1ebd9c4f239cf2) - Adds a static Trackio mode via `trackio.sync(sdk="static")` and support for the `TRACKIO_SPACE_ID` environment variable. Thanks @abidlabs! + +## 0.20.0 + +### Features + +- [#450](https://github.com/gradio-app/trackio/pull/450) [`b0571ef`](https://github.com/gradio-app/trackio/commit/b0571ef6207a1ce346696f858ad2b7b584dd194f) - Use Svelte source for Gradio components directly in Trackio dashboard. Thanks @abidlabs! + +## 0.19.0 + +### Features + +- [#445](https://github.com/gradio-app/trackio/pull/445) [`cef4a58`](https://github.com/gradio-app/trackio/commit/cef4a583cb76f4091fc6c0e5783124ee84f8e243) - Add remote HF Space support to CLI. Thanks @abidlabs! +- [#444](https://github.com/gradio-app/trackio/pull/444) [`358f2a9`](https://github.com/gradio-app/trackio/commit/358f2a9ca238ee8b90b5a8c96220da287e0698fb) - Fix alerts placeholder flashing on reports page. Thanks @abidlabs! + +## 0.18.0 + +### Features + +- [#435](https://github.com/gradio-app/trackio/pull/435) [`4a47112`](https://github.com/gradio-app/trackio/commit/4a471128e18a39e45fad48a67fd711c5ae9e4aed) - feat: allow hiding section header accordions. Thanks @Saba9! +- [#439](https://github.com/gradio-app/trackio/pull/439) [`18e9650`](https://github.com/gradio-app/trackio/commit/18e96503d5a3a7cf926e92782d457e23c19942bd) - Add alerts with webhooks, CLI, and documentation. Thanks @abidlabs! +- [#438](https://github.com/gradio-app/trackio/pull/438) [`0875ccd`](https://github.com/gradio-app/trackio/commit/0875ccd3d8a41b1376f64030f21cfe8cdcc73b05) - Add "share this view" functionality. Thanks @qgallouedec! +- [#409](https://github.com/gradio-app/trackio/pull/409) [`9282403`](https://github.com/gradio-app/trackio/commit/9282403d8896d48679b0f888208a7ba5bdd4271a) - Add Apple Silicon GPU and system monitoring support. Thanks @znation! +- [#434](https://github.com/gradio-app/trackio/pull/434) [`4193223`](https://github.com/gradio-app/trackio/commit/41932230a3a2e1c16405dba08ecba5a42f11d1a8) - fix: table slider crash. Thanks @Saba9! + +### Fixes + +- [#441](https://github.com/gradio-app/trackio/pull/441) [`3a2d11d`](https://github.com/gradio-app/trackio/commit/3a2d11dab0b4b37c925abc30ef84b0e2910321ee) - preserve x-axis step when toggling run checkboxes. Thanks @Saba9! + +## 0.17.0 + +### Features + +- [#428](https://github.com/gradio-app/trackio/pull/428) [`f7dd1ce`](https://github.com/gradio-app/trackio/commit/f7dd1ce2dc8a1936f9983467fcbcf93bfef01e09) - feat: add ability to rename runs. Thanks @Saba9! +- [#437](https://github.com/gradio-app/trackio/pull/437) [`2727c0b`](https://github.com/gradio-app/trackio/commit/2727c0b0755f48f7f186162ea45185c98f6b5516) - Add markdown reports across Trackio. Thanks @abidlabs! +- [#427](https://github.com/gradio-app/trackio/pull/427) [`5aeb9ed`](https://github.com/gradio-app/trackio/commit/5aeb9edcfd2068d309d9d64f172dcbcc327be1ab) - Make Trackio logging much more robust. Thanks @abidlabs! + +## 0.16.1 + +### Features + +- [#431](https://github.com/gradio-app/trackio/pull/431) [`c7ce55b`](https://github.com/gradio-app/trackio/commit/c7ce55b14dd5eb0c2165fb15df17dd60721c9325) - Lazy load the UI when trackio is imported. Thanks @abidlabs! + +## 0.16.0 + +### Features + +- [#426](https://github.com/gradio-app/trackio/pull/426) [`ead4dc8`](https://github.com/gradio-app/trackio/commit/ead4dc8e74ee2d8e47d61bca0a7668456acf49be) - Fix redundant double rendering of group checkboxes. Thanks @abidlabs! +- [#413](https://github.com/gradio-app/trackio/pull/413) [`39c4750`](https://github.com/gradio-app/trackio/commit/39c4750951d554ba6eb4d58847c6bb444b2891a8) - Check `dist-packages` when checking for source installation. Thanks @sergiopaniego! +- [#423](https://github.com/gradio-app/trackio/pull/423) [`2e52ab3`](https://github.com/gradio-app/trackio/commit/2e52ab303e3041718a6a56fbf84d0848aca9ad67) - Fix legend outline visibility issue. Thanks @Raghunath-Balaji! +- [#407](https://github.com/gradio-app/trackio/pull/407) [`c8a384d`](https://github.com/gradio-app/trackio/commit/c8a384ddfe5a295cecf862a26178d40e48acb424) - Fix pytests that were failling locally on MacOS. Thanks @abidlabs! +- [#405](https://github.com/gradio-app/trackio/pull/405) [`35aae4e`](https://github.com/gradio-app/trackio/commit/35aae4e3aa3e2b2888887528478b9dc6a9808bda) - Add conditional padding for HF Space dashboard when not in iframe. Thanks @znation! + +## 0.15.0 + +### Features + +- [#397](https://github.com/gradio-app/trackio/pull/397) [`6b38ad0`](https://github.com/gradio-app/trackio/commit/6b38ad02e5d73a0df49c4eede7e91331282ece04) - Adds `--host` cli option support. Thanks @abidlabs! +- [#396](https://github.com/gradio-app/trackio/pull/396) [`4a4d1ab`](https://github.com/gradio-app/trackio/commit/4a4d1ab85e63d923132a3fa7afa5d90e16431bec) - Fix run selection issue. Thanks @abidlabs! +- [#394](https://github.com/gradio-app/trackio/pull/394) [`c47a3a3`](https://github.com/gradio-app/trackio/commit/c47a3a31f8c4b83bce1aa7fc22eeba3d9021ad3d) - Add wandb-compatible API for trackio. Thanks @abidlabs! +- [#378](https://github.com/gradio-app/trackio/pull/378) [`b02046a`](https://github.com/gradio-app/trackio/commit/b02046a5b0dad7c9854e099a87f884afba4aecb2) - Add JSON export button for line plots and upgrade gradio dependency. Thanks @JamshedAli18! + +## 0.14.2 + +### Features + +- [#386](https://github.com/gradio-app/trackio/pull/386) [`f9452cd`](https://github.com/gradio-app/trackio/commit/f9452cdb8f0819368f3610f7ac0ed08957305275) - Fixing some issues related to deployed Trackio Spaces. Thanks @abidlabs! + +## 0.14.1 + +### Features + +- [#382](https://github.com/gradio-app/trackio/pull/382) [`44fe9bb`](https://github.com/gradio-app/trackio/commit/44fe9bb264fb2aafb0ec302ff15227c045819a2c) - Fix app file path when Trackio is not installed from source. Thanks @abidlabs! +- [#380](https://github.com/gradio-app/trackio/pull/380) [`c3f4cff`](https://github.com/gradio-app/trackio/commit/c3f4cff74bc5676e812773d8571454894fcdc7cc) - Add CLI commands for querying projects, runs, and metrics. Thanks @abidlabs! + +## 0.14.0 + +### Features + +- [#377](https://github.com/gradio-app/trackio/pull/377) [`5c5015b`](https://github.com/gradio-app/trackio/commit/5c5015b68c85c5de51111dad983f735c27b9a05f) - fixed wrapping issue in Runs table. Thanks @gaganchapa! +- [#374](https://github.com/gradio-app/trackio/pull/374) [`388e26b`](https://github.com/gradio-app/trackio/commit/388e26b9e9f24cd7ad203affe9b709be885b3d24) - Save Optimized Parquet files. Thanks @lhoestq! +- [#371](https://github.com/gradio-app/trackio/pull/371) [`fbace9c`](https://github.com/gradio-app/trackio/commit/fbace9cd7732c166f34d268f54b05bb06846cc5d) - Add GPU metrics logging. Thanks @kashif! +- [#367](https://github.com/gradio-app/trackio/pull/367) [`862840c`](https://github.com/gradio-app/trackio/commit/862840c13e30fc960cbee5b9eac4d3c25beba9de) - Add option to only show latest run, and fix the double logo issue. Thanks @abidlabs! + +## 0.13.1 + +### Features + +- [#369](https://github.com/gradio-app/trackio/pull/369) [`767e9fe`](https://github.com/gradio-app/trackio/commit/767e9fe095d7c6ed102016caf927c1517fb8618c) - tiny pr removing unnecessary code. Thanks @abidlabs! + +## 0.13.0 + +### Features + +- [#358](https://github.com/gradio-app/trackio/pull/358) [`073715d`](https://github.com/gradio-app/trackio/commit/073715d1caf8282f68890117f09c3ac301205312) - Improvements to `trackio.sync()`. Thanks @abidlabs! + +## 0.12.0 + +### Features + +- [#357](https://github.com/gradio-app/trackio/pull/357) [`02ba815`](https://github.com/gradio-app/trackio/commit/02ba815358060f1966052de051a5bdb09702920e) - Redesign media and tables to show up on separate page. Thanks @abidlabs! +- [#359](https://github.com/gradio-app/trackio/pull/359) [`08fe9c9`](https://github.com/gradio-app/trackio/commit/08fe9c9ddd7fe99ee811555fdfb62df9ab88e939) - docs: Improve docstrings. Thanks @qgallouedec! + +## 0.11.0 + +### Features + +- [#355](https://github.com/gradio-app/trackio/pull/355) [`ea51f49`](https://github.com/gradio-app/trackio/commit/ea51f4954922f21be76ef828700420fe9a912c4b) - Color code run checkboxes and match with plot lines. Thanks @abidlabs! +- [#353](https://github.com/gradio-app/trackio/pull/353) [`8abe691`](https://github.com/gradio-app/trackio/commit/8abe6919aeefe21fc7a23af814883efbb037c21f) - Remove show_api from demo.launch. Thanks @sergiopaniego! +- [#351](https://github.com/gradio-app/trackio/pull/351) [`8a8957e`](https://github.com/gradio-app/trackio/commit/8a8957e530dd7908d1fef7f2df030303f808101f) - Add `trackio.save()`. Thanks @abidlabs! + +## 0.10.0 + +### Features + +- [#305](https://github.com/gradio-app/trackio/pull/305) [`e64883a`](https://github.com/gradio-app/trackio/commit/e64883a51f7b8b93f7d48b8afe55acdb62238b71) - bump to gradio 6.0, make `trackio` compatible, and fix related issues. Thanks @abidlabs! + +## 0.9.1 + +### Features + +- [#344](https://github.com/gradio-app/trackio/pull/344) [`7e01024`](https://github.com/gradio-app/trackio/commit/7e010241d9a34794e0ce0dc19c1a6f0cf94ba856) - Avoid redundant calls to /whoami-v2. Thanks @Wauplin! + +## 0.9.0 + +### Features + +- [#343](https://github.com/gradio-app/trackio/pull/343) [`51bea30`](https://github.com/gradio-app/trackio/commit/51bea30f2877adff8e6497466d3a799400a0a049) - Sync offline projects to Hugging Face spaces. Thanks @candemircan! +- [#341](https://github.com/gradio-app/trackio/pull/341) [`4fd841f`](https://github.com/gradio-app/trackio/commit/4fd841fa190e15071b02f6fba7683ef4f393a654) - Adds a basic UI test to `trackio`. Thanks @abidlabs! +- [#339](https://github.com/gradio-app/trackio/pull/339) [`011d91b`](https://github.com/gradio-app/trackio/commit/011d91bb6ae266516fd250a349285670a8049d05) - Allow customzing the trackio color palette. Thanks @abidlabs! + +## 0.8.1 + +### Features + +- [#336](https://github.com/gradio-app/trackio/pull/336) [`5f9f51d`](https://github.com/gradio-app/trackio/commit/5f9f51dac8677f240d7c42c3e3b2660a22aee138) - Support a list of `Trackio.Image` in a `trackio.Table` cell. Thanks @abidlabs! + +## 0.8.0 + +### Features + +- [#331](https://github.com/gradio-app/trackio/pull/331) [`2c02d0f`](https://github.com/gradio-app/trackio/commit/2c02d0fd0a5824160528782402bb0dd4083396d5) - Truncate table string values that are greater than 250 characters (configuirable via env variable). Thanks @abidlabs! +- [#324](https://github.com/gradio-app/trackio/pull/324) [`50b2122`](https://github.com/gradio-app/trackio/commit/50b2122e7965ac82a72e6cb3b7d048bc10a2a6b1) - Add log y-axis functionality to UI. Thanks @abidlabs! +- [#326](https://github.com/gradio-app/trackio/pull/326) [`61dc1f4`](https://github.com/gradio-app/trackio/commit/61dc1f40af2f545f8e70395ddf0dbb8aee6b60d5) - Fix: improve table rendering for metrics in Trackio Dashboard. Thanks @vigneshwaran! +- [#328](https://github.com/gradio-app/trackio/pull/328) [`6857cbb`](https://github.com/gradio-app/trackio/commit/6857cbbe557a59a4642f210ec42566d108294e63) - Support trackio.Table with trackio.Image columns. Thanks @abidlabs! +- [#323](https://github.com/gradio-app/trackio/pull/323) [`6857cbb`](https://github.com/gradio-app/trackio/commit/6857cbbe557a59a4642f210ec42566d108294e63) - add Trackio client implementations in Go, Rust, and JS. Thanks @vaibhav-research! + +## 0.7.0 + +### Features + +- [#277](https://github.com/gradio-app/trackio/pull/277) [`db35601`](https://github.com/gradio-app/trackio/commit/db35601b9c023423c4654c9909b8ab73e58737de) - fix: make grouped runs view reflect live updates. Thanks @Saba9! +- [#320](https://github.com/gradio-app/trackio/pull/320) [`24ae739`](https://github.com/gradio-app/trackio/commit/24ae73969b09fb3126acd2f91647cdfbf8cf72a1) - Add additional query parms for xmin, xmax, and smoothing. Thanks @abidlabs! +- [#270](https://github.com/gradio-app/trackio/pull/270) [`cd1dfc3`](https://github.com/gradio-app/trackio/commit/cd1dfc3dc641b4499ac6d4a1b066fa8e2b52c57b) - feature: add support for logging audio. Thanks @Saba9! + +## 0.6.0 + +### Features + +- [#309](https://github.com/gradio-app/trackio/pull/309) [`1df2353`](https://github.com/gradio-app/trackio/commit/1df23534d6c01938c8db9c0f584ffa23e8d6021d) - Add histogram support with wandb-compatible API. Thanks @abidlabs! +- [#315](https://github.com/gradio-app/trackio/pull/315) [`76ba060`](https://github.com/gradio-app/trackio/commit/76ba06055dc43ca8f03b79f3e72d761949bd19a8) - Add guards to avoid silent fails. Thanks @Xmaster6y! +- [#313](https://github.com/gradio-app/trackio/pull/313) [`a606b3e`](https://github.com/gradio-app/trackio/commit/a606b3e1c5edf3d4cf9f31bd50605226a5a1c5d0) - No longer prevent certain keys from being used. Instead, dunderify them to prevent collisions with internal usage. Thanks @abidlabs! +- [#317](https://github.com/gradio-app/trackio/pull/317) [`27370a5`](https://github.com/gradio-app/trackio/commit/27370a595d0dbdf7eebbe7159d2ba778f039da44) - quick fixes for trackio.histogram. Thanks @abidlabs! +- [#312](https://github.com/gradio-app/trackio/pull/312) [`aa0f3bf`](https://github.com/gradio-app/trackio/commit/aa0f3bf372e7a0dd592a38af699c998363830eeb) - Fix video logging by adding TRACKIO_DIR to allowed_paths. Thanks @abidlabs! + +## 0.5.3 + +### Features + +- [#300](https://github.com/gradio-app/trackio/pull/300) [`5e4cacf`](https://github.com/gradio-app/trackio/commit/5e4cacf2e7ce527b4ce60de3a5bc05d2c02c77fb) - Adds more environment variables to allow customization of Trackio dashboard. Thanks @abidlabs! + +## 0.5.2 + +### Features + +- [#293](https://github.com/gradio-app/trackio/pull/293) [`64afc28`](https://github.com/gradio-app/trackio/commit/64afc28d3ea1dfd821472dc6bf0b8ed35a9b74be) - Ensures that the TRACKIO_DIR environment variable is respected. Thanks @abidlabs! +- [#287](https://github.com/gradio-app/trackio/pull/287) [`cd3e929`](https://github.com/gradio-app/trackio/commit/cd3e9294320949e6b8b829239069a43d5d7ff4c1) - fix(sqlite): unify .sqlite extension, allow export when DBs exist, clean WAL sidecars on import. Thanks @vaibhav-research! + +### Fixes + +- [#291](https://github.com/gradio-app/trackio/pull/291) [`3b5adc3`](https://github.com/gradio-app/trackio/commit/3b5adc3d1f452dbab7a714d235f4974782f93730) - Fix the wheel build. Thanks @pngwn! + +## 0.5.1 + +### Fixes + +- [#278](https://github.com/gradio-app/trackio/pull/278) [`314c054`](https://github.com/gradio-app/trackio/commit/314c05438007ddfea3383e06fd19143e27468e2d) - Fix row orientation of metrics plots. Thanks @abidlabs! \ No newline at end of file diff --git a/trackio/__init__.py b/trackio/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2315a6c8d289eb8f831350afc2bca682c1150b63 --- /dev/null +++ b/trackio/__init__.py @@ -0,0 +1,1095 @@ +import atexit +import glob +import json +import logging +import os +import shutil +import warnings +import webbrowser +from pathlib import Path +from typing import Any + +import huggingface_hub +from gradio_client import handle_file +from huggingface_hub import SpaceStorage +from huggingface_hub.errors import LocalTokenNotFoundError + +from trackio import context_vars, deploy, utils +from trackio.alerts import AlertLevel +from trackio.api import Api +from trackio.apple_gpu import apple_gpu_available +from trackio.apple_gpu import log_apple_gpu as _log_apple_gpu +from trackio.deploy import freeze, sync +from trackio.gpu import gpu_available +from trackio.gpu import log_gpu as _log_nvidia_gpu +from trackio.histogram import Histogram +from trackio.imports import import_csv, import_tf_events +from trackio.launch import launch_trackio_dashboard +from trackio.markdown import Markdown +from trackio.media import ( + TrackioAudio, + TrackioImage, + TrackioVideo, + get_project_media_path, +) +from trackio.remote_client import RemoteClient +from trackio.run import Run +from trackio.server import TrackioDashboardApp, build_starlette_app_only +from trackio.sqlite_storage import SQLiteStorage +from trackio.table import Table +from trackio.typehints import UploadEntry +from trackio.utils import TRACKIO_DIR, TRACKIO_LOGO_DIR, _emit_nonfatal_warning + +logging.getLogger("httpx").setLevel(logging.WARNING) + +__version__ = json.loads(Path(__file__).parent.joinpath("package.json").read_text())[ + "version" +] + + +class _TupleNoPrint(tuple): + def __repr__(self) -> str: + return "" + + +__all__ = [ + "init", + "log", + "log_system", + "log_gpu", + "finish", + "alert", + "AlertLevel", + "show", + "sync", + "freeze", + "delete_project", + "import_csv", + "import_tf_events", + "save", + "Image", + "Video", + "Audio", + "Table", + "Histogram", + "Markdown", + "Api", + "TRACKIO_LOGO_DIR", +] + +Audio = TrackioAudio +Image = TrackioImage +Video = TrackioVideo + + +config = {} + +_atexit_registered = False +_projects_notified_auto_log_hw: set[str] = set() + + +def _cleanup_current_run(): + run = context_vars.current_run.get() + if run is not None: + try: + run.finish() + except Exception: + pass + + +def _safe_get_runs_for_init( + project: str, + space_id: str | None, + server_base_url: str | None, + write_token: str | None, + resume: str, + remote_client: RemoteClient | None = None, + check_existing_for_never: bool = False, +) -> list[str]: + if space_id is not None: + if resume == "never" and not check_existing_for_never: + return [] + try: + client = remote_client or RemoteClient( + space_id, + hf_token=huggingface_hub.utils.get_token(), + verbose=False, + ) + runs = client.predict(project=project, api_name="/get_runs_for_project") + return runs if isinstance(runs, list) else [] + except Exception as e: + _emit_nonfatal_warning( + f"trackio.init() could not inspect existing runs for project '{project}' on Space '{space_id}': {e}. Continuing without resume metadata." + ) + return [] + if server_base_url is not None: + if resume == "never" and not check_existing_for_never: + return [] + try: + client = remote_client or RemoteClient( + server_base_url, + hf_token=None, + write_token=write_token, + verbose=False, + ) + runs = client.predict(project=project, api_name="/get_runs_for_project") + return runs if isinstance(runs, list) else [] + except Exception as e: + _emit_nonfatal_warning( + f"trackio.init() could not inspect existing runs for project '{project}' on self-hosted server '{server_base_url}': {e}. Continuing without resume metadata." + ) + return [] + try: + return SQLiteStorage.get_runs(project) + except Exception as e: + _emit_nonfatal_warning( + f"trackio.init() could not inspect existing runs for project '{project}': {e}. Continuing without resume metadata." + ) + return [] + + +def _safe_get_latest_run_for_init( + project: str, + name: str, + space_id: str | None = None, + server_base_url: str | None = None, + write_token: str | None = None, + remote_client: RemoteClient | None = None, +) -> dict | None: + if space_id is not None: + try: + client = remote_client or RemoteClient( + space_id, + hf_token=huggingface_hub.utils.get_token(), + verbose=False, + ) + runs = client.predict(project=project, api_name="/get_runs_for_project") + if not isinstance(runs, list): + return None + matches = [r for r in runs if isinstance(r, dict) and r.get("name") == name] + if not matches: + return None + matches.sort(key=lambda r: r.get("created_at") or "", reverse=True) + return matches[0] + except Exception as e: + _emit_nonfatal_warning( + f"trackio.init() could not inspect existing runs for project '{project}' on Space '{space_id}': {e}. Continuing without resume metadata." + ) + return None + if server_base_url is not None: + try: + client = remote_client or RemoteClient( + server_base_url, + hf_token=None, + write_token=write_token, + verbose=False, + ) + runs = client.predict(project=project, api_name="/get_runs_for_project") + if not isinstance(runs, list): + return None + matches = [r for r in runs if isinstance(r, dict) and r.get("name") == name] + if not matches: + return None + matches.sort(key=lambda r: r.get("created_at") or "", reverse=True) + return matches[0] + except Exception as e: + _emit_nonfatal_warning( + f"trackio.init() could not inspect existing runs for project '{project}' on self-hosted server '{server_base_url}': {e}. Continuing without resume metadata." + ) + return None + try: + return SQLiteStorage.get_latest_run_record_by_name(project, name) + except Exception as e: + _emit_nonfatal_warning( + f"trackio.init() could not inspect existing runs for project '{project}': {e}. Continuing without resume metadata." + ) + return None + + +def _safe_get_last_step_for_init( + project: str, + run_name: str, + space_id: str | None, + server_base_url: str | None, + write_token: str | None, + resumed: bool, + run_id: str | None = None, + remote_client: RemoteClient | None = None, +) -> int | None: + if not resumed: + return None + if space_id is not None: + try: + client = remote_client or RemoteClient( + space_id, + hf_token=huggingface_hub.utils.get_token(), + verbose=False, + ) + summary_kwargs: dict[str, Any] = { + "project": project, + "api_name": "/get_run_summary", + } + if run_id is not None: + summary_kwargs["run_id"] = run_id + else: + summary_kwargs["run"] = run_name + summary = client.predict(**summary_kwargs) + if isinstance(summary, dict): + last_step = summary.get("last_step") + return last_step if isinstance(last_step, int) else None + return None + except Exception as e: + _emit_nonfatal_warning( + f"trackio.init() could not recover the previous step for run '{run_name}' on Space '{space_id}': {e}. Continuing from step 0." + ) + return None + if server_base_url is not None: + try: + client = remote_client or RemoteClient( + server_base_url, + hf_token=None, + write_token=write_token, + verbose=False, + ) + summary_kwargs = { + "project": project, + "api_name": "/get_run_summary", + } + if run_id is not None: + summary_kwargs["run_id"] = run_id + else: + summary_kwargs["run"] = run_name + summary = client.predict(**summary_kwargs) + if isinstance(summary, dict): + last_step = summary.get("last_step") + return last_step if isinstance(last_step, int) else None + return None + except Exception as e: + _emit_nonfatal_warning( + f"trackio.init() could not recover the previous step for run '{run_name}' on self-hosted server '{server_base_url}': {e}. Continuing from step 0." + ) + return None + try: + return SQLiteStorage.get_max_step_for_run(project, run_name, run_id=run_id) + except Exception as e: + _emit_nonfatal_warning( + f"trackio.init() could not recover the previous step for run '{run_name}': {e}. Continuing from step 0." + ) + return None + + +def init( + project: str, + name: str | None = None, + group: str | None = None, + space_id: str | None = None, + server_url: str | None = None, + space_storage: SpaceStorage | None = None, + dataset_id: str | None = None, + bucket_id: str | None = None, + config: dict | None = None, + resume: str = "never", + settings: Any = None, + private: bool | None = None, + embed: bool = True, + auto_log_gpu: bool | None = None, + gpu_log_interval: float = 10.0, + webhook_url: str | None = None, + webhook_min_level: AlertLevel | str | None = None, +) -> Run: + """ + Creates a new Trackio project and returns a [`Run`] object. + + Args: + project (`str`): + The name of the project (can be an existing project to continue tracking or + a new project to start tracking from scratch). + name (`str`, *optional*): + The name of the run (if not provided, a default name will be generated). + group (`str`, *optional*): + The name of the group which this run belongs to in order to help organize + related runs together. You can toggle the entire group's visibilitiy in the + dashboard. + space_id (`str`, *optional*): + If provided, the project will be logged to a Hugging Face Space instead of + a local directory. Should be a complete Space name like + `"username/reponame"` or `"orgname/reponame"`, or just `"reponame"` in which + case the Space will be created in the currently-logged-in Hugging Face + user's namespace. If the Space does not exist, it will be created. If the + Space already exists, the project will be logged to it. Can also be set + via the `TRACKIO_SPACE_ID` environment variable. You cannot log to a + Space that has been **frozen** (converted to the static SDK); use + ``trackio.sync(..., sdk="static")`` only after you are done logging. + Takes precedence over `server_url` and `TRACKIO_SERVER_URL` when more than + one is set. + server_url (`str`, *optional*): + Base URL of a self-hosted Trackio server (``http://`` or ``https://``), or the + write-access URL from ``trackio.show()`` which may include a ``write_token`` query + parameter. The client sends that token on each request (``X-Trackio-Write-Token``); + you can also set ``TRACKIO_WRITE_TOKEN`` instead of embedding the token in the URL. + When set, metrics are sent to that server over HTTP instead of creating or syncing + to a Hugging Face Space. Can also be set via the ``TRACKIO_SERVER_URL`` environment + variable. Ignored when ``space_id`` or ``TRACKIO_SPACE_ID`` is set. + space_storage ([`~huggingface_hub.SpaceStorage`], *optional*): + Choice of persistent storage tier. + dataset_id (`str`, *optional*): + Deprecated. Use `bucket_id` instead. + bucket_id (`str`, *optional*): + The ID of the Hugging Face Bucket to use for metric persistence. By default, + when a `space_id` is provided and `bucket_id` is not explicitly set, a + bucket is auto-generated from the space_id. Buckets provide + S3-like storage without git overhead - the SQLite database is stored directly + via `hf-mount` in the Space. Specify a Bucket with name like + `"username/bucketname"` or just `"bucketname"`. + config (`dict`, *optional*): + A dictionary of configuration options. Provided for compatibility with + `wandb.init()`. + resume (`str`, *optional*, defaults to `"never"`): + Controls how to handle resuming a run. Can be one of: + + - `"must"`: Must resume the run with the given name, raises error if run + doesn't exist + - `"allow"`: Resume the run if it exists, otherwise create a new run + - `"never"`: Never resume a run, always create a new one + private (`bool`, *optional*): + Whether to make the Space private. If None (default), the repo will be + public unless the organization's default is private. This value is ignored + if the repo already exists. + settings (`Any`, *optional*): + Not used. Provided for compatibility with `wandb.init()`. + embed (`bool`, *optional*, defaults to `True`): + If running inside a Jupyter/Colab notebook, whether the dashboard should + automatically be embedded in the cell when trackio.init() is called. For + local runs, this launches a local Trackio dashboard and embeds it. For Space runs, + this embeds the Space URL. In Colab, the local dashboard will be accessible + via a public share URL when `share=True`. + auto_log_gpu (`bool` or `None`, *optional*, defaults to `None`): + Controls automatic GPU metrics logging. If `None` (default), GPU logging + is automatically enabled when `nvidia-ml-py` is installed and an NVIDIA + GPU or Apple M series is detected. Set to `True` to force enable or + `False` to disable. + gpu_log_interval (`float`, *optional*, defaults to `10.0`): + The interval in seconds between automatic GPU metric logs. + Only used when `auto_log_gpu=True`. + webhook_url (`str`, *optional*): + A webhook URL to POST alert payloads to when `trackio.alert()` is + called. Supports Slack and Discord webhook URLs natively (payloads + are formatted automatically). Can also be set via the + `TRACKIO_WEBHOOK_URL` environment variable. Individual alerts can + override this URL by passing `webhook_url` to `trackio.alert()`. + webhook_min_level (`AlertLevel` or `str`, *optional*): + Minimum alert level that should trigger webhook delivery. + For example, `AlertLevel.WARN` sends only `WARN` and `ERROR` + alerts to the webhook destination. Can also be set via + `TRACKIO_WEBHOOK_MIN_LEVEL`. + Returns: + `Run`: A [`Run`] object that can be used to log metrics and finish the run. + """ + if settings is not None: + _emit_nonfatal_warning( + "* Warning: settings is not used. Provided for compatibility with wandb.init(). Please create an issue at: https://github.com/gradio-app/trackio/issues if you need a specific feature implemented." + ) + + bucket_id_was_explicit = bucket_id is not None + space_id, server_url = utils.resolve_space_id_and_server_url(space_id, server_url) + if bucket_id is None and utils.on_spaces(): + bucket_id = os.environ.get("TRACKIO_BUCKET_ID") + if server_url is not None and not server_url.startswith(("http://", "https://")): + raise ValueError( + f"`server_url` must be a full URL starting with http:// or https://, got: {server_url!r}" + ) + server_base_url: str | None = None + write_token_resolved: str | None = None + if server_url is not None: + server_base_url, tok = utils.parse_trackio_server_url(server_url) + write_token_resolved = tok or os.environ.get("TRACKIO_WRITE_TOKEN") + if not write_token_resolved: + raise ValueError( + "Self-hosted logging requires a write token: add write_token to the server URL, " + "or set the TRACKIO_WRITE_TOKEN environment variable." + ) + if server_url is not None and (dataset_id is not None or bucket_id is not None): + raise ValueError( + "`dataset_id` and `bucket_id` are Hugging Face Spaces concepts and are not " + "compatible with `server_url`. Configure storage on the self-hosted server." + ) + if space_id is None and dataset_id is not None: + raise ValueError("Must provide a `space_id` when `dataset_id` is provided.") + if dataset_id is not None and bucket_id is not None: + raise ValueError("Cannot provide both `dataset_id` and `bucket_id`.") + try: + space_id, dataset_id, bucket_id = utils.preprocess_space_and_dataset_ids( + space_id, dataset_id, bucket_id + ) + if ( + space_id is not None + and dataset_id is None + and bucket_id is not None + and not bucket_id_was_explicit + and not utils.on_spaces() + ): + bucket_id = deploy.resolve_auto_bucket_id(space_id, bucket_id) + except LocalTokenNotFoundError as e: + raise LocalTokenNotFoundError( + f"You must be logged in to Hugging Face locally when `space_id` is provided to deploy to a Space. {e}" + ) from e + + if space_id is None and bucket_id is not None: + _emit_nonfatal_warning( + "trackio.init() has `bucket_id` set but `space_id` is None: metrics will be logged " + "locally only. Pass `space_id` to create or use a Hugging Face Space, which will be " + "attached to the Hugging Face Bucket.", + UserWarning, + stacklevel=2, + ) + + if space_id is not None: + deploy.raise_if_space_is_frozen_for_logging(space_id) + + remote_source = space_id or server_base_url + + url = context_vars.current_server.get() + + if remote_source is not None: + if url is None: + url = remote_source + context_vars.current_server.set(url) + if space_id is not None: + context_vars.current_space_id.set(space_id) + context_vars.current_server_write_token.set(None) + elif server_base_url is not None: + context_vars.current_server_write_token.set(write_token_resolved) + + _should_embed_local = False + + if ( + context_vars.current_project.get() is None + or context_vars.current_project.get() != project + ): + print(f"* Trackio project initialized: {project}") + + if bucket_id is not None: + if utils.on_spaces(): + os.environ["TRACKIO_BUCKET_ID"] = bucket_id + bucket_url = f"https://huggingface.co/buckets/{bucket_id}" + print( + f"* Trackio metrics will be synced to Hugging Face Bucket: {bucket_url}" + ) + elif dataset_id is not None: + if utils.on_spaces(): + os.environ["TRACKIO_DATASET_ID"] = dataset_id + print( + f"* Trackio metrics will be synced to Hugging Face Dataset: {dataset_id}" + ) + if remote_source is None: + print(f"* Trackio metrics logged to: {TRACKIO_DIR}") + _should_embed_local = embed and utils.is_in_notebook() + if not _should_embed_local: + utils.print_dashboard_instructions(project) + elif server_base_url is not None: + print( + f"* Trackio metrics will be sent to self-hosted server: {server_base_url}" + ) + if utils.is_in_notebook() and embed: + utils.embed_url_in_notebook(server_base_url) + else: + try: + deploy.create_space_if_not_exists( + space_id, + space_storage, + dataset_id, + bucket_id, + private, + ) + user_name, space_name = space_id.split("/") + space_url = deploy.SPACE_HOST_URL.format( + user_name=user_name, space_name=space_name + ) + if utils.is_in_notebook() and embed: + utils.embed_url_in_notebook(space_url) + except Exception as e: + _emit_nonfatal_warning( + f"trackio.init() could not prepare Space '{space_id}': {e}. Logging will continue in local fallback mode until the Space is reachable." + ) + context_vars.current_project.set(project) + + remote_client = None + if space_id is not None: + try: + remote_client = RemoteClient( + space_id, + hf_token=huggingface_hub.utils.get_token(), + verbose=False, + ) + except Exception as e: + _emit_nonfatal_warning( + f"trackio.init() could not create a remote client for Space '{space_id}': {e}. Continuing with local fallback metadata lookups." + ) + elif server_base_url is not None: + try: + remote_client = RemoteClient( + server_base_url, + hf_token=None, + write_token=write_token_resolved, + verbose=False, + ) + except Exception as e: + _emit_nonfatal_warning( + f"trackio.init() could not create a remote client for '{server_base_url}': {e}. Continuing with local fallback metadata lookups." + ) + + existing_run_records = _safe_get_runs_for_init( + project, + space_id, + server_base_url, + write_token_resolved, + resume, + remote_client=remote_client, + check_existing_for_never=name is not None, + ) + existing_runs = [ + r["name"] if isinstance(r, dict) else r for r in existing_run_records + ] + + existing_run = ( + _safe_get_latest_run_for_init( + project, + name, + space_id=space_id, + server_base_url=server_base_url, + write_token=write_token_resolved, + remote_client=remote_client, + ) + if name is not None + else None + ) + resolved_run_id = None + + if resume == "must": + if name is None: + raise ValueError("Must provide a run name when resume='must'") + if existing_run is None: + raise ValueError(f"Run '{name}' does not exist in project '{project}'") + resumed = True + resolved_run_id = existing_run["id"] + elif resume == "allow": + resumed = existing_run is not None + if resumed: + resolved_run_id = existing_run["id"] + elif resume == "never": + resumed = False + else: + raise ValueError("resume must be one of: 'must', 'allow', or 'never'") + + initial_last_step = ( + _safe_get_last_step_for_init( + project, + name, + space_id, + server_base_url, + write_token_resolved, + resumed, + run_id=resolved_run_id, + remote_client=remote_client, + ) + if name is not None + else None + ) + + if auto_log_gpu is None: + nvidia_available = gpu_available() + apple_available = apple_gpu_available() + auto_log_gpu = nvidia_available or apple_available + if project not in _projects_notified_auto_log_hw: + if nvidia_available: + print("* NVIDIA GPU detected, enabling automatic GPU metrics logging") + elif apple_available: + print( + "* Apple Silicon detected, enabling automatic system metrics logging" + ) + if nvidia_available or apple_available: + _projects_notified_auto_log_hw.add(project) + + run = Run( + url=url, + project=project, + client=None, + name=name, + run_id=resolved_run_id, + group=group, + config=config, + space_id=space_id, + server_base_url=server_base_url, + write_token=write_token_resolved, + existing_runs=existing_runs, + initial_last_step=initial_last_step, + auto_log_gpu=auto_log_gpu, + gpu_log_interval=gpu_log_interval, + webhook_url=webhook_url, + webhook_min_level=webhook_min_level, + ) + + if space_id is not None: + try: + SQLiteStorage.set_project_metadata(project, "space_id", space_id) + except Exception as e: + _emit_nonfatal_warning( + f"trackio.init() could not persist Space metadata for project '{project}': {e}. Logging will continue." + ) + try: + if SQLiteStorage.has_pending_data(project): + run._has_local_buffer = True + except Exception as e: + _emit_nonfatal_warning( + f"trackio.init() could not inspect pending buffered data for project '{project}': {e}. Logging will continue." + ) + + global _atexit_registered + if not _atexit_registered: + atexit.register(_cleanup_current_run) + _atexit_registered = True + + if resumed: + print(f"* Resumed existing run: {run.name}") + else: + print(f"* Created new run: {run.name}") + + context_vars.current_run.set(run) + globals()["config"] = run.config + + if _should_embed_local: + try: + show(project=project, open_browser=False, block_thread=False) + except Exception as e: + _emit_nonfatal_warning( + f"trackio.init() could not auto-launch the dashboard: {e}. Logging will continue." + ) + + return run + + +def log(metrics: dict, step: int | None = None) -> None: + """ + Logs metrics to the current run. + + Args: + metrics (`dict`): + A dictionary of metrics to log. + step (`int`, *optional*): + The step number. If not provided, the step will be incremented + automatically. + """ + run = context_vars.current_run.get() + if run is None: + raise RuntimeError("Call trackio.init() before trackio.log().") + run.log( + metrics=metrics, + step=step, + ) + + +def log_system(metrics: dict) -> None: + """ + Logs system metrics (GPU, etc.) to the current run using timestamps instead of steps. + + Args: + metrics (`dict`): + A dictionary of system metrics to log. + """ + run = context_vars.current_run.get() + if run is None: + raise RuntimeError("Call trackio.init() before trackio.log_system().") + run.log_system(metrics=metrics) + + +def log_gpu(run: Run | None = None, device: int | None = None) -> dict: + """ + Log GPU metrics to the current or specified run as system metrics. + Automatically detects whether an NVIDIA or Apple GPU is available and calls + the appropriate logging method. + + Args: + run: Optional Run instance. If None, uses current run from context. + device: CUDA device index to collect metrics from (NVIDIA GPUs only). + If None, collects from all GPUs visible to this process. + This parameter is ignored for Apple GPUs. + + Returns: + dict: The GPU metrics that were logged. + + Example: + ```python + import trackio + + run = trackio.init(project="my-project") + trackio.log({"loss": 0.5}) + trackio.log_gpu() + trackio.log_gpu(device=0) + ``` + """ + if run is None: + run = context_vars.current_run.get() + if run is None: + raise RuntimeError("Call trackio.init() before trackio.log_gpu().") + + if gpu_available(): + return _log_nvidia_gpu(run=run, device=device) + elif apple_gpu_available(): + return _log_apple_gpu(run=run) + else: + _emit_nonfatal_warning( + "No GPU detected. Install nvidia-ml-py for NVIDIA GPU support " + "or psutil for Apple Silicon support." + ) + return {} + + +def finish(): + """ + Finishes the current run. + """ + run = context_vars.current_run.get() + if run is None: + raise RuntimeError("Call trackio.init() before trackio.finish().") + try: + run.finish() + finally: + context_vars.current_run.set(None) + + +def alert( + title: str, + text: str | None = None, + level: AlertLevel = AlertLevel.WARN, + webhook_url: str | None = None, +) -> None: + """ + Fires an alert immediately on the current run. The alert is printed to the + terminal, stored in the database, and displayed in the dashboard. If a + webhook URL is configured (via `trackio.init()`, the `TRACKIO_WEBHOOK_URL` + environment variable, or the `webhook_url` parameter here), the alert is + also POSTed to that URL. + + Args: + title (`str`): + A short title for the alert. + text (`str`, *optional*): + A longer description with details about the alert. + level (`AlertLevel`, *optional*, defaults to `AlertLevel.WARN`): + The severity level. One of `AlertLevel.INFO`, `AlertLevel.WARN`, + or `AlertLevel.ERROR`. + webhook_url (`str`, *optional*): + A webhook URL to send this specific alert to. Overrides any + URL set in `trackio.init()` or the `TRACKIO_WEBHOOK_URL` + environment variable. Supports Slack and Discord webhook + URLs natively. + """ + run = context_vars.current_run.get() + if run is None: + raise RuntimeError("Call trackio.init() before trackio.alert().") + run.alert(title=title, text=text, level=level, webhook_url=webhook_url) + + +def delete_project(project: str, force: bool = False) -> bool: + """ + Deletes a project by removing its local SQLite database. + + Args: + project (`str`): + The name of the project to delete. + force (`bool`, *optional*, defaults to `False`): + If `True`, deletes the project without prompting for confirmation. + If `False`, prompts the user to confirm before deleting. + + Returns: + `bool`: `True` if the project was deleted, `False` otherwise. + """ + db_path = SQLiteStorage.get_project_db_path(project) + + if not db_path.exists(): + print(f"* Project '{project}' does not exist.") + return False + + if not force: + response = input( + f"Are you sure you want to delete project '{project}'? " + f"This will permanently delete all runs and metrics. (y/N): " + ) + if response.lower() not in ["y", "yes"]: + print("* Deletion cancelled.") + return False + + try: + db_path.unlink() + + for suffix in ("-wal", "-shm"): + sidecar = Path(str(db_path) + suffix) + if sidecar.exists(): + sidecar.unlink() + + print(f"* Project '{project}' has been deleted.") + return True + except Exception as e: + print(f"* Error deleting project '{project}': {e}") + return False + + +def save( + glob_str: str | Path, + project: str | None = None, +) -> str: + """ + Saves files to a project (not linked to a specific run). If Trackio is running + locally, the file(s) will be copied to the project's files directory. If Trackio is + running in a Space, the file(s) will be uploaded to the Space's files directory. + + Args: + glob_str (`str` or `Path`): + The file path or glob pattern to save. Can be a single file or a pattern + matching multiple files (e.g., `"*.py"`, `"models/**/*.pth"`). + project (`str`, *optional*): + The name of the project to save files to. If not provided, uses the current + project from `trackio.init()`. If no project is initialized, raises an + error. + + Returns: + `str`: The path where the file(s) were saved (project's files directory). + + Example: + ```python + import trackio + + trackio.init(project="my-project") + trackio.save("config.yaml") + trackio.save("models/*.pth") + ``` + """ + if project is None: + project = context_vars.current_project.get() + if project is None: + raise RuntimeError( + "No project specified. Either call trackio.init() first or provide a " + "project parameter to trackio.save()." + ) + + glob_str = Path(glob_str) + base_path = Path.cwd().resolve() + + matched_files = [] + if glob_str.is_file(): + matched_files = [glob_str.resolve()] + else: + pattern = str(glob_str) + if not glob_str.is_absolute(): + pattern = str((Path.cwd() / glob_str).resolve()) + matched_files = [ + Path(f).resolve() + for f in glob.glob(pattern, recursive=True) + if Path(f).is_file() + ] + + if not matched_files: + raise ValueError(f"No files found matching pattern: {glob_str}") + + current_run = context_vars.current_run.get() + is_local = ( + current_run._is_local + if current_run is not None + else ( + context_vars.current_space_id.get() is None + and context_vars.current_server.get() is None + ) + ) + + if is_local: + for file_path in matched_files: + try: + relative_to_base = file_path.relative_to(base_path) + except ValueError: + relative_to_base = Path(file_path.name) + + if current_run is not None: + current_run._queue_upload( + file_path, + step=None, + relative_path=str(relative_to_base.parent), + use_run_name=False, + ) + else: + media_path = get_project_media_path( + project=project, + run=None, + step=None, + relative_path=str(relative_to_base), + ) + shutil.copy(str(file_path), str(media_path)) + else: + url = context_vars.current_server.get() + + upload_entries = [] + for file_path in matched_files: + try: + relative_to_base = file_path.relative_to(base_path) + except ValueError: + relative_to_base = Path(file_path.name) + + if current_run is not None: + current_run._queue_upload( + file_path, + step=None, + relative_path=str(relative_to_base.parent), + use_run_name=False, + ) + else: + upload_entry: UploadEntry = { + "project": project, + "run": None, + "step": None, + "relative_path": str(relative_to_base), + "uploaded_file": handle_file(file_path), + } + upload_entries.append(upload_entry) + + if upload_entries: + if url is None: + raise RuntimeError( + "No server available. Call trackio.init() before trackio.save() to start the server." + ) + + try: + wt = context_vars.current_server_write_token.get() + if wt is not None: + client = RemoteClient( + url, + hf_token=None, + write_token=wt, + httpx_kwargs={"timeout": 90}, + ) + else: + client = RemoteClient( + url, + hf_token=huggingface_hub.utils.get_token(), + httpx_kwargs={"timeout": 90}, + ) + client.predict( + api_name="/bulk_upload_media", + uploads=upload_entries, + hf_token=huggingface_hub.utils.get_token() if wt is None else None, + ) + except Exception as e: + _emit_nonfatal_warning( + f"Failed to upload files: {e}. " + "Files may not be available in the dashboard." + ) + + return str(utils.MEDIA_DIR / project / "files") + + +def show( + project: str | None = None, + *, + theme: Any = None, + mcp_server: bool | None = None, + footer: bool = True, + color_palette: list[str] | None = None, + open_browser: bool = True, + block_thread: bool | None = None, + host: str | None = None, + share: bool | None = None, + server_port: int | None = None, +): + """ + Launches the Trackio dashboard. + + Args: + project (`str`, *optional*): + The name of the project whose runs to show. If not provided, all projects + will be shown and the user can select one. + theme (`Any`, *optional*): + Ignored. Kept for backward compatibility; Trackio no longer uses Gradio themes. + mcp_server (`bool`, *optional*): + If `True`, the dashboard exposes an MCP server at `/mcp` when the optional + `trackio[mcp]` dependency is installed. If `None` (default), the + `GRADIO_MCP_SERVER` environment variable is used (e.g. on Spaces). + footer (`bool`, *optional*, defaults to `True`): + Whether to include `footer=false` in the write-token URL when `False`. + This can also be controlled via the `footer` query parameter in the URL. + color_palette (`list[str]`, *optional*): + A list of hex color codes to use for plot lines. If not provided, the + `TRACKIO_COLOR_PALETTE` environment variable will be used (comma-separated + hex codes), or if that is not set, the default color palette will be used. + Example: `['#FF0000', '#00FF00', '#0000FF']` + open_browser (`bool`, *optional*, defaults to `True`): + If `True` and not in a notebook, a new browser tab will be opened with the + dashboard. If `False`, the browser will not be opened. + block_thread (`bool`, *optional*): + If `True`, the main thread will be blocked until the dashboard is closed. + If `None` (default behavior), then the main thread will not be blocked if the + dashboard is launched in a notebook, otherwise the main thread will be blocked. + host (`str`, *optional*): + The host to bind the server to. If not provided, defaults to `'127.0.0.1'` + (localhost only). Set to `'0.0.0.0'` to allow remote access. + share (`bool`, *optional*): + If `True`, creates a temporary public URL (Gradio-compatible tunnel). On Colab + or hosted notebooks, defaults to `True` unless overridden. + server_port (`int`, *optional*): + Port to bind. If not set, scans from `GRADIO_SERVER_PORT` (default 7860). + + Returns: + `app`: The dashboard handle (`.close()` stops the server). + `url`: The local URL of the dashboard. + `share_url`: The public share URL, if any. + `full_url`: The full URL including the write token (share URL when sharing, else local). + """ + if theme is not None and theme != "default": + warnings.warn( + "The theme argument is ignored; Trackio no longer depends on Gradio themes.", + UserWarning, + stacklevel=2, + ) + + if color_palette is not None: + os.environ["TRACKIO_COLOR_PALETTE"] = ",".join(color_palette) + + _mcp_server = ( + mcp_server + if mcp_server is not None + else os.environ.get("GRADIO_MCP_SERVER", "False") == "True" + ) + + starlette_app, wt = build_starlette_app_only(mcp_server=_mcp_server) + local_url, share_url, _local_api_url, uv_server = launch_trackio_dashboard( + starlette_app, + server_name=host, + server_port=server_port, + share=share, + mcp_server=_mcp_server, + quiet=True, + ) + server = TrackioDashboardApp(starlette_app, uv_server, wt) + + base_root = (share_url or local_url).rstrip("/") + base_url = base_root + "/" + dashboard_url = base_url + if project: + dashboard_url += f"?project={project}" + full_url = utils.get_full_url( + base_root, + project=project, + write_token=wt, + footer=footer, + ) + + if not utils.is_in_notebook(): + print(f"\033[1m\033[38;5;208m* Trackio UI launched at: {dashboard_url}\033[0m") + utils.print_write_token_instructions(full_url) + if open_browser: + webbrowser.open(full_url) + block_thread = block_thread if block_thread is not None else True + else: + utils.embed_url_in_notebook(full_url) + block_thread = block_thread if block_thread is not None else False + + if block_thread: + utils.block_main_thread_until_keyboard_interrupt() + return _TupleNoPrint((server, local_url, share_url, full_url)) diff --git a/trackio/_vendor/__init__.py b/trackio/_vendor/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/trackio/_vendor/gradio_exceptions.py b/trackio/_vendor/gradio_exceptions.py new file mode 100644 index 0000000000000000000000000000000000000000..34696b9e534896b66557bab78a2d78709c07e351 --- /dev/null +++ b/trackio/_vendor/gradio_exceptions.py @@ -0,0 +1,6 @@ +class ChecksumMismatchError(Exception): + pass + + +class ShareCertificateWriteError(Exception): + pass diff --git a/trackio/_vendor/networking.py b/trackio/_vendor/networking.py new file mode 100644 index 0000000000000000000000000000000000000000..04ef35763f89eb813059ea6fc91a7316ecd2f2de --- /dev/null +++ b/trackio/_vendor/networking.py @@ -0,0 +1,81 @@ +from __future__ import annotations + +import os +import time +import warnings +from pathlib import Path +from urllib.parse import urlparse, urlunparse + +import httpx + +from trackio._vendor.gradio_exceptions import ShareCertificateWriteError +from trackio._vendor.tunneling import CERTIFICATE_PATH, Tunnel + +GRADIO_API_SERVER = "https://api.gradio.app/v3/tunnel-request" +GRADIO_SHARE_SERVER_ADDRESS = os.getenv("GRADIO_SHARE_SERVER_ADDRESS") + + +def setup_tunnel( + local_host: str, + local_port: int, + share_token: str, + share_server_address: str | None, + share_server_tls_certificate: str | None, +) -> str: + share_server_address = ( + GRADIO_SHARE_SERVER_ADDRESS + if share_server_address is None + else share_server_address + ) + if share_server_address is None: + try: + response = httpx.get(GRADIO_API_SERVER, timeout=30) + payload = response.json()[0] + remote_host, remote_port = payload["host"], int(payload["port"]) + certificate = payload["root_ca"] + except Exception as e: + raise RuntimeError( + "Could not get share link from Gradio API Server." + ) from e + try: + Path(CERTIFICATE_PATH).parent.mkdir(parents=True, exist_ok=True) + with open(CERTIFICATE_PATH, "w") as f: + f.write(certificate) + except Exception as e: + raise ShareCertificateWriteError( + f"{e}. This can happen if the current working directory is read-only." + ) from e + share_server_tls_certificate = CERTIFICATE_PATH + + else: + remote_host, remote_port = share_server_address.split(":") + remote_port = int(remote_port) + tunnel = Tunnel( + remote_host, + remote_port, + local_host, + local_port, + share_token, + share_server_tls_certificate, + ) + address = tunnel.start_tunnel() + return address + + +def url_ok(url: str) -> bool: + try: + for _ in range(5): + with warnings.catch_warnings(): + warnings.filterwarnings("ignore") + r = httpx.head(url, timeout=3, verify=False) + if r.status_code in (200, 401, 302, 303, 307): + return True + time.sleep(0.500) + except (ConnectionError, httpx.ConnectError, httpx.TimeoutException): + return False + return False + + +def normalize_share_url(share_url: str, share_server_protocol: str) -> str: + parsed_url = urlparse(share_url) + return urlunparse((share_server_protocol,) + parsed_url[1:]) diff --git a/trackio/_vendor/tunneling.py b/trackio/_vendor/tunneling.py new file mode 100644 index 0000000000000000000000000000000000000000..6bd6258ed043ba70b07bb1e928bf009864206000 --- /dev/null +++ b/trackio/_vendor/tunneling.py @@ -0,0 +1,191 @@ +import atexit +import hashlib +import os +import platform +import re +import stat +import subprocess +import sys +import time +from pathlib import Path + +import httpx +from huggingface_hub.constants import HF_HOME + +from trackio._vendor.gradio_exceptions import ChecksumMismatchError + +VERSION = "0.3" +CURRENT_TUNNELS: list["Tunnel"] = [] + +machine = platform.machine() +if machine == "x86_64": + machine = "amd64" +elif machine == "aarch64": + machine = "arm64" + +BINARY_REMOTE_NAME = f"frpc_{platform.system().lower()}_{machine.lower()}" +EXTENSION = ".exe" if os.name == "nt" else "" +BINARY_URL = f"https://cdn-media.huggingface.co/frpc-gradio-{VERSION}/{BINARY_REMOTE_NAME}{EXTENSION}" + +CHECKSUMS = { + "https://cdn-media.huggingface.co/frpc-gradio-0.3/frpc_windows_amd64.exe": "14bc0ea470be5d67d79a07412bd21de8a0a179c6ac1116d7764f68e942dc9ceb", + "https://cdn-media.huggingface.co/frpc-gradio-0.3/frpc_linux_amd64": "c791d1f047b41ff5885772fc4bf20b797c6059bbd82abb9e31de15e55d6a57c4", + "https://cdn-media.huggingface.co/frpc-gradio-0.3/frpc_linux_arm64": "823ced25104de6dc3c9f4798dbb43f20e681207279e6ab89c40e2176ccbf70cd", + "https://cdn-media.huggingface.co/frpc-gradio-0.3/frpc_darwin_amd64": "930f8face3365810ce16689da81b7d1941fda4466225a7bbcbced9a2916a6e15", + "https://cdn-media.huggingface.co/frpc-gradio-0.3/frpc_darwin_arm64": "dfac50c690aca459ed5158fad8bfbe99f9282baf4166cf7c410a6673fbc1f327", + "https://cdn-media.huggingface.co/frpc-gradio-0.3/frpc_linux_arm": "4b563beb2e36c448cc688174e20b53af38dc1ff2b5e362d4ddd1401f2affbfb7", + "https://cdn-media.huggingface.co/frpc-gradio-0.3/frpc_freebsd_386": "cb0a56c764ecf96dd54ed601d240c564f060ee4e58202d65ffca17c1a51ce19c", + "https://cdn-media.huggingface.co/frpc-gradio-0.3/frpc_freebsd_amd64": "516d9e6903513869a011ddcd1ec206167ad1eb5dd6640d21057acc258edecbbb", + "https://cdn-media.huggingface.co/frpc-gradio-0.3/frpc_linux_386": "4c2f2a48cd71571498c0ac8a4d42a055f22cb7f14b4b5a2b0d584220fd60a283", + "https://cdn-media.huggingface.co/frpc-gradio-0.3/frpc_linux_mips": "b309ecd594d4f0f7f33e556a80d4b67aef9319c00a8334648a618e56b23cb9e0", + "https://cdn-media.huggingface.co/frpc-gradio-0.3/frpc_linux_mips64": "0372ef5505baa6f3b64c6295a86541b24b7b0dbe4ef28b344992e21f47624b7b", + "https://cdn-media.huggingface.co/frpc-gradio-0.3/frpc_linux_riscv64": "1658eed7e8c14ea76e1d95749d58441ce24147c3d559381832c725c29cfc3df3", + "https://cdn-media.huggingface.co/frpc-gradio-0.3/frpc_linux_mipsle": "a2aaba16961d3372b79bd7a28976fcd0f0bbaebc2b50d5a7a71af2240747960f", + "https://cdn-media.huggingface.co/frpc-gradio-0.3/frpc_windows_386.exe": "721b90550195a83e15f2176d8f85a48d5a25822757cb872e9723d4bccc4e5bb6", + "https://cdn-media.huggingface.co/frpc-gradio-0.3/frpc_linux_mips64le": "796481edd609f31962b45cc0ab4c9798d040205ae3bf354ed1b72fb432d796b8", +} + +CHUNK_SIZE = 128 + +BINARY_FILENAME = f"{BINARY_REMOTE_NAME}_v{VERSION}" +BINARY_FOLDER = Path(HF_HOME) / "trackio" / "frpc" +BINARY_PATH = str(BINARY_FOLDER / BINARY_FILENAME) + +TUNNEL_TIMEOUT_SECONDS = 30 +TUNNEL_ERROR_MESSAGE = ( + "Could not create share URL. " + "Please check the appended log from frpc for more information:" +) + +CERTIFICATE_PATH = ".trackio/certificate.pem" + + +class Tunnel: + def __init__( + self, + remote_host: str, + remote_port: int, + local_host: str, + local_port: int, + share_token: str, + share_server_tls_certificate: str | None, + ): + self.proc = None + self.url = None + self.remote_host = remote_host + self.remote_port = remote_port + self.local_host = local_host + self.local_port = local_port + self.share_token = share_token + self.share_server_tls_certificate = share_server_tls_certificate + + @staticmethod + def download_binary(): + if not Path(BINARY_PATH).exists(): + Path(BINARY_FOLDER).mkdir(parents=True, exist_ok=True) + resp = httpx.get(BINARY_URL, timeout=30) + + if resp.status_code == 403: + raise OSError( + f"Cannot set up a share link as this platform is incompatible. Please " + f"create a GitHub issue with information about your platform: {platform.uname()}" + ) + + resp.raise_for_status() + + with open(BINARY_PATH, "wb") as file: + file.write(resp.content) + st = os.stat(BINARY_PATH) + os.chmod(BINARY_PATH, st.st_mode | stat.S_IEXEC) + + if BINARY_URL in CHECKSUMS: + sha = hashlib.sha256() + with open(BINARY_PATH, "rb") as f: + for chunk in iter(lambda: f.read(CHUNK_SIZE * sha.block_size), b""): + sha.update(chunk) + calculated_hash = sha.hexdigest() + + if calculated_hash != CHECKSUMS[BINARY_URL]: + raise ChecksumMismatchError() + + def start_tunnel(self) -> str: + self.download_binary() + self.url = self._start_tunnel(BINARY_PATH) + return self.url + + def kill(self): + if self.proc is not None: + print(f"Killing tunnel {self.local_host}:{self.local_port} <> {self.url}") + self.proc.terminate() + self.proc = None + + def _start_tunnel(self, binary: str) -> str: + CURRENT_TUNNELS.append(self) + command = [ + binary, + "http", + "-n", + self.share_token, + "-l", + str(self.local_port), + "-i", + self.local_host, + "--uc", + "--sd", + "random", + "--ue", + "--server_addr", + f"{self.remote_host}:{self.remote_port}", + "--disable_log_color", + ] + if self.share_server_tls_certificate is not None: + command.extend( + [ + "--tls_enable", + "--tls_trusted_ca_file", + self.share_server_tls_certificate, + ] + ) + self.proc = subprocess.Popen( + command, stdout=subprocess.PIPE, stderr=subprocess.PIPE + ) + atexit.register(self.kill) + return self._read_url_from_tunnel_stream() + + def _read_url_from_tunnel_stream(self) -> str: + start_timestamp = time.time() + + log = [] + url = "" + + def _raise_tunnel_error(): + log_text = "\n".join(log) + print(log_text, file=sys.stderr) + raise ValueError(f"{TUNNEL_ERROR_MESSAGE}\n{log_text}") + + while url == "": + if time.time() - start_timestamp >= TUNNEL_TIMEOUT_SECONDS: + _raise_tunnel_error() + + assert self.proc is not None # noqa: S101 + if self.proc.stdout is None: + continue + + line = self.proc.stdout.readline() + line = line.decode("utf-8") + + if line == "": + continue + + log.append(line.strip()) + + if "start proxy success" in line: + result = re.search("start proxy success: (.+)\n", line) + if result is None: + _raise_tunnel_error() + else: + url = result.group(1) + elif "login to server failed" in line: + _raise_tunnel_error() + + return url diff --git a/trackio/alerts.py b/trackio/alerts.py new file mode 100644 index 0000000000000000000000000000000000000000..646b9786eacb91fca5270189a78280293e045fc0 --- /dev/null +++ b/trackio/alerts.py @@ -0,0 +1,184 @@ +import json +import logging +import ssl +import urllib.request +from enum import Enum + +try: + import certifi + + _SSL_CONTEXT = ssl.create_default_context(cafile=certifi.where()) +except ImportError: + _SSL_CONTEXT = None + +logger = logging.getLogger(__name__) + + +class AlertLevel(str, Enum): + INFO = "info" + WARN = "warn" + ERROR = "error" + + +ALERT_LEVEL_ORDER = { + AlertLevel.INFO: 0, + AlertLevel.WARN: 1, + AlertLevel.ERROR: 2, +} + +ALERT_COLORS = { + AlertLevel.INFO: "\033[94m", + AlertLevel.WARN: "\033[93m", + AlertLevel.ERROR: "\033[91m", +} +RESET_COLOR = "\033[0m" + +LEVEL_EMOJI = { + AlertLevel.INFO: "ℹ️", + AlertLevel.WARN: "⚠️", + AlertLevel.ERROR: "🚨", +} + + +def format_alert_terminal( + level: AlertLevel, title: str, text: str | None, step: int | None +) -> str: + color = ALERT_COLORS.get(level, "") + step_str = f" (step {step})" if step is not None else "" + if text: + return f"{color}[TRACKIO {level.value.upper()}]{RESET_COLOR} {title}: {text}{step_str}" + return f"{color}[TRACKIO {level.value.upper()}]{RESET_COLOR} {title}{step_str}" + + +def _is_slack_url(url: str) -> bool: + return "hooks.slack.com" in url + + +def _is_discord_url(url: str) -> bool: + return "discord.com/api/webhooks" in url or "discordapp.com/api/webhooks" in url + + +def _build_slack_payload( + level: AlertLevel, + title: str, + text: str | None, + project: str, + run: str, + step: int | None, +) -> dict: + emoji = LEVEL_EMOJI.get(level, "") + step_str = f" • Step {step}" if step is not None else "" + header = f"{emoji} *[{level.value.upper()}] {title}*" + context = f"Project: {project} • Run: {run}{step_str}" + blocks = [ + {"type": "section", "text": {"type": "mrkdwn", "text": header}}, + ] + if text: + blocks.append({"type": "section", "text": {"type": "mrkdwn", "text": text}}) + blocks.append( + {"type": "context", "elements": [{"type": "mrkdwn", "text": context}]} + ) + return {"blocks": blocks} + + +def _build_discord_payload( + level: AlertLevel, + title: str, + text: str | None, + project: str, + run: str, + step: int | None, +) -> dict: + color_map = { + AlertLevel.INFO: 3447003, + AlertLevel.WARN: 16776960, + AlertLevel.ERROR: 15158332, + } + emoji = LEVEL_EMOJI.get(level, "") + step_str = f" • Step {step}" if step is not None else "" + embed = { + "title": f"{emoji} [{level.value.upper()}] {title}", + "color": color_map.get(level, 0), + "footer": {"text": f"Project: {project} • Run: {run}{step_str}"}, + } + if text: + embed["description"] = text + return {"embeds": [embed]} + + +def _build_generic_payload( + level: AlertLevel, + title: str, + text: str | None, + project: str, + run: str, + step: int | None, + timestamp: str | None, +) -> dict: + return { + "level": level.value, + "title": title, + "text": text, + "project": project, + "run": run, + "step": step, + "timestamp": timestamp, + } + + +def parse_alert_level(level: AlertLevel | str) -> AlertLevel: + if isinstance(level, AlertLevel): + return level + normalized = level.lower().strip() + try: + return AlertLevel(normalized) + except ValueError as e: + allowed = ", ".join(lvl.value for lvl in AlertLevel) + raise ValueError( + f"Invalid alert level '{level}'. Expected one of: {allowed}." + ) from e + + +def resolve_webhook_min_level( + webhook_min_level: AlertLevel | str | None, +) -> AlertLevel | None: + if webhook_min_level is None: + return None + return parse_alert_level(webhook_min_level) + + +def should_send_webhook( + level: AlertLevel, webhook_min_level: AlertLevel | None +) -> bool: + if webhook_min_level is None: + return True + return ALERT_LEVEL_ORDER[level] >= ALERT_LEVEL_ORDER[webhook_min_level] + + +def send_webhook( + url: str, + level: AlertLevel, + title: str, + text: str | None, + project: str, + run: str, + step: int | None, + timestamp: str | None = None, +) -> None: + if _is_slack_url(url): + payload = _build_slack_payload(level, title, text, project, run, step) + elif _is_discord_url(url): + payload = _build_discord_payload(level, title, text, project, run, step) + else: + payload = _build_generic_payload( + level, title, text, project, run, step, timestamp + ) + + data = json.dumps(payload).encode("utf-8") + req = urllib.request.Request( + url, data=data, headers={"Content-Type": "application/json"} + ) + try: + urllib.request.urlopen(req, timeout=10, context=_SSL_CONTEXT) + except Exception as e: + logger.warning(f"Failed to send webhook to {url}: {e}") diff --git a/trackio/api.py b/trackio/api.py new file mode 100644 index 0000000000000000000000000000000000000000..666f4b34bbfbcae4171e26d0bcc0572b8753b4cc --- /dev/null +++ b/trackio/api.py @@ -0,0 +1,93 @@ +from typing import Iterator + +from trackio.sqlite_storage import SQLiteStorage + + +class Run: + def __init__(self, project: str, name: str, run_id: str | None = None): + self.project = project + self.name = name + self._id = run_id or name + self._config = None + + @property + def id(self) -> str: + return self._id + + @property + def config(self) -> dict | None: + if self._config is None: + self._config = SQLiteStorage.get_run_config( + self.project, self.name, run_id=self.id + ) + return self._config + + def alerts(self, level: str | None = None, since: str | None = None) -> list[dict]: + return SQLiteStorage.get_alerts( + self.project, run_name=self.name, run_id=self.id, level=level, since=since + ) + + def delete(self) -> bool: + return SQLiteStorage.delete_run(self.project, self.name, run_id=self.id) + + def move(self, new_project: str) -> bool: + success = SQLiteStorage.move_run(self.project, self.name, new_project) + if success: + self.project = new_project + return success + + def rename(self, new_name: str) -> "Run": + SQLiteStorage.rename_run(self.project, self.name, new_name) + self.name = new_name + return self + + def __repr__(self) -> str: + return f"" + + +class Runs: + def __init__(self, project: str): + self.project = project + self._runs = None + + def _load_runs(self): + if self._runs is None: + records = SQLiteStorage.get_run_records(self.project) + self._runs = [ + Run(self.project, str(record["name"]), run_id=str(record["id"])) + for record in records + ] + + def __iter__(self) -> Iterator[Run]: + self._load_runs() + return iter(self._runs) + + def __getitem__(self, index: int) -> Run: + self._load_runs() + return self._runs[index] + + def __len__(self) -> int: + self._load_runs() + return len(self._runs) + + def __repr__(self) -> str: + self._load_runs() + return f"" + + +class Api: + def runs(self, project: str) -> Runs: + if not SQLiteStorage.get_project_db_path(project).exists(): + raise ValueError(f"Project '{project}' does not exist") + return Runs(project) + + def alerts( + self, + project: str, + run: str | None = None, + level: str | None = None, + since: str | None = None, + ) -> list[dict]: + if not SQLiteStorage.get_project_db_path(project).exists(): + raise ValueError(f"Project '{project}' does not exist") + return SQLiteStorage.get_alerts(project, run_name=run, level=level, since=since) diff --git a/trackio/apple_gpu.py b/trackio/apple_gpu.py new file mode 100644 index 0000000000000000000000000000000000000000..6192319db2aa56b6a7c752e97ceabdb0678ca367 --- /dev/null +++ b/trackio/apple_gpu.py @@ -0,0 +1,253 @@ +import platform +import subprocess +import sys +import threading +import warnings +from typing import TYPE_CHECKING, Any + +if TYPE_CHECKING: + from trackio.run import Run + +psutil: Any = None +PSUTIL_AVAILABLE = False +_monitor_lock = threading.Lock() + + +def _ensure_psutil(): + global PSUTIL_AVAILABLE, psutil + if PSUTIL_AVAILABLE: + return psutil + try: + import psutil as _psutil + + psutil = _psutil + PSUTIL_AVAILABLE = True + return psutil + except ImportError: + raise ImportError( + "psutil is required for Apple Silicon monitoring. " + "Install it with: pip install psutil" + ) + + +def is_apple_silicon() -> bool: + """Check if running on Apple Silicon (M1/M2/M3/M4).""" + if platform.system() != "Darwin": + return False + + try: + result = subprocess.run( + ["sysctl", "-n", "machdep.cpu.brand_string"], + capture_output=True, + text=True, + timeout=1, + ) + cpu_brand = result.stdout.strip() + return "Apple" in cpu_brand + except Exception: + return False + + +def get_gpu_info() -> dict[str, Any]: + """Get Apple GPU information using ioreg.""" + try: + result = subprocess.run( + ["ioreg", "-r", "-d", "1", "-w", "0", "-c", "IOAccelerator"], + capture_output=True, + text=True, + timeout=2, + ) + + if result.returncode == 0 and result.stdout: + lines = result.stdout.strip().split("\n") + for line in lines: + if "IOAccelerator" in line and "class" in line: + return {"detected": True, "type": "Apple GPU"} + else: + print("Error collecting Apple GPU info. ioreg stdout was:", file=sys.stderr) + print(result.stdout, file=sys.stderr) + print("ioreg stderr was:", file=sys.stderr) + print(result.stderr, file=sys.stderr) + + result = subprocess.run( + ["system_profiler", "SPDisplaysDataType"], + capture_output=True, + text=True, + timeout=3, + ) + + if result.returncode == 0 and "Apple" in result.stdout: + for line in result.stdout.split("\n"): + if "Chipset Model:" in line: + model = line.split(":")[-1].strip() + return {"detected": True, "type": model} + + except Exception: + pass + + return {"detected": False} + + +def apple_gpu_available() -> bool: + """ + Check if Apple GPU monitoring is available. + + Returns True if running on Apple Silicon (M-series chips) and psutil is installed. + """ + try: + _ensure_psutil() + return is_apple_silicon() + except ImportError: + return False + except Exception: + return False + + +def collect_apple_metrics() -> dict: + """ + Collect system metrics for Apple Silicon. + + Returns: + Dictionary of system metrics including CPU, memory, and GPU info. + """ + if not PSUTIL_AVAILABLE: + try: + _ensure_psutil() + except ImportError: + return {} + + metrics = {} + + try: + cpu_percent = psutil.cpu_percent(interval=0.1, percpu=False) + metrics["cpu/utilization"] = cpu_percent + except Exception: + pass + + try: + cpu_percents = psutil.cpu_percent(interval=0.1, percpu=True) + for i, percent in enumerate(cpu_percents): + metrics[f"cpu/{i}/utilization"] = percent + except Exception: + pass + + try: + cpu_freq = psutil.cpu_freq() + if cpu_freq: + metrics["cpu/frequency"] = cpu_freq.current + if cpu_freq.max > 0: + metrics["cpu/frequency_max"] = cpu_freq.max + except Exception: + pass + + try: + mem = psutil.virtual_memory() + metrics["memory/used"] = mem.used / (1024**3) + metrics["memory/total"] = mem.total / (1024**3) + metrics["memory/available"] = mem.available / (1024**3) + metrics["memory/percent"] = mem.percent + except Exception: + pass + + try: + swap = psutil.swap_memory() + metrics["swap/used"] = swap.used / (1024**3) + metrics["swap/total"] = swap.total / (1024**3) + metrics["swap/percent"] = swap.percent + except Exception: + pass + + try: + sensors_temps = psutil.sensors_temperatures() + if sensors_temps: + for name, entries in sensors_temps.items(): + for i, entry in enumerate(entries): + label = entry.label or f"{name}_{i}" + metrics[f"temp/{label}"] = entry.current + except Exception: + pass + + gpu_info = get_gpu_info() + if gpu_info.get("detected"): + metrics["gpu/detected"] = 1 + if "type" in gpu_info: + pass + + return metrics + + +class AppleGpuMonitor: + def __init__(self, run: "Run", interval: float = 10.0): + self._run = run + self._interval = interval + self._stop_flag = threading.Event() + self._thread: "threading.Thread | None" = None + + def start(self): + if not is_apple_silicon(): + warnings.warn( + "auto_log_gpu=True but not running on Apple Silicon. " + "Apple GPU logging disabled." + ) + return + + if not PSUTIL_AVAILABLE: + try: + _ensure_psutil() + except ImportError: + warnings.warn( + "auto_log_gpu=True but psutil not installed. " + "Install with: pip install psutil" + ) + return + + self._thread = threading.Thread(target=self._monitor_loop, daemon=True) + self._thread.start() + + def stop(self): + self._stop_flag.set() + if self._thread is not None: + self._thread.join(timeout=2.0) + + def _monitor_loop(self): + while not self._stop_flag.is_set(): + try: + metrics = collect_apple_metrics() + if metrics: + self._run.log_system(metrics) + except Exception: + pass + + self._stop_flag.wait(timeout=self._interval) + + +def log_apple_gpu(run: "Run | None" = None) -> dict: + """ + Log Apple Silicon system metrics to the current or specified run. + + Args: + run: Optional Run instance. If None, uses current run from context. + + Returns: + dict: The system metrics that were logged. + + Example: + ```python + import trackio + + run = trackio.init(project="my-project") + trackio.log({"loss": 0.5}) + trackio.log_apple_gpu() + ``` + """ + from trackio import context_vars + + if run is None: + run = context_vars.current_run.get() + if run is None: + raise RuntimeError("Call trackio.init() before trackio.log_apple_gpu().") + + metrics = collect_apple_metrics() + if metrics: + run.log_system(metrics) + return metrics diff --git a/trackio/asgi_app.py b/trackio/asgi_app.py new file mode 100644 index 0000000000000000000000000000000000000000..bff1f38caa9267d8df6ad698d92593849332ec30 --- /dev/null +++ b/trackio/asgi_app.py @@ -0,0 +1,483 @@ +from __future__ import annotations + +import inspect +import json +import logging +import math +import secrets +import tempfile +import threading +from pathlib import Path +from typing import Any, get_args, get_origin +from urllib.parse import unquote + +from starlette.applications import Starlette +from starlette.requests import Request +from starlette.responses import FileResponse, JSONResponse, Response, StreamingResponse +from starlette.routing import Route + +from trackio.exceptions import TrackioAPIError +from trackio.remote_client import HTTP_API_VERSION +from trackio.utils import on_spaces + +logger = logging.getLogger("trackio.asgi_app") + +_PACKAGE_JSON_PATH = Path(__file__).parent / "package.json" +_TRACKIO_PACKAGE_VERSION = json.loads(_PACKAGE_JSON_PATH.read_text())["version"] + + +def _normalize_allowed_file_roots( + allowed_file_roots: list[str | Path] | None, +) -> tuple[Path, ...]: + roots = [] + for root in allowed_file_roots or []: + roots.append(Path(root).resolve()) + return tuple(roots) + + +def _is_allowed_file_path(path: Path, allowed_roots: tuple[Path, ...]) -> bool: + resolved_path = path.resolve(strict=False) + for root in allowed_roots: + try: + resolved_path.relative_to(root) + return True + except ValueError: + continue + return False + + +def _json_safe(data: Any) -> Any: + if data is None or isinstance(data, (str, bool, int)): + return data + if isinstance(data, float): + return data if math.isfinite(data) else None + if isinstance(data, dict): + return {k: _json_safe(v) for k, v in data.items()} + if isinstance(data, (list, tuple)): + return [_json_safe(v) for v in data] + if hasattr(data, "item"): + try: + return _json_safe(data.item()) + except Exception: + pass + return str(data) + + +def register_uploaded_temp_file(request: Request, file_path: str | Path) -> None: + resolved_path = Path(file_path).resolve(strict=False) + with request.app.state.uploaded_temp_files_lock: + request.app.state.uploaded_temp_files.add(resolved_path) + + +def consume_uploaded_temp_file(request: Request, file_data: Any) -> Path: + file_path = file_data.get("path") if isinstance(file_data, dict) else None + if not isinstance(file_path, str) or not file_path: + raise TrackioAPIError("Expected uploaded file metadata with a valid path.") + + resolved_path = Path(file_path).resolve(strict=False) + with request.app.state.uploaded_temp_files_lock: + if resolved_path not in request.app.state.uploaded_temp_files: + raise TrackioAPIError( + "Uploaded file was not created by this Trackio server." + ) + request.app.state.uploaded_temp_files.remove(resolved_path) + + if not resolved_path.is_file(): + raise TrackioAPIError("Uploaded file is missing.") + + return resolved_path + + +def _invoke_handler( + fn: Any, + request: Request, + args: list[Any] | None = None, + kwargs: dict[str, Any] | None = None, +) -> Any: + sig = inspect.signature(fn) + params = list(sig.parameters.values()) + positional_args: list[Any] = [] + keyword_args: dict[str, Any] = {} + args = list(args or []) + kwargs = dict(kwargs or {}) + data_index = 0 + + for param in params: + if param.name == "request": + keyword_args["request"] = request + elif param.kind == inspect.Parameter.VAR_POSITIONAL: + positional_args.extend(args[data_index:]) + data_index = len(args) + elif param.kind == inspect.Parameter.VAR_KEYWORD: + keyword_args.update(kwargs) + kwargs.clear() + elif param.name in kwargs: + keyword_args[param.name] = kwargs.pop(param.name) + elif param.kind in ( + inspect.Parameter.POSITIONAL_ONLY, + inspect.Parameter.POSITIONAL_OR_KEYWORD, + ) and data_index < len(args): + positional_args.append(args[data_index]) + data_index += 1 + elif param.default is inspect.Signature.empty and param.kind not in ( + inspect.Parameter.VAR_POSITIONAL, + inspect.Parameter.VAR_KEYWORD, + ): + raise TrackioAPIError(f"Missing required parameter: {param.name}") + + return fn(*positional_args, **keyword_args) + + +async def version_handler(request: Request) -> Response: + mcp_enabled = bool(getattr(request.app.state, "mcp_enabled", False)) + return JSONResponse( + { + "version": _TRACKIO_PACKAGE_VERSION, + "api_version": HTTP_API_VERSION, + "api_transport": "http", + "mcp_enabled": mcp_enabled, + "mcp_path": "/mcp" if mcp_enabled else None, + } + ) + + +def _json_schema_and_python_type(annotation: Any) -> tuple[dict[str, Any], str]: + if annotation is inspect.Parameter.empty: + return {"type": "object"}, "Any" + origin = get_origin(annotation) + args = get_args(annotation) + if origin is not None and args: + non_none = tuple(a for a in args if a is not type(None)) + if len(non_none) == 1 and len(args) > 1: + return _json_schema_and_python_type(non_none[0]) + if origin is list: + inner, py_inner = _json_schema_and_python_type( + args[0] if args else inspect.Parameter.empty + ) + return {"type": "array", "items": inner}, f"list[{py_inner}]" + if origin is dict: + return {"type": "object"}, "dict" + if annotation in (str, bytes): + return {"type": "string"}, "str" + if annotation is int: + return {"type": "integer"}, "int" + if annotation is float: + return {"type": "number"}, "float" + if annotation is bool: + return {"type": "boolean"}, "bool" + name = getattr(annotation, "__name__", None) + if name: + return {"type": "object"}, name + return {"type": "object"}, "Any" + + +def build_gradio_api_info(api_registry: dict[str, Any]) -> dict[str, Any]: + named_endpoints: dict[str, Any] = {} + for name in sorted(api_registry.keys()): + fn = api_registry[name] + if not callable(fn): + continue + sig = inspect.signature(fn) + parameters: list[dict[str, Any]] = [] + for pname, param in sig.parameters.items(): + if pname == "request": + continue + jtype, pytype = _json_schema_and_python_type(param.annotation) + has_default = param.default is not inspect.Parameter.empty + parameters.append( + { + "label": pname, + "parameter_name": pname, + "parameter_has_default": has_default, + "parameter_default": None if not has_default else param.default, + "type": jtype, + "python_type": {"type": pytype, "description": ""}, + "component": "Api", + "example_input": None, + } + ) + ret_ann = sig.return_annotation + if ret_ann is inspect.Signature.empty: + ret_ann = Any + rjtype, rpytype = _json_schema_and_python_type(ret_ann) + returns = [ + { + "label": "result", + "type": rjtype, + "python_type": {"type": rpytype, "description": ""}, + "component": "Api", + } + ] + named_endpoints[f"/{name}"] = { + "parameters": parameters, + "returns": returns, + "api_visibility": "public", + } + return {"named_endpoints": named_endpoints, "unnamed_endpoints": {}} + + +_MAX_GRADIO_CALL_EVENTS = 256 + + +def _hf_token_value_is_unset(value: Any) -> bool: + if value is None: + return True + if isinstance(value, str) and value.strip() == "": + return True + return False + + +def _authorization_bearer_token(request: Request) -> str | None: + auth = request.headers.get("authorization") or request.headers.get("Authorization") + if not auth: + return None + parts = auth.split() + if len(parts) != 2 or parts[0].lower() != "bearer": + return None + tok = parts[1].strip() + return tok or None + + +def _maybe_apply_hf_token_from_authorization( + request: Request, fn: Any, args: list[Any], kwargs: dict[str, Any] +) -> None: + if not on_spaces(): + return + token = _authorization_bearer_token(request) + if not token: + return + sig = inspect.signature(fn) + if "hf_token" not in sig.parameters: + return + params = [p for p in sig.parameters.values() if p.name != "request"] + names = [p.name for p in params] + if "hf_token" not in names: + return + idx = names.index("hf_token") + if "hf_token" in kwargs: + if _hf_token_value_is_unset(kwargs["hf_token"]): + kwargs["hf_token"] = token + return + if idx < len(args): + if _hf_token_value_is_unset(args[idx]): + args[idx] = token + return + kwargs["hf_token"] = token + + +def _store_gradio_call_result( + request: Request, event_id: str, api_name: str, data: Any +) -> None: + with request.app.state.gradio_call_events_lock: + d = request.app.state.gradio_call_events + while len(d) >= _MAX_GRADIO_CALL_EVENTS: + d.pop(next(iter(d))) + d[event_id] = {"api_name": api_name, "data": data} + + +async def run_api_request(request: Request, api_name: str) -> Response: + api_registry = request.app.state.api_registry + fn = api_registry.get(api_name) + if fn is None: + return JSONResponse({"error": f"Unknown API: {api_name}"}, status_code=404) + + try: + body = await request.json() + except Exception: + body = {} + + args: list[Any] = [] + kwargs: dict[str, Any] = {} + if isinstance(body, dict): + if "args" in body or "kwargs" in body: + args = body.get("args") or [] + kwargs = body.get("kwargs") or {} + elif "data" in body and isinstance(body["data"], list): + args = body["data"] + else: + kwargs = body + elif isinstance(body, list): + args = body + elif body is not None: + args = [body] + + if not isinstance(args, list): + args = [args] + if not isinstance(kwargs, dict): + kwargs = {} + + _maybe_apply_hf_token_from_authorization(request, fn, args, kwargs) + + try: + result = _invoke_handler(fn, request, args=args, kwargs=kwargs) + return JSONResponse({"data": _json_safe(result)}) + except TrackioAPIError as e: + return JSONResponse({"error": str(e)}, status_code=400) + except Exception as e: + return JSONResponse({"error": str(e)}, status_code=500) + + +async def api_handler(request: Request) -> Response: + return await run_api_request(request, request.path_params["api_name"]) + + +async def gradio_api_info_handler(request: Request) -> Response: + return JSONResponse( + build_gradio_api_info(request.app.state.api_registry), + headers={"Cache-Control": "no-store"}, + ) + + +async def gradio_call_post_handler(request: Request) -> Response: + api_name = request.path_params["api_name"] + resp = await run_api_request(request, api_name) + if resp.status_code != 200: + return resp + body = json.loads(bytes(resp.body).decode()) + event_id = secrets.token_urlsafe(16) + _store_gradio_call_result(request, event_id, api_name, body["data"]) + return JSONResponse({"event_id": event_id}) + + +async def gradio_call_poll_handler(request: Request) -> Response: + api_name = request.path_params["api_name"] + event_id = request.path_params["event_id"] + with request.app.state.gradio_call_events_lock: + event = request.app.state.gradio_call_events.pop(event_id, None) + if event is None: + logger.info("gradio_api poll: unknown or expired event_id") + return JSONResponse({"error": "Unknown or expired event_id"}, status_code=404) + if event.get("api_name") != api_name: + logger.info( + "gradio_api poll: api_name mismatch (path=%r, stored=%r)", + api_name, + event.get("api_name"), + ) + with request.app.state.gradio_call_events_lock: + d = request.app.state.gradio_call_events + while len(d) >= _MAX_GRADIO_CALL_EVENTS: + d.pop(next(iter(d))) + d[event_id] = event + return JSONResponse({"error": "Unknown or expired event_id"}, status_code=404) + + data = event["data"] + payload = json.dumps(_json_safe([data])) + + async def sse() -> Any: + yield f"event: complete\ndata: {payload}\n\n" + + return StreamingResponse( + sse(), + media_type="text/event-stream", + headers={ + "Cache-Control": "no-store", + "X-Accel-Buffering": "no", + }, + ) + + +async def upload_handler(request: Request) -> Response: + form = await request.form() + uploads = form.getlist("files") + saved_paths = [] + for upload in uploads: + suffix = Path(getattr(upload, "filename", "") or "").suffix + with tempfile.NamedTemporaryFile( + delete=False, + prefix="trackio-upload-", + suffix=suffix, + ) as tmp: + tmp.write(await upload.read()) + register_uploaded_temp_file(request, tmp.name) + saved_paths.append(tmp.name) + return JSONResponse({"paths": saved_paths}) + + +async def gradio_upload_alias_handler(request: Request) -> Response: + return await upload_handler(request) + + +async def file_handler(request: Request) -> Response: + fs_path = request.query_params.get("path") + if fs_path is None: + return Response("Missing path", status_code=400) + fp = Path(unquote(fs_path)).resolve(strict=False) + allowed_roots = getattr(request.app.state, "allowed_file_roots", ()) + if fp.is_file() and _is_allowed_file_path(fp, allowed_roots): + return FileResponse(str(fp)) + return Response("Not found", status_code=404) + + +def create_trackio_starlette_app( + oauth_routes: list[Route], + api_registry: dict[str, Any], + extra_routes: list[Any] | None = None, + mcp_lifespan: Any = None, + mcp_enabled: bool = False, + allowed_file_roots: list[str | Path] | None = None, +) -> Starlette: + routes: list[Any] = list(oauth_routes) + routes.extend( + [ + Route("/version", endpoint=version_handler, methods=["GET"]), + Route("/api/upload", endpoint=upload_handler, methods=["POST"]), + Route("/api/{api_name:str}", endpoint=api_handler, methods=["POST"]), + Route("/file", endpoint=file_handler, methods=["GET"]), + ] + ) + if on_spaces(): + routes.extend( + [ + Route( + "/gradio_api/info", + endpoint=gradio_api_info_handler, + methods=["GET"], + ), + Route( + "/gradio_api/info/", + endpoint=gradio_api_info_handler, + methods=["GET"], + ), + Route( + "/gradio_api/upload", + endpoint=gradio_upload_alias_handler, + methods=["POST"], + ), + Route( + "/gradio_api/upload/", + endpoint=gradio_upload_alias_handler, + methods=["POST"], + ), + Route( + "/gradio_api/call/{api_name:str}", + endpoint=gradio_call_post_handler, + methods=["POST"], + ), + Route( + "/gradio_api/call/{api_name:str}/", + endpoint=gradio_call_post_handler, + methods=["POST"], + ), + Route( + "/gradio_api/call/{api_name:str}/{event_id:str}", + endpoint=gradio_call_poll_handler, + methods=["GET"], + ), + Route( + "/gradio_api/call/{api_name:str}/{event_id:str}/", + endpoint=gradio_call_poll_handler, + methods=["GET"], + ), + ] + ) + routes.extend(extra_routes or []) + app = Starlette(routes=routes, lifespan=mcp_lifespan) + app.state.api_registry = api_registry + app.state.mcp_enabled = mcp_enabled + app.state.allowed_file_roots = _normalize_allowed_file_roots(allowed_file_roots) + app.state.uploaded_temp_files = set() + app.state.uploaded_temp_files_lock = threading.Lock() + if on_spaces(): + app.state.gradio_call_events = {} + app.state.gradio_call_events_lock = threading.Lock() + return app diff --git a/trackio/assets/badge.png b/trackio/assets/badge.png new file mode 100644 index 0000000000000000000000000000000000000000..03e0340873223b3ecb45f3ebf09512f6d6746ec2 --- /dev/null +++ b/trackio/assets/badge.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:206b7847247e83279f498510a2760338a03116bb5141a658d71ec14429f9ea9e +size 169669 diff --git a/trackio/assets/trackio_logo_dark.png b/trackio/assets/trackio_logo_dark.png new file mode 100644 index 0000000000000000000000000000000000000000..e6ff7e0a3580bca040d9ec3ea8c91e8cca469e61 Binary files /dev/null and b/trackio/assets/trackio_logo_dark.png differ diff --git a/trackio/assets/trackio_logo_light.png b/trackio/assets/trackio_logo_light.png new file mode 100644 index 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b/trackio/bucket_storage.py @@ -0,0 +1,152 @@ +import shutil +import tempfile +from pathlib import Path + +import huggingface_hub +from huggingface_hub import copy_files, sync_bucket + +from trackio.sqlite_storage import SQLiteStorage +from trackio.utils import MEDIA_DIR, TRACKIO_DIR + + +def create_bucket_if_not_exists(bucket_id: str, private: bool | None = None) -> None: + huggingface_hub.create_bucket(bucket_id, private=private, exist_ok=True) + + +def _list_bucket_file_paths(bucket_id: str, prefix: str | None = None) -> list[str]: + items = huggingface_hub.list_bucket_tree(bucket_id, prefix=prefix, recursive=True) + return [ + item.path + for item in items + if getattr(item, "type", None) == "file" and getattr(item, "path", None) + ] + + +def download_bucket_to_trackio_dir(bucket_id: str) -> None: + TRACKIO_DIR.mkdir(parents=True, exist_ok=True) + sync_bucket( + source=f"hf://buckets/{bucket_id}/trackio", + dest=str(TRACKIO_DIR), + quiet=True, + ) + + +def upload_project_to_bucket(project: str, bucket_id: str) -> None: + db_path = SQLiteStorage.get_project_db_path(project) + if not db_path.exists(): + raise FileNotFoundError(f"No database found for project '{project}'") + + with SQLiteStorage._get_connection( + db_path, configure_pragmas=False, row_factory=None + ) as conn: + conn.execute("PRAGMA wal_checkpoint(TRUNCATE)") + + files_to_add = [(str(db_path), f"trackio/{db_path.name}")] + + media_dir = MEDIA_DIR / project + if media_dir.exists(): + for media_file in media_dir.rglob("*"): + if media_file.is_file(): + rel = media_file.relative_to(TRACKIO_DIR) + files_to_add.append((str(media_file), f"trackio/{rel}")) + + huggingface_hub.batch_bucket_files(bucket_id, add=files_to_add) + + +def _download_db_from_bucket( + project: str, bucket_id: str, dest_path: Path | None = None +) -> bool: + db_filename = SQLiteStorage.get_project_db_filename(project) + remote_path = f"trackio/{db_filename}" + local_path = dest_path or SQLiteStorage.get_project_db_path(project) + local_path.parent.mkdir(parents=True, exist_ok=True) + try: + huggingface_hub.download_bucket_files( + bucket_id, + files=[(remote_path, str(local_path))], + token=huggingface_hub.utils.get_token(), + ) + return local_path.exists() + except Exception: + return False + + +def _local_db_has_data(project: str) -> bool: + db_path = SQLiteStorage.get_project_db_path(project) + if not db_path.exists() or db_path.stat().st_size == 0: + return False + try: + with SQLiteStorage._get_connection( + db_path, configure_pragmas=False, row_factory=None + ) as conn: + count = conn.execute("SELECT COUNT(*) FROM metrics").fetchone()[0] + return count > 0 + except Exception: + return False + + +def _export_and_upload_static( + project: str, + dest_bucket_id: str, + db_path: Path, + media_dir: Path | None = None, +) -> None: + with tempfile.TemporaryDirectory() as tmp_dir: + output_dir = Path(tmp_dir) + SQLiteStorage.export_for_static_space( + project, output_dir, db_path_override=db_path + ) + + if media_dir and media_dir.exists(): + shutil.copytree(media_dir, output_dir / "media") + + files_to_add = [] + for f in output_dir.rglob("*"): + if f.is_file(): + rel = f.relative_to(output_dir) + files_to_add.append((str(f), str(rel))) + + huggingface_hub.batch_bucket_files(dest_bucket_id, add=files_to_add) + + +def _copy_project_media_between_buckets( + source_bucket_id: str, dest_bucket_id: str, project: str +) -> None: + source_media_prefix = f"trackio/media/{project}/" + media_to_copy = _list_bucket_file_paths( + source_bucket_id, prefix=source_media_prefix + ) + if not media_to_copy: + return + + copy_files( + f"hf://buckets/{source_bucket_id}/{source_media_prefix}", + f"hf://buckets/{dest_bucket_id}/media/", + ) + + +def upload_project_to_bucket_for_static(project: str, bucket_id: str) -> None: + if not _local_db_has_data(project): + _download_db_from_bucket(project, bucket_id) + + db_path = SQLiteStorage.get_project_db_path(project) + _export_and_upload_static(project, bucket_id, db_path, MEDIA_DIR / project) + + +def export_from_bucket_for_static( + source_bucket_id: str, + dest_bucket_id: str, + project: str, +) -> None: + with tempfile.TemporaryDirectory() as work_dir: + work_path = Path(work_dir) + db_path = work_path / SQLiteStorage.get_project_db_filename(project) + + if not _download_db_from_bucket(project, source_bucket_id, dest_path=db_path): + raise FileNotFoundError( + f"Could not download database for project '{project}' " + f"from bucket '{source_bucket_id}'." + ) + + _export_and_upload_static(project, dest_bucket_id, db_path) + _copy_project_media_between_buckets(source_bucket_id, dest_bucket_id, project) diff --git a/trackio/cli.py b/trackio/cli.py new file mode 100644 index 0000000000000000000000000000000000000000..d8c5131d76ad2f281d520934fb7db5436a5291ac --- /dev/null +++ b/trackio/cli.py @@ -0,0 +1,1326 @@ +import argparse +import os + +from trackio import freeze, show, sync +from trackio.cli_helpers import ( + error_exit, + format_alerts, + format_json, + format_list, + format_metric_values, + format_project_summary, + format_query_result, + format_run_summary, + format_snapshot, + format_system_metric_names, + format_system_metrics, +) +from trackio.markdown import Markdown +from trackio.server import get_project_summary, get_run_summary +from trackio.sqlite_storage import SQLiteStorage + + +def _get_space(args): + return getattr(args, "space", None) + + +def _get_remote(args): + from trackio.remote_client import RemoteClient + + space = _get_space(args) + if not space: + return None + hf_token = getattr(args, "hf_token", None) + return RemoteClient(space, hf_token=hf_token) + + +def _handle_status(): + print("Reading local Trackio projects...\n") + projects = SQLiteStorage.get_projects() + if not projects: + print("No Trackio projects found.") + return + + local_projects = [] + synced_projects = [] + unsynced_projects = [] + + for project in projects: + space_id = SQLiteStorage.get_space_id(project) + if space_id is None: + local_projects.append(project) + elif SQLiteStorage.has_pending_data(project): + unsynced_projects.append(project) + else: + synced_projects.append(project) + + print("Finished reading Trackio projects") + if local_projects: + print(f" * {len(local_projects)} local trackio project(s) [OK]") + if synced_projects: + print(f" * {len(synced_projects)} trackio project(s) synced to Spaces [OK]") + if unsynced_projects: + print( + f" * {len(unsynced_projects)} trackio project(s) with unsynced changes [WARNING]:" + ) + for p in unsynced_projects: + print(f" - {p}") + + if unsynced_projects: + print( + f"\nRun `trackio sync --project {unsynced_projects[0]}` to sync. " + "Or run `trackio sync --all` to sync all unsynced changes." + ) + + +def _handle_sync(args): + from trackio.deploy import sync_incremental + + if args.sync_all and args.project: + error_exit("Cannot use --all and --project together.") + if not args.sync_all and not args.project: + error_exit("Must provide either --project or --all.") + + if args.sync_all: + projects = SQLiteStorage.get_projects() + synced_any = False + for project in projects: + space_id = SQLiteStorage.get_space_id(project) + if space_id and SQLiteStorage.has_pending_data(project): + sync_incremental( + project, space_id, private=args.private, pending_only=True + ) + synced_any = True + if not synced_any: + print("No projects with unsynced data found.") + else: + space_id = args.space_id + if space_id is None: + space_id = SQLiteStorage.get_space_id(args.project) + sync( + project=args.project, + space_id=space_id, + private=args.private, + force=args.force, + sdk=args.sdk, + ) + + +def _extract_reports( + run: str, logs: list[dict], report_name: str | None = None +) -> list[dict]: + reports = [] + for log in logs: + timestamp = log.get("timestamp") + step = log.get("step") + for key, value in log.items(): + if report_name is not None and key != report_name: + continue + if isinstance(value, dict) and value.get("_type") == Markdown.TYPE: + content = value.get("_value") + if isinstance(content, str): + reports.append( + { + "run": run, + "report": key, + "step": step, + "timestamp": timestamp, + "content": content, + } + ) + return reports + + +def _handle_query(args): + remote = _get_remote(args) + try: + if remote: + result = remote.predict(args.project, args.sql, api_name="/query_project") + else: + result = SQLiteStorage.query_project(args.project, args.sql) + except FileNotFoundError as e: + error_exit(str(e)) + except ValueError as e: + error_exit(str(e)) + + if args.json: + print(format_json(result)) + else: + print(format_query_result(result)) + + +def main(): + parser = argparse.ArgumentParser(description="Trackio CLI") + parser.add_argument( + "--space", + required=False, + help="HF Space ID (e.g. 'user/space') or Space URL to query remotely.", + ) + parser.add_argument( + "--hf-token", + required=False, + help="HF token for accessing private Spaces.", + ) + subparsers = parser.add_subparsers(dest="command") + + ui_parser = subparsers.add_parser( + "show", help="Show the Trackio dashboard UI for a project" + ) + ui_parser.add_argument( + "--project", required=False, help="Project name to show in the dashboard" + ) + ui_parser.add_argument( + "--theme", + required=False, + default="default", + help="A Gradio Theme to use for the dashboard instead of the default, can be a built-in theme (e.g. 'soft', 'citrus'), or a theme from the Hub (e.g. 'gstaff/xkcd').", + ) + ui_parser.add_argument( + "--mcp-server", + action="store_true", + help="Enable MCP server functionality. The Trackio dashboard will be set up as an MCP server and certain functions will be exposed as MCP tools.", + ) + ui_parser.add_argument( + "--footer", + action="store_true", + default=True, + help="Show the Gradio footer. Use --no-footer to hide it.", + ) + ui_parser.add_argument( + "--no-footer", + dest="footer", + action="store_false", + help="Hide the Gradio footer.", + ) + ui_parser.add_argument( + "--color-palette", + required=False, + help="Comma-separated list of hex color codes for plot lines (e.g. '#FF0000,#00FF00,#0000FF'). If not provided, the TRACKIO_COLOR_PALETTE environment variable will be used, or the default palette if not set.", + ) + ui_parser.add_argument( + "--host", + required=False, + help="Host to bind the server to (e.g. '0.0.0.0' for remote access). If not provided, defaults to '127.0.0.1' (localhost only).", + ) + + subparsers.add_parser( + "status", + help="Show the status of all local Trackio projects, including sync status.", + ) + + sync_parser = subparsers.add_parser( + "sync", + help="Sync a local project's database to a Hugging Face Space. If the Space does not exist, it will be created.", + ) + sync_parser.add_argument( + "--project", + required=False, + help="The name of the local project.", + ) + sync_parser.add_argument( + "--space-id", + required=False, + help="The Hugging Face Space ID where the project will be synced (e.g. username/space_id). If not provided, uses the previously-configured Space.", + ) + sync_parser.add_argument( + "--all", + action="store_true", + dest="sync_all", + help="Sync all projects that have unsynced data to their configured Spaces.", + ) + sync_parser.add_argument( + "--private", + action="store_true", + help="Make the Hugging Face Space private if creating a new Space. By default, the repo will be public unless the organization's default is private. This value is ignored if the repo already exists.", + ) + sync_parser.add_argument( + "--force", + action="store_true", + help="Overwrite the existing database without prompting for confirmation.", + ) + sync_parser.add_argument( + "--sdk", + choices=["gradio", "static"], + default="gradio", + help="The type of Space to deploy. 'gradio' (default) deploys a live Gradio server. 'static' deploys a static Space that reads from an HF Bucket.", + ) + + freeze_parser = subparsers.add_parser( + "freeze", + help="Create a one-time static Space snapshot from a project's data.", + ) + freeze_parser.add_argument( + "--space-id", + required=True, + help="The source Gradio Space ID (e.g. username/space_id).", + ) + freeze_parser.add_argument( + "--project", + required=True, + help="The name of the project to freeze into a static snapshot.", + ) + freeze_parser.add_argument( + "--new-space-id", + required=False, + help="The Space ID for the new static Space. Defaults to {space_id}_static.", + ) + freeze_parser.add_argument( + "--private", + action="store_true", + help="Make the new static Space private.", + ) + + list_parser = subparsers.add_parser( + "list", + help="List projects, runs, or metrics", + ) + list_subparsers = list_parser.add_subparsers(dest="list_type", required=True) + + list_projects_parser = list_subparsers.add_parser( + "projects", + help="List all projects", + ) + list_projects_parser.add_argument( + "--json", + action="store_true", + help="Output in JSON format", + ) + + list_runs_parser = list_subparsers.add_parser( + "runs", + help="List runs for a project", + ) + list_runs_parser.add_argument( + "--project", + required=True, + help="Project name", + ) + list_runs_parser.add_argument( + "--json", + action="store_true", + help="Output in JSON format", + ) + + list_metrics_parser = list_subparsers.add_parser( + "metrics", + help="List metrics for a run", + ) + list_metrics_parser.add_argument( + "--project", + required=True, + help="Project name", + ) + list_metrics_parser.add_argument( + "--run", + required=True, + help="Run name", + ) + list_metrics_parser.add_argument( + "--json", + action="store_true", + help="Output in JSON format", + ) + + list_system_metrics_parser = list_subparsers.add_parser( + "system-metrics", + help="List system metrics for a run", + ) + list_system_metrics_parser.add_argument( + "--project", + required=True, + help="Project name", + ) + list_system_metrics_parser.add_argument( + "--run", + required=True, + help="Run name", + ) + list_system_metrics_parser.add_argument( + "--json", + action="store_true", + help="Output in JSON format", + ) + + list_alerts_parser = list_subparsers.add_parser( + "alerts", + help="List alerts for a project or run", + ) + list_alerts_parser.add_argument( + "--project", + required=True, + help="Project name", + ) + list_alerts_parser.add_argument( + "--run", + required=False, + help="Run name (optional)", + ) + list_alerts_parser.add_argument( + "--level", + required=False, + help="Filter by alert level (info, warn, error)", + ) + list_alerts_parser.add_argument( + "--json", + action="store_true", + help="Output in JSON format", + ) + list_alerts_parser.add_argument( + "--since", + required=False, + help="Only show alerts after this ISO 8601 timestamp", + ) + + list_reports_parser = list_subparsers.add_parser( + "reports", + help="List markdown reports for a project or run", + ) + list_reports_parser.add_argument( + "--project", + required=True, + help="Project name", + ) + list_reports_parser.add_argument( + "--run", + required=False, + help="Run name (optional)", + ) + list_reports_parser.add_argument( + "--json", + action="store_true", + help="Output in JSON format", + ) + + get_parser = subparsers.add_parser( + "get", + help="Get project, run, or metric information", + ) + get_subparsers = get_parser.add_subparsers(dest="get_type", required=True) + + get_project_parser = get_subparsers.add_parser( + "project", + help="Get project summary", + ) + get_project_parser.add_argument( + "--project", + required=True, + help="Project name", + ) + get_project_parser.add_argument( + "--json", + action="store_true", + help="Output in JSON format", + ) + + get_run_parser = get_subparsers.add_parser( + "run", + help="Get run summary", + ) + get_run_parser.add_argument( + "--project", + required=True, + help="Project name", + ) + get_run_parser.add_argument( + "--run", + required=True, + help="Run name", + ) + get_run_parser.add_argument( + "--json", + action="store_true", + help="Output in JSON format", + ) + + get_metric_parser = get_subparsers.add_parser( + "metric", + help="Get metric values for a run", + ) + get_metric_parser.add_argument( + "--project", + required=True, + help="Project name", + ) + get_metric_parser.add_argument( + "--run", + required=True, + help="Run name", + ) + get_metric_parser.add_argument( + "--metric", + required=True, + help="Metric name", + ) + get_metric_parser.add_argument( + "--step", + type=int, + required=False, + help="Get metric at exactly this step", + ) + get_metric_parser.add_argument( + "--around", + type=int, + required=False, + help="Get metrics around this step (use with --window)", + ) + get_metric_parser.add_argument( + "--at-time", + required=False, + help="Get metrics around this ISO 8601 timestamp (use with --window)", + ) + get_metric_parser.add_argument( + "--window", + type=int, + required=False, + default=10, + help="Window size: ±steps for --around, ±seconds for --at-time (default: 10)", + ) + get_metric_parser.add_argument( + "--json", + action="store_true", + help="Output in JSON format", + ) + + get_snapshot_parser = get_subparsers.add_parser( + "snapshot", + help="Get all metrics at/around a step or timestamp", + ) + get_snapshot_parser.add_argument( + "--project", + required=True, + help="Project name", + ) + get_snapshot_parser.add_argument( + "--run", + required=True, + help="Run name", + ) + get_snapshot_parser.add_argument( + "--step", + type=int, + required=False, + help="Get all metrics at exactly this step", + ) + get_snapshot_parser.add_argument( + "--around", + type=int, + required=False, + help="Get all metrics around this step (use with --window)", + ) + get_snapshot_parser.add_argument( + "--at-time", + required=False, + help="Get all metrics around this ISO 8601 timestamp (use with --window)", + ) + get_snapshot_parser.add_argument( + "--window", + type=int, + required=False, + default=10, + help="Window size: ±steps for --around, ±seconds for --at-time (default: 10)", + ) + get_snapshot_parser.add_argument( + "--json", + action="store_true", + help="Output in JSON format", + ) + + get_system_metric_parser = get_subparsers.add_parser( + "system-metric", + help="Get system metric values for a run", + ) + get_system_metric_parser.add_argument( + "--project", + required=True, + help="Project name", + ) + get_system_metric_parser.add_argument( + "--run", + required=True, + help="Run name", + ) + get_system_metric_parser.add_argument( + "--metric", + required=False, + help="System metric name (optional, if not provided returns all system metrics)", + ) + get_system_metric_parser.add_argument( + "--json", + action="store_true", + help="Output in JSON format", + ) + + get_alerts_parser = get_subparsers.add_parser( + "alerts", + help="Get alerts for a project or run", + ) + get_alerts_parser.add_argument( + "--project", + required=True, + help="Project name", + ) + get_alerts_parser.add_argument( + "--run", + required=False, + help="Run name (optional)", + ) + get_alerts_parser.add_argument( + "--level", + required=False, + help="Filter by alert level (info, warn, error)", + ) + get_alerts_parser.add_argument( + "--json", + action="store_true", + help="Output in JSON format", + ) + get_alerts_parser.add_argument( + "--since", + required=False, + help="Only show alerts after this ISO 8601 timestamp", + ) + + get_report_parser = get_subparsers.add_parser( + "report", + help="Get markdown report entries for a run", + ) + get_report_parser.add_argument( + "--project", + required=True, + help="Project name", + ) + get_report_parser.add_argument( + "--run", + required=True, + help="Run name", + ) + get_report_parser.add_argument( + "--report", + required=True, + help="Report metric name", + ) + get_report_parser.add_argument( + "--json", + action="store_true", + help="Output in JSON format", + ) + + query_parser = subparsers.add_parser( + "query", + help="Run a read-only SQL query against a project database", + ) + query_subparsers = query_parser.add_subparsers(dest="query_type", required=True) + query_project_parser = query_subparsers.add_parser( + "project", + help="Run a read-only SQL query against a project's SQLite database", + ) + query_project_parser.add_argument( + "--project", + required=True, + help="Project name", + ) + query_project_parser.add_argument( + "--sql", + required=True, + help="Read-only SQL query to execute", + ) + query_project_parser.add_argument( + "--json", + action="store_true", + help="Output in JSON format", + ) + + skills_parser = subparsers.add_parser( + "skills", + help="Manage Trackio skills for AI coding assistants", + ) + skills_subparsers = skills_parser.add_subparsers( + dest="skills_action", required=True + ) + skills_add_parser = skills_subparsers.add_parser( + "add", + help="Download and install the Trackio skill for an AI assistant", + ) + skills_add_parser.add_argument( + "--cursor", + action="store_true", + help="Install for Cursor", + ) + skills_add_parser.add_argument( + "--claude", + action="store_true", + help="Install for Claude Code", + ) + skills_add_parser.add_argument( + "--codex", + action="store_true", + help="Install for Codex", + ) + skills_add_parser.add_argument( + "--opencode", + action="store_true", + help="Install for OpenCode", + ) + skills_add_parser.add_argument( + "--global", + dest="global_", + action="store_true", + help="Install globally (user-level) instead of in the current project directory", + ) + skills_add_parser.add_argument( + "--dest", + type=str, + required=False, + help="Install into a custom destination (path to skills directory)", + ) + skills_add_parser.add_argument( + "--force", + action="store_true", + help="Overwrite existing skill if it already exists", + ) + + args, unknown_args = parser.parse_known_args() + if unknown_args: + trailing_global_parser = argparse.ArgumentParser(add_help=False) + trailing_global_parser.add_argument("--space", required=False) + trailing_global_parser.add_argument("--hf-token", required=False) + trailing_globals, remaining_unknown = trailing_global_parser.parse_known_args( + unknown_args + ) + if remaining_unknown: + parser.error(f"unrecognized arguments: {' '.join(remaining_unknown)}") + if trailing_globals.space is not None: + args.space = trailing_globals.space + if trailing_globals.hf_token is not None: + args.hf_token = trailing_globals.hf_token + + if args.command in ("show", "status", "sync", "freeze", "skills") and _get_space( + args + ): + error_exit( + f"The '{args.command}' command does not support --space (remote mode)." + ) + + if args.command == "show": + color_palette = None + if args.color_palette: + color_palette = [color.strip() for color in args.color_palette.split(",")] + show( + project=args.project, + theme=args.theme, + mcp_server=args.mcp_server, + footer=args.footer, + color_palette=color_palette, + host=args.host, + ) + elif args.command == "status": + _handle_status() + elif args.command == "sync": + _handle_sync(args) + elif args.command == "freeze": + freeze( + space_id=args.space_id, + project=args.project, + new_space_id=args.new_space_id, + private=args.private, + ) + elif args.command == "list": + remote = _get_remote(args) + if args.list_type == "projects": + if remote: + projects = remote.predict(api_name="/get_all_projects") + else: + projects = SQLiteStorage.get_projects() + if args.json: + print(format_json({"projects": projects})) + else: + print(format_list(projects, "Projects")) + elif args.list_type == "runs": + if remote: + run_records = remote.predict( + args.project, api_name="/get_runs_for_project" + ) + runs = [r["name"] if isinstance(r, dict) else r for r in run_records] + else: + db_path = SQLiteStorage.get_project_db_path(args.project) + if not db_path.exists(): + error_exit(f"Project '{args.project}' not found.") + runs = SQLiteStorage.get_runs(args.project) + if args.json: + print(format_json({"project": args.project, "runs": runs})) + else: + print(format_list(runs, f"Runs in '{args.project}'")) + elif args.list_type == "metrics": + if remote: + metrics = remote.predict( + args.project, args.run, api_name="/get_metrics_for_run" + ) + else: + db_path = SQLiteStorage.get_project_db_path(args.project) + if not db_path.exists(): + error_exit(f"Project '{args.project}' not found.") + runs = SQLiteStorage.get_runs(args.project) + if args.run not in runs: + error_exit( + f"Run '{args.run}' not found in project '{args.project}'." + ) + metrics = SQLiteStorage.get_all_metrics_for_run(args.project, args.run) + if args.json: + print( + format_json( + {"project": args.project, "run": args.run, "metrics": metrics} + ) + ) + else: + print( + format_list( + metrics, f"Metrics for '{args.run}' in '{args.project}'" + ) + ) + elif args.list_type == "system-metrics": + if remote: + system_metrics = remote.predict( + args.project, args.run, api_name="/get_system_metrics_for_run" + ) + else: + db_path = SQLiteStorage.get_project_db_path(args.project) + if not db_path.exists(): + error_exit(f"Project '{args.project}' not found.") + runs = SQLiteStorage.get_runs(args.project) + if args.run not in runs: + error_exit( + f"Run '{args.run}' not found in project '{args.project}'." + ) + system_metrics = SQLiteStorage.get_all_system_metrics_for_run( + args.project, args.run + ) + if args.json: + print( + format_json( + { + "project": args.project, + "run": args.run, + "system_metrics": system_metrics, + } + ) + ) + else: + print(format_system_metric_names(system_metrics)) + elif args.list_type == "alerts": + if remote: + alerts = remote.predict( + args.project, + args.run, + args.level, + args.since, + api_name="/get_alerts", + ) + else: + db_path = SQLiteStorage.get_project_db_path(args.project) + if not db_path.exists(): + error_exit(f"Project '{args.project}' not found.") + alerts = SQLiteStorage.get_alerts( + args.project, + run_name=args.run, + level=args.level, + since=args.since, + ) + if args.json: + print( + format_json( + { + "project": args.project, + "run": args.run, + "level": args.level, + "since": args.since, + "alerts": alerts, + } + ) + ) + else: + print(format_alerts(alerts)) + elif args.list_type == "reports": + if remote: + run_records = remote.predict( + args.project, api_name="/get_runs_for_project" + ) + runs = [r["name"] if isinstance(r, dict) else r for r in run_records] + else: + db_path = SQLiteStorage.get_project_db_path(args.project) + if not db_path.exists(): + error_exit(f"Project '{args.project}' not found.") + runs = SQLiteStorage.get_runs(args.project) + if args.run and args.run not in runs: + error_exit(f"Run '{args.run}' not found in project '{args.project}'.") + + target_runs = [args.run] if args.run else runs + all_reports = [] + for run_name in target_runs: + if remote: + logs = remote.predict(args.project, run_name, api_name="/get_logs") + else: + logs = SQLiteStorage.get_logs(args.project, run_name) + all_reports.extend(_extract_reports(run_name, logs)) + + if args.json: + print( + format_json( + { + "project": args.project, + "run": args.run, + "reports": all_reports, + } + ) + ) + else: + report_lines = [ + f"{entry['run']} | {entry['report']} | step={entry['step']} | {entry['timestamp']}" + for entry in all_reports + ] + if args.run: + print( + format_list( + report_lines, + f"Reports for '{args.run}' in '{args.project}'", + ) + ) + else: + print(format_list(report_lines, f"Reports in '{args.project}'")) + elif args.command == "get": + remote = _get_remote(args) + if args.get_type == "project": + if remote: + summary = remote.predict(args.project, api_name="/get_project_summary") + else: + db_path = SQLiteStorage.get_project_db_path(args.project) + if not db_path.exists(): + error_exit(f"Project '{args.project}' not found.") + summary = get_project_summary(args.project) + if args.json: + print(format_json(summary)) + else: + print(format_project_summary(summary)) + elif args.get_type == "run": + if remote: + summary = remote.predict( + args.project, args.run, api_name="/get_run_summary" + ) + else: + db_path = SQLiteStorage.get_project_db_path(args.project) + if not db_path.exists(): + error_exit(f"Project '{args.project}' not found.") + runs = SQLiteStorage.get_runs(args.project) + if args.run not in runs: + error_exit( + f"Run '{args.run}' not found in project '{args.project}'." + ) + summary = get_run_summary(args.project, args.run) + if args.json: + print(format_json(summary)) + else: + print(format_run_summary(summary)) + elif args.get_type == "metric": + at_time = getattr(args, "at_time", None) + if remote: + values = remote.predict( + args.project, + args.run, + args.metric, + args.step, + args.around, + at_time, + args.window, + api_name="/get_metric_values", + ) + else: + db_path = SQLiteStorage.get_project_db_path(args.project) + if not db_path.exists(): + error_exit(f"Project '{args.project}' not found.") + runs = SQLiteStorage.get_runs(args.project) + if args.run not in runs: + error_exit( + f"Run '{args.run}' not found in project '{args.project}'." + ) + metrics = SQLiteStorage.get_all_metrics_for_run(args.project, args.run) + if args.metric not in metrics: + error_exit( + f"Metric '{args.metric}' not found in run '{args.run}' of project '{args.project}'." + ) + values = SQLiteStorage.get_metric_values( + args.project, + args.run, + args.metric, + step=args.step, + around_step=args.around, + at_time=at_time, + window=args.window, + ) + if args.json: + print( + format_json( + { + "project": args.project, + "run": args.run, + "metric": args.metric, + "values": values, + } + ) + ) + else: + print(format_metric_values(values)) + elif args.get_type == "snapshot": + if not args.step and not args.around and not getattr(args, "at_time", None): + error_exit( + "Provide --step, --around (with --window), or --at-time (with --window)." + ) + at_time = getattr(args, "at_time", None) + if remote: + snapshot = remote.predict( + args.project, + args.run, + args.step, + args.around, + at_time, + args.window, + api_name="/get_snapshot", + ) + else: + db_path = SQLiteStorage.get_project_db_path(args.project) + if not db_path.exists(): + error_exit(f"Project '{args.project}' not found.") + runs = SQLiteStorage.get_runs(args.project) + if args.run not in runs: + error_exit( + f"Run '{args.run}' not found in project '{args.project}'." + ) + snapshot = SQLiteStorage.get_snapshot( + args.project, + args.run, + step=args.step, + around_step=args.around, + at_time=at_time, + window=args.window, + ) + if args.json: + result = { + "project": args.project, + "run": args.run, + "metrics": snapshot, + } + if args.step is not None: + result["step"] = args.step + if args.around is not None: + result["around"] = args.around + result["window"] = args.window + if at_time is not None: + result["at_time"] = at_time + result["window"] = args.window + print(format_json(result)) + else: + print(format_snapshot(snapshot)) + elif args.get_type == "system-metric": + if remote: + system_metrics = remote.predict( + args.project, args.run, api_name="/get_system_logs" + ) + if args.metric: + all_system_metric_names = remote.predict( + args.project, + args.run, + api_name="/get_system_metrics_for_run", + ) + if args.metric not in all_system_metric_names: + error_exit( + f"System metric '{args.metric}' not found in run '{args.run}' of project '{args.project}'." + ) + filtered_metrics = [ + { + k: v + for k, v in entry.items() + if k == "timestamp" or k == args.metric + } + for entry in system_metrics + if args.metric in entry + ] + if args.json: + print( + format_json( + { + "project": args.project, + "run": args.run, + "metric": args.metric, + "values": filtered_metrics, + } + ) + ) + else: + print(format_system_metrics(filtered_metrics)) + else: + if args.json: + print( + format_json( + { + "project": args.project, + "run": args.run, + "system_metrics": system_metrics, + } + ) + ) + else: + print(format_system_metrics(system_metrics)) + else: + db_path = SQLiteStorage.get_project_db_path(args.project) + if not db_path.exists(): + error_exit(f"Project '{args.project}' not found.") + runs = SQLiteStorage.get_runs(args.project) + if args.run not in runs: + error_exit( + f"Run '{args.run}' not found in project '{args.project}'." + ) + if args.metric: + system_metrics = SQLiteStorage.get_system_logs( + args.project, args.run + ) + all_system_metric_names = ( + SQLiteStorage.get_all_system_metrics_for_run( + args.project, args.run + ) + ) + if args.metric not in all_system_metric_names: + error_exit( + f"System metric '{args.metric}' not found in run '{args.run}' of project '{args.project}'." + ) + filtered_metrics = [ + { + k: v + for k, v in entry.items() + if k == "timestamp" or k == args.metric + } + for entry in system_metrics + if args.metric in entry + ] + if args.json: + print( + format_json( + { + "project": args.project, + "run": args.run, + "metric": args.metric, + "values": filtered_metrics, + } + ) + ) + else: + print(format_system_metrics(filtered_metrics)) + else: + system_metrics = SQLiteStorage.get_system_logs( + args.project, args.run + ) + if args.json: + print( + format_json( + { + "project": args.project, + "run": args.run, + "system_metrics": system_metrics, + } + ) + ) + else: + print(format_system_metrics(system_metrics)) + elif args.get_type == "alerts": + if remote: + alerts = remote.predict( + args.project, + args.run, + args.level, + args.since, + api_name="/get_alerts", + ) + else: + db_path = SQLiteStorage.get_project_db_path(args.project) + if not db_path.exists(): + error_exit(f"Project '{args.project}' not found.") + alerts = SQLiteStorage.get_alerts( + args.project, + run_name=args.run, + level=args.level, + since=args.since, + ) + if args.json: + print( + format_json( + { + "project": args.project, + "run": args.run, + "level": args.level, + "since": args.since, + "alerts": alerts, + } + ) + ) + else: + print(format_alerts(alerts)) + elif args.get_type == "report": + if remote: + logs = remote.predict(args.project, args.run, api_name="/get_logs") + else: + db_path = SQLiteStorage.get_project_db_path(args.project) + if not db_path.exists(): + error_exit(f"Project '{args.project}' not found.") + runs = SQLiteStorage.get_runs(args.project) + if args.run not in runs: + error_exit( + f"Run '{args.run}' not found in project '{args.project}'." + ) + logs = SQLiteStorage.get_logs(args.project, args.run) + + reports = _extract_reports(args.run, logs, report_name=args.report) + if not reports: + error_exit( + f"Report '{args.report}' not found in run '{args.run}' of project '{args.project}'." + ) + + if args.json: + print( + format_json( + { + "project": args.project, + "run": args.run, + "report": args.report, + "values": reports, + } + ) + ) + else: + output = [] + for idx, entry in enumerate(reports, start=1): + output.append( + f"Entry {idx} | step={entry['step']} | timestamp={entry['timestamp']}" + ) + output.append(entry["content"]) + if idx < len(reports): + output.append("-" * 80) + print("\n".join(output)) + elif args.command == "query": + if args.query_type == "project": + _handle_query(args) + elif args.command == "skills": + if args.skills_action == "add": + _handle_skills_add(args) + else: + parser.print_help() + + +def _handle_skills_add(args): + import shutil + from pathlib import Path + + try: + from huggingface_hub.cli.skills import ( + CENTRAL_GLOBAL, + CENTRAL_LOCAL, + GLOBAL_TARGETS, + LOCAL_TARGETS, + ) + except (ImportError, ModuleNotFoundError): + error_exit( + "The 'trackio skills' command requires huggingface_hub >= 1.4.0.\n" + "Please upgrade: pip install --upgrade huggingface_hub" + ) + + SKILL_ID = "trackio" + GITHUB_RAW = "https://raw.githubusercontent.com/gradio-app/trackio/main" + SKILL_PREFIX = ".agents/skills/trackio" + SKILL_FILES = [ + "SKILL.md", + "alerts.md", + "logging_metrics.md", + "retrieving_metrics.md", + "storage_schema.md", + ] + + if not (args.cursor or args.claude or args.codex or args.opencode or args.dest): + error_exit( + "Pick a destination via --cursor, --claude, --codex, --opencode, or --dest." + ) + + def download(url: str) -> str: + from huggingface_hub.utils import get_session + + try: + response = get_session().get(url) + response.raise_for_status() + except Exception as e: + error_exit( + f"Failed to download {url}\n{e}\n\n" + "Make sure you have internet access. The skill files are fetched from " + "the Trackio GitHub repository." + ) + return response.text + + def remove_existing(path: Path, force: bool): + if not (path.exists() or path.is_symlink()): + return + if not force: + error_exit( + f"Skill already exists at {path}.\nRe-run with --force to overwrite." + ) + if path.is_dir() and not path.is_symlink(): + shutil.rmtree(path) + else: + path.unlink() + + def install_to(skills_dir: Path, force: bool) -> Path: + skills_dir = skills_dir.expanduser().resolve() + skills_dir.mkdir(parents=True, exist_ok=True) + dest = skills_dir / SKILL_ID + remove_existing(dest, force) + dest.mkdir() + for fname in SKILL_FILES: + content = download(f"{GITHUB_RAW}/{SKILL_PREFIX}/{fname}") + (dest / fname).write_text(content, encoding="utf-8") + return dest + + def create_symlink( + agent_skills_dir: Path, central_skill_path: Path, force: bool + ) -> Path: + agent_skills_dir = agent_skills_dir.expanduser().resolve() + agent_skills_dir.mkdir(parents=True, exist_ok=True) + link_path = agent_skills_dir / SKILL_ID + remove_existing(link_path, force) + link_path.symlink_to(os.path.relpath(central_skill_path, agent_skills_dir)) + return link_path + + global_targets = {**GLOBAL_TARGETS, "cursor": Path("~/.cursor/skills")} + local_targets = {**LOCAL_TARGETS, "cursor": Path(".cursor/skills")} + targets_dict = global_targets if args.global_ else local_targets + + if args.dest: + if args.cursor or args.claude or args.codex or args.opencode or args.global_: + error_exit("--dest cannot be combined with agent flags or --global.") + skill_dest = install_to(Path(args.dest), args.force) + print(f"Installed '{SKILL_ID}' to {skill_dest}") + return + + agent_targets = [] + if args.cursor: + agent_targets.append(targets_dict["cursor"]) + if args.claude: + agent_targets.append(targets_dict["claude"]) + if args.codex: + agent_targets.append(targets_dict["codex"]) + if args.opencode: + agent_targets.append(targets_dict["opencode"]) + + central_path = CENTRAL_GLOBAL if args.global_ else CENTRAL_LOCAL + central_skill_path = install_to(central_path, args.force) + print(f"Installed '{SKILL_ID}' to central location: {central_skill_path}") + + for agent_target in agent_targets: + link_path = create_symlink(agent_target, central_skill_path, args.force) + print(f"Created symlink: {link_path}") + + +if __name__ == "__main__": + main() diff --git a/trackio/cli_helpers.py b/trackio/cli_helpers.py new file mode 100644 index 0000000000000000000000000000000000000000..13287d05a92a824a9a779142f56a74d445f25254 --- /dev/null +++ b/trackio/cli_helpers.py @@ -0,0 +1,204 @@ +import json +import sys +from typing import Any + + +def format_json(data: Any) -> str: + """Format data as JSON.""" + return json.dumps(data, indent=2) + + +def format_list(items: list[str], title: str | None = None) -> str: + """Format a list of items in human-readable format.""" + if not items: + return f"No {title.lower() if title else 'items'} found." + + output = [] + if title: + output.append(f"{title}:") + + for item in items: + output.append(f" - {item}") + + return "\n".join(output) + + +def format_project_summary(summary: dict) -> str: + """Format project summary in human-readable format.""" + output = [f"Project: {summary['project']}"] + output.append(f"Number of runs: {summary['num_runs']}") + + if summary["runs"]: + output.append("\nRuns:") + for run in summary["runs"]: + output.append(f" - {run}") + else: + output.append("\nNo runs found.") + + if summary.get("last_activity"): + output.append(f"\nLast activity (max step): {summary['last_activity']}") + + return "\n".join(output) + + +def format_run_summary(summary: dict) -> str: + """Format run summary in human-readable format.""" + output = [f"Project: {summary['project']}"] + output.append(f"Run: {summary['run']}") + output.append(f"Number of logs: {summary['num_logs']}") + + if summary.get("last_step") is not None: + output.append(f"Last step: {summary['last_step']}") + + if summary.get("metrics"): + output.append("\nMetrics:") + for metric in summary["metrics"]: + output.append(f" - {metric}") + else: + output.append("\nNo metrics found.") + + config = summary.get("config") + if config: + output.append("\nConfig:") + config_display = {k: v for k, v in config.items() if not k.startswith("_")} + if config_display: + for key, value in config_display.items(): + output.append(f" {key}: {value}") + else: + output.append(" (no config)") + else: + output.append("\nConfig: (no config)") + + return "\n".join(output) + + +def format_metric_values(values: list[dict]) -> str: + """Format metric values in human-readable format.""" + if not values: + return "No metric values found." + + output = [f"Found {len(values)} value(s):\n"] + output.append("Step | Timestamp | Value") + output.append("-" * 50) + + for value in values: + step = value.get("step", "N/A") + timestamp = value.get("timestamp", "N/A") + val = value.get("value", "N/A") + output.append(f"{step} | {timestamp} | {val}") + + return "\n".join(output) + + +def format_system_metrics(metrics: list[dict]) -> str: + """Format system metrics in human-readable format.""" + if not metrics: + return "No system metrics found." + + output = [f"Found {len(metrics)} system metric entry/entries:\n"] + + for i, entry in enumerate(metrics): + timestamp = entry.get("timestamp", "N/A") + output.append(f"\nEntry {i + 1} (Timestamp: {timestamp}):") + for key, value in entry.items(): + if key != "timestamp": + output.append(f" {key}: {value}") + + return "\n".join(output) + + +def format_system_metric_names(names: list[str]) -> str: + """Format system metric names in human-readable format.""" + return format_list(names, "System Metrics") + + +def format_snapshot(snapshot: dict[str, list[dict]]) -> str: + """Format a metrics snapshot in human-readable format.""" + if not snapshot: + return "No metrics found in the specified range." + + output = [] + for metric_name, values in sorted(snapshot.items()): + output.append(f"\n{metric_name}:") + output.append(" Step | Timestamp | Value") + output.append(" " + "-" * 48) + for v in values: + step = v.get("step", "N/A") + ts = v.get("timestamp", "N/A") + val = v.get("value", "N/A") + output.append(f" {step} | {ts} | {val}") + + return "\n".join(output) + + +def format_alerts(alerts: list[dict]) -> str: + """Format alerts in human-readable format.""" + if not alerts: + return "No alerts found." + + output = [f"Found {len(alerts)} alert(s):\n"] + output.append("Timestamp | Run | Level | Title | Text | Step") + output.append("-" * 80) + + for a in alerts: + ts = a.get("timestamp", "N/A") + run = a.get("run", "N/A") + level = a.get("level", "N/A").upper() + title = a.get("title", "") + text = a.get("text", "") or "" + step = a.get("step", "N/A") + output.append(f"{ts} | {run} | {level} | {title} | {text} | {step}") + + return "\n".join(output) + + +def format_query_result(result: dict[str, Any]) -> str: + """Format SQL query results in human-readable format.""" + columns = result.get("columns", []) + rows = result.get("rows", []) + row_count = result.get("row_count", 0) + + if not columns: + return f"Query returned {row_count} row(s)." + + rendered_rows = [] + for row in rows: + rendered_rows.append( + [ + "" if row.get(column) is None else str(row.get(column)) + for column in columns + ] + ) + + widths = [] + for idx, column in enumerate(columns): + cell_width = max( + (len(rendered_row[idx]) for rendered_row in rendered_rows), default=0 + ) + widths.append(max(len(column), cell_width)) + + header = " | ".join( + column.ljust(width) for column, width in zip(columns, widths, strict=False) + ) + separator = "-+-".join("-" * width for width in widths) + output = [f"Query returned {row_count} row(s).", header, separator] + + if not rendered_rows: + output.append("(no rows)") + return "\n".join(output) + + for rendered_row in rendered_rows: + output.append( + " | ".join( + value.ljust(width) + for value, width in zip(rendered_row, widths, strict=False) + ) + ) + + return "\n".join(output) + + +def error_exit(message: str, code: int = 1) -> None: + """Print error message and exit.""" + print(f"Error: {message}", file=sys.stderr) + sys.exit(code) diff --git a/trackio/commit_scheduler.py b/trackio/commit_scheduler.py new file mode 100644 index 0000000000000000000000000000000000000000..2e1cbabc2f9e576def2332cd4fbdc5788828ace6 --- /dev/null +++ b/trackio/commit_scheduler.py @@ -0,0 +1,310 @@ +# Originally copied from https://github.com/huggingface/huggingface_hub/blob/d0a948fc2a32ed6e557042a95ef3e4af97ec4a7c/src/huggingface_hub/_commit_scheduler.py + +import atexit +import logging +import time +from concurrent.futures import Future +from dataclasses import dataclass +from pathlib import Path +from threading import Lock, Thread +from typing import Callable, Dict, List, Union + +from huggingface_hub.hf_api import ( + DEFAULT_IGNORE_PATTERNS, + CommitInfo, + CommitOperationAdd, + HfApi, +) +from huggingface_hub.utils import filter_repo_objects + +logger = logging.getLogger(__name__) + + +@dataclass(frozen=True) +class _FileToUpload: + """Temporary dataclass to store info about files to upload. Not meant to be used directly.""" + + local_path: Path + path_in_repo: str + size_limit: int + last_modified: float + + +class CommitScheduler: + """ + Scheduler to upload a local folder to the Hub at regular intervals (e.g. push to hub every 5 minutes). + + The recommended way to use the scheduler is to use it as a context manager. This ensures that the scheduler is + properly stopped and the last commit is triggered when the script ends. The scheduler can also be stopped manually + with the `stop` method. Checkout the [upload guide](https://huggingface.co/docs/huggingface_hub/guides/upload#scheduled-uploads) + to learn more about how to use it. + + Args: + repo_id (`str`): + The id of the repo to commit to. + folder_path (`str` or `Path`): + Path to the local folder to upload regularly. + every (`int` or `float`, *optional*): + The number of minutes between each commit. Defaults to 5 minutes. + path_in_repo (`str`, *optional*): + Relative path of the directory in the repo, for example: `"checkpoints/"`. Defaults to the root folder + of the repository. + repo_type (`str`, *optional*): + The type of the repo to commit to. Defaults to `model`. + revision (`str`, *optional*): + The revision of the repo to commit to. Defaults to `main`. + private (`bool`, *optional*): + Whether to make the repo private. If `None` (default), the repo will be public unless the organization's default is private. This value is ignored if the repo already exists. + token (`str`, *optional*): + The token to use to commit to the repo. Defaults to the token saved on the machine. + allow_patterns (`List[str]` or `str`, *optional*): + If provided, only files matching at least one pattern are uploaded. + ignore_patterns (`List[str]` or `str`, *optional*): + If provided, files matching any of the patterns are not uploaded. + squash_history (`bool`, *optional*): + Whether to squash the history of the repo after each commit. Defaults to `False`. Squashing commits is + useful to avoid degraded performances on the repo when it grows too large. + hf_api (`HfApi`, *optional*): + The [`HfApi`] client to use to commit to the Hub. Can be set with custom settings (user agent, token,...). + on_before_commit (`Callable[[], None]`, *optional*): + If specified, a function that will be called before the CommitScheduler lists files to create a commit. + + Example: + ```py + >>> from pathlib import Path + >>> from huggingface_hub import CommitScheduler + + # Scheduler uploads every 10 minutes + >>> csv_path = Path("watched_folder/data.csv") + >>> CommitScheduler(repo_id="test_scheduler", repo_type="dataset", folder_path=csv_path.parent, every=10) + + >>> with csv_path.open("a") as f: + ... f.write("first line") + + # Some time later (...) + >>> with csv_path.open("a") as f: + ... f.write("second line") + ``` + + Example using a context manager: + ```py + >>> from pathlib import Path + >>> from huggingface_hub import CommitScheduler + + >>> with CommitScheduler(repo_id="test_scheduler", repo_type="dataset", folder_path="watched_folder", every=10) as scheduler: + ... csv_path = Path("watched_folder/data.csv") + ... with csv_path.open("a") as f: + ... f.write("first line") + ... (...) + ... with csv_path.open("a") as f: + ... f.write("second line") + + # Scheduler is now stopped and last commit have been triggered + ``` + """ + + def __init__( + self, + *, + repo_id: str, + folder_path: Union[str, Path], + every: Union[int, float] = 5, + path_in_repo: str | None = None, + repo_type: str | None = None, + revision: str | None = None, + private: bool | None = None, + token: str | None = None, + allow_patterns: list[str] | str | None = None, + ignore_patterns: list[str] | str | None = None, + squash_history: bool = False, + hf_api: HfApi | None = None, + on_before_commit: Callable[[], None] | None = None, + ) -> None: + self.api = hf_api or HfApi(token=token) + self.on_before_commit = on_before_commit + + # Folder + self.folder_path = Path(folder_path).expanduser().resolve() + self.path_in_repo = path_in_repo or "" + self.allow_patterns = allow_patterns + + if ignore_patterns is None: + ignore_patterns = [] + elif isinstance(ignore_patterns, str): + ignore_patterns = [ignore_patterns] + self.ignore_patterns = ignore_patterns + DEFAULT_IGNORE_PATTERNS + + if self.folder_path.is_file(): + raise ValueError( + f"'folder_path' must be a directory, not a file: '{self.folder_path}'." + ) + self.folder_path.mkdir(parents=True, exist_ok=True) + + # Repository + repo_url = self.api.create_repo( + repo_id=repo_id, private=private, repo_type=repo_type, exist_ok=True + ) + self.repo_id = repo_url.repo_id + self.repo_type = repo_type + self.revision = revision + self.token = token + + self.last_uploaded: Dict[Path, float] = {} + self.last_push_time: float | None = None + + if not every > 0: + raise ValueError(f"'every' must be a positive integer, not '{every}'.") + self.lock = Lock() + self.every = every + self.squash_history = squash_history + + logger.info( + f"Scheduled job to push '{self.folder_path}' to '{self.repo_id}' every {self.every} minutes." + ) + self._scheduler_thread = Thread(target=self._run_scheduler, daemon=True) + self._scheduler_thread.start() + atexit.register(self._push_to_hub) + + self.__stopped = False + + def stop(self) -> None: + """Stop the scheduler. + + A stopped scheduler cannot be restarted. Mostly for tests purposes. + """ + self.__stopped = True + + def __enter__(self) -> "CommitScheduler": + return self + + def __exit__(self, exc_type, exc_value, traceback) -> None: + # Upload last changes before exiting + self.trigger().result() + self.stop() + return + + def _run_scheduler(self) -> None: + """Dumb thread waiting between each scheduled push to Hub.""" + while True: + self.last_future = self.trigger() + time.sleep(self.every * 60) + if self.__stopped: + break + + def trigger(self) -> Future: + """Trigger a `push_to_hub` and return a future. + + This method is automatically called every `every` minutes. You can also call it manually to trigger a commit + immediately, without waiting for the next scheduled commit. + """ + return self.api.run_as_future(self._push_to_hub) + + def _push_to_hub(self) -> CommitInfo | None: + if self.__stopped: # If stopped, already scheduled commits are ignored + return None + + logger.info("(Background) scheduled commit triggered.") + try: + value = self.push_to_hub() + if self.squash_history: + logger.info("(Background) squashing repo history.") + self.api.super_squash_history( + repo_id=self.repo_id, repo_type=self.repo_type, branch=self.revision + ) + return value + except Exception as e: + logger.error( + f"Error while pushing to Hub: {e}" + ) # Depending on the setup, error might be silenced + raise + + def push_to_hub(self) -> CommitInfo | None: + """ + Push folder to the Hub and return the commit info. + + + + This method is not meant to be called directly. It is run in the background by the scheduler, respecting a + queue mechanism to avoid concurrent commits. Making a direct call to the method might lead to concurrency + issues. + + + + The default behavior of `push_to_hub` is to assume an append-only folder. It lists all files in the folder and + uploads only changed files. If no changes are found, the method returns without committing anything. If you want + to change this behavior, you can inherit from [`CommitScheduler`] and override this method. This can be useful + for example to compress data together in a single file before committing. For more details and examples, check + out our [integration guide](https://huggingface.co/docs/huggingface_hub/main/en/guides/upload#scheduled-uploads). + """ + # Check files to upload (with lock) + with self.lock: + if self.on_before_commit is not None: + self.on_before_commit() + + logger.debug("Listing files to upload for scheduled commit.") + + # List files from folder (taken from `_prepare_upload_folder_additions`) + relpath_to_abspath = { + path.relative_to(self.folder_path).as_posix(): path + for path in sorted( + self.folder_path.glob("**/*") + ) # sorted to be deterministic + if path.is_file() + } + prefix = f"{self.path_in_repo.strip('/')}/" if self.path_in_repo else "" + + # Filter with pattern + filter out unchanged files + retrieve current file size + files_to_upload: List[_FileToUpload] = [] + for relpath in filter_repo_objects( + relpath_to_abspath.keys(), + allow_patterns=self.allow_patterns, + ignore_patterns=self.ignore_patterns, + ): + local_path = relpath_to_abspath[relpath] + stat = local_path.stat() + if ( + self.last_uploaded.get(local_path) is None + or self.last_uploaded[local_path] != stat.st_mtime + ): + files_to_upload.append( + _FileToUpload( + local_path=local_path, + path_in_repo=prefix + relpath, + size_limit=stat.st_size, + last_modified=stat.st_mtime, + ) + ) + + # Return if nothing to upload + if len(files_to_upload) == 0: + logger.debug("Dropping schedule commit: no changed file to upload.") + return None + + # Convert `_FileToUpload` as `CommitOperationAdd` (=> compute file shas + limit to file size) + logger.debug("Removing unchanged files since previous scheduled commit.") + add_operations = [ + CommitOperationAdd( + # TODO: Cap the file to its current size, even if the user append data to it while a scheduled commit is happening + # (requires an upstream fix for XET-535: `hf_xet` should support `BinaryIO` for upload) + path_or_fileobj=file_to_upload.local_path, + path_in_repo=file_to_upload.path_in_repo, + ) + for file_to_upload in files_to_upload + ] + + # Upload files (append mode expected - no need for lock) + logger.debug("Uploading files for scheduled commit.") + commit_info = self.api.create_commit( + repo_id=self.repo_id, + repo_type=self.repo_type, + operations=add_operations, + commit_message="Scheduled Commit", + revision=self.revision, + ) + + for file in files_to_upload: + self.last_uploaded[file.local_path] = file.last_modified + + self.last_push_time = time.time() + + return commit_info diff --git a/trackio/context_vars.py b/trackio/context_vars.py new file mode 100644 index 0000000000000000000000000000000000000000..ef1404001b8856446ec2940eee81578d634a51fd --- /dev/null +++ b/trackio/context_vars.py @@ -0,0 +1,21 @@ +import contextvars +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from trackio.run import Run + +current_run: contextvars.ContextVar["Run | None"] = contextvars.ContextVar( + "current_run", default=None +) +current_project: contextvars.ContextVar[str | None] = contextvars.ContextVar( + "current_project", default=None +) +current_server: contextvars.ContextVar[str | None] = contextvars.ContextVar( + "current_server", default=None +) +current_space_id: contextvars.ContextVar[str | None] = contextvars.ContextVar( + "current_space_id", default=None +) +current_server_write_token: contextvars.ContextVar[str | None] = contextvars.ContextVar( + "current_server_write_token", default=None +) diff --git a/trackio/deploy.py b/trackio/deploy.py new file mode 100644 index 0000000000000000000000000000000000000000..7fe48ad33750e7d3c0fd57050378cec355888051 --- /dev/null +++ b/trackio/deploy.py @@ -0,0 +1,1107 @@ +import importlib.metadata +import io +import json as json_mod +import os +import shutil +import sys +import tempfile +import threading +import time +from collections import Counter +from importlib.resources import files +from pathlib import Path + +if sys.version_info >= (3, 11): + import tomllib +else: + import tomli as tomllib + +import httpx +import huggingface_hub +from gradio_client import handle_file +from httpx import ReadTimeout +from huggingface_hub import Volume +from huggingface_hub.errors import ( + BucketNotFoundError, + HfHubHTTPError, + RepositoryNotFoundError, +) + +import trackio +from trackio.bucket_storage import ( + create_bucket_if_not_exists, + export_from_bucket_for_static, + upload_project_to_bucket, + upload_project_to_bucket_for_static, +) +from trackio.remote_client import RemoteClient +from trackio.sqlite_storage import SQLiteStorage +from trackio.utils import ( + MEDIA_DIR, + get_or_create_project_hash, + on_spaces, + preprocess_space_and_dataset_ids, +) + +SPACE_HOST_URL = "https://{user_name}-{space_name}.hf.space/" +SPACE_URL = "https://huggingface.co/spaces/{space_id}" +_BOLD_ORANGE = "\033[1m\033[38;5;208m" +_RESET = "\033[0m" + + +def raise_if_space_is_frozen_for_logging(space_id: str) -> None: + try: + info = huggingface_hub.HfApi().space_info(space_id) + except RepositoryNotFoundError: + return + if getattr(info, "sdk", None) == "static": + raise RuntimeError( + f"Cannot log to Hugging Face Space '{space_id}' because it has been frozen " + f"(it uses the static SDK: a read-only dashboard with no live Trackio server).\n\n" + f"Use a different space_id for training, or create a new Gradio Trackio Space. " + f"Freezing converts a live Gradio Space to static after a run; a frozen Space " + f'cannot accept new logs. See trackio.sync(..., sdk="static") in the Trackio docs.' + ) + + +def _readme_linked_hub_yaml(dataset_id: str | None) -> str: + if dataset_id is not None: + return f"datasets:\n - {dataset_id}\n" + return "" + + +_SPACE_APP_PY = "import trackio\ntrackio.show()\n" + + +def _retry_hf_write(op_name: str, fn, retries: int = 4, initial_delay: float = 1.5): + delay = initial_delay + for attempt in range(1, retries + 1): + try: + return fn() + except ReadTimeout: + if attempt == retries: + raise + print( + f"* {op_name} timed out (attempt {attempt}/{retries}). Retrying in {delay:.1f}s..." + ) + time.sleep(delay) + delay = min(delay * 2, 12) + except HfHubHTTPError as e: + status = e.response.status_code if e.response is not None else None + if status is None or status < 500 or attempt == retries: + raise + print( + f"* {op_name} failed with HTTP {status} (attempt {attempt}/{retries}). Retrying in {delay:.1f}s..." + ) + time.sleep(delay) + delay = min(delay * 2, 12) + + +def _get_space_volumes(space_id: str) -> list[Volume]: + """ + Return mounted volumes for a Space. + + `HfApi.get_space_runtime()` does not always populate `volumes`, even when the + mount exists. Fall back to `space_info().runtime.volumes`, which currently + carries the volume metadata for running Spaces. + """ + hf_api = huggingface_hub.HfApi() + runtime = hf_api.get_space_runtime(space_id) + if runtime.volumes: + return list(runtime.volumes) + + info = hf_api.space_info(space_id) + if info.runtime and info.runtime.volumes: + return list(info.runtime.volumes) + + return [] + + +def _get_space_bucket_at_data_mount(space_id: str) -> str | None: + for volume in _get_space_volumes(space_id): + if volume.type == "bucket" and volume.mount_path == "/data": + return volume.source + return None + + +def _get_existing_space_bucket(space_id: str) -> str | None: + """Return the Trackio bucket for a Space, preferring the canonical /data mount.""" + bucket_at_data = _get_space_bucket_at_data_mount(space_id) + if bucket_at_data is not None: + return bucket_at_data + + for volume in _get_space_volumes(space_id): + if volume.type == "bucket": + return volume.source + return None + + +def _ensure_bucket_mounted_at_data( + space_id: str, + bucket_id: str, + hf_api: huggingface_hub.HfApi | None = None, +) -> None: + hf_api = hf_api or huggingface_hub.HfApi() + existing = _get_space_volumes(space_id) + already_mounted = any( + v.type == "bucket" and v.source == bucket_id and v.mount_path == "/data" + for v in existing + ) + if not already_mounted: + preserved = [ + v + for v in existing + if not ( + v.type == "bucket" + and (v.source == bucket_id or v.mount_path == "/data") + ) + ] + hf_api.set_space_volumes( + space_id, + preserved + [Volume(type="bucket", source=bucket_id, mount_path="/data")], + ) + print(f"* Attached bucket {bucket_id} at '/data'") + + huggingface_hub.add_space_variable(space_id, "TRACKIO_DIR", "/data/trackio") + + +def _bucket_exists(bucket_id: str, hf_api: huggingface_hub.HfApi | None = None) -> bool: + hf_api = hf_api or huggingface_hub.HfApi() + try: + hf_api.bucket_info(bucket_id) + return True + except BucketNotFoundError: + return False + + +def _find_available_bucket_id( + preferred_bucket_id: str, hf_api: huggingface_hub.HfApi | None = None +) -> str: + hf_api = hf_api or huggingface_hub.HfApi() + if not _bucket_exists(preferred_bucket_id, hf_api): + return preferred_bucket_id + + suffix = 2 + while True: + candidate = f"{preferred_bucket_id}-{suffix}" + if not _bucket_exists(candidate, hf_api): + return candidate + suffix += 1 + + +def resolve_auto_bucket_id( + space_id: str, + preferred_bucket_id: str, + hf_api: huggingface_hub.HfApi | None = None, +) -> str: + """ + Resolve the bucket to use for an auto-generated bucket ID. + + Rules: + - Existing Space with a bucket mounted at /data -> reuse that bucket. + - Otherwise -> use the preferred auto bucket ID if free, or a suffixed variant. + """ + hf_api = hf_api or huggingface_hub.HfApi() + + try: + hf_api.space_info(space_id) + except RepositoryNotFoundError: + existing_bucket_id = None + else: + existing_bucket_id = _get_existing_space_bucket(space_id) + if existing_bucket_id is not None: + return existing_bucket_id + + bucket_id = _find_available_bucket_id(preferred_bucket_id, hf_api) + if bucket_id != preferred_bucket_id: + print( + f"* Auto-generated bucket {preferred_bucket_id} already exists; " + f"using {bucket_id} instead" + ) + return bucket_id + + +def _get_source_install_dependencies() -> str: + """Get trackio dependencies from pyproject.toml for source installs.""" + trackio_path = files("trackio") + pyproject_path = Path(trackio_path).parent / "pyproject.toml" + with open(pyproject_path, "rb") as f: + pyproject = tomllib.load(f) + deps = pyproject["project"]["dependencies"] + spaces_deps = ( + pyproject["project"].get("optional-dependencies", {}).get("spaces", []) + ) + mcp_deps = pyproject["project"].get("optional-dependencies", {}).get("mcp", []) + return "\n".join(deps + spaces_deps + mcp_deps) + + +def _get_space_install_requirement() -> str: + return f"trackio[spaces,mcp]=={trackio.__version__}" + + +def _is_trackio_installed_from_source() -> bool: + """Check if trackio is installed from source/editable install vs PyPI.""" + try: + trackio_file = trackio.__file__ + if "site-packages" not in trackio_file and "dist-packages" not in trackio_file: + return True + + dist = importlib.metadata.distribution("trackio") + if dist.files: + files = list(dist.files) + has_pth = any(".pth" in str(f) for f in files) + if has_pth: + return True + + return False + except ( + AttributeError, + importlib.metadata.PackageNotFoundError, + importlib.metadata.MetadataError, + ValueError, + TypeError, + ): + return True + + +def deploy_as_space( + space_id: str, + space_storage: huggingface_hub.SpaceStorage | None = None, + dataset_id: str | None = None, + bucket_id: str | None = None, + private: bool | None = None, +): + if on_spaces(): # in case a repo with this function is uploaded to spaces + return + + if dataset_id is not None and bucket_id is not None: + raise ValueError( + "Cannot use bucket volume options together with dataset_id; use one persistence mode." + ) + + trackio_path = files("trackio") + + hf_api = huggingface_hub.HfApi() + + try: + huggingface_hub.create_repo( + space_id, + private=private, + space_sdk="gradio", + space_storage=space_storage, + repo_type="space", + exist_ok=True, + ) + except HfHubHTTPError as e: + if e.response.status_code in [401, 403]: # unauthorized or forbidden + print("Need 'write' access token to create a Spaces repo.") + huggingface_hub.login(add_to_git_credential=False) + huggingface_hub.create_repo( + space_id, + private=private, + space_sdk="gradio", + space_storage=space_storage, + repo_type="space", + exist_ok=True, + ) + else: + raise ValueError(f"Failed to create Space: {e}") + + # We can assume huggingface-hub is available; requirements.txt pins trackio. + # Make sure necessary dependencies are installed by creating a requirements.txt. + is_source_install = _is_trackio_installed_from_source() + + if bucket_id is not None: + create_bucket_if_not_exists(bucket_id, private=private) + + with open(Path(trackio_path, "README.md"), "r") as f: + readme_content = f.read() + readme_content = readme_content.replace("sdk_version: {GRADIO_VERSION}\n", "") + readme_content = readme_content.replace("{APP_FILE}", "app.py") + readme_content = readme_content.replace( + "{LINKED_HUB_METADATA}", _readme_linked_hub_yaml(dataset_id) + ) + readme_buffer = io.BytesIO(readme_content.encode("utf-8")) + hf_api.upload_file( + path_or_fileobj=readme_buffer, + path_in_repo="README.md", + repo_id=space_id, + repo_type="space", + ) + + if is_source_install: + requirements_content = _get_source_install_dependencies() + else: + requirements_content = _get_space_install_requirement() + + requirements_buffer = io.BytesIO(requirements_content.encode("utf-8")) + hf_api.upload_file( + path_or_fileobj=requirements_buffer, + path_in_repo="requirements.txt", + repo_id=space_id, + repo_type="space", + ) + + huggingface_hub.utils.disable_progress_bars() + + if is_source_install: + dist_index = ( + Path(trackio.__file__).resolve().parent / "frontend" / "dist" / "index.html" + ) + if not dist_index.is_file(): + raise ValueError( + "The Trackio frontend build is missing. From the repository root run " + "`cd trackio/frontend && npm ci && npm run build`, then deploy again." + ) + hf_api.upload_folder( + repo_id=space_id, + repo_type="space", + folder_path=trackio_path, + path_in_repo="trackio", + ignore_patterns=[ + "README.md", + "frontend/node_modules/**", + "frontend/src/**", + "frontend/.gitignore", + "frontend/package.json", + "frontend/package-lock.json", + "frontend/vite.config.js", + "frontend/svelte.config.js", + "**/__pycache__/**", + "*.pyc", + ], + ) + + app_file_content = _SPACE_APP_PY + app_file_buffer = io.BytesIO(app_file_content.encode("utf-8")) + hf_api.upload_file( + path_or_fileobj=app_file_buffer, + path_in_repo="app.py", + repo_id=space_id, + repo_type="space", + ) + + if hf_token := huggingface_hub.utils.get_token(): + huggingface_hub.add_space_secret(space_id, "HF_TOKEN", hf_token) + if bucket_id is not None: + _ensure_bucket_mounted_at_data(space_id, bucket_id, hf_api) + elif dataset_id is not None: + huggingface_hub.add_space_variable(space_id, "TRACKIO_DATASET_ID", dataset_id) + if logo_light_url := os.environ.get("TRACKIO_LOGO_LIGHT_URL"): + huggingface_hub.add_space_variable( + space_id, "TRACKIO_LOGO_LIGHT_URL", logo_light_url + ) + if logo_dark_url := os.environ.get("TRACKIO_LOGO_DARK_URL"): + huggingface_hub.add_space_variable( + space_id, "TRACKIO_LOGO_DARK_URL", logo_dark_url + ) + if plot_order := os.environ.get("TRACKIO_PLOT_ORDER"): + huggingface_hub.add_space_variable(space_id, "TRACKIO_PLOT_ORDER", plot_order) + if theme := os.environ.get("TRACKIO_THEME"): + huggingface_hub.add_space_variable(space_id, "TRACKIO_THEME", theme) + huggingface_hub.add_space_variable(space_id, "GRADIO_MCP_SERVER", "True") + + +def create_space_if_not_exists( + space_id: str, + space_storage: huggingface_hub.SpaceStorage | None = None, + dataset_id: str | None = None, + bucket_id: str | None = None, + private: bool | None = None, +) -> None: + """ + Creates a new Hugging Face Space if it does not exist. + + Args: + space_id (`str`): + The ID of the Space to create. + space_storage ([`~huggingface_hub.SpaceStorage`], *optional*): + Choice of persistent storage tier for the Space. + dataset_id (`str`, *optional*): + Deprecated. Use `bucket_id` instead. + bucket_id (`str`, *optional*): + Full Hub bucket id (`namespace/name`) to attach via the Hub volumes API (platform mount). + Sets `TRACKIO_DIR` to the mount path. + private (`bool`, *optional*): + Whether to make the Space private. If `None` (default), the repo will be + public unless the organization's default is private. This value is ignored + if the repo already exists. + """ + if "/" not in space_id: + raise ValueError( + f"Invalid space ID: {space_id}. Must be in the format: username/reponame or orgname/reponame." + ) + if dataset_id is not None and "/" not in dataset_id: + raise ValueError( + f"Invalid dataset ID: {dataset_id}. Must be in the format: username/datasetname or orgname/datasetname." + ) + if bucket_id is not None and "/" not in bucket_id: + raise ValueError( + f"Invalid bucket ID: {bucket_id}. Must be in the format: username/bucketname or orgname/bucketname." + ) + try: + huggingface_hub.repo_info(space_id, repo_type="space") + print( + f"* Found existing space: {_BOLD_ORANGE}{SPACE_URL.format(space_id=space_id)}{_RESET}" + ) + if bucket_id is not None: + create_bucket_if_not_exists(bucket_id, private=private) + _ensure_bucket_mounted_at_data(space_id, bucket_id) + elif dataset_id is not None: + huggingface_hub.add_space_variable(space_id, "TRACKIO_DATASET_ID", dataset_id) + return + except RepositoryNotFoundError: + pass + except HfHubHTTPError as e: + if e.response.status_code in [401, 403]: # unauthorized or forbidden + print("Need 'write' access token to create a Spaces repo.") + huggingface_hub.login(add_to_git_credential=False) + else: + raise ValueError(f"Failed to create Space: {e}") + + print( + f"* Creating new space: {_BOLD_ORANGE}{SPACE_URL.format(space_id=space_id)}{_RESET}" + ) + deploy_as_space( + space_id, + space_storage, + dataset_id, + bucket_id, + private, + ) + print("* Waiting for Space to be ready...") + _wait_until_space_running(space_id) + + +def _wait_until_space_running(space_id: str, timeout: int = 300) -> None: + hf_api = huggingface_hub.HfApi() + start = time.time() + delay = 2 + request_timeout = 45.0 + failure_stages = frozenset( + ("NO_APP_FILE", "CONFIG_ERROR", "BUILD_ERROR", "RUNTIME_ERROR") + ) + while time.time() - start < timeout: + try: + info = hf_api.space_info(space_id, timeout=request_timeout) + if info.runtime: + stage = str(info.runtime.stage) + if stage in failure_stages: + raise RuntimeError( + f"Space {space_id} entered terminal stage {stage}. " + "Fix README.md or app files; see build logs on the Hub." + ) + if stage == "RUNNING": + return + except RuntimeError: + raise + except (huggingface_hub.utils.HfHubHTTPError, httpx.RequestError): + pass + time.sleep(delay) + delay = min(delay * 1.5, 15) + raise TimeoutError( + f"Space {space_id} did not reach RUNNING within {timeout}s. " + "Check status and build logs on the Hub." + ) + + +def wait_until_space_exists( + space_id: str, +) -> None: + """ + Blocks the current thread until the Space exists. + + Args: + space_id (`str`): + The ID of the Space to wait for. + + Raises: + `TimeoutError`: If waiting for the Space takes longer than expected. + """ + hf_api = huggingface_hub.HfApi() + delay = 1 + for _ in range(30): + try: + hf_api.space_info(space_id) + return + except (huggingface_hub.utils.HfHubHTTPError, httpx.RequestError): + time.sleep(delay) + delay = min(delay * 2, 60) + raise TimeoutError("Waiting for space to exist took longer than expected") + + +def upload_db_to_space(project: str, space_id: str, force: bool = False) -> None: + """ + Uploads the database of a local Trackio project to a Hugging Face Space. + + This uses the Trackio remote client so newer Trackio Spaces can speak the direct + HTTP API while older Gradio-based Spaces still work through `gradio_client`. + + Args: + project (`str`): + The name of the project to upload. + space_id (`str`): + The ID of the Space to upload to. + force (`bool`, *optional*, defaults to `False`): + If `True`, overwrites the existing database without prompting. If `False`, + prompts for confirmation. + """ + db_path = SQLiteStorage.get_project_db_path(project) + client = RemoteClient( + space_id, + hf_token=huggingface_hub.utils.get_token(), + httpx_kwargs={"timeout": 90}, + ) + + if not force: + try: + existing_projects = client.predict(api_name="/get_all_projects") + if project in existing_projects: + response = input( + f"Database for project '{project}' already exists on Space '{space_id}'. " + f"Overwrite it? (y/N): " + ) + if response.lower() not in ["y", "yes"]: + print("* Upload cancelled.") + return + except Exception as e: + print(f"* Warning: Could not check if project exists on Space: {e}") + print("* Proceeding with upload...") + + client.predict( + api_name="/upload_db_to_space", + project=project, + uploaded_db=handle_file(db_path), + hf_token=huggingface_hub.utils.get_token(), + ) + + +SYNC_BATCH_SIZE = 500 + + +def sync_incremental( + project: str, + space_id: str, + private: bool | None = None, + pending_only: bool = False, +) -> None: + """ + Syncs a local Trackio project to a Space via the bulk_log API endpoints + instead of uploading the entire DB file. Supports incremental sync. + + Args: + project: The name of the project to sync. + space_id: The HF Space ID to sync to. + private: Whether to make the Space private if creating. + pending_only: If True, only sync rows tagged with space_id (pending data). + """ + print( + f"* Syncing project '{project}' to: {SPACE_URL.format(space_id=space_id)} (please wait...)" + ) + create_space_if_not_exists(space_id, private=private) + wait_until_space_exists(space_id) + hf_token = huggingface_hub.utils.get_token() + expected_run_counts: Counter[str] = Counter() + + client = RemoteClient( + space_id, + hf_token=hf_token, + httpx_kwargs={"timeout": 90}, + ) + + if pending_only: + pending_logs = SQLiteStorage.get_pending_logs(project) + if pending_logs: + logs = pending_logs["logs"] + expected_run_counts.update(log["run"] for log in logs) + for i in range(0, len(logs), SYNC_BATCH_SIZE): + batch = logs[i : i + SYNC_BATCH_SIZE] + print( + f" Syncing metrics: {min(i + SYNC_BATCH_SIZE, len(logs))}/{len(logs)}..." + ) + client.predict(api_name="/bulk_log", logs=batch, hf_token=hf_token) + SQLiteStorage.clear_pending_logs(project, pending_logs["ids"]) + + pending_sys = SQLiteStorage.get_pending_system_logs(project) + if pending_sys: + logs = pending_sys["logs"] + for i in range(0, len(logs), SYNC_BATCH_SIZE): + batch = logs[i : i + SYNC_BATCH_SIZE] + print( + f" Syncing system metrics: {min(i + SYNC_BATCH_SIZE, len(logs))}/{len(logs)}..." + ) + client.predict( + api_name="/bulk_log_system", logs=batch, hf_token=hf_token + ) + SQLiteStorage.clear_pending_system_logs(project, pending_sys["ids"]) + + pending_uploads = SQLiteStorage.get_pending_uploads(project) + if pending_uploads: + upload_entries = [] + for u in pending_uploads["uploads"]: + fp = u["file_path"] + if os.path.exists(fp): + upload_entries.append( + { + "project": u["project"], + "run": u["run"], + "step": u["step"], + "relative_path": u["relative_path"], + "uploaded_file": handle_file(fp), + } + ) + if upload_entries: + print(f" Syncing {len(upload_entries)} media files...") + client.predict( + api_name="/bulk_upload_media", + uploads=upload_entries, + hf_token=hf_token, + ) + SQLiteStorage.clear_pending_uploads(project, pending_uploads["ids"]) + else: + all_logs = SQLiteStorage.get_all_logs_for_sync(project) + if all_logs: + expected_run_counts.update(log["run"] for log in all_logs) + for i in range(0, len(all_logs), SYNC_BATCH_SIZE): + batch = all_logs[i : i + SYNC_BATCH_SIZE] + print( + f" Syncing metrics: {min(i + SYNC_BATCH_SIZE, len(all_logs))}/{len(all_logs)}..." + ) + client.predict(api_name="/bulk_log", logs=batch, hf_token=hf_token) + + all_sys_logs = SQLiteStorage.get_all_system_logs_for_sync(project) + if all_sys_logs: + for i in range(0, len(all_sys_logs), SYNC_BATCH_SIZE): + batch = all_sys_logs[i : i + SYNC_BATCH_SIZE] + print( + f" Syncing system metrics: {min(i + SYNC_BATCH_SIZE, len(all_sys_logs))}/{len(all_sys_logs)}..." + ) + client.predict( + api_name="/bulk_log_system", logs=batch, hf_token=hf_token + ) + + _wait_for_remote_sync(client, project, expected_run_counts) + SQLiteStorage.set_project_metadata(project, "space_id", space_id) + print( + f"* Synced successfully to space: {_BOLD_ORANGE}{SPACE_URL.format(space_id=space_id)}{_RESET}" + ) + + +def _wait_for_remote_sync( + client: RemoteClient, + project: str, + expected_run_counts: Counter[str], + timeout: int = 180, +) -> None: + if not expected_run_counts: + return + + deadline = time.time() + timeout + delay = 2 + last_error: Exception | None = None + pending = dict(expected_run_counts) + + while time.time() < deadline and pending: + completed = [] + for run_name, expected_num_logs in pending.items(): + try: + summary = client.predict( + project=project, run=run_name, api_name="/get_run_summary" + ) + if summary.get("num_logs") == expected_num_logs: + completed.append(run_name) + except Exception as e: + last_error = e + for run_name in completed: + pending.pop(run_name, None) + if pending: + time.sleep(delay) + delay = min(delay * 1.5, 15) + + if pending: + raise TimeoutError( + f"Remote sync for project '{project}' did not become visible for runs " + f"{sorted(pending.items())} within {timeout}s. " + f"Last error: {last_error!r}" + ) + + +def upload_dataset_for_static( + project: str, + dataset_id: str, + private: bool | None = None, +) -> None: + hf_api = huggingface_hub.HfApi() + + try: + huggingface_hub.create_repo( + dataset_id, + private=private, + repo_type="dataset", + exist_ok=True, + ) + except HfHubHTTPError as e: + if e.response.status_code in [401, 403]: + print("Need 'write' access token to create a Dataset repo.") + huggingface_hub.login(add_to_git_credential=False) + huggingface_hub.create_repo( + dataset_id, + private=private, + repo_type="dataset", + exist_ok=True, + ) + else: + raise ValueError(f"Failed to create Dataset: {e}") + + with tempfile.TemporaryDirectory() as tmp_dir: + output_dir = Path(tmp_dir) + SQLiteStorage.export_for_static_space(project, output_dir) + + media_dir = MEDIA_DIR / project + if media_dir.exists(): + dest = output_dir / "media" + shutil.copytree(media_dir, dest) + + _retry_hf_write( + "Dataset upload", + lambda: hf_api.upload_folder( + repo_id=dataset_id, + repo_type="dataset", + folder_path=str(output_dir), + ), + ) + + print(f"* Dataset uploaded: https://huggingface.co/datasets/{dataset_id}") + + +def deploy_as_static_space( + space_id: str, + dataset_id: str | None, + project: str, + bucket_id: str | None = None, + private: bool | None = None, + hf_token: str | None = None, +) -> None: + if on_spaces(): + return + + hf_api = huggingface_hub.HfApi() + + try: + huggingface_hub.create_repo( + space_id, + private=private, + space_sdk="static", + repo_type="space", + exist_ok=True, + ) + except HfHubHTTPError as e: + if e.response.status_code in [401, 403]: + print("Need 'write' access token to create a Spaces repo.") + huggingface_hub.login(add_to_git_credential=False) + huggingface_hub.create_repo( + space_id, + private=private, + space_sdk="static", + repo_type="space", + exist_ok=True, + ) + else: + raise ValueError(f"Failed to create Space: {e}") + + linked = _readme_linked_hub_yaml(dataset_id) + readme_content = ( + f"---\nsdk: static\npinned: false\ntags:\n - trackio\n{linked}---\n" + ) + _retry_hf_write( + "Static Space README upload", + lambda: hf_api.upload_file( + path_or_fileobj=io.BytesIO(readme_content.encode("utf-8")), + path_in_repo="README.md", + repo_id=space_id, + repo_type="space", + ), + ) + + trackio_path = files("trackio") + dist_dir = Path(trackio_path).parent / "trackio" / "frontend" / "dist" + if not dist_dir.is_dir(): + dist_dir = Path(trackio.__file__).resolve().parent / "frontend" / "dist" + if not dist_dir.is_dir(): + raise ValueError( + "The Trackio frontend build is missing. From the repository root run " + "`cd trackio/frontend && npm ci && npm run build`, then deploy again." + ) + + _retry_hf_write( + "Static Space frontend upload", + lambda: hf_api.upload_folder( + repo_id=space_id, + repo_type="space", + folder_path=str(dist_dir), + ), + ) + + config = { + "mode": "static", + "project": project, + "private": bool(private), + } + if bucket_id is not None: + config["bucket_id"] = bucket_id + if dataset_id is not None: + config["dataset_id"] = dataset_id + if hf_token and private: + config["hf_token"] = hf_token + + _retry_hf_write( + "Static Space config upload", + lambda: hf_api.upload_file( + path_or_fileobj=io.BytesIO(json_mod.dumps(config).encode("utf-8")), + path_in_repo="config.json", + repo_id=space_id, + repo_type="space", + ), + ) + + assets_dir = Path(trackio.__file__).resolve().parent / "assets" + if assets_dir.is_dir(): + _retry_hf_write( + "Static Space assets upload", + lambda: hf_api.upload_folder( + repo_id=space_id, + repo_type="space", + folder_path=str(assets_dir), + path_in_repo="assets", + ), + ) + + print( + f"* Static Space deployed: {_BOLD_ORANGE}{SPACE_URL.format(space_id=space_id)}{_RESET}" + ) + + +def sync( + project: str, + space_id: str | None = None, + private: bool | None = None, + force: bool = False, + run_in_background: bool = False, + sdk: str = "gradio", + dataset_id: str | None = None, + bucket_id: str | None = None, +) -> str: + """ + Syncs a local Trackio project's database to a Hugging Face Space. + If the Space does not exist, it will be created. Local data is never deleted. + + **Freezing:** Passing ``sdk="static"`` deploys a static Space backed by an HF Bucket + (read-only dashboard, no Gradio server). You can sync the same project again later to + refresh that static Space. If you want a one-time snapshot of an existing Gradio Space, + use ``freeze()`` instead. + + Args: + project (`str`): The name of the project to upload. + space_id (`str`, *optional*): The ID of the Space to upload to (e.g., `"username/space_id"`). + If not provided, checks project metadata first, then generates a random space_id. + private (`bool`, *optional*): + Whether to make the Space private. If None (default), the repo will be + public unless the organization's default is private. This value is ignored + if the repo already exists. + force (`bool`, *optional*, defaults to `False`): + If `True`, overwrite the existing database without prompting for confirmation. + If `False`, prompt the user before overwriting an existing database. + run_in_background (`bool`, *optional*, defaults to `False`): + If `True`, the Space creation and database upload will be run in a background thread. + If `False`, all the steps will be run synchronously. + sdk (`str`, *optional*, defaults to `"gradio"`): + The type of Space to deploy. `"gradio"` deploys a Gradio Space with a live + server. `"static"` freezes the Space: deploys a static Space that reads from an HF Bucket + (no server needed). + dataset_id (`str`, *optional*): + Deprecated. Use `bucket_id` instead. + bucket_id (`str`, *optional*): + The ID of the HF Bucket to sync to. By default, a bucket is auto-generated + from the space_id. + Returns: + `str`: The Space ID of the synced project. + """ + if sdk not in ("gradio", "static"): + raise ValueError(f"sdk must be 'gradio' or 'static', got '{sdk}'") + bucket_id_was_explicit = bucket_id is not None + + if space_id is None: + space_id = SQLiteStorage.get_space_id(project) + if space_id is None: + space_id = f"{project}-{get_or_create_project_hash(project)}" + space_id, dataset_id, bucket_id = preprocess_space_and_dataset_ids( + space_id, dataset_id, bucket_id + ) + if dataset_id is None and bucket_id is not None and not bucket_id_was_explicit: + bucket_id = resolve_auto_bucket_id(space_id, bucket_id) + + def _do_sync(): + try: + info = huggingface_hub.HfApi().space_info(space_id) + existing_sdk = info.sdk + if existing_sdk and existing_sdk != sdk: + raise ValueError( + f"Space '{space_id}' is a '{existing_sdk}' Space but sdk='{sdk}' was requested. " + f"The sdk must match the existing Space type." + ) + except RepositoryNotFoundError: + pass + + if sdk == "static": + if dataset_id is not None: + upload_dataset_for_static(project, dataset_id, private=private) + hf_token = huggingface_hub.utils.get_token() if private else None + deploy_as_static_space( + space_id, + dataset_id, + project, + private=private, + hf_token=hf_token, + ) + elif bucket_id is not None: + create_bucket_if_not_exists(bucket_id, private=private) + upload_project_to_bucket_for_static(project, bucket_id) + print( + f"* Project data uploaded to bucket: https://huggingface.co/buckets/{bucket_id}" + ) + deploy_as_static_space( + space_id, + None, + project, + bucket_id=bucket_id, + private=private, + hf_token=huggingface_hub.utils.get_token() if private else None, + ) + else: + if bucket_id is not None: + create_bucket_if_not_exists(bucket_id, private=private) + upload_project_to_bucket(project, bucket_id) + print( + f"* Project data uploaded to bucket: https://huggingface.co/buckets/{bucket_id}" + ) + create_space_if_not_exists( + space_id, bucket_id=bucket_id, private=private + ) + _wait_for_remote_sync( + RemoteClient(space_id, verbose=False, httpx_kwargs={"timeout": 90}), + project, + Counter( + log["run"] + for log in SQLiteStorage.get_all_logs_for_sync(project) + ), + ) + else: + sync_incremental(project, space_id, private=private, pending_only=False) + SQLiteStorage.set_project_metadata(project, "space_id", space_id) + + if run_in_background: + threading.Thread(target=_do_sync).start() + else: + _do_sync() + return space_id + + +def _get_source_bucket(space_id: str) -> str: + bucket_id = _get_existing_space_bucket(space_id) + if bucket_id is not None: + _ensure_bucket_mounted_at_data(space_id, bucket_id) + return bucket_id + raise ValueError( + f"Space '{space_id}' has no bucket mounted at '/data'. " + f"freeze() requires the source Space to use bucket storage." + ) + + +def freeze( + space_id: str, + project: str, + new_space_id: str | None = None, + private: bool | None = None, + bucket_id: str | None = None, +) -> str: + """ + Creates a new static Hugging Face Space containing a read-only snapshot of + the data for the specified project from the source Gradio Space. The data is + read from the bucket attached to the source Space at freeze time. The original + Space is not modified, and the new static Space does not automatically reflect + metrics uploaded to the original Gradio Space after the freeze completes. + + Args: + space_id (`str`): + The ID of the source Gradio Space (e.g., `"username/my-space"` or a + short repo name with the logged-in namespace inferred, like `init()`). + Must be a Gradio Space with a bucket mounted at `/data`. + project (`str`): + The name of the project whose data to include in the frozen Space. + new_space_id (`str`, *optional*): + The ID for the new static Space. If not provided, defaults to + `"{space_id}_static"`. + private (`bool`, *optional*): + Whether to make the new Space private. If None (default), the repo + will be public unless the organization's default is private. + bucket_id (`str`, *optional*): + The ID of the HF Bucket for the new static Space's data storage. + If not provided, one is auto-generated from the new Space ID. + + Returns: + `str`: The Space ID of the newly created static Space. + """ + space_id, _, _ = preprocess_space_and_dataset_ids(space_id, None, None) + + try: + info = huggingface_hub.HfApi().space_info(space_id) + if info.sdk != "gradio": + raise ValueError( + f"Space '{space_id}' is not a Gradio Space (sdk='{info.sdk}'). " + f"freeze() requires a Gradio Space as the source." + ) + except RepositoryNotFoundError: + raise ValueError( + f"Space '{space_id}' not found. Provide an existing Gradio Space ID." + ) + + source_bucket_id = _get_source_bucket(space_id) + print(f"* Reading project data from bucket: {source_bucket_id}") + + bucket_id_was_explicit = bucket_id is not None + + if new_space_id is None: + new_space_id = f"{space_id}_static" + new_space_id, _dataset_id, bucket_id = preprocess_space_and_dataset_ids( + new_space_id, None, bucket_id + ) + if bucket_id is not None and not bucket_id_was_explicit: + bucket_id = resolve_auto_bucket_id(new_space_id, bucket_id) + + hf_api = huggingface_hub.HfApi() + try: + dest_info = hf_api.space_info(new_space_id) + tags = dest_info.tags or [] + if dest_info.sdk != "static" or "trackio" not in tags: + raise ValueError( + f"Space '{new_space_id}' already exists and is not a Trackio static Space " + f"(sdk='{dest_info.sdk}', tags={tags}). Choose a different new_space_id " + f"or delete the existing Space first." + ) + except RepositoryNotFoundError: + pass + + create_bucket_if_not_exists(bucket_id, private=private) + export_from_bucket_for_static(source_bucket_id, bucket_id, project) + print( + f"* Project data uploaded to bucket: https://huggingface.co/buckets/{bucket_id}" + ) + deploy_as_static_space( + new_space_id, + None, + project, + bucket_id=bucket_id, + private=private, + hf_token=huggingface_hub.utils.get_token() if private else None, + ) + return new_space_id diff --git a/trackio/dummy_commit_scheduler.py b/trackio/dummy_commit_scheduler.py new file mode 100644 index 0000000000000000000000000000000000000000..6068fe8a36b8647ade92dd300af0855f4338826f --- /dev/null +++ b/trackio/dummy_commit_scheduler.py @@ -0,0 +1,19 @@ +from concurrent.futures import Future + + +class DummyCommitSchedulerLock: + def __enter__(self): + return None + + def __exit__(self, exception_type, exception_value, exception_traceback): + pass + + +class DummyCommitScheduler: + def __init__(self): + self.lock = DummyCommitSchedulerLock() + + def trigger(self) -> Future: + fut: Future = Future() + fut.set_result(None) + return fut diff --git a/trackio/exceptions.py b/trackio/exceptions.py new file mode 100644 index 0000000000000000000000000000000000000000..189f56d718245b65790ff43e14005b846aad2402 --- /dev/null +++ b/trackio/exceptions.py @@ -0,0 +1,2 @@ +class TrackioAPIError(Exception): + pass diff --git a/trackio/frontend/dist/assets/index-BgSTPqpC.css b/trackio/frontend/dist/assets/index-BgSTPqpC.css new file mode 100644 index 0000000000000000000000000000000000000000..ab898d78b1c167b07b13792801e432e7761c10fe --- /dev/null +++ b/trackio/frontend/dist/assets/index-BgSTPqpC.css @@ -0,0 +1 @@ +:root{--primary-50: #fff7ed;--primary-100: #ffedd5;--primary-200: #fed7aa;--primary-300: #fdba74;--primary-400: #fb923c;--primary-500: #f97316;--primary-600: #ea580c;--primary-700: #c2410c;--primary-800: #9a3412;--primary-900: 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h={enter:c={opacity:Ct},update:d={opacity:ns},exit:f={opacity:Ct}};dr(h,{stroke:s("gridColor"),strokeCap:s("gridCap"),strokeDash:s("gridDash"),strokeDashOffset:s("gridDashOffset"),strokeOpacity:s("gridOpacity"),strokeWidth:s("gridWidth")});const p={scale:e.scale,field:ts,band:i.band,extra:i.extra,offset:i.offset,round:s("tickRound")},g=Yn(o,{signal:"height"},{signal:"width"}),m=a?{scale:a,range:0,mult:u,offset:l}:{value:0,offset:l},v=a?{scale:a,range:1,mult:u,offset:l}:ut(g,{mult:u,offset:l});return c.x=d.x=Yn(o,p,m),c.y=d.y=Ts(o,p,m),c.x2=d.x2=Ts(o,v),c.y2=d.y2=Yn(o,v),f.x=Yn(o,p),f.y=Ts(o,p),Ii({type:K3,role:kfe,key:ts,from:r,encode:h},n)}function rpe(e,t){if(t!==1)if(!Fe(e))e=In(t)?{signal:`(${t.signal}) * (${e||0})`}:t*(e||0);else{let n=e=ut({},e);for(;n.mult!=null;)if(Fe(n.mult))n=n.mult=ut({},n.mult);else return n.mult=In(t)?{signal:`(${n.mult}) * (${t.signal})`}:n.mult*t,e;n.mult=t}return e}function ipe(e,t,n,r,i,s){const o=mi(e,t),a=e.orient,u=Qy(a,-1,1);let l,c,f;const d={enter:l={opacity:Ct},update:f={opacity:ns},exit:c={opacity:Ct}};dr(d,{stroke:o("tickColor"),strokeCap:o("tickCap"),strokeDash:o("tickDash"),strokeDashOffset:o("tickDashOffset"),strokeOpacity:o("tickOpacity"),strokeWidth:o("tickWidth")});const h=Gn(i);h.mult=u;const p={scale:e.scale,field:ts,band:s.band,extra:s.extra,offset:s.offset,round:o("tickRound")};return f.y=l.y=Yn(a,Ct,p),f.y2=l.y2=Yn(a,h),c.x=Yn(a,p),f.x=l.x=Ts(a,Ct,p),f.x2=l.x2=Ts(a,h),c.y=Ts(a,p),Ii({type:K3,role:Cfe,key:ts,from:r,encode:d},n)}function mx(e,t,n,r,i){return{signal:'flush(range("'+e+'"), scale("'+e+'", datum.value), '+t+","+n+","+r+","+i+")"}}function spe(e,t,n,r,i,s){const o=mi(e,t),a=e.orient,u=e.scale,l=Qy(a,-1,1),c=Hl(o("labelFlush")),f=Hl(o("labelFlushOffset")),d=o("labelAlign"),h=o("labelBaseline");let p=c===0||!!c,g;const m=Gn(i);m.mult=l,m.offset=Gn(o("labelPadding")||0),m.offset.mult=l;const v={scale:u,field:ts,band:.5,offset:AP(s.offset,o("labelOffset"))},y=Yn(a,p?mx(u,c,'"left"','"right"','"center"'):{value:"center"},Ghe(a,"left","right")),x=Yn(a,DP(a,"bottom","top"),p?mx(u,c,'"top"','"bottom"','"middle"'):{value:"middle"}),_=mx(u,c,`-(${f})`,f,0);p=p&&f;const w={opacity:Ct,x:Yn(a,v,m),y:Ts(a,v,m)},A={enter:w,update:g={opacity:ns,text:{field:Y3},x:w.x,y:w.y,align:y,baseline:x},exit:{opacity:Ct,x:w.x,y:w.y}};dr(A,{dx:!d&&p?Yn(a,_):null,dy:!h&&p?Ts(a,_):null}),dr(A,{angle:o("labelAngle"),fill:o("labelColor"),fillOpacity:o("labelOpacity"),font:o("labelFont"),fontSize:o("labelFontSize"),fontWeight:o("labelFontWeight"),fontStyle:o("labelFontStyle"),limit:o("labelLimit"),lineHeight:o("labelLineHeight")},{align:d,baseline:h});const E=o("labelBound");let k=o("labelOverlap");return k=k||E?{separation:o("labelSeparation"),method:k,order:"datum.index",bound:E?{scale:u,orient:a,tolerance:E}:null}:void 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s=t.add(Gs()),o=t.add(hP(lP(cpe(e.encode),Xy,q3,e.style,t,{pulse:We(s)}))),a=t.add(mP({layout:t.objectProperty(e.layout),legends:t.legends,autosize:t.signalRef("autosize"),mark:r,pulse:We(o)}));t.operators.pop(),t.pushState(We(o),We(a),null),RP(e,t,i),t.operators.push(a);let u=t.add(dP({mark:r,pulse:We(a)}));return u=t.add(gP({pulse:We(u)})),u=t.add(Lc({pulse:We(u)})),t.addData("root",new mc(t,s,s,u)),t}function $h(e,t){return t&&t.signal?{name:e,update:t.signal}:{name:e,value:t}}function dpe(e,t){const n=o=>_s(e[o],t[o]),r=[$h("background",n("background")),$h("autosize",xfe(n("autosize"))),$h("padding",wfe(n("padding"))),$h("width",n("width")||0),$h("height",n("height")||0)],i=r.reduce((o,a)=>(o[a.name]=a,o),{}),s={};return Me(e.signals).forEach(o=>{Ne(i,o.name)?o=ut(i[o.name],o):r.push(o),s[o.name]=o}),Me(t.signals).forEach(o=>{!Ne(s,o.name)&&!Ne(i,o.name)&&r.push(o)}),r}function TP(e,t){this.config=e||{},this.options=t||{},this.bindings=[],this.field={},this.signals={},this.lambdas={},this.scales={},this.events={},this.data={},this.streams=[],this.updates=[],this.operators=[],this.eventConfig=null,this.locale=null,this._id=0,this._subid=0,this._nextsub=[0],this._parent=[],this._encode=[],this._lookup=[],this._markpath=[]}function n4(e){this.config=e.config,this.options=e.options,this.legends=e.legends,this.field=Object.create(e.field),this.signals=Object.create(e.signals),this.lambdas=Object.create(e.lambdas),this.scales=Object.create(e.scales),this.events=Object.create(e.events),this.data=Object.create(e.data),this.streams=[],this.updates=[],this.operators=[],this._id=0,this._subid=++e._nextsub[0],this._nextsub=e._nextsub,this._parent=e._parent.slice(),this._encode=e._encode.slice(),this._lookup=e._lookup.slice(),this._markpath=e._markpath}TP.prototype=n4.prototype={parse(e){return RP(e,this)},fork(){return new n4(this)},isSubscope(){return this._subid>0},toRuntime(){return this.finish(),{description:this.description,operators:this.operators,streams:this.streams,updates:this.updates,bindings:this.bindings,eventConfig:this.eventConfig,locale:this.locale}},id(){return(this._subid?this._subid+":":0)+this._id++},add(e){return this.operators.push(e),e.id=this.id(),e.refs&&(e.refs.forEach(t=>{t.$ref=e.id}),e.refs=null),e},proxy(e){const t=e instanceof Qw?We(e):e;return this.add(Ede({value:t}))},addStream(e){return this.streams.push(e),e.id=this.id(),e},addUpdate(e){return this.updates.push(e),e},finish(){let e,t;this.root&&(this.root.root=!0);for(e in this.signals)this.signals[e].signal=e;for(e in this.scales)this.scales[e].scale=e;function n(r,i,s){let o,a;r&&(o=r.data||(r.data={}),a=o[i]||(o[i]=[]),a.push(s))}for(e in this.data){t=this.data[e],n(t.input,e,"input"),n(t.output,e,"output"),n(t.values,e,"values");for(const r in t.index)n(t.index[r],e,"index:"+r)}return this},pushState(e,t,n){this._encode.push(We(this.add(Lc({pulse:e})))),this._parent.push(t),this._lookup.push(n?We(this.proxy(n)):null),this._markpath.push(-1)},popState(){this._encode.pop(),this._parent.pop(),this._lookup.pop(),this._markpath.pop()},parent(){return kt(this._parent)},encode(){return kt(this._encode)},lookup(){return kt(this._lookup)},markpath(){const e=this._markpath;return++e[e.length-1]},fieldRef(e,t){if(De(e))return Xp(e,t);e.signal||Y("Unsupported field reference: "+ge(e));const n=e.signal;let r=this.field[n];if(!r){const i={name:this.signalRef(n)};t&&(i.as=t),this.field[n]=r=We(this.add(hde(i)))}return r},compareRef(e){let t=!1;const n=s=>In(s)?(t=!0,this.signalRef(s.signal)):Jfe(s)?(t=!0,this.exprRef(s.expr)):s,r=Me(e.field).map(n),i=Me(e.order).map(n);return t?We(this.add(UM({fields:r,orders:i}))):zM(r,i)},keyRef(e,t){let n=!1;const r=s=>In(s)?(n=!0,We(i[s.signal])):s,i=this.signals;return e=Me(e).map(r),n?We(this.add(pde({fields:e,flat:t}))):Vfe(e,t)},sortRef(e){if(!e)return e;const t=Yy(e.op,e.field),n=e.order||Xfe;return n.signal?We(this.add(UM({fields:t,orders:this.signalRef(n.signal)}))):zM(t,n)},event(e,t){const n=e+":"+t;if(!this.events[n]){const r=this.id();this.streams.push({id:r,source:e,type:t}),this.events[n]=r}return this.events[n]},hasOwnSignal(e){return Ne(this.signals,e)},addSignal(e,t){this.hasOwnSignal(e)&&Y("Duplicate signal name: "+ge(e));const n=t instanceof Qw?t:this.add(cv(t));return this.signals[e]=n},getSignal(e){return this.signals[e]||Y("Unrecognized signal name: "+ge(e)),this.signals[e]},signalRef(e){return this.signals[e]?We(this.signals[e]):(Ne(this.lambdas,e)||(this.lambdas[e]=this.add(cv(null))),We(this.lambdas[e]))},parseLambdas(){const e=Object.keys(this.lambdas);for(let t=0,n=e.length;t0?",":"")+(Fe(i)?i.signal||nC(i):ge(i))}return n+"]"}function ppe(e){let t="{",n=0,r,i;for(r in 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Using "field": ${ge(r)}.`}const Cge="Axes cannot be shared in concatenated or repeated views yet (https://github.com/vega/vega-lite/issues/2415).";function $ge(e){return`Unrecognized parse "${e}".`}function d4(e,t,n){return`An ancestor parsed field "${e}" as ${n} but a child wants to parse the field as ${t}.`}const Fge="Attempt to add the same child twice.";function Dge(e){return`Ignoring an invalid transform: ${Kt(e)}.`}const Mge='If "from.fields" is not specified, "as" has to be a string that specifies the key to be used for the data from the secondary source.';function h4(e){return`Config.customFormatTypes is not true, thus custom format type and format for channel ${e} are dropped.`}function Rge(e){const{parentProjection:t,projection:n}=e;return`Layer's shared projection ${Kt(t)} is overridden by a child projection ${Kt(n)}.`}const Tge="Arc marks uses theta channel rather than angle, replacing angle with theta.";function Nge(e){return`${e}Offset dropped because ${e} is continuous`}function Oge(e,t,n){return`Channel ${e} is a ${t}. 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You may want to use trail marks instead.";function lb(e,t,n){return`${e} dropped as it is incompatible with "${t}".`}function zge(e){return`${e}-encoding is dropped as ${e} is not a valid encoding channel.`}function jge(e){return`${e} encoding should be discrete (ordinal / nominal / binned).`}function Uge(e){return`${e} encoding should be discrete (ordinal / nominal / binned) or use a discretizing scale (e.g. threshold).`}function qge(e){return`Facet encoding dropped as ${e.join(" and ")} ${e.length>1?"are":"is"} also specified.`}function bx(e,t){return`Using discrete channel "${e}" to encode "${t}" field can be misleading as it does not encode ${t==="ordinal"?"order":"magnitude"}.`}function Wge(e){return`The ${e} for range marks cannot be an expression`}function Hge(e,t){return`Line mark is for continuous lines and thus cannot be used with ${e&&t?"x2 and y2":e?"x2":"y2"}. We will use the rule mark (line segments) instead.`}function Gge(e,t){return`Specified orient "${e}" overridden with "${t}".`}function Yge(e){return`Cannot use the scale property "${e}" with non-color channel.`}function Vge(e){return`Cannot use the relative band size with ${e} scale.`}function Xge(e){return`Using unaggregated domain with raw field has no effect (${Kt(e)}).`}function Kge(e){return`Unaggregated domain not applicable for "${e}" since it produces values outside the origin domain of the source data.`}function Zge(e){return`Unaggregated domain is currently unsupported for log scale (${Kt(e)}).`}function Jge(e){return`Cannot apply size to non-oriented mark "${e}".`}function Qge(e,t,n){return`Channel "${e}" does not work with "${t}" scale. We are using "${n}" scale instead.`}function e0e(e,t){return`FieldDef does not work with "${e}" scale. We are using "${t}" scale instead.`}function dB(e,t,n){return`${n}-scale's "${t}" is dropped as it does not work with ${e} scale.`}function hB(e){return`The step for "${e}" is dropped because the ${e==="width"?"x":"y"} is continuous.`}function t0e(e,t,n,r){return`Conflicting ${t.toString()} property "${e.toString()}" (${Kt(n)} and ${Kt(r)}). Using ${Kt(n)}.`}function n0e(e,t,n,r){return`Conflicting ${t.toString()} property "${e.toString()}" (${Kt(n)} and ${Kt(r)}). Using the union of the two domains.`}function r0e(e){return`Setting the scale to be independent for "${e}" means we also have to set the guide (axis or legend) to be independent.`}function i0e(e){return`Dropping sort property ${Kt(e)} as unioned domains only support boolean or op "count", "min", and "max".`}const p4="Domains that should be unioned has conflicting sort properties. Sort will be set to true.",s0e="Detected faceted independent scales that union domain of multiple fields from different data sources. 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n2e(e){const{legends:t,resolve:n}=e.component;for(const r of e.children){Pj(r);for(const i of ue(r.component.legends))n.legend[i]=y$(e.component.resolve,i),n.legend[i]==="shared"&&(t[i]=Bj(t[i],r.component.legends[i]),t[i]||(n.legend[i]="independent",delete t[i]))}for(const r of ue(t))for(const i of e.children)i.component.legends[r]&&n.legend[r]==="shared"&&delete i.component.legends[r];return t}function Bj(e,t){var s,o,a,u;if(!e)return t.clone();const n=e.getWithExplicit("orient"),r=t.getWithExplicit("orient");if(n.explicit&&r.explicit&&n.value!==r.value)return;let i=!1;for(const l of Nj){const c=Bu(e.getWithExplicit(l),t.getWithExplicit(l),l,"legend",(f,d)=>{switch(l){case"symbolType":return r2e(f,d);case"title":return uB(f,d);case"type":return i=!0,bi("symbol")}return kb(f,d,l,"legend")});e.setWithExplicit(l,c)}return i&&((o=(s=e.implicit)==null?void 0:s.encode)!=null&&o.gradient&&fv(e.implicit,["encode","gradient"]),(u=(a=e.explicit)==null?void 0:a.encode)!=null&&u.gradient&&fv(e.explicit,["encode","gradient"])),e}function r2e(e,t){return t.value==="circle"?t:e}function i2e(e,t,n,r){var i,s;e.encode??(e.encode={}),(i=e.encode)[t]??(i[t]={}),(s=e.encode[t]).update??(s.update={}),e.encode[t].update[n]=r}function zj(e){const t=e.component.legends,n={};for(const i of ue(t)){const s=e.getScaleComponent(i),o=Kt(s.get("domains"));if(n[o])for(const a of n[o])Bj(a,t[i])||n[o].push(t[i]);else n[o]=[t[i].clone()]}return cr(n).flat().map(i=>s2e(i,e.config)).filter(i=>i!==void 0)}function s2e(e,t){var o,a,u;const{disable:n,labelExpr:r,selections:i,...s}=e.combine();if(!n){if(t.aria===!1&&s.aria==null&&(s.aria=!1),(o=s.encode)!=null&&o.symbols){const l=s.encode.symbols.update;l.fill&&l.fill.value!=="transparent"&&!l.stroke&&!s.stroke&&(l.stroke={value:"transparent"});for(const c of Az)s[c]&&delete l[c]}if(s.title||delete s.title,r!==void 0){let l=r;(u=(a=s.encode)==null?void 0:a.labels)!=null&&u.update&&Be(s.encode.labels.update.text)&&(l=vc(r,"datum.label",s.encode.labels.update.text.signal)),i2e(s,"labels","text",{signal:l})}return s}}function o2e(e){return oh(e)||A$(e)?a2e(e):jj(e)}function a2e(e){return e.children.reduce((t,n)=>t.concat(n.assembleProjections()),jj(e))}function jj(e){const t=e.component.projection;if(!t||t.merged)return[];const n=t.combine(),{name:r}=n;if(t.data){const i={signal:`[${t.size.map(o=>o.signal).join(", ")}]`},s=t.data.reduce((o,a)=>{const u=Be(a)?a.signal:`data('${e.lookupDataSource(a)}')`;return vt(o,u)||o.push(u),o},[]);if(s.length<=0)throw new Error("Projection's fit didn't find any data sources");return[{name:r,size:i,fit:{signal:s.length>1?`[${s.join(", ")}]`:s[0]},...n}]}else return[{name:r,translate:{signal:"[width / 2, height / 2]"},...n}]}const u2e=["type","clipAngle","clipExtent","center","rotate","precision","reflectX","reflectY","coefficient","distance","fraction","lobes","parallel","radius","ratio","spacing","tilt"];class Uj extends Ha{constructor(t,n,r,i){super({...n},{name:t}),this.specifiedProjection=n,this.size=r,this.data=i,this.merged=!1}get isFit(){return!!this.data}}function qj(e){e.component.projection=pn(e)?l2e(e):d2e(e)}function l2e(e){if(e.hasProjection){const t=rr(e.specifiedProjection),n=!(t&&(t.scale!=null||t.translate!=null)),r=n?[e.getSizeSignalRef("width"),e.getSizeSignalRef("height")]:void 0,i=n?c2e(e):void 0,s=new Uj(e.projectionName(!0),{...rr(e.config.projection),...t},r,i);return s.get("type")||s.set("type","equalEarth",!1),s}}function c2e(e){const t=[],{encoding:n}=e;for(const r of[[Zs,Ks],[ls,Js]])(Ln(n[r[0]])||Ln(n[r[1]]))&&t.push({signal:e.getName(`geojson_${t.length}`)});return e.channelHasField(di)&&e.typedFieldDef(di).type===th&&t.push({signal:e.getName(`geojson_${t.length}`)}),t.length===0&&t.push(e.requestDataName(mn.Main)),t}function f2e(e,t){const n=oC(u2e,i=>!!(!Ne(e.explicit,i)&&!Ne(t.explicit,i)||Ne(e.explicit,i)&&Ne(t.explicit,i)&&Ci(e.get(i),t.get(i))));if(Ci(e.size,t.size)){if(n)return e;if(Ci(e.explicit,{}))return t;if(Ci(t.explicit,{}))return e}return null}function d2e(e){if(e.children.length===0)return;let t;for(const r of e.children)qj(r);const n=oC(e.children,r=>{const i=r.component.projection;if(i)if(t){const s=f2e(t,i);return s&&(t=s),!!s}else return t=i,!0;else return!0});if(t&&n){const r=e.projectionName(!0),i=new Uj(r,t.specifiedProjection,t.size,nt(t.data));for(const s of e.children){const o=s.component.projection;o&&(o.isFit&&i.data.push(...s.component.projection.data),s.renameProjection(o.get("name"),r),o.merged=!0)}return i}}function h2e(e,t,n,r){if(o0(t,n)){const i=pn(e)?e.axis(n)??e.legend(n)??{}:{},s=Ee(t,{expr:"datum"}),o=Ee(t,{expr:"datum",binSuffix:"end"});return{formulaAs:Ee(t,{binSuffix:"range",forAs:!0}),formula:n0(s,o,i.format,i.formatType,r)}}return{}}function Wj(e,t){return`${eB(e)}_${t}`}function p2e(e,t){return{signal:e.getName(`${t}_bins`),extentSignal:e.getName(`${t}_extent`)}}function b$(e,t,n){const r=bb(n,void 0)??{},i=Wj(r,t);return e.getName(`${i}_bins`)}function g2e(e){return"as"in e}function u6(e,t,n){let r,i;g2e(e)?r=De(e.as)?[e.as,`${e.as}_end`]:[e.as[0],e.as[1]]:r=[Ee(e,{forAs:!0}),Ee(e,{binSuffix:"end",forAs:!0})];const s={...bb(t,void 0)},o=Wj(s,e.field),{signal:a,extentSignal:u}=p2e(n,o);if(ub(s.extent)){const c=s.extent;i=wj(n,c.param,c),delete s.extent}const l={bin:s,field:e.field,as:[r],...a?{signal:a}:{},...u?{extentSignal:u}:{},...i?{span:i}:{}};return{key:o,binComponent:l}}class $o extends Wt{clone(){return new $o(null,nt(this.bins))}constructor(t,n){super(t),this.bins=n}static makeFromEncoding(t,n){const r=n.reduceFieldDef((i,s,o)=>{if(Yr(s)&&rn(s.bin)){const{key:a,binComponent:u}=u6(s,s.bin,n);i[a]={...u,...i[a],...h2e(n,s,o,n.config)}}return i},{});return Xt(r)?null:new $o(t,r)}static makeFromTransform(t,n,r){const{key:i,binComponent:s}=u6(n,n.bin,r);return new $o(t,{[i]:s})}merge(t,n){for(const r of ue(t.bins))r in this.bins?(n(t.bins[r].signal,this.bins[r].signal),this.bins[r].as=_o([...this.bins[r].as,...t.bins[r].as],_t)):this.bins[r]=t.bins[r];for(const r of t.children)t.removeChild(r),r.parent=this;t.remove()}producedFields(){return new Set(cr(this.bins).map(t=>t.as).flat(2))}dependentFields(){return new Set(cr(this.bins).map(t=>t.field))}hash(){return`Bin ${_t(this.bins)}`}assemble(){return cr(this.bins).flatMap(t=>{const n=[],[r,...i]=t.as,{extent:s,...o}=t.bin,a={type:"bin",field:us(t.field),as:r,signal:t.signal,...ub(s)?{extent:null}:{extent:s},...t.span?{span:{signal:`span(${t.span})`}}:{},...o};!s&&t.extentSignal&&(n.push({type:"extent",field:us(t.field),signal:t.extentSignal}),a.extent={signal:t.extentSignal}),n.push(a);for(const u of i)for(let l=0;l<2;l++)n.push({type:"formula",expr:Ee({field:r[l]},{expr:"datum"}),as:u[l]});return t.formula&&n.push({type:"formula",expr:t.formula,as:t.formulaAs}),n})}}function m2e(e,t,n,r){var s;const i=pn(r)?r.encoding[Zo(t)]:void 0;if(Yr(n)&&pn(r)&&VB(n,i,r.markDef,r.config)){e.add(Ee(n,{})),e.add(Ee(n,{suffix:"end"}));const{mark:o,markDef:a,config:u}=r,l=Pu({fieldDef:n,markDef:a,config:u});eg(o)&&l!==.5&&Fn(t)&&(e.add(Ee(n,{suffix:Sb})),e.add(Ee(n,{suffix:Cb}))),n.bin&&o0(n,t)&&e.add(Ee(n,{binSuffix:"range"}))}else if(HP(t)){const o=WP(t);e.add(r.getName(o))}else e.add(Ee(n));return Wc(n)&&V0e((s=n.scale)==null?void 0:s.range)&&e.add(n.scale.range.field),e}function v2e(e,t){for(const n of ue(t)){const r=t[n];for(const i of ue(r))n in e?e[n][i]=new Set([...e[n][i]??[],...r[i]]):e[n]={[i]:r[i]}}}class Ns extends Wt{clone(){return new Ns(null,new Set(this.dimensions),nt(this.measures))}constructor(t,n,r){super(t),this.dimensions=n,this.measures=r}get groupBy(){return this.dimensions}static makeFromEncoding(t,n){let r=!1;n.forEachFieldDef(o=>{o.aggregate&&(r=!0)});const i={},s=new Set;return!r||(n.forEachFieldDef((o,a)=>{const{aggregate:u,field:l}=o;if(u)if(u==="count")i["*"]??(i["*"]={}),i["*"].count=new Set([Ee(o,{forAs:!0})]);else{if(Na(u)||sl(u)){const c=Na(u)?"argmin":"argmax",f=u[c];i[f]??(i[f]={}),i[f][c]=new Set([Ee({op:c,field:f},{forAs:!0})])}else i[l]??(i[l]={}),i[l][u]=new Set([Ee(o,{forAs:!0})]);Jo(a)&&n.scaleDomain(a)==="unaggregated"&&(i[l]??(i[l]={}),i[l].min=new Set([Ee({field:l,aggregate:"min"},{forAs:!0})]),i[l].max=new Set([Ee({field:l,aggregate:"max"},{forAs:!0})]))}else m2e(s,a,o,n)}),s.size+ue(i).length===0)?null:new Ns(t,s,i)}static makeFromTransform(t,n){var r;const i=new Set,s={};for(const o of n.aggregate){const{op:a,field:u,as:l}=o;a&&(a==="count"?(s["*"]??(s["*"]={}),s["*"].count=new Set([l||Ee(o,{forAs:!0})])):(s[u]??(s[u]={}),(r=s[u])[a]??(r[a]=new Set),s[u][a].add(l||Ee(o,{forAs:!0}))))}for(const o of n.groupby??[])i.add(o);return i.size+ue(s).length===0?null:new Ns(t,i,s)}merge(t){return LP(this.dimensions,t.dimensions)?(v2e(this.measures,t.measures),!0):(b0e("different dimensions, cannot merge"),!1)}addDimensions(t){t.forEach(this.dimensions.add,this.dimensions)}dependentFields(){return new Set([...this.dimensions,...ue(this.measures)])}producedFields(){const t=new Set;for(const n of ue(this.measures))for(const r of ue(this.measures[n])){const i=this.measures[n][r];i.size===0?t.add(`${r}_${n}`):i.forEach(t.add,t)}return t}hash(){return`Aggregate ${_t({dimensions:this.dimensions,measures:this.measures})}`}assemble(){const t=[],n=[],r=[];for(const s of ue(this.measures))for(const o of ue(this.measures[s]))for(const a of this.measures[s][o])r.push(a),t.push(o),n.push(s==="*"?null:us(s));return{type:"aggregate",groupby:[...this.dimensions].map(us),ops:t,fields:n,as:r}}}class ih extends Wt{constructor(t,n,r,i){super(t),this.model=n,this.name=r,this.data=i;for(const s of Zi){const o=n.facet[s];if(o){const{bin:a,sort:u}=o;this[s]={name:n.getName(`${s}_domain`),fields:[Ee(o),...rn(a)?[Ee(o,{binSuffix:"end"})]:[]],...Ca(u)?{sortField:u}:ee(u)?{sortIndexField:Fd(o,s)}:{}}}}this.childModel=n.child}hash(){let t="Facet";for(const n of Zi)this[n]&&(t+=` ${n.charAt(0)}:${_t(this[n])}`);return t}get fields(){var n;const t=[];for(const r of Zi)(n=this[r])!=null&&n.fields&&t.push(...this[r].fields);return t}dependentFields(){const t=new Set(this.fields);for(const n of Zi)this[n]&&(this[n].sortField&&t.add(this[n].sortField.field),this[n].sortIndexField&&t.add(this[n].sortIndexField));return t}producedFields(){return new Set}getSource(){return this.name}getChildIndependentFieldsWithStep(){const t={};for(const n of qa){const r=this.childModel.component.scales[n];if(r&&!r.merged){const i=r.get("type"),s=r.get("range");if(Zn(i)&&jc(s)){const o=Lb(this.childModel,n),a=E$(o);a?t[n]=a:de(xC(n))}}}return t}assembleRowColumnHeaderData(t,n,r){const i={row:"y",column:"x",facet:void 0}[t],s=[],o=[],a=[];i&&r&&r[i]&&(n?(s.push(`distinct_${r[i]}`),o.push("max")):(s.push(r[i]),o.push("distinct")),a.push(`distinct_${r[i]}`));const{sortField:u,sortIndexField:l}=this[t];if(u){const{op:c=mb,field:f}=u;s.push(f),o.push(c),a.push(Ee(u,{forAs:!0}))}else l&&(s.push(l),o.push("max"),a.push(l));return{name:this[t].name,source:n??this.data,transform:[{type:"aggregate",groupby:this[t].fields,...s.length?{fields:s,ops:o,as:a}:{}}]}}assembleFacetHeaderData(t){var u;const{columns:n}=this.model.layout,{layoutHeaders:r}=this.model.component,i=[],s={};for(const l of g$){for(const c of m$){const f=(r[l]&&r[l][c])??[];for(const d of f)if(((u=d.axes)==null?void 0:u.length)>0){s[l]=!0;break}}if(s[l]){const c=`length(data("${this.facet.name}"))`,f=l==="row"?n?{signal:`ceil(${c} / ${n})`}:1:n?{signal:`min(${c}, ${n})`}:{signal:c};i.push({name:`${this.facet.name}_${l}`,transform:[{type:"sequence",start:0,stop:f}]})}}const{row:o,column:a}=s;return(o||a)&&i.unshift(this.assembleRowColumnHeaderData("facet",null,t)),i}assemble(){const t=[];let n=null;const r=this.getChildIndependentFieldsWithStep(),{column:i,row:s,facet:o}=this;if(i&&s&&(r.x||r.y)){n=`cross_${this.column.name}_${this.row.name}`;const a=[].concat(r.x??[],r.y??[]),u=a.map(()=>"distinct");t.push({name:n,source:this.data,transform:[{type:"aggregate",groupby:this.fields,fields:a,ops:u}]})}for(const a of[Sa,ka])this[a]&&t.push(this.assembleRowColumnHeaderData(a,n,r));if(o){const a=this.assembleFacetHeaderData(r);a&&t.push(...a)}return t}}function l6(e){return e.startsWith("'")&&e.endsWith("'")||e.startsWith('"')&&e.endsWith('"')?e.slice(1,-1):e}function y2e(e,t){const n=lC(e);if(t==="number")return`toNumber(${n})`;if(t==="boolean")return`toBoolean(${n})`;if(t==="string")return`toString(${n})`;if(t==="date")return`toDate(${n})`;if(t==="flatten")return n;if(t.startsWith("date:")){const r=l6(t.slice(5,t.length));return`timeParse(${n},'${r}')`}else if(t.startsWith("utc:")){const r=l6(t.slice(4,t.length));return`utcParse(${n},'${r}')`}else return de($ge(t)),null}function b2e(e){const t={};return Bm(e.filter,n=>{if(kB(n)){let r=null;SC(n)?r=ki(n.equal):$C(n)?r=ki(n.lte):CC(n)?r=ki(n.lt):FC(n)?r=ki(n.gt):DC(n)?r=ki(n.gte):MC(n)?r=n.range[0]:RC(n)&&(r=(n.oneOf??n.in)[0]),r&&(Uc(r)?t[n.field]="date":Nt(r)?t[n.field]="number":De(r)&&(t[n.field]="string")),n.timeUnit&&(t[n.field]="date")}}),t}function x2e(e){const t={};function n(r){kd(r)?t[r.field]="date":r.type==="quantitative"&&nge(r.aggregate)?t[r.field]="number":bd(r.field)>1?r.field in t||(t[r.field]="flatten"):Wc(r)&&Ca(r.sort)&&bd(r.sort.field)>1&&(r.sort.field in t||(t[r.sort.field]="flatten"))}if((pn(e)||Ri(e))&&e.forEachFieldDef((r,i)=>{if(Yr(r))n(r);else{const s=Bc(i),o=e.fieldDef(s);n({...r,type:o.type})}}),pn(e)){const{mark:r,markDef:i,encoding:s}=e;if(ol(r)&&!e.encoding.order){const o=i.orient==="horizontal"?"y":"x",a=s[o];pe(a)&&a.type==="quantitative"&&!(a.field in t)&&(t[a.field]="number")}}return t}function _2e(e){const t={};if(pn(e)&&e.component.selection)for(const n of ue(e.component.selection)){const r=e.component.selection[n];for(const i of r.project.items)!i.channel&&bd(i.field)>1&&(t[i.field]="flatten")}return t}class wr extends Wt{clone(){return new wr(null,nt(this._parse))}constructor(t,n){super(t),this._parse=n}hash(){return`Parse ${_t(this._parse)}`}static makeExplicit(t,n,r){var o;let i={};const s=n.data;return!Au(s)&&((o=s==null?void 0:s.format)!=null&&o.parse)&&(i=s.format.parse),this.makeWithAncestors(t,i,{},r)}static makeWithAncestors(t,n,r,i){for(const a of ue(r)){const u=i.getWithExplicit(a);u.value!==void 0&&(u.explicit||u.value===r[a]||u.value==="derived"||r[a]==="flatten"?delete r[a]:de(d4(a,r[a],u.value)))}for(const a of ue(n)){const u=i.get(a);u!==void 0&&(u===n[a]?delete n[a]:de(d4(a,n[a],u)))}const s=new Ha(n,r);i.copyAll(s);const o={};for(const a of ue(s.combine())){const u=s.get(a);u!==null&&(o[a]=u)}return ue(o).length===0||i.parseNothing?null:new wr(t,o)}get parse(){return this._parse}merge(t){this._parse={...this._parse,...t.parse},t.remove()}assembleFormatParse(){const t={};for(const n of ue(this._parse)){const r=this._parse[n];bd(n)===1&&(t[n]=r)}return t}producedFields(){return new Set(ue(this._parse))}dependentFields(){return new Set(ue(this._parse))}assembleTransforms(t=!1){return ue(this._parse).filter(n=>t?bd(n)>1:!0).map(n=>{const r=y2e(n,this._parse[n]);return r?{type:"formula",expr:r,as:Qd(n)}:null}).filter(n=>n!==null)}}class zu extends Wt{clone(){return new zu(null)}constructor(t){super(t)}dependentFields(){return new Set}producedFields(){return new Set([qs])}hash(){return"Identifier"}assemble(){return{type:"identifier",as:qs}}}class d0 extends Wt{clone(){return new d0(null,this.params)}constructor(t,n){super(t),this.params=n}dependentFields(){return new Set}producedFields(){}hash(){return`Graticule ${_t(this.params)}`}assemble(){return{type:"graticule",...this.params===!0?{}:this.params}}}class h0 extends Wt{clone(){return new h0(null,this.params)}constructor(t,n){super(t),this.params=n}dependentFields(){return new Set}producedFields(){return new Set([this.params.as??"data"])}hash(){return`Hash ${_t(this.params)}`}assemble(){return{type:"sequence",...this.params}}}class $c extends Wt{constructor(t){super(null),t??(t={name:"source"});let n;if(Au(t)||(n=t.format?{...oi(t.format,["parse"])}:{}),tg(t))this._data={values:t.values};else if(Sd(t)){if(this._data={url:t.url},!n.type){let r=/(?:\.([^.]+))?$/.exec(t.url)[1];vt(["json","csv","tsv","dsv","topojson"],r)||(r="json"),n.type=r}}else Uz(t)?this._data={values:[{type:"Sphere"}]}:(zz(t)||Au(t))&&(this._data={});this._generator=Au(t),t.name&&(this._name=t.name),n&&!Xt(n)&&(this._data.format=n)}dependentFields(){return new Set}producedFields(){}get data(){return this._data}hasName(){return!!this._name}get isGenerator(){return this._generator}get dataName(){return this._name}set dataName(t){this._name=t}set parent(t){throw new Error("Source nodes have to be roots.")}remove(){throw new Error("Source nodes are roots and cannot be removed.")}hash(){throw new Error("Cannot hash sources")}assemble(){return{name:this._name,...this._data,transform:[]}}}var c6=function(e,t,n,r,i){if(r==="m")throw new TypeError("Private method is not writable");if(r==="a"&&!i)throw new TypeError("Private accessor was defined without a setter");if(typeof t=="function"?e!==t||!i:!t.has(e))throw new TypeError("Cannot write private member to an object whose class did not declare it");return r==="a"?i.call(e,n):i?i.value=n:t.set(e,n),n},w2e=function(e,t,n,r){if(n==="a"&&!r)throw new TypeError("Private accessor was defined without a getter");if(typeof t=="function"?e!==t||!r:!t.has(e))throw new TypeError("Cannot read private member from an object whose class did not declare it");return n==="m"?r:n==="a"?r.call(e):r?r.value:t.get(e)},sp;function x$(e){return e instanceof $c||e instanceof d0||e instanceof h0}class _${constructor(){sp.set(this,void 0),c6(this,sp,!1,"f")}setModified(){c6(this,sp,!0,"f")}get modifiedFlag(){return w2e(this,sp,"f")}}sp=new WeakMap;class Gc extends _${getNodeDepths(t,n,r){r.set(t,n);for(const i of t.children)this.getNodeDepths(i,n+1,r);return r}optimize(t){const r=[...this.getNodeDepths(t,0,new Map).entries()].sort((i,s)=>s[1]-i[1]);for(const i of r)this.run(i[0]);return this.modifiedFlag}}class w$ extends _${optimize(t){this.run(t);for(const n of t.children)this.optimize(n);return this.modifiedFlag}}class E2e extends w${mergeNodes(t,n){const r=n.shift();for(const i of n)t.removeChild(i),i.parent=r,i.remove()}run(t){const n=t.children.map(i=>i.hash()),r={};for(let i=0;i1&&(this.setModified(),this.mergeNodes(t,r[i]))}}class A2e extends w${constructor(t){super(),this.requiresSelectionId=t&&f$(t)}run(t){t instanceof zu&&(this.requiresSelectionId&&(x$(t.parent)||t.parent instanceof Ns||t.parent instanceof wr)||(this.setModified(),t.remove()))}}class k2e extends _${optimize(t){return this.run(t,new Set),this.modifiedFlag}run(t,n){let r=new Set;t instanceof So&&(r=t.producedFields(),aC(r,n)&&(this.setModified(),t.removeFormulas(n),t.producedFields.length===0&&t.remove()));for(const i of t.children)this.run(i,new Set([...n,...r]))}}class S2e extends w${constructor(){super()}run(t){t instanceof ai&&!t.isRequired()&&(this.setModified(),t.remove())}}class C2e extends Gc{run(t){if(!x$(t)&&!(t.numChildren()>1)){for(const n of t.children)if(n instanceof wr)if(t instanceof wr)this.setModified(),t.merge(n);else{if(uC(t.producedFields(),n.dependentFields()))continue;this.setModified(),n.swapWithParent()}}}}class $2e extends Gc{run(t){const n=[...t.children],r=t.children.filter(i=>i instanceof wr);if(t.numChildren()>1&&r.length>=1){const i={},s=new Set;for(const o of r){const a=o.parse;for(const u of ue(a))u in i?i[u]!==a[u]&&s.add(u):i[u]=a[u]}for(const o of s)delete i[o];if(!Xt(i)){this.setModified();const o=new wr(t,i);for(const a of n){if(a instanceof wr)for(const u of ue(i))delete a.parse[u];t.removeChild(a),a.parent=o,a instanceof wr&&ue(a.parse).length===0&&a.remove()}}}}}class F2e extends Gc{run(t){t instanceof ai||t.numChildren()>0||t instanceof ih||t instanceof $c||(this.setModified(),t.remove())}}class D2e extends Gc{run(t){const n=t.children.filter(i=>i instanceof So),r=n.pop();for(const i of n)this.setModified(),r.merge(i)}}class M2e extends Gc{run(t){const n=t.children.filter(i=>i instanceof Ns),r={};for(const i of n){const s=_t(i.groupBy);s in r||(r[s]=[]),r[s].push(i)}for(const i of ue(r)){const s=r[i];if(s.length>1){const o=s.pop();for(const a of s)o.merge(a)&&(t.removeChild(a),a.parent=o,a.remove(),this.setModified())}}}}class R2e extends Gc{constructor(t){super(),this.model=t}run(t){const n=!(x$(t)||t instanceof rh||t instanceof wr||t instanceof zu),r=[],i=[];for(const s of t.children)s instanceof $o&&(n&&!uC(t.producedFields(),s.dependentFields())?r.push(s):i.push(s));if(r.length>0){const s=r.pop();for(const o of r)s.merge(o,this.model.renameSignal.bind(this.model));this.setModified(),t instanceof $o?t.merge(s,this.model.renameSignal.bind(this.model)):s.swapWithParent()}if(i.length>1){const s=i.pop();for(const o of i)s.merge(o,this.model.renameSignal.bind(this.model));this.setModified()}}}class T2e extends Gc{run(t){const n=[...t.children];if(!yd(n,o=>o instanceof ai)||t.numChildren()<=1)return;const i=[];let s;for(const o of n)if(o instanceof ai){let a=o;for(;a.numChildren()===1;){const[u]=a.children;if(u instanceof ai)a=u;else break}i.push(...a.children),s?(t.removeChild(o),o.parent=s.parent,s.parent.removeChild(s),s.parent=a,this.setModified()):s=a}else i.push(o);if(i.length){this.setModified();for(const o of i)o.parent.removeChild(o),o.parent=s}}}class Yc extends Wt{clone(){return new Yc(null,nt(this.transform))}constructor(t,n){super(t),this.transform=n}addDimensions(t){this.transform.groupby=_o(this.transform.groupby.concat(t),n=>n)}dependentFields(){const t=new Set;return this.transform.groupby&&this.transform.groupby.forEach(t.add,t),this.transform.joinaggregate.map(n=>n.field).filter(n=>n!==void 0).forEach(t.add,t),t}producedFields(){return new Set(this.transform.joinaggregate.map(this.getDefaultName))}getDefaultName(t){return t.as??Ee(t)}hash(){return`JoinAggregateTransform ${_t(this.transform)}`}assemble(){const t=[],n=[],r=[];for(const s of this.transform.joinaggregate)n.push(s.op),r.push(this.getDefaultName(s)),t.push(s.field===void 0?null:s.field);const i=this.transform.groupby;return{type:"joinaggregate",as:r,ops:n,fields:t,...i!==void 0?{groupby:i}:{}}}}class Md extends Wt{clone(){return new Md(null,{...this.filter})}constructor(t,n){super(t),this.filter=n}static make(t,n,r){const{config:i,markDef:s}=n,{marks:o,scales:a}=r;if(o==="include-invalid-values"&&a==="include-invalid-values")return null;const u=n.reduceFieldDef((l,c,f)=>{const d=Jo(f)&&n.getScaleComponent(f);if(d){const h=d.get("type"),{aggregate:p}=c,g=BC({scaleChannel:f,markDef:s,config:i,scaleType:h,isCountAggregate:ab(p)});g!=="show"&&g!=="always-valid"&&(l[c.field]=c)}return l},{});return ue(u).length?new Md(t,u):null}dependentFields(){return new Set(ue(this.filter))}producedFields(){return new Set}hash(){return`FilterInvalid ${_t(this.filter)}`}assemble(){const t=ue(this.filter).reduce((n,r)=>{const i=this.filter[r],s=Ee(i,{expr:"datum"});return i!==null&&(i.type==="temporal"?n.push(`(isDate(${s}) || (${ME(s)}))`):i.type==="quantitative"&&n.push(ME(s))),n},[]);return t.length>0?{type:"filter",expr:t.join(" && ")}:null}}function ME(e){return`isValid(${e}) && isFinite(+${e})`}function N2e(e){return e.stack.stackBy.reduce((t,n)=>{const r=n.fieldDef,i=Ee(r);return i&&t.push(i),t},[])}function O2e(e){return ee(e)&&e.every(t=>De(t))&&e.length>1}class Fa extends Wt{clone(){return new Fa(null,nt(this._stack))}constructor(t,n){super(t),this._stack=n}static makeFromTransform(t,n){const{stack:r,groupby:i,as:s,offset:o="zero"}=n,a=[],u=[];if(n.sort!==void 0)for(const f of n.sort)a.push(f.field),u.push($n(f.order,"ascending"));const l={field:a,order:u};let c;return O2e(s)?c=s:De(s)?c=[s,`${s}_end`]:c=[`${n.stack}_start`,`${n.stack}_end`],new Fa(t,{dimensionFieldDefs:[],stackField:r,groupby:i,offset:o,sort:l,facetby:[],as:c})}static makeFromEncoding(t,n){const r=n.stack,{encoding:i}=n;if(!r)return null;const{groupbyChannels:s,fieldChannel:o,offset:a,impute:u}=r,l=s.map(h=>{const p=i[h];return Us(p)}).filter(h=>!!h),c=N2e(n),f=n.encoding.order;let d;if(ee(f)||pe(f))d=sB(f);else{const h=XB(f)?f.sort:o==="y"?"descending":"ascending";d=c.reduce((p,g)=>(p.field.includes(g)||(p.field.push(g),p.order.push(h)),p),{field:[],order:[]})}return new Fa(t,{dimensionFieldDefs:l,stackField:n.vgField(o),facetby:[],stackby:c,sort:d,offset:a,impute:u,as:[n.vgField(o,{suffix:"start",forAs:!0}),n.vgField(o,{suffix:"end",forAs:!0})]})}get stack(){return this._stack}addDimensions(t){this._stack.facetby.push(...t)}dependentFields(){const t=new Set;return t.add(this._stack.stackField),this.getGroupbyFields().forEach(t.add,t),this._stack.facetby.forEach(t.add,t),this._stack.sort.field.forEach(t.add,t),t}producedFields(){return new Set(this._stack.as)}hash(){return`Stack ${_t(this._stack)}`}getGroupbyFields(){const{dimensionFieldDefs:t,impute:n,groupby:r}=this._stack;return t.length>0?t.map(i=>i.bin?n?[Ee(i,{binSuffix:"mid"})]:[Ee(i,{}),Ee(i,{binSuffix:"end"})]:[Ee(i)]).flat():r??[]}assemble(){const t=[],{facetby:n,dimensionFieldDefs:r,stackField:i,stackby:s,sort:o,offset:a,impute:u,as:l}=this._stack;if(u)for(const c of r){const{bandPosition:f=.5,bin:d}=c;if(d){const h=Ee(c,{expr:"datum"}),p=Ee(c,{expr:"datum",binSuffix:"end"});t.push({type:"formula",expr:`${ME(h)} ? ${f}*${h}+${1-f}*${p} : ${h}`,as:Ee(c,{binSuffix:"mid",forAs:!0})})}t.push({type:"impute",field:i,groupby:[...s,...n],key:Ee(c,{binSuffix:"mid"}),method:"value",value:0})}return t.push({type:"stack",groupby:[...this.getGroupbyFields(),...n],field:i,sort:o,as:l,offset:a}),t}}class sh extends Wt{clone(){return new sh(null,nt(this.transform))}constructor(t,n){super(t),this.transform=n}addDimensions(t){this.transform.groupby=_o(this.transform.groupby.concat(t),n=>n)}dependentFields(){const t=new Set;return(this.transform.groupby??[]).forEach(t.add,t),(this.transform.sort??[]).forEach(n=>t.add(n.field)),this.transform.window.map(n=>n.field).filter(n=>n!==void 0).forEach(t.add,t),t}producedFields(){return new Set(this.transform.window.map(this.getDefaultName))}getDefaultName(t){return t.as??Ee(t)}hash(){return`WindowTransform ${_t(this.transform)}`}assemble(){const t=[],n=[],r=[],i=[];for(const f of this.transform.window)n.push(f.op),r.push(this.getDefaultName(f)),i.push(f.param===void 0?null:f.param),t.push(f.field===void 0?null:f.field);const s=this.transform.frame,o=this.transform.groupby;if(s&&s[0]===null&&s[1]===null&&n.every(f=>yC(f)))return{type:"joinaggregate",as:r,ops:n,fields:t,...o!==void 0?{groupby:o}:{}};const a=[],u=[];if(this.transform.sort!==void 0)for(const f of this.transform.sort)a.push(f.field),u.push(f.order??"ascending");const l={field:a,order:u},c=this.transform.ignorePeers;return{type:"window",params:i,as:r,ops:n,fields:t,sort:l,...c!==void 0?{ignorePeers:c}:{},...o!==void 0?{groupby:o}:{},...s!==void 0?{frame:s}:{}}}}function L2e(e){function t(n){if(!(n instanceof ih)){const r=n.clone();if(r instanceof ai){const i=TE+r.getSource();r.setSource(i),e.model.component.data.outputNodes[i]=r}else(r instanceof Ns||r instanceof Fa||r instanceof sh||r instanceof Yc)&&r.addDimensions(e.fields);for(const i of n.children.flatMap(t))i.parent=r;return[r]}return n.children.flatMap(t)}return t}function RE(e){if(e instanceof ih)if(e.numChildren()===1&&!(e.children[0]instanceof ai)){const t=e.children[0];(t instanceof Ns||t instanceof Fa||t instanceof sh||t instanceof Yc)&&t.addDimensions(e.fields),t.swapWithParent(),RE(e)}else{const t=e.model.component.data.main;Hj(t);const n=L2e(e),r=e.children.map(n).flat();for(const i of r)i.parent=t}else e.children.map(RE)}function Hj(e){if(e instanceof ai&&e.type===mn.Main&&e.numChildren()===1){const t=e.children[0];t instanceof ih||(t.swapWithParent(),Hj(e))}}const TE="scale_",X0=5;function NE(e){for(const t of e){for(const n of t.children)if(n.parent!==t)return!1;if(!NE(t.children))return!1}return!0}function ps(e,t){let n=!1;for(const r of t)n=e.optimize(r)||n;return n}function f6(e,t,n){let r=e.sources,i=!1;return i=ps(new S2e,r)||i,i=ps(new A2e(t),r)||i,r=r.filter(s=>s.numChildren()>0),i=ps(new F2e,r)||i,r=r.filter(s=>s.numChildren()>0),n||(i=ps(new C2e,r)||i,i=ps(new R2e(t),r)||i,i=ps(new k2e,r)||i,i=ps(new $2e,r)||i,i=ps(new M2e,r)||i,i=ps(new D2e,r)||i,i=ps(new E2e,r)||i,i=ps(new T2e,r)||i),e.sources=r,i}function I2e(e,t){NE(e.sources);let n=0,r=0;for(let i=0;it(n))}}function Gj(e){pn(e)?P2e(e):B2e(e)}function P2e(e){const t=e.component.scales;for(const n of ue(t)){const r=j2e(e,n);if(t[n].setWithExplicit("domains",r),q2e(e,n),e.component.data.isFaceted){let s=e;for(;!Ri(s)&&s.parent;)s=s.parent;if(s.component.resolve.scale[n]==="shared")for(const a of r.value)xa(a)&&(a.data=TE+a.data.replace(TE,""))}}}function B2e(e){for(const n of e.children)Gj(n);const t=e.component.scales;for(const n of ue(t)){let r,i=null;for(const s of e.children){const o=s.component.scales[n];if(o){r===void 0?r=o.getWithExplicit("domains"):r=Bu(r,o.getWithExplicit("domains"),"domains","scale",OE);const a=o.get("selectionExtent");i&&a&&i.param!==a.param&&de(_ge),i=a}}t[n].setWithExplicit("domains",r),i&&t[n].set("selectionExtent",i,!0)}}function z2e(e,t,n,r){if(e==="unaggregated"){const{valid:i,reason:s}=d6(t,n);if(!i){de(s);return}}else if(e===void 0&&r.useUnaggregatedDomain){const{valid:i}=d6(t,n);if(i)return"unaggregated"}return e}function j2e(e,t){const n=e.getScaleComponent(t).get("type"),{encoding:r}=e,i=z2e(e.scaleDomain(t),e.typedFieldDef(t),n,e.config.scale);return i!==e.scaleDomain(t)&&(e.specifiedScales[t]={...e.specifiedScales[t],domain:i}),t==="x"&&Ln(r.x2)?Ln(r.x)?Bu(Ka(n,i,e,"x"),Ka(n,i,e,"x2"),"domain","scale",OE):Ka(n,i,e,"x2"):t==="y"&&Ln(r.y2)?Ln(r.y)?Bu(Ka(n,i,e,"y"),Ka(n,i,e,"y2"),"domain","scale",OE):Ka(n,i,e,"y2"):Ka(n,i,e,t)}function U2e(e,t,n){return e.map(r=>({signal:`{data: ${xb(r,{timeUnit:n,type:t})}}`}))}function Rx(e,t,n){var i;const r=(i=Xn(n))==null?void 0:i.unit;return t==="temporal"||r?U2e(e,t,r):[e]}function Ka(e,t,n,r){const{encoding:i,markDef:s,mark:o,config:a,stack:u}=n,l=Ln(i[r]),{type:c}=l,f=l.timeUnit,d=kve({invalid:Lo("invalid",s,a),isPath:ol(o)});if(Y0e(t)){const g=Ka(e,void 0,n,r),m=Rx(t.unionWith,c,f);return uo([...m,...g.value])}else{if(Be(t))return uo([t]);if(t&&t!=="unaggregated"&&!RB(t))return uo(Rx(t,c,f))}if(u&&r===u.fieldChannel){if(u.offset==="normalize")return bi([[0,1]]);const g=n.requestDataName(d);return bi([{data:g,field:n.vgField(r,{suffix:"start"})},{data:g,field:n.vgField(r,{suffix:"end"})}])}const h=Jo(r)&&pe(l)?W2e(n,r,e):void 0;if(Qo(l)){const g=Rx([l.datum],c,f);return bi(g)}const p=l;if(t==="unaggregated"){const{field:g}=l;return bi([{data:n.requestDataName(d),field:Ee({field:g,aggregate:"min"})},{data:n.requestDataName(d),field:Ee({field:g,aggregate:"max"})}])}else if(rn(p.bin)){if(Zn(e))return 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Nx,this.data=t.data,this.description=t.description,this.transforms=hve(t.transform??[]),this.layout=n==="layer"||n==="unit"?{}:v1e(t,n,s),this.component={data:{sources:r?r.component.data.sources:[],outputNodes:r?r.component.data.outputNodes:{},outputNodeRefCounts:r?r.component.data.outputNodeRefCounts:{},isFaceted:vb(t)||(r==null?void 0:r.component.data.isFaceted)&&t.data===void 0},layoutSize:new Ha,layoutHeaders:{row:{},column:{},facet:{}},mark:null,resolve:{scale:{},axis:{},legend:{},...o?nt(o):{}},selection:null,scales:null,projection:null,axes:{},legends:{}}}get width(){return this.getSizeSignalRef("width")}get height(){return this.getSizeSignalRef("height")}parse(){this.parseScale(),this.parseLayoutSize(),this.renameTopLevelLayoutSizeSignal(),this.parseSelections(),this.parseProjection(),this.parseData(),this.parseAxesAndHeaders(),this.parseLegends(),this.parseMarkGroup()}parseScale(){pxe(this)}parseProjection(){qj(this)}renameTopLevelLayoutSizeSignal(){this.getName("width")!=="width"&&this.renameSignal(this.getName("width"),"width"),this.getName("height")!=="height"&&this.renameSignal(this.getName("height"),"height")}parseLegends(){Pj(this)}assembleEncodeFromView(t){const{style:n,...r}=t,i={};for(const s of ue(r)){const o=r[s];o!==void 0&&(i[s]=on(o))}return i}assembleGroupEncodeEntry(t){let n={};return this.view&&(n=this.assembleEncodeFromView(this.view)),!t&&(this.description&&(n.description=on(this.description)),this.type==="unit"||this.type==="layer")?{width:this.getSizeSignalRef("width"),height:this.getSizeSignalRef("height"),...n}:Xt(n)?void 0:n}assembleLayout(){if(!this.layout)return;const{spacing:t,...n}=this.layout,{component:r,config:i}=this,s=Ibe(r.layoutHeaders,i);return{padding:t,...this.assembleDefaultLayout(),...n,...s?{titleBand:s}:{}}}assembleDefaultLayout(){return{}}assembleHeaderMarks(){const{layoutHeaders:t}=this.component;let n=[];for(const r of Zi)t[r].title&&n.push(Mbe(this,r));for(const r of g$)n=n.concat(Rbe(this,r));return n}assembleAxes(){return ybe(this.component.axes,this.config)}assembleLegends(){return zj(this)}assembleProjections(){return o2e(this)}assembleTitle(){const{encoding:t,...n}=this.title??{},r={...tB(this.config.title).nonMarkTitleProperties,...n,...t?{encode:{update:t}}:{}};if(r.text)return vt(["unit","layer"],this.type)?vt(["middle",void 0],r.anchor)&&(r.frame??(r.frame="group")):r.anchor??(r.anchor="start"),Xt(r)?void 0:r}assembleGroup(t=[]){const n={};t=t.concat(this.assembleSignals()),t.length>0&&(n.signals=t);const r=this.assembleLayout();r&&(n.layout=r),n.marks=[].concat(this.assembleHeaderMarks(),this.assembleMarks());const i=!this.parent||Ri(this.parent)?Yj(this):[];i.length>0&&(n.scales=i);const s=this.assembleAxes();s.length>0&&(n.axes=s);const o=this.assembleLegends();return o.length>0&&(n.legends=o),n}getName(t){return cn((this.name?`${this.name}_`:"")+t)}getDataName(t){return this.getName(mn[t].toLowerCase())}requestDataName(t){const n=this.getDataName(t),r=this.component.data.outputNodeRefCounts;return r[n]=(r[n]||0)+1,n}getSizeSignalRef(t){if(Ri(this.parent)){const n=Mj(t),r=ob(n),i=this.component.scales[r];if(i&&!i.merged){const s=i.get("type"),o=i.get("range");if(Zn(s)&&jc(o)){const a=i.get("name"),u=Lb(this,r),l=E$(u);if(l){const c=Ee({aggregate:"distinct",field:l},{expr:"datum"});return{signal:Dj(a,i,c)}}else return de(xC(r)),null}}}return{signal:this.signalNameMap.get(this.getName(t))}}lookupDataSource(t){const n=this.component.data.outputNodes[t];return n?n.getSource():t}getSignalName(t){return this.signalNameMap.get(t)}renameSignal(t,n){this.signalNameMap.rename(t,n)}renameScale(t,n){this.scaleNameMap.rename(t,n)}renameProjection(t,n){this.projectionNameMap.rename(t,n)}scaleName(t,n){if(n)return this.getName(t);if(YP(t)&&Jo(t)&&this.component.scales[t]||this.scaleNameMap.has(this.getName(t)))return this.scaleNameMap.get(this.getName(t))}projectionName(t){if(t)return this.getName("projection");if(this.component.projection&&!this.component.projection.merged||this.projectionNameMap.has(this.getName("projection")))return this.projectionNameMap.get(this.getName("projection"))}getScaleComponent(t){if(!this.component.scales)throw new Error("getScaleComponent cannot be called before parseScale(). Make sure you have called parseScale or use parseUnitModelWithScale().");const n=this.component.scales[t];return n&&!n.merged?n:this.parent?this.parent.getScaleComponent(t):void 0}getScaleType(t){const n=this.getScaleComponent(t);return n?n.get("type"):void 0}getSelectionComponent(t,n){let r=this.component.selection[t];if(!r&&this.parent&&(r=this.parent.getSelectionComponent(t,n)),!r)throw new Error(gge(n));return r}hasAxisOrientSignalRef(){var t,n;return((t=this.component.axes.x)==null?void 0:t.some(r=>r.hasOrientSignalRef()))||((n=this.component.axes.y)==null?void 0:n.some(r=>r.hasOrientSignalRef()))}}class rU extends k${vgField(t,n={}){const r=this.fieldDef(t);if(r)return Ee(r,n)}reduceFieldDef(t,n){return Gme(this.getMapping(),(r,i,s)=>{const o=Us(i);return o?t(r,o,s):r},n)}forEachFieldDef(t,n){VC(this.getMapping(),(r,i)=>{const s=Us(r);s&&t(s,i)},n)}}class Ib extends Wt{clone(){return new Ib(null,nt(this.transform))}constructor(t,n){super(t),this.transform=n,this.transform=nt(n);const r=this.transform.as??[void 0,void 0];this.transform.as=[r[0]??"value",r[1]??"density"];const i=this.transform.resolve??"shared";this.transform.resolve=i}dependentFields(){return new Set([this.transform.density,...this.transform.groupby??[]])}producedFields(){return new Set(this.transform.as)}hash(){return`DensityTransform ${_t(this.transform)}`}assemble(){const{density:t,...n}=this.transform,r={type:"kde",field:t,...n};return r.resolve=this.transform.resolve,r}}class Pb extends Wt{clone(){return new Pb(null,nt(this.transform))}constructor(t,n){super(t),this.transform=n,this.transform=nt(n)}dependentFields(){return new Set([this.transform.extent])}producedFields(){return new Set([])}hash(){return`ExtentTransform ${_t(this.transform)}`}assemble(){const{extent:t,param:n}=this.transform;return{type:"extent",field:t,signal:n}}}class Bb extends Wt{clone(){return new Bb(this.parent,nt(this.transform))}constructor(t,n){super(t),this.transform=n,this.transform=nt(n);const{flatten:r,as:i=[]}=this.transform;this.transform.as=r.map((s,o)=>i[o]??s)}dependentFields(){return new Set(this.transform.flatten)}producedFields(){return new Set(this.transform.as)}hash(){return`FlattenTransform ${_t(this.transform)}`}assemble(){const{flatten:t,as:n}=this.transform;return{type:"flatten",fields:t,as:n}}}class zb extends Wt{clone(){return new zb(null,nt(this.transform))}constructor(t,n){super(t),this.transform=n,this.transform=nt(n);const r=this.transform.as??[void 0,void 0];this.transform.as=[r[0]??"key",r[1]??"value"]}dependentFields(){return new Set(this.transform.fold)}producedFields(){return new Set(this.transform.as)}hash(){return`FoldTransform ${_t(this.transform)}`}assemble(){const{fold:t,as:n}=this.transform;return{type:"fold",fields:t,as:n}}}class Nf extends Wt{clone(){return new Nf(null,nt(this.fields),this.geojson,this.signal)}static parseAll(t,n){if(n.component.projection&&!n.component.projection.isFit)return t;let r=0;for(const i of[[Zs,Ks],[ls,Js]]){const s=i.map(o=>{const a=Ln(n.encoding[o]);return pe(a)?a.field:Qo(a)?{expr:`${a.datum}`}:js(a)?{expr:`${a.value}`}:void 0});(s[0]||s[1])&&(t=new Nf(t,s,null,n.getName(`geojson_${r++}`)))}if(n.channelHasField(di)){const i=n.typedFieldDef(di);i.type===th&&(t=new Nf(t,null,i.field,n.getName(`geojson_${r++}`)))}return t}constructor(t,n,r,i){super(t),this.fields=n,this.geojson=r,this.signal=i}dependentFields(){const t=(this.fields??[]).filter(De);return new Set([...this.geojson?[this.geojson]:[],...t])}producedFields(){return new Set}hash(){return`GeoJSON ${this.geojson} ${this.signal} ${_t(this.fields)}`}assemble(){return[...this.geojson?[{type:"filter",expr:`isValid(datum["${this.geojson}"])`}]:[],{type:"geojson",...this.fields?{fields:this.fields}:{},...this.geojson?{geojson:this.geojson}:{},signal:this.signal}]}}class ig extends Wt{clone(){return new ig(null,this.projection,nt(this.fields),nt(this.as))}constructor(t,n,r,i){super(t),this.projection=n,this.fields=r,this.as=i}static parseAll(t,n){if(!n.projectionName())return t;for(const r of[[Zs,Ks],[ls,Js]]){const i=r.map(o=>{const a=Ln(n.encoding[o]);return pe(a)?a.field:Qo(a)?{expr:`${a.datum}`}:js(a)?{expr:`${a.value}`}:void 0}),s=r[0]===ls?"2":"";(i[0]||i[1])&&(t=new ig(t,n.projectionName(),i,[n.getName(`x${s}`),n.getName(`y${s}`)]))}return t}dependentFields(){return new Set(this.fields.filter(De))}producedFields(){return new Set(this.as)}hash(){return`Geopoint ${this.projection} ${_t(this.fields)} ${_t(this.as)}`}assemble(){return{type:"geopoint",projection:this.projection,fields:this.fields,as:this.as}}}class Xl extends Wt{clone(){return new Xl(null,nt(this.transform))}constructor(t,n){super(t),this.transform=n}dependentFields(){return new Set([this.transform.impute,this.transform.key,...this.transform.groupby??[]])}producedFields(){return new Set([this.transform.impute])}processSequence(t){const{start:n=0,stop:r,step:i}=t;return{signal:`sequence(${[n,r,...i?[i]:[]].join(",")})`}}static makeFromTransform(t,n){return new Xl(t,n)}static makeFromEncoding(t,n){const r=n.encoding,i=r.x,s=r.y;if(pe(i)&&pe(s)){const o=i.impute?i:s.impute?s:void 0;if(o===void 0)return;const a=i.impute?s:s.impute?i:void 0,{method:u,value:l,frame:c,keyvals:f}=o.impute,d=cz(n.mark,r);return new Xl(t,{impute:o.field,key:a.field,...u?{method:u}:{},...l!==void 0?{value:l}:{},...c?{frame:c}:{},...f!==void 0?{keyvals:f}:{},...d.length?{groupby:d}:{}})}return null}hash(){return`Impute ${_t(this.transform)}`}assemble(){const{impute:t,key:n,keyvals:r,method:i,groupby:s,value:o,frame:a=[null,null]}=this.transform,u={type:"impute",field:t,key:n,...r?{keyvals:V1e(r)?this.processSequence(r):r}:{},method:"value",...s?{groupby:s}:{},value:!i||i==="value"?o:null};if(i&&i!=="value"){const l={type:"window",as:[`imputed_${t}_value`],ops:[i],fields:[t],frame:a,ignorePeers:!1,...s?{groupby:s}:{}},c={type:"formula",expr:`datum.${t} === null ? datum.imputed_${t}_value : datum.${t}`,as:t};return[u,l,c]}else return[u]}}class jb extends Wt{clone(){return new jb(null,nt(this.transform))}constructor(t,n){super(t),this.transform=n,this.transform=nt(n);const r=this.transform.as??[void 0,void 0];this.transform.as=[r[0]??n.on,r[1]??n.loess]}dependentFields(){return new Set([this.transform.loess,this.transform.on,...this.transform.groupby??[]])}producedFields(){return new Set(this.transform.as)}hash(){return`LoessTransform ${_t(this.transform)}`}assemble(){const{loess:t,on:n,...r}=this.transform;return{type:"loess",x:n,y:t,...r}}}class sg extends Wt{clone(){return new sg(null,nt(this.transform),this.secondary)}constructor(t,n,r){super(t),this.transform=n,this.secondary=r}static make(t,n,r,i){const s=n.component.data.sources,{from:o}=r;let a=null;if(X1e(o)){let u=oU(o.data,s);u||(u=new $c(o.data),s.push(u));const l=n.getName(`lookup_${i}`);a=new ai(u,l,mn.Lookup,n.component.data.outputNodeRefCounts),n.component.data.outputNodes[l]=a}else if(K1e(o)){const u=o.param;r={as:u,...r};let l;try{l=n.getSelectionComponent(cn(u),u)}catch{throw new Error(bge(u))}if(a=l.materialized,!a)throw new Error(xge(u))}return new sg(t,r,a.getSource())}dependentFields(){return new Set([this.transform.lookup])}producedFields(){return new Set(this.transform.as?Me(this.transform.as):this.transform.from.fields)}hash(){return`Lookup ${_t({transform:this.transform,secondary:this.secondary})}`}assemble(){let t;if(this.transform.from.fields)t={values:this.transform.from.fields,...this.transform.as?{as:Me(this.transform.as)}:{}};else{let n=this.transform.as;De(n)||(de(Mge),n="_lookup"),t={as:[n]}}return{type:"lookup",from:this.secondary,key:this.transform.from.key,fields:[this.transform.lookup],...t,...this.transform.default?{default:this.transform.default}:{}}}}class Ub extends Wt{clone(){return new Ub(null,nt(this.transform))}constructor(t,n){super(t),this.transform=n,this.transform=nt(n);const r=this.transform.as??[void 0,void 0];this.transform.as=[r[0]??"prob",r[1]??"value"]}dependentFields(){return new Set([this.transform.quantile,...this.transform.groupby??[]])}producedFields(){return new Set(this.transform.as)}hash(){return`QuantileTransform ${_t(this.transform)}`}assemble(){const{quantile:t,...n}=this.transform;return{type:"quantile",field:t,...n}}}class qb extends Wt{clone(){return new qb(null,nt(this.transform))}constructor(t,n){super(t),this.transform=n,this.transform=nt(n);const r=this.transform.as??[void 0,void 0];this.transform.as=[r[0]??n.on,r[1]??n.regression]}dependentFields(){return new Set([this.transform.regression,this.transform.on,...this.transform.groupby??[]])}producedFields(){return new Set(this.transform.as)}hash(){return`RegressionTransform ${_t(this.transform)}`}assemble(){const{regression:t,on:n,...r}=this.transform;return{type:"regression",x:n,y:t,...r}}}class Wb extends Wt{clone(){return new Wb(null,nt(this.transform))}constructor(t,n){super(t),this.transform=n}addDimensions(t){this.transform.groupby=_o((this.transform.groupby??[]).concat(t),n=>n)}producedFields(){}dependentFields(){return new Set([this.transform.pivot,this.transform.value,...this.transform.groupby??[]])}hash(){return`PivotTransform ${_t(this.transform)}`}assemble(){const{pivot:t,value:n,groupby:r,limit:i,op:s}=this.transform;return{type:"pivot",field:t,value:n,...i!==void 0?{limit:i}:{},...s!==void 0?{op:s}:{},...r!==void 0?{groupby:r}:{}}}}class Hb extends Wt{clone(){return new Hb(null,nt(this.transform))}constructor(t,n){super(t),this.transform=n}dependentFields(){return new Set}producedFields(){return new Set}hash(){return`SampleTransform ${_t(this.transform)}`}assemble(){return{type:"sample",size:this.transform.sample}}}function iU(e){let t=0;function n(r,i){if(r instanceof $c&&!r.isGenerator&&!Sd(r.data)&&(e.push(i),i={name:null,source:i.name,transform:[]}),r instanceof wr&&(r.parent instanceof $c&&!i.source?(i.format={...i.format,parse:r.assembleFormatParse()},i.transform.push(...r.assembleTransforms(!0))):i.transform.push(...r.assembleTransforms())),r instanceof ih){i.name||(i.name=`data_${t++}`),!i.source||i.transform.length>0?(e.push(i),r.data=i.name):r.data=i.source,e.push(...r.assemble());return}switch((r instanceof d0||r instanceof h0||r instanceof Md||r instanceof rh||r instanceof $d||r instanceof ig||r instanceof Ns||r instanceof sg||r instanceof sh||r instanceof Yc||r instanceof zb||r instanceof Bb||r instanceof Ib||r instanceof jb||r instanceof Ub||r instanceof qb||r instanceof zu||r instanceof Hb||r instanceof Wb||r instanceof Pb)&&i.transform.push(r.assemble()),(r instanceof $o||r instanceof So||r instanceof Xl||r instanceof Fa||r instanceof Nf)&&i.transform.push(...r.assemble()),r instanceof ai&&(i.source&&i.transform.length===0?r.setSource(i.source):r.parent instanceof ai?r.setSource(i.name):(i.name||(i.name=`data_${t++}`),r.setSource(i.name),r.numChildren()===1&&(e.push(i),i={name:null,source:i.name,transform:[]}))),r.numChildren()){case 0:r instanceof ai&&(!i.source||i.transform.length>0)&&e.push(i);break;case 1:n(r.children[0],i);break;default:{i.name||(i.name=`data_${t++}`);let s=i.name;!i.source||i.transform.length>0?e.push(i):s=i.source;for(const o of r.children)n(o,{name:null,source:s,transform:[]});break}}}return n}function yxe(e){const t=[],n=iU(t);for(const r of e.children)n(r,{source:e.name,name:null,transform:[]});return t}function bxe(e,t){const n=[],r=iU(n);let i=0;for(const o of e.sources){o.hasName()||(o.dataName=`source_${i++}`);const a=o.assemble();r(o,a)}for(const o of n)o.transform.length===0&&delete o.transform;let s=0;for(const[o,a]of n.entries())(a.transform??[]).length===0&&!a.source&&n.splice(s++,0,n.splice(o,1)[0]);for(const o of n)for(const a of o.transform??[])a.type==="lookup"&&(a.from=e.outputNodes[a.from].getSource());for(const o of n)o.name in t&&(o.values=t[o.name]);return n}function xxe(e){return e==="top"||e==="left"||Be(e)?"header":"footer"}function _xe(e){for(const t of Zi)wxe(e,t);y6(e,"x"),y6(e,"y")}function wxe(e,t){var o;const{facet:n,config:r,child:i,component:s}=e;if(e.channelHasField(t)){const a=n[t],u=Dd("title",null,r,t);let l=Rf(a,r,{allowDisabling:!0,includeDefault:u===void 0||!!u});i.component.layoutHeaders[t].title&&(l=ee(l)?l.join(", "):l,l+=` / ${i.component.layoutHeaders[t].title}`,i.component.layoutHeaders[t].title=null);const c=Dd("labelOrient",a.header,r,t),f=a.header!==null?$n((o=a.header)==null?void 0:o.labels,r.header.labels,!0):!1,d=vt(["bottom","right"],c)?"footer":"header";s.layoutHeaders[t]={title:a.header!==null?l:null,facetFieldDef:a,[d]:t==="facet"?[]:[sU(e,t,f)]}}}function sU(e,t,n){const 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F$(e,t={fromPrefix:""}){const{mark:n,markDef:r,encoding:i,config:s}=e,o=$n(r.clip,c_e(e),f_e(e)),a=rB(r),u=i.key,l=l_e(e),c=d_e(e),f=Vt("aria",r,s),d=Z0[n].postEncodingTransform?Z0[n].postEncodingTransform(e):null;return[{name:e.getName("marks"),type:Z0[n].vgMark,...o?{clip:o}:{},...a?{style:a}:{},...u?{key:u.field}:{},...l?{sort:l}:{},...c||{},...f===!1?{aria:f}:{},from:{data:t.fromPrefix+e.requestDataName(mn.Main)},encode:{update:Z0[n].encodeEntry(e)},...d?{transform:d}:{}}]}function c_e(e){const t=e.getScaleComponent("x"),n=e.getScaleComponent("y");return t!=null&&t.get("selectionExtent")||n!=null&&n.get("selectionExtent")?!0:void 0}function f_e(e){const t=e.component.projection;return t&&!t.isFit?!0:void 0}function d_e(e){if(!e.component.selection)return null;const t=ue(e.component.selection).length;let n=t,r=e.parent;for(;r&&n===0;)n=ue(r.component.selection).length,r=r.parent;return n?{interactive:t>0||e.mark==="geoshape"||!!e.encoding.tooltip||!!e.markDef.tooltip}:null}class uU extends rU{constructor(t,n,r,i={},s){super(t,"unit",n,r,s,void 0,k4(t)?t.view:void 0),this.specifiedScales={},this.specifiedAxes={},this.specifiedLegends={},this.specifiedProjection={},this.selection=[],this.children=[],this.correctDataNames=l=>{var c,f,d;return(c=l.from)!=null&&c.data&&(l.from.data=this.lookupDataSource(l.from.data),"time"in this.encoding&&(l.from.data=l.from.data+Hz)),(d=(f=l.from)==null?void 0:f.facet)!=null&&d.data&&(l.from.facet.data=this.lookupDataSource(l.from.facet.data)),l};const o=Io(t.mark)?{...t.mark}:{type:t.mark},a=o.type;o.filled===void 0&&(o.filled=z1e(o,s,{graticule:t.data&&o$(t.data)}));const u=this.encoding=Wme(t.encoding||{},a,o.filled,s);this.markDef=Mz(o,u,s),this.size=Uxe({encoding:u,size:k4(t)?{...i,...t.width?{width:t.width}:{},...t.height?{height:t.height}:{}}:i}),this.stack=Dz(this.markDef,u),this.specifiedScales=this.initScales(a,u),this.specifiedAxes=this.initAxes(u),this.specifiedLegends=this.initLegends(u),this.specifiedProjection=t.projection,this.selection=(t.params??[]).filter(l=>t$(l))}get hasProjection(){const{encoding:t}=this,n=this.mark===LB,r=t&&Lpe.some(i=>rt(t[i]));return n||r}scaleDomain(t){const n=this.specifiedScales[t];return n?n.domain:void 0}axis(t){return this.specifiedAxes[t]}legend(t){return this.specifiedLegends[t]}initScales(t,n){return vC.reduce((r,i)=>{const s=Ln(n[i]);return s&&(r[i]=this.initScale(s.scale??{})),r},{})}initScale(t){const{domain:n,range:r}=t,i=rr(t);return ee(n)&&(i.domain=n.map(ki)),ee(r)&&(i.range=r.map(ki)),i}initAxes(t){return qa.reduce((n,r)=>{const i=t[r];if(rt(i)||r===dn&&rt(t.x2)||r===Qn&&rt(t.y2)){const 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ue(t.component.selection))this.component.selection[n]=t.component.selection[n]}Object.values(this.component.selection).some(t=>Co(t))&&wC(_C)}parseMarkGroup(){for(const t of this.children)t.parseMarkGroup()}parseAxesAndHeaders(){Lxe(this)}assembleSelectionTopLevelSignals(t){return this.children.reduce((n,r)=>r.assembleSelectionTopLevelSignals(n),t)}assembleSignals(){return this.children.reduce((t,n)=>t.concat(n.assembleSignals()),Ej(this))}assembleLayoutSignals(){return this.children.reduce((t,n)=>t.concat(n.assembleLayoutSignals()),v$(this))}assembleSelectionData(t){return this.children.reduce((n,r)=>r.assembleSelectionData(n),t)}assembleGroupStyle(){const t=new Set;for(const r of this.children)for(const i of Me(r.assembleGroupStyle()))t.add(i);const n=Array.from(t);return n.length>1?n:n.length===1?n[0]:void 0}assembleTitle(){let t=super.assembleTitle();if(t)return t;for(const n of this.children)if(t=n.assembleTitle(),t)return t}assembleLayout(){return null}assembleMarks(){return 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Array(t.num_values);return ju(e,Jb(r),i,t.definition_levels_byte_length),i}}function w8(e,{groupStart:t,selectStart:n,selectEnd:r},i,s){const{pathInSchema:o,schemaPath:a}=i,u=zU(a),l=[];let c,f,d=0,h=0;const p=s&&(()=>{f&&s({pathInSchema:o,columnData:f,rowStart:t+d-f.length,rowEnd:t+d})});for(;(u?d=e.view.byteLength-1);){const g=IAe(e);if(g.type==="DICTIONARY_PAGE"){const{data:m}=E8(e,g,i,c,void 0,0);m&&(c=NU(m,i))}else{const m=(f==null?void 0:f.length)||0,v=E8(e,g,i,c,f,n-d);v.skipped?(l.length||(h+=v.skipped),d+=v.skipped):v.data&&f===v.data?d+=v.data.length-m:v.data&&v.data.length&&(p==null||p(),l.push(v.data),d+=v.data.length,f=v.data)}}return p==null||p(),{data:l,skipped:h}}function E8(e,t,n,r,i,s){const{type:o,element:a,schemaPath:u,codec:l,compressors:c}=n,f=new Uint8Array(e.view.buffer,e.view.byteOffset+e.offset,t.compressed_page_size);if(e.offset+=t.compressed_page_size,t.type==="DATA_PAGE"){const d=t.data_page_header;if(!d)throw new Error("parquet data page header is 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t=Zb(e),n=RU[t.field_1],r=t.field_2,i=t.field_3,s=t.field_4,o=t.field_5&&{num_values:t.field_5.field_1,encoding:Cl[t.field_5.field_2],definition_level_encoding:Cl[t.field_5.field_3],repetition_level_encoding:Cl[t.field_5.field_4],statistics:t.field_5.field_5&&{max:t.field_5.field_5.field_1,min:t.field_5.field_5.field_2,null_count:t.field_5.field_5.field_3,distinct_count:t.field_5.field_5.field_4,max_value:t.field_5.field_5.field_5,min_value:t.field_5.field_5.field_6}},a=t.field_6,u=t.field_7&&{num_values:t.field_7.field_1,encoding:Cl[t.field_7.field_2],is_sorted:t.field_7.field_3},l=t.field_8&&{num_values:t.field_8.field_1,num_nulls:t.field_8.field_2,num_rows:t.field_8.field_3,encoding:Cl[t.field_8.field_4],definition_levels_byte_length:t.field_8.field_5,repetition_levels_byte_length:t.field_8.field_6,is_compressed:t.field_8.field_7===void 0?!0:t.field_8.field_7,statistics:t.field_8.field_8};return{type:n,uncompressed_page_size:r,compressed_page_size:i,crc:s,data_page_header:o,index_page_header:a,dictionary_page_header:u,data_page_header_v2:l}}function PAe(e,{metadata:t},n){const{file:r,compressors:i,utf8:s}=e,o=[],a={...Kb,...e.parsers};for(const u of n.chunks){const{columnMetadata:l}=u,c=PU(t.schema,l.path_in_schema),f={pathInSchema:l.path_in_schema,type:l.type,element:c[c.length-1].element,schemaPath:c,codec:l.codec,parsers:a,compressors:i,utf8:s};if(!("offsetIndex"in u)){o.push({pathInSchema:l.path_in_schema,data:Promise.resolve(r.slice(u.range.startByte,u.range.endByte)).then(d=>{const h={view:new DataView(d),offset:0};return w8(h,n,f,e.onPage)})});continue}o.push({pathInSchema:l.path_in_schema,data:Promise.resolve(r.slice(u.offsetIndex.startByte,u.offsetIndex.endByte)).then(async d=>{const h=aAe({view:new DataView(d),offset:0}),{selectStart:p,selectEnd:g}=n,m=h.page_locations;let v=NaN,y=NaN,x=0;for(let 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        + + diff --git a/trackio/frontend/eslint.config.js b/trackio/frontend/eslint.config.js new file mode 100644 index 0000000000000000000000000000000000000000..c0bd968851d9ee35d98943b9c2b04b91af245322 --- /dev/null +++ b/trackio/frontend/eslint.config.js @@ -0,0 +1,42 @@ +import js from "@eslint/js"; +import svelte from "eslint-plugin-svelte"; +import svelteParser from "svelte-eslint-parser"; +import globals from "globals"; + +export default [ + { ignores: ["dist/**", "node_modules/**"] }, + { + files: ["**/*.js"], + languageOptions: { + globals: { + ...globals.browser, + ...globals.es2021, + $state: "readonly", + $derived: "readonly", + $effect: "readonly", + $props: "readonly", + $bindable: "readonly", + $inspect: "readonly", + }, + }, + rules: { + ...js.configs.recommended.rules, + "no-unused-vars": ["error", { argsIgnorePattern: "^_" }], + "no-empty": "off", + }, + }, + { + files: ["**/*.svelte"], + languageOptions: { + parser: svelteParser, + globals: { ...globals.browser, ...globals.es2021 }, + }, + plugins: { svelte }, + rules: { + ...js.configs.recommended.rules, + ...svelte.configs.recommended.rules, + "no-unused-vars": ["error", { argsIgnorePattern: "^_", varsIgnorePattern: "^\\$" }], + "no-empty": "off", + }, + }, +]; diff --git a/trackio/frontend/index.html b/trackio/frontend/index.html new file mode 100644 index 0000000000000000000000000000000000000000..c3d5e2df6bb7db117ed1d554ea7d8d180b076799 --- /dev/null +++ b/trackio/frontend/index.html @@ -0,0 +1,13 @@ + + + + + + Trackio Dashboard + + + +
        + + + diff --git a/trackio/frontend_server.py b/trackio/frontend_server.py new file mode 100644 index 0000000000000000000000000000000000000000..017e9cd9557b969988bf796d6f9e24154f4cabf6 --- /dev/null +++ b/trackio/frontend_server.py @@ -0,0 +1,64 @@ +"""Serves the built Svelte frontend alongside the Trackio HTTP API.""" + +import logging +import re +from pathlib import Path + +from starlette.responses import HTMLResponse +from starlette.routing import Mount, Route +from starlette.staticfiles import StaticFiles + +FRONTEND_DIR = Path(__file__).parent / "frontend" / "dist" +ASSETS_DIR = Path(__file__).parent / "assets" + +_logger = logging.getLogger(__name__) + +_SPA_SEGMENTS = ( + "metrics", + "system", + "media", + "reports", + "runs", + "run", + "files", + "settings", +) + + +def mount_frontend(app): + if not FRONTEND_DIR.exists(): + _logger.warning( + "Trackio dashboard UI was not mounted: %s is missing. " + "Build the frontend with `npm ci && npm run build` in trackio/frontend.", + FRONTEND_DIR, + ) + return + + index_html_path = FRONTEND_DIR / "index.html" + if not index_html_path.exists(): + _logger.warning( + "Trackio dashboard UI was not mounted: %s is missing.", + index_html_path, + ) + return + + index_html_content = index_html_path.read_text() + patched_html = re.sub( + r'/assets/(index-[^"]+)', + r"/assets/app/\1", + index_html_content, + ) + + async def serve_frontend(request): + return HTMLResponse(patched_html) + + vite_assets = StaticFiles(directory=str(FRONTEND_DIR / "assets")) + static_assets = StaticFiles(directory=str(ASSETS_DIR)) + + app.routes.insert(0, Mount("/static/trackio", app=static_assets)) + app.routes.insert(0, Mount("/assets/app", app=vite_assets)) + + for seg in reversed(_SPA_SEGMENTS): + app.routes.insert(0, Route(f"/{seg}/", serve_frontend, methods=["GET"])) + app.routes.insert(0, Route(f"/{seg}", serve_frontend, methods=["GET"])) + app.routes.insert(0, Route("/", serve_frontend, methods=["GET"])) diff --git a/trackio/gpu.py b/trackio/gpu.py new file mode 100644 index 0000000000000000000000000000000000000000..a84d903884069e1133532629aa1c63d22c3c792a --- /dev/null +++ b/trackio/gpu.py @@ -0,0 +1,381 @@ +import os +import threading +import warnings +from typing import TYPE_CHECKING, Any + +if TYPE_CHECKING: + from trackio.run import Run + +pynvml: Any = None +PYNVML_AVAILABLE = False +_nvml_initialized = False +_nvml_lock = threading.Lock() +_energy_baseline: dict[int, float] = {} + + +def _ensure_pynvml(): + global PYNVML_AVAILABLE, pynvml + if PYNVML_AVAILABLE: + return pynvml + try: + import pynvml as _pynvml + + pynvml = _pynvml + PYNVML_AVAILABLE = True + return pynvml + except ImportError: + raise ImportError( + "nvidia-ml-py is required for GPU monitoring. " + "Install it with: pip install nvidia-ml-py" + ) + + +def _init_nvml() -> bool: + global _nvml_initialized + with _nvml_lock: + if _nvml_initialized: + return True + try: + nvml = _ensure_pynvml() + nvml.nvmlInit() + _nvml_initialized = True + return True + except Exception: + return False + + +def get_gpu_count() -> tuple[int, list[int]]: + """ + Get the number of GPUs visible to this process and their physical indices. + Respects CUDA_VISIBLE_DEVICES environment variable. + + Returns: + Tuple of (count, physical_indices) where: + - count: Number of visible GPUs + - physical_indices: List mapping logical index to physical GPU index. + e.g., if CUDA_VISIBLE_DEVICES=2,3 returns (2, [2, 3]) + meaning logical GPU 0 = physical GPU 2, logical GPU 1 = physical GPU 3 + """ + if not _init_nvml(): + return 0, [] + + cuda_visible = os.environ.get("CUDA_VISIBLE_DEVICES") + if cuda_visible is not None and cuda_visible.strip(): + try: + indices = [int(x.strip()) for x in cuda_visible.split(",") if x.strip()] + return len(indices), indices + except ValueError: + pass + + try: + total = pynvml.nvmlDeviceGetCount() + return total, list(range(total)) + except Exception: + return 0, [] + + +def get_all_gpu_count() -> tuple[int, list[int]]: + """ + Get the total number of physical GPUs on the machine, ignoring CUDA_VISIBLE_DEVICES. + + Returns: + Tuple of (count, physical_indices) for ALL GPUs on the machine. + e.g., on a 4-GPU machine returns (4, [0, 1, 2, 3]) regardless of + CUDA_VISIBLE_DEVICES setting. + """ + if not _init_nvml(): + return 0, [] + + try: + total = pynvml.nvmlDeviceGetCount() + return total, list(range(total)) + except Exception: + return 0, [] + + +def gpu_available() -> bool: + """ + Check if GPU monitoring is available. + + Returns True if nvidia-ml-py is installed and at least one NVIDIA GPU is detected. + This is used for auto-detection of GPU logging. + """ + try: + _ensure_pynvml() + count, _ = get_gpu_count() + return count > 0 + except ImportError: + return False + except Exception: + return False + + +def reset_energy_baseline(): + """Reset the energy baseline for all GPUs. Called when a new run starts.""" + global _energy_baseline + _energy_baseline = {} + + +def collect_gpu_metrics(device: int | None = None, all_gpus: bool = False) -> dict: + """ + Collect GPU metrics for visible GPUs. + + Args: + device: CUDA device index to collect metrics from. If None, collects + from all GPUs visible to this process (respects CUDA_VISIBLE_DEVICES). + The device index is the logical CUDA index (0, 1, 2...), not the + physical GPU index. + all_gpus: If True and device is None, collect metrics for ALL physical GPUs + on the machine, ignoring CUDA_VISIBLE_DEVICES. Used by GpuMonitor + to report system-wide GPU metrics in distributed training. + + Returns: + Dictionary of GPU metrics. Keys use device indices (gpu/0/, gpu/1/, etc.). + """ + if not _init_nvml(): + return {} + + if all_gpus and device is None: + gpu_count, visible_gpus = get_all_gpu_count() + else: + gpu_count, visible_gpus = get_gpu_count() + if gpu_count == 0: + return {} + + if device is not None: + if device < 0 or device >= gpu_count: + return {} + gpu_indices = [(device, visible_gpus[device])] + else: + gpu_indices = list(enumerate(visible_gpus)) + + metrics = {} + total_util = 0.0 + total_mem_used_gib = 0.0 + total_power = 0.0 + max_temp = 0.0 + valid_util_count = 0 + + for logical_idx, physical_idx in gpu_indices: + prefix = f"gpu/{logical_idx}" + try: + handle = pynvml.nvmlDeviceGetHandleByIndex(physical_idx) + + try: + util = pynvml.nvmlDeviceGetUtilizationRates(handle) + metrics[f"{prefix}/utilization"] = util.gpu + metrics[f"{prefix}/memory_utilization"] = util.memory + total_util += util.gpu + valid_util_count += 1 + except Exception: + pass + + try: + mem = pynvml.nvmlDeviceGetMemoryInfo(handle) + mem_used_gib = mem.used / (1024**3) + mem_total_gib = mem.total / (1024**3) + metrics[f"{prefix}/allocated_memory"] = mem_used_gib + metrics[f"{prefix}/total_memory"] = mem_total_gib + if mem.total > 0: + metrics[f"{prefix}/memory_usage"] = mem.used / mem.total + total_mem_used_gib += mem_used_gib + except Exception: + pass + + try: + power_mw = pynvml.nvmlDeviceGetPowerUsage(handle) + power_w = power_mw / 1000.0 + metrics[f"{prefix}/power"] = power_w + total_power += power_w + except Exception: + pass + + try: + power_limit_mw = pynvml.nvmlDeviceGetPowerManagementLimit(handle) + power_limit_w = power_limit_mw / 1000.0 + metrics[f"{prefix}/power_limit"] = power_limit_w + if power_limit_w > 0 and f"{prefix}/power" in metrics: + metrics[f"{prefix}/power_percent"] = ( + metrics[f"{prefix}/power"] / power_limit_w + ) * 100 + except Exception: + pass + + try: + temp = pynvml.nvmlDeviceGetTemperature( + handle, pynvml.NVML_TEMPERATURE_GPU + ) + metrics[f"{prefix}/temp"] = temp + max_temp = max(max_temp, temp) + except Exception: + pass + + try: + sm_clock = pynvml.nvmlDeviceGetClockInfo(handle, pynvml.NVML_CLOCK_SM) + metrics[f"{prefix}/sm_clock"] = sm_clock + except Exception: + pass + + try: + mem_clock = pynvml.nvmlDeviceGetClockInfo(handle, pynvml.NVML_CLOCK_MEM) + metrics[f"{prefix}/memory_clock"] = mem_clock + except Exception: + pass + + try: + fan_speed = pynvml.nvmlDeviceGetFanSpeed(handle) + metrics[f"{prefix}/fan_speed"] = fan_speed + except Exception: + pass + + try: + pstate = pynvml.nvmlDeviceGetPerformanceState(handle) + metrics[f"{prefix}/performance_state"] = pstate + except Exception: + pass + + try: + energy_mj = pynvml.nvmlDeviceGetTotalEnergyConsumption(handle) + if physical_idx not in _energy_baseline: + _energy_baseline[physical_idx] = energy_mj + energy_consumed_mj = energy_mj - _energy_baseline[physical_idx] + metrics[f"{prefix}/energy_consumed"] = energy_consumed_mj / 1000.0 + except Exception: + pass + + try: + pcie_tx = pynvml.nvmlDeviceGetPcieThroughput( + handle, pynvml.NVML_PCIE_UTIL_TX_BYTES + ) + pcie_rx = pynvml.nvmlDeviceGetPcieThroughput( + handle, pynvml.NVML_PCIE_UTIL_RX_BYTES + ) + metrics[f"{prefix}/pcie_tx"] = pcie_tx / 1024.0 + metrics[f"{prefix}/pcie_rx"] = pcie_rx / 1024.0 + except Exception: + pass + + try: + throttle = pynvml.nvmlDeviceGetCurrentClocksThrottleReasons(handle) + metrics[f"{prefix}/throttle_thermal"] = int( + bool(throttle & pynvml.nvmlClocksThrottleReasonSwThermalSlowdown) + ) + metrics[f"{prefix}/throttle_power"] = int( + bool(throttle & pynvml.nvmlClocksThrottleReasonSwPowerCap) + ) + metrics[f"{prefix}/throttle_hw_slowdown"] = int( + bool(throttle & pynvml.nvmlClocksThrottleReasonHwSlowdown) + ) + metrics[f"{prefix}/throttle_apps"] = int( + bool( + throttle + & pynvml.nvmlClocksThrottleReasonApplicationsClocksSetting + ) + ) + except Exception: + pass + + try: + ecc_corrected = pynvml.nvmlDeviceGetTotalEccErrors( + handle, + pynvml.NVML_MEMORY_ERROR_TYPE_CORRECTED, + pynvml.NVML_VOLATILE_ECC, + ) + metrics[f"{prefix}/corrected_memory_errors"] = ecc_corrected + except Exception: + pass + + try: + ecc_uncorrected = pynvml.nvmlDeviceGetTotalEccErrors( + handle, + pynvml.NVML_MEMORY_ERROR_TYPE_UNCORRECTED, + pynvml.NVML_VOLATILE_ECC, + ) + metrics[f"{prefix}/uncorrected_memory_errors"] = ecc_uncorrected + except Exception: + pass + + except Exception: + continue + + if valid_util_count > 0: + metrics["gpu/mean_utilization"] = total_util / valid_util_count + if total_mem_used_gib > 0: + metrics["gpu/total_allocated_memory"] = total_mem_used_gib + if total_power > 0: + metrics["gpu/total_power"] = total_power + if max_temp > 0: + metrics["gpu/max_temp"] = max_temp + + return metrics + + +class GpuMonitor: + def __init__(self, run: "Run", interval: float = 10.0): + self._run = run + self._interval = interval + self._stop_flag = threading.Event() + self._thread: "threading.Thread | None" = None + + def start(self): + count, _ = get_all_gpu_count() + if count == 0: + warnings.warn( + "auto_log_gpu=True but no NVIDIA GPUs detected. GPU logging disabled." + ) + return + + reset_energy_baseline() + self._thread = threading.Thread(target=self._monitor_loop, daemon=True) + self._thread.start() + + def stop(self): + self._stop_flag.set() + if self._thread is not None: + self._thread.join(timeout=2.0) + + def _monitor_loop(self): + while not self._stop_flag.is_set(): + try: + metrics = collect_gpu_metrics(all_gpus=True) + if metrics: + self._run.log_system(metrics) + except Exception: + pass + + self._stop_flag.wait(timeout=self._interval) + + +def log_gpu(run: "Run | None" = None, device: int | None = None) -> dict: + """ + Log GPU metrics to the current or specified run as system metrics. + + Args: + run: Optional Run instance. If None, uses current run from context. + device: CUDA device index to collect metrics from. If None, collects + from all GPUs visible to this process (respects CUDA_VISIBLE_DEVICES). + + Returns: + dict: The GPU metrics that were logged. + + Example: + ```python + import trackio + + run = trackio.init(project="my-project") + trackio.log({"loss": 0.5}) + trackio.log_gpu() # logs all visible GPUs + trackio.log_gpu(device=0) # logs only CUDA device 0 + ``` + """ + from trackio import context_vars + + if run is None: + run = context_vars.current_run.get() + if run is None: + raise RuntimeError("Call trackio.init() before trackio.log_gpu().") + + metrics = collect_gpu_metrics(device=device) + if metrics: + run.log_system(metrics) + return metrics diff --git a/trackio/histogram.py b/trackio/histogram.py new file mode 100644 index 0000000000000000000000000000000000000000..1e00477907d55f7f5dcc1bf2bff05029d43b3ac1 --- /dev/null +++ b/trackio/histogram.py @@ -0,0 +1,71 @@ +from typing import Sequence + +import numpy as np + + +class Histogram: + """ + Histogram data type for Trackio, compatible with wandb.Histogram. + + Args: + sequence (`np.ndarray` or `Sequence[float]` or `Sequence[int]`, *optional*): + Sequence of values to create the histogram from. + np_histogram (`tuple`, *optional*): + Pre-computed NumPy histogram as a `(hist, bins)` tuple. + num_bins (`int`, *optional*, defaults to `64`): + Number of bins for the histogram (maximum `512`). + + Example: + ```python + import trackio + import numpy as np + + # Create histogram from sequence + data = np.random.randn(1000) + trackio.log({"distribution": trackio.Histogram(data)}) + + # Create histogram from numpy histogram + hist, bins = np.histogram(data, bins=30) + trackio.log({"distribution": trackio.Histogram(np_histogram=(hist, bins))}) + + # Specify custom number of bins + trackio.log({"distribution": trackio.Histogram(data, num_bins=50)}) + ``` + """ + + TYPE = "trackio.histogram" + + def __init__( + self, + sequence: np.ndarray | Sequence[float] | Sequence[int] | None = None, + np_histogram: tuple | None = None, + num_bins: int = 64, + ): + if sequence is None and np_histogram is None: + raise ValueError("Must provide either sequence or np_histogram") + + if sequence is not None and np_histogram is not None: + raise ValueError("Cannot provide both sequence and np_histogram") + + num_bins = min(num_bins, 512) + + if np_histogram is not None: + self.histogram, self.bins = np_histogram + self.histogram = np.asarray(self.histogram) + self.bins = np.asarray(self.bins) + else: + data = np.asarray(sequence).flatten() + data = data[np.isfinite(data)] + if len(data) == 0: + self.histogram = np.array([]) + self.bins = np.array([]) + else: + self.histogram, self.bins = np.histogram(data, bins=num_bins) + + def _to_dict(self) -> dict: + """Convert histogram to dictionary for storage.""" + return { + "_type": self.TYPE, + "bins": self.bins.tolist(), + "values": self.histogram.tolist(), + } diff --git a/trackio/imports.py b/trackio/imports.py new file mode 100644 index 0000000000000000000000000000000000000000..a52c4f67d61ea71c6ab89283821779131859c1b1 --- /dev/null +++ b/trackio/imports.py @@ -0,0 +1,300 @@ +import csv +import os +from pathlib import Path + +from trackio import deploy, utils +from trackio.sqlite_storage import SQLiteStorage + + +def import_csv( + csv_path: str | Path, + project: str, + name: str | None = None, + space_id: str | None = None, + dataset_id: str | None = None, + private: bool | None = None, + force: bool = False, +) -> None: + """ + Imports a CSV file into a Trackio project. The CSV file must contain a `"step"` + column, may optionally contain a `"timestamp"` column, and any other columns will be + treated as metrics. It should also include a header row with the column names. + + TODO: call init() and return a Run object so that the user can continue to log metrics to it. + + Args: + csv_path (`str` or `Path`): + The str or Path to the CSV file to import. + project (`str`): + The name of the project to import the CSV file into. Must not be an existing + project. + name (`str`, *optional*): + The name of the Run to import the CSV file into. If not provided, a default + name will be generated. + name (`str`, *optional*): + The name of the run (if not provided, a default name will be generated). + space_id (`str`, *optional*): + If provided, the project will be logged to a Hugging Face Space instead of a + local directory. Should be a complete Space name like `"username/reponame"` + or `"orgname/reponame"`, or just `"reponame"` in which case the Space will + be created in the currently-logged-in Hugging Face user's namespace. If the + Space does not exist, it will be created. If the Space already exists, the + project will be logged to it. + dataset_id (`str`, *optional*): + Deprecated. Use `bucket_id` instead. + private (`bool`, *optional*): + Whether to make the Space private. If None (default), the repo will be + public unless the organization's default is private. This value is ignored + if the repo already exists. + """ + if SQLiteStorage.get_runs(project): + raise ValueError( + f"Project '{project}' already exists. Cannot import CSV into existing project." + ) + + csv_path = Path(csv_path) + if not csv_path.exists(): + raise FileNotFoundError(f"CSV file not found: {csv_path}") + + with csv_path.open(newline="", encoding="utf-8") as csv_file: + reader = csv.DictReader(csv_file) + source_columns = reader.fieldnames or [] + rows = list(reader) + + if not rows: + raise ValueError("CSV file is empty") + + column_mapping = utils.simplify_column_names(source_columns) + normalized_rows = [ + {column_mapping[key]: value for key, value in row.items()} for row in rows + ] + columns = list(normalized_rows[0].keys()) + + step_column = None + for col in columns: + if col.lower() == "step": + step_column = col + break + + if step_column is None: + raise ValueError("CSV file must contain a 'step' or 'Step' column") + + if name is None: + name = csv_path.stem + + metrics_list = [] + steps = [] + timestamps = [] + + numeric_columns = [] + for column in columns: + if column == step_column: + continue + if column == "timestamp": + continue + + try: + for row in normalized_rows: + value = row[column] + if value in ("", None): + continue + float(value) + except (ValueError, TypeError): + continue + numeric_columns.append(column) + + for row in normalized_rows: + metrics = {} + for column in numeric_columns: + value = row[column] + if value not in ("", None): + metrics[column] = float(value) + + if metrics: + metrics_list.append(metrics) + steps.append(int(float(row[step_column]))) + + if "timestamp" in row and row["timestamp"] not in ("", None): + timestamps.append(str(row["timestamp"])) + else: + timestamps.append("") + + if metrics_list: + SQLiteStorage.bulk_log( + project=project, + run=name, + metrics_list=metrics_list, + steps=steps, + timestamps=timestamps, + ) + + print( + f"* Imported {len(metrics_list)} rows from {csv_path} into project '{project}' as run '{name}'" + ) + print(f"* Metrics found: {', '.join(metrics_list[0].keys())}") + + space_id, dataset_id, _ = utils.preprocess_space_and_dataset_ids( + space_id, dataset_id + ) + if dataset_id is not None: + os.environ["TRACKIO_DATASET_ID"] = dataset_id + print(f"* Trackio metrics will be synced to Hugging Face Dataset: {dataset_id}") + + if space_id is None: + utils.print_dashboard_instructions(project) + else: + deploy.create_space_if_not_exists( + space_id=space_id, dataset_id=dataset_id, private=private + ) + deploy.wait_until_space_exists(space_id=space_id) + deploy.upload_db_to_space(project=project, space_id=space_id, force=force) + print( + f"* View dashboard by going to: {deploy.SPACE_URL.format(space_id=space_id)}" + ) + + +def import_tf_events( + log_dir: str | Path, + project: str, + name: str | None = None, + space_id: str | None = None, + dataset_id: str | None = None, + private: bool | None = None, + force: bool = False, +) -> None: + """ + Imports TensorFlow Events files from a directory into a Trackio project. Each + subdirectory in the log directory will be imported as a separate run. + + Args: + log_dir (`str` or `Path`): + The str or Path to the directory containing TensorFlow Events files. + project (`str`): + The name of the project to import the TensorFlow Events files into. Must not + be an existing project. + name (`str`, *optional*): + The name prefix for runs (if not provided, will use directory names). Each + subdirectory will create a separate run. + space_id (`str`, *optional*): + If provided, the project will be logged to a Hugging Face Space instead of a + local directory. Should be a complete Space name like `"username/reponame"` + or `"orgname/reponame"`, or just `"reponame"` in which case the Space will + be created in the currently-logged-in Hugging Face user's namespace. If the + Space does not exist, it will be created. If the Space already exists, the + project will be logged to it. + dataset_id (`str`, *optional*): + Deprecated. Use `bucket_id` instead. + private (`bool`, *optional*): + Whether to make the Space private. If None (default), the repo will be + public unless the organization's default is private. This value is ignored + if the repo already exists. + """ + try: + from tbparse import SummaryReader + except ImportError: + raise ImportError( + "The `tbparse` package is not installed but is required for `import_tf_events`. Please install trackio with the `tensorboard` extra: `pip install trackio[tensorboard]`." + ) + + if SQLiteStorage.get_runs(project): + raise ValueError( + f"Project '{project}' already exists. Cannot import TF events into existing project." + ) + + path = Path(log_dir) + if not path.exists(): + raise FileNotFoundError(f"TF events directory not found: {path}") + + # Use tbparse to read all tfevents files in the directory structure + reader = SummaryReader(str(path), extra_columns={"dir_name"}) + df = reader.scalars + + if df.empty: + raise ValueError(f"No TensorFlow events data found in {path}") + + total_imported = 0 + imported_runs = [] + + # Group by dir_name to create separate runs + for dir_name, group_df in df.groupby("dir_name"): + try: + # Determine run name based on directory name + if dir_name == "": + run_name = "main" # For files in the root directory + else: + run_name = dir_name # Use directory name + + if name: + run_name = f"{name}_{run_name}" + + if group_df.empty: + print(f"* Skipping directory {dir_name}: no scalar data found") + continue + + metrics_list = [] + steps = [] + timestamps = [] + + for _, row in group_df.iterrows(): + # Convert row values to appropriate types + tag = str(row["tag"]) + value = float(row["value"]) + step = int(row["step"]) + + metrics = {tag: value} + metrics_list.append(metrics) + steps.append(step) + + # Use wall_time if present, else fallback + if "wall_time" in group_df.columns and not utils.is_missing_value( + row["wall_time"] + ): + timestamps.append(str(row["wall_time"])) + else: + timestamps.append("") + + if metrics_list: + SQLiteStorage.bulk_log( + project=project, + run=str(run_name), + metrics_list=metrics_list, + steps=steps, + timestamps=timestamps, + ) + + total_imported += len(metrics_list) + imported_runs.append(run_name) + + print( + f"* Imported {len(metrics_list)} scalar events from directory '{dir_name}' as run '{run_name}'" + ) + print(f"* Metrics in this run: {', '.join(set(group_df['tag']))}") + + except Exception as e: + print(f"* Error processing directory {dir_name}: {e}") + continue + + if not imported_runs: + raise ValueError("No valid TensorFlow events data could be imported") + + print(f"* Total imported events: {total_imported}") + print(f"* Created runs: {', '.join(imported_runs)}") + + space_id, dataset_id, _ = utils.preprocess_space_and_dataset_ids( + space_id, dataset_id + ) + if dataset_id is not None: + os.environ["TRACKIO_DATASET_ID"] = dataset_id + print(f"* Trackio metrics will be synced to Hugging Face Dataset: {dataset_id}") + + if space_id is None: + utils.print_dashboard_instructions(project) + else: + deploy.create_space_if_not_exists( + space_id, dataset_id=dataset_id, private=private + ) + deploy.wait_until_space_exists(space_id) + deploy.upload_db_to_space(project, space_id, force=force) + print( + f"* View dashboard by going to: {deploy.SPACE_URL.format(space_id=space_id)}" + ) diff --git a/trackio/launch.py b/trackio/launch.py new file mode 100644 index 0000000000000000000000000000000000000000..9fe4843dceb64c7c6ee74f95cbc22e5447a273c6 --- /dev/null +++ b/trackio/launch.py @@ -0,0 +1,202 @@ +from __future__ import annotations + +import os +import secrets +import socket +import threading +import time +import warnings +from pathlib import Path +from typing import Any + +import httpx +import uvicorn +from uvicorn.config import Config + +from trackio._vendor.gradio_exceptions import ChecksumMismatchError +from trackio._vendor.networking import normalize_share_url, setup_tunnel, url_ok +from trackio._vendor.tunneling import BINARY_PATH +from trackio.launch_utils import colab_check, is_hosted_notebook + +INITIAL_PORT_VALUE = int(os.getenv("GRADIO_SERVER_PORT", "7860")) +TRY_NUM_PORTS = int(os.getenv("GRADIO_NUM_PORTS", "100")) +LOCALHOST_NAME = os.getenv("GRADIO_SERVER_NAME", "127.0.0.1") + + +class _UvicornServer(uvicorn.Server): + def install_signal_handlers(self) -> None: + pass + + def run_in_thread(self) -> None: + self.thread = threading.Thread(target=self.run, daemon=True) + self.thread.start() + start = time.time() + while not self.started: + time.sleep(1e-3) + if time.time() - start > 60: + raise RuntimeError( + "Server failed to start. Please check that the port is available." + ) + + +def _bind_host(server_name: str) -> str: + if server_name.startswith("[") and server_name.endswith("]"): + return server_name[1:-1] + return server_name + + +def start_server( + app: Any, + server_name: str | None = None, + server_port: int | None = None, + ssl_keyfile: str | None = None, + ssl_certfile: str | None = None, + ssl_keyfile_password: str | None = None, +) -> tuple[str, int, str, _UvicornServer]: + server_name = server_name or LOCALHOST_NAME + url_host_name = "localhost" if server_name == "0.0.0.0" else server_name + + host = _bind_host(server_name) + + server_ports = ( + [server_port] + if server_port is not None + else range(INITIAL_PORT_VALUE, INITIAL_PORT_VALUE + TRY_NUM_PORTS) + ) + + port_used = None + server = None + for port in server_ports: + try: + socket_family = socket.AF_INET6 if ":" in host else socket.AF_INET + with socket.socket(socket_family, socket.SOCK_STREAM) as s: + s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) + s.bind((host, port)) + config = Config( + app=app, + port=port, + host=host, + log_level="warning", + ssl_keyfile=ssl_keyfile, + ssl_certfile=ssl_certfile, + ssl_keyfile_password=ssl_keyfile_password, + ) + server = _UvicornServer(config=config) + server.run_in_thread() + port_used = port + break + except (OSError, RuntimeError): + continue + else: + raise OSError( + f"Cannot find empty port in range: {min(server_ports)}-{max(server_ports)}. " + "Set GRADIO_SERVER_PORT or pass server_port to trackio.show()." + ) + + assert port_used is not None and server is not None + + if ssl_keyfile is not None: + path_to_local_server = f"https://{url_host_name}:{port_used}/" + else: + path_to_local_server = f"http://{url_host_name}:{port_used}/" + + return server_name, port_used, path_to_local_server, server + + +def launch_trackio_dashboard( + starlette_app: Any, + *, + server_name: str | None = None, + server_port: int | None = None, + share: bool | None = None, + share_server_address: str | None = None, + share_server_protocol: str | None = None, + share_server_tls_certificate: str | None = None, + mcp_server: bool = False, + ssl_verify: bool = True, + quiet: bool = False, +) -> tuple[str | None, str | None, str | None, Any]: + is_colab = colab_check() + is_hosted_nb = is_hosted_notebook() + space_id = os.getenv("SPACE_ID") + + if share is None: + if is_colab or is_hosted_nb: + if not quiet: + print( + "It looks like you are running Trackio on a hosted Jupyter notebook, which requires " + "`share=True`. Automatically setting `share=True` " + "(set `share=False` in `show()` to disable).\n" + ) + share = True + else: + share = os.getenv("GRADIO_SHARE", "").lower() == "true" + + sn = server_name + if sn is None and os.getenv("SYSTEM") == "spaces": + sn = "0.0.0.0" + elif sn is None: + sn = LOCALHOST_NAME + + server_name_r, server_port_r, local_url, uv_server = start_server( + starlette_app, + server_name=sn, + server_port=server_port, + ) + + local_api_url = f"{local_url.rstrip('/')}/api/" + try: + httpx.get(f"{local_url.rstrip('/')}/version", verify=ssl_verify, timeout=10) + except Exception as e: + raise RuntimeError( + f"Could not reach Trackio server at {local_url.rstrip('/')}/version: {e}" + ) from e + + if share and space_id: + warnings.warn("Setting share=True is not supported on Hugging Face Spaces") + share = False + + share_url: str | None = None + if share: + try: + share_tok = secrets.token_urlsafe(32) + proto = share_server_protocol or ( + "http" if share_server_address is not None else "https" + ) + raw = setup_tunnel( + local_host=server_name_r, + local_port=server_port_r, + share_token=share_tok, + share_server_address=share_server_address, + share_server_tls_certificate=share_server_tls_certificate, + ) + share_url = normalize_share_url(raw, proto) + if not quiet: + print(f"* Running on public URL: {share_url}") + print( + "\nThis share link expires in 1 week. For permanent hosting, deploy to Hugging Face Spaces." + ) + except Exception as e: + share_url = None + if not quiet: + if isinstance(e, ChecksumMismatchError): + print( + "\nCould not create share link. Checksum mismatch for frpc binary." + ) + elif Path(BINARY_PATH).exists(): + print( + "\nCould not create share link. Check your internet connection or https://status.gradio.app." + ) + else: + print( + f"\nCould not create share link. Missing frpc at {BINARY_PATH}. {e}" + ) + + if not share_url and not quiet: + print("* To create a public link, set `share=True` in `trackio.show()`.") + + return local_url, share_url, local_api_url, uv_server + + +def url_ok_local(local_url: str) -> bool: + return url_ok(local_url) diff --git a/trackio/launch_utils.py b/trackio/launch_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4215f7564472ad42441be63ecec13d5d385a20fb --- /dev/null +++ b/trackio/launch_utils.py @@ -0,0 +1,33 @@ +import os + + +def colab_check() -> bool: + is_colab = False + try: + from IPython.core.getipython import get_ipython # noqa: PLC0415 + + from_ipynb = get_ipython() + if "google.colab" in str(from_ipynb): + is_colab = True + except (ImportError, NameError): + pass + return is_colab + + +def is_hosted_notebook() -> bool: + return bool( + os.environ.get("KAGGLE_KERNEL_RUN_TYPE") + or os.path.exists("/home/ec2-user/SageMaker") + ) + + +def ipython_check() -> bool: + is_ipython = False + try: + from IPython.core.getipython import get_ipython # noqa: PLC0415 + + if get_ipython() is not None: + is_ipython = True + except (ImportError, NameError): + pass + return is_ipython diff --git a/trackio/markdown.py b/trackio/markdown.py new file mode 100644 index 0000000000000000000000000000000000000000..910a7749517980578449801da53a279f3f5c11d1 --- /dev/null +++ b/trackio/markdown.py @@ -0,0 +1,21 @@ +class Markdown: + """ + Markdown report data type for Trackio. + + Args: + text (`str`): + Markdown content to log. + """ + + TYPE = "trackio.markdown" + + def __init__(self, text: str = ""): + if not isinstance(text, str): + raise ValueError("Markdown text must be a string") + self.text = text + + def _to_dict(self) -> dict: + return { + "_type": self.TYPE, + "_value": self.text, + } diff --git a/trackio/mcp_setup.py b/trackio/mcp_setup.py new file mode 100644 index 0000000000000000000000000000000000000000..178ff83dcc5eb98877fa5e3157b736025ed2df5e --- /dev/null +++ b/trackio/mcp_setup.py @@ -0,0 +1,156 @@ +from __future__ import annotations + +import os +import secrets +from contextlib import asynccontextmanager +from typing import Any + +from starlette.routing import Mount + +from trackio.sqlite_storage import SQLiteStorage + + +def _assert_mcp_mutation_access( + *, + hf_token: str | None = None, + write_token: str | None = None, +) -> None: + import trackio.server as trackio_server # noqa: PLC0415 + + if os.getenv("SYSTEM") == "spaces": + try: + trackio_server.check_hf_token_has_write_access(hf_token) + except PermissionError as e: + raise ValueError(str(e)) from e + return + + if not secrets.compare_digest(write_token or "", trackio_server.write_token or ""): + raise ValueError( + "A write_token is required for Trackio MCP mutations. " + "Use the write token from the dashboard URL." + ) + + +def create_mcp_integration() -> tuple[list[Any], Any]: + from mcp.server.fastmcp import FastMCP # noqa: PLC0415 + + import trackio.server as trackio_server # noqa: PLC0415 + + mcp = FastMCP( + "Trackio", + instructions="Inspect and manage Trackio experiment data.", + streamable_http_path="/", + log_level="WARNING", + ) + + mcp.add_tool( + trackio_server.get_all_projects, + description="List all Trackio projects available on this server.", + structured_output=True, + ) + mcp.add_tool( + trackio_server.get_runs_for_project, + description="List runs for a given Trackio project.", + structured_output=True, + ) + mcp.add_tool( + trackio_server.get_metrics_for_run, + description="List metric names recorded for a given Trackio run.", + structured_output=True, + ) + mcp.add_tool( + trackio_server.get_project_summary, + description="Return summary metadata for a Trackio project.", + structured_output=True, + ) + mcp.add_tool( + trackio_server.get_run_summary, + description="Return summary metadata for a Trackio run.", + structured_output=True, + ) + mcp.add_tool( + trackio_server.get_metric_values, + description="Fetch metric values for a run, optionally around a step or time.", + structured_output=True, + ) + mcp.add_tool( + trackio_server.get_system_metrics_for_run, + description="List system metric names recorded for a run.", + structured_output=True, + ) + mcp.add_tool( + trackio_server.get_system_logs, + description="Fetch system metric logs for a run.", + structured_output=True, + ) + mcp.add_tool( + trackio_server.get_snapshot, + description="Fetch a single Trackio snapshot around a step or timestamp.", + structured_output=True, + ) + mcp.add_tool( + trackio_server.get_logs, + description="Fetch Trackio metric logs for a run.", + structured_output=True, + ) + mcp.add_tool( + trackio_server.get_alerts, + description="Fetch alerts for a project, optionally filtered by run or level.", + structured_output=True, + ) + mcp.add_tool( + trackio_server.get_settings, + description="Return Trackio dashboard settings and asset configuration.", + structured_output=True, + ) + + @mcp.tool( + description="Delete a run. On Spaces, pass an hf_token with write access.", + structured_output=True, + ) + def delete_run( + project: str, + run: str, + hf_token: str | None = None, + write_token: str | None = None, + ) -> bool: + _assert_mcp_mutation_access(hf_token=hf_token, write_token=write_token) + return SQLiteStorage.delete_run(project, run) + + @mcp.tool( + description="Rename a run. On Spaces, pass an hf_token with write access.", + structured_output=True, + ) + def rename_run( + project: str, + old_name: str, + new_name: str, + hf_token: str | None = None, + write_token: str | None = None, + ) -> bool: + _assert_mcp_mutation_access(hf_token=hf_token, write_token=write_token) + SQLiteStorage.rename_run(project, old_name, new_name) + return True + + @mcp.tool( + description=( + "Trigger a Trackio export/sync pass. On Spaces, pass an hf_token with " + "write access." + ), + structured_output=True, + ) + def trigger_sync( + hf_token: str | None = None, + write_token: str | None = None, + ) -> bool: + _assert_mcp_mutation_access(hf_token=hf_token, write_token=write_token) + return trackio_server.force_sync() + + mcp_app = mcp.streamable_http_app() + + @asynccontextmanager + async def mcp_lifespan_context(app): + async with mcp.session_manager.run(): + yield + + return [Mount("/mcp", app=mcp_app)], mcp_lifespan_context diff --git a/trackio/media/__init__.py b/trackio/media/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0fb247008abc2493044fe0a66a691f2d23a909c2 --- /dev/null +++ b/trackio/media/__init__.py @@ -0,0 +1,27 @@ +""" +Media module for Trackio. + +This module contains all media-related functionality including: +- TrackioImage, TrackioVideo, TrackioAudio classes +- Video writing utilities +- Audio conversion utilities +""" + +from trackio.media.audio import TrackioAudio +from trackio.media.image import TrackioImage +from trackio.media.media import TrackioMedia +from trackio.media.utils import get_project_media_path +from trackio.media.video import TrackioVideo + +write_audio = TrackioAudio.write_audio +write_video = TrackioVideo.write_video + +__all__ = [ + "TrackioMedia", + "TrackioImage", + "TrackioVideo", + "TrackioAudio", + "get_project_media_path", + "write_video", + "write_audio", +] diff --git a/trackio/media/audio.py b/trackio/media/audio.py new file mode 100644 index 0000000000000000000000000000000000000000..90026d7be9d5620786034b41c31d55161a80a28a --- /dev/null +++ b/trackio/media/audio.py @@ -0,0 +1,167 @@ +import os +import shutil +import warnings +from pathlib import Path +from typing import Literal + +import numpy as np +from pydub import AudioSegment + +from trackio.media.media import TrackioMedia +from trackio.media.utils import check_ffmpeg_installed, check_path + +SUPPORTED_FORMATS = ["wav", "mp3"] +AudioFormatType = Literal["wav", "mp3"] +TrackioAudioSourceType = str | Path | np.ndarray + + +class TrackioAudio(TrackioMedia): + """ + Initializes an Audio object. + + Example: + ```python + import trackio + import numpy as np + + # Generate a 1-second 440 Hz sine wave (mono) + sr = 16000 + t = np.linspace(0, 1, sr, endpoint=False) + wave = 0.2 * np.sin(2 * np.pi * 440 * t) + audio = trackio.Audio(wave, caption="A4 sine", sample_rate=sr, format="wav") + trackio.log({"tone": audio}) + + # Stereo from numpy array (shape: samples, 2) + stereo = np.stack([wave, wave], axis=1) + audio = trackio.Audio(stereo, caption="Stereo", sample_rate=sr, format="mp3") + trackio.log({"stereo": audio}) + + # From an existing file + audio = trackio.Audio("path/to/audio.wav", caption="From file") + trackio.log({"file_audio": audio}) + ``` + + Args: + value (`str`, `Path`, or `numpy.ndarray`, *optional*): + A path to an audio file, or a numpy array. + The array should be shaped `(samples,)` for mono or `(samples, 2)` for stereo. + Float arrays will be peak-normalized and converted to 16-bit PCM; integer arrays will be converted to 16-bit PCM as needed. + caption (`str`, *optional*): + A string caption for the audio. + sample_rate (`int`, *optional*): + Sample rate in Hz. Required when `value` is a numpy array. + format (`Literal["wav", "mp3"]`, *optional*): + Audio format used when `value` is a numpy array. Default is "wav". + """ + + TYPE = "trackio.audio" + + def __init__( + self, + value: TrackioAudioSourceType, + caption: str | None = None, + sample_rate: int | None = None, + format: AudioFormatType | None = None, + ): + super().__init__(value, caption) + if isinstance(value, np.ndarray): + if sample_rate is None: + raise ValueError("Sample rate is required when value is an ndarray") + if format is None: + format = "wav" + self._format = format + self._sample_rate = sample_rate + + def _save_media(self, file_path: Path): + if isinstance(self._value, np.ndarray): + TrackioAudio.write_audio( + data=self._value, + sample_rate=self._sample_rate, + filename=file_path, + format=self._format, + ) + elif isinstance(self._value, str | Path): + if os.path.isfile(self._value): + shutil.copy(self._value, file_path) + else: + raise ValueError(f"File not found: {self._value}") + + @staticmethod + def ensure_int16_pcm(data: np.ndarray) -> np.ndarray: + """ + Convert input audio array to contiguous int16 PCM. + Peak normalization is applied to floating inputs. + """ + arr = np.asarray(data) + if arr.ndim not in (1, 2): + raise ValueError("Audio data must be 1D (mono) or 2D ([samples, channels])") + + if arr.dtype != np.int16: + warnings.warn( + f"Converting {arr.dtype} audio to int16 PCM; pass int16 to avoid conversion.", + stacklevel=2, + ) + + arr = np.nan_to_num(arr, copy=False) + + # Floating types: normalize to peak 1.0, then scale to int16 + if np.issubdtype(arr.dtype, np.floating): + max_abs = float(np.max(np.abs(arr))) if arr.size else 0.0 + if max_abs > 0.0: + arr = arr / max_abs + out = (arr * 32767.0).clip(-32768, 32767).astype(np.int16, copy=False) + return np.ascontiguousarray(out) + + converters: dict[np.dtype, callable] = { + np.dtype(np.int16): lambda a: a, + np.dtype(np.int32): lambda a: (a.astype(np.int32) // 65536).astype( + np.int16, copy=False + ), + np.dtype(np.uint16): lambda a: (a.astype(np.int32) - 32768).astype( + np.int16, copy=False + ), + np.dtype(np.uint8): lambda a: (a.astype(np.int32) * 257 - 32768).astype( + np.int16, copy=False + ), + np.dtype(np.int8): lambda a: (a.astype(np.int32) * 256).astype( + np.int16, copy=False + ), + } + + conv = converters.get(arr.dtype) + if conv is not None: + out = conv(arr) + return np.ascontiguousarray(out) + raise TypeError(f"Unsupported audio dtype: {arr.dtype}") + + @staticmethod + def write_audio( + data: np.ndarray, + sample_rate: int, + filename: str | Path, + format: AudioFormatType = "wav", + ) -> None: + if not isinstance(sample_rate, int) or sample_rate <= 0: + raise ValueError(f"Invalid sample_rate: {sample_rate}") + if format not in SUPPORTED_FORMATS: + raise ValueError( + f"Unsupported format: {format}. Supported: {SUPPORTED_FORMATS}" + ) + + check_path(filename) + + pcm = TrackioAudio.ensure_int16_pcm(data) + + if format != "wav": + check_ffmpeg_installed() + + channels = 1 if pcm.ndim == 1 else pcm.shape[1] + audio = AudioSegment( + pcm.tobytes(), + frame_rate=sample_rate, + sample_width=2, # int16 + channels=channels, + ) + + file = audio.export(str(filename), format=format) + file.close() diff --git a/trackio/media/image.py b/trackio/media/image.py new file mode 100644 index 0000000000000000000000000000000000000000..0f070df0fe4e05dbbaed34867a360abc483d2e72 --- /dev/null +++ b/trackio/media/image.py @@ -0,0 +1,84 @@ +import os +import shutil +from pathlib import Path + +import numpy as np +from PIL import Image as PILImage + +from trackio.media.media import TrackioMedia + +TrackioImageSourceType = str | Path | np.ndarray | PILImage.Image + + +class TrackioImage(TrackioMedia): + """ + Initializes an Image object. + + Example: + ```python + import trackio + import numpy as np + from PIL import Image + + # Create an image from numpy array + image_data = np.random.randint(0, 255, (64, 64, 3), dtype=np.uint8) + image = trackio.Image(image_data, caption="Random image") + trackio.log({"my_image": image}) + + # Create an image from PIL Image + pil_image = Image.new('RGB', (100, 100), color='red') + image = trackio.Image(pil_image, caption="Red square") + trackio.log({"red_image": image}) + + # Create an image from file path + image = trackio.Image("path/to/image.jpg", caption="Photo from file") + trackio.log({"file_image": image}) + ``` + + Args: + value (`str`, `Path`, `numpy.ndarray`, or `PIL.Image`, *optional*): + A path to an image, a PIL Image, or a numpy array of shape (height, width, channels). + If numpy array, should be of type `np.uint8` with RGB values in the range `[0, 255]`. + caption (`str`, *optional*): + A string caption for the image. + """ + + TYPE = "trackio.image" + + def __init__(self, value: TrackioImageSourceType, caption: str | None = None): + super().__init__(value, caption) + self._format: str | None = None + + if not isinstance(self._value, TrackioImageSourceType): + raise ValueError( + f"Invalid value type, expected {TrackioImageSourceType}, got {type(self._value)}" + ) + if isinstance(self._value, np.ndarray) and self._value.dtype != np.uint8: + raise ValueError( + f"Invalid value dtype, expected np.uint8, got {self._value.dtype}" + ) + if ( + isinstance(self._value, np.ndarray | PILImage.Image) + and self._format is None + ): + self._format = "png" + + def _as_pil(self) -> PILImage.Image | None: + try: + if isinstance(self._value, np.ndarray): + arr = np.asarray(self._value).astype("uint8") + return PILImage.fromarray(arr).convert("RGBA") + if isinstance(self._value, PILImage.Image): + return self._value.convert("RGBA") + except Exception as e: + raise ValueError(f"Failed to process image data: {self._value}") from e + return None + + def _save_media(self, file_path: Path): + if pil := self._as_pil(): + pil.save(file_path, format=self._format) + elif isinstance(self._value, str | Path): + if os.path.isfile(self._value): + shutil.copy(self._value, file_path) + else: + raise ValueError(f"File not found: {self._value}") diff --git a/trackio/media/media.py b/trackio/media/media.py new file mode 100644 index 0000000000000000000000000000000000000000..6ec3d65939c81d2e32089a6fb3d192fbab0e4209 --- /dev/null +++ b/trackio/media/media.py @@ -0,0 +1,79 @@ +import os +import uuid +from abc import ABC, abstractmethod +from pathlib import Path + +from trackio.media.utils import get_project_media_path +from trackio.utils import MEDIA_DIR + + +class TrackioMedia(ABC): + """ + Abstract base class for Trackio media objects + Provides shared functionality for file handling and serialization. + """ + + TYPE: str + + def __init_subclass__(cls, **kwargs): + """Ensure subclasses define the TYPE attribute.""" + super().__init_subclass__(**kwargs) + if not hasattr(cls, "TYPE") or cls.TYPE is None: + raise TypeError(f"Class {cls.__name__} must define TYPE attribute") + + def __init__(self, value, caption: str | None = None): + """ + Saves the value and caption, and if the value is a file path, checks if the file exists. + """ + self.caption = caption + self._value = value + self._file_path: Path | None = None + + if isinstance(self._value, str | Path): + if not os.path.isfile(self._value): + raise ValueError(f"File not found: {self._value}") + + def _file_extension(self) -> str: + if self._file_path: + return self._file_path.suffix[1:].lower() + if isinstance(self._value, str | Path): + path = Path(self._value) + return path.suffix[1:].lower() + if hasattr(self, "_format") and self._format: + return self._format + return "unknown" + + def _get_relative_file_path(self) -> Path | None: + return self._file_path + + def _get_absolute_file_path(self) -> Path | None: + if self._file_path: + return MEDIA_DIR / self._file_path + return None + + def _save(self, project: str, run: str, step: int = 0): + if self._file_path: + return + + media_dir = get_project_media_path(project=project, run=run, step=step) + filename = f"{uuid.uuid4()}.{self._file_extension()}" + file_path = media_dir / filename + + self._save_media(file_path) + self._file_path = file_path.relative_to(MEDIA_DIR) + + @abstractmethod + def _save_media(self, file_path: Path): + """ + Performs the actual media saving logic. + """ + pass + + def _to_dict(self) -> dict: + if not self._file_path: + raise ValueError("Media must be saved to file before serialization") + return { + "_type": self.TYPE, + "file_path": str(self._get_relative_file_path()), + "caption": self.caption, + } diff --git a/trackio/media/utils.py b/trackio/media/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4e86cb23760cc74c11e404f092564a18d4dc1c73 --- /dev/null +++ b/trackio/media/utils.py @@ -0,0 +1,60 @@ +import shutil +from pathlib import Path + +from trackio.utils import MEDIA_DIR + + +def check_path(file_path: str | Path) -> None: + """Raise an error if the parent directory does not exist.""" + file_path = Path(file_path) + if not file_path.parent.exists(): + try: + file_path.parent.mkdir(parents=True, exist_ok=True) + except OSError as e: + raise ValueError( + f"Failed to create parent directory {file_path.parent}: {e}" + ) + + +def check_ffmpeg_installed() -> None: + """Raise an error if ffmpeg is not available on the system PATH.""" + if shutil.which("ffmpeg") is None: + raise RuntimeError( + "ffmpeg is required to write video but was not found on your system. " + "Please install ffmpeg and ensure it is available on your PATH." + ) + + +def get_project_media_path( + project: str, + run: str | None = None, + step: int | None = None, + relative_path: str | Path | None = None, +) -> Path: + """ + Get the full path where uploaded files are stored for a Trackio project (and create the directory if it doesn't exist). + If a run is not provided, the files are stored in a project-level directory with the given relative path. + + Args: + project: The project name + run: The run name + step: The step number + relative_path: The relative path within the directory (only used if run is not provided) + + Returns: + The full path to the media file + """ + if step is not None and run is None: + raise ValueError("Uploading files at a specific step requires a run") + + path = MEDIA_DIR / project + if run: + path /= run + if step is not None: + path /= str(step) + else: + path /= "files" + if relative_path: + path /= relative_path + path.mkdir(parents=True, exist_ok=True) + return path diff --git a/trackio/media/video.py b/trackio/media/video.py new file mode 100644 index 0000000000000000000000000000000000000000..01210bca73d801322e9c4450917f9fc71556f634 --- /dev/null +++ b/trackio/media/video.py @@ -0,0 +1,246 @@ +import os +import shutil +import subprocess +from pathlib import Path +from typing import Literal + +import numpy as np + +from trackio.media.media import TrackioMedia +from trackio.media.utils import check_ffmpeg_installed, check_path + +TrackioVideoSourceType = str | Path | np.ndarray +TrackioVideoFormatType = Literal["gif", "mp4", "webm"] +VideoCodec = Literal["h264", "vp9", "gif"] + + +class TrackioVideo(TrackioMedia): + """ + Initializes a Video object. + + Example: + ```python + import trackio + import numpy as np + + # Create a simple video from numpy array + frames = np.random.randint(0, 255, (10, 3, 64, 64), dtype=np.uint8) + video = trackio.Video(frames, caption="Random video", fps=30) + + # Create a batch of videos + batch_frames = np.random.randint(0, 255, (3, 10, 3, 64, 64), dtype=np.uint8) + batch_video = trackio.Video(batch_frames, caption="Batch of videos", fps=15) + + # Create video from file path + video = trackio.Video("path/to/video.mp4", caption="Video from file") + ``` + + Args: + value (`str`, `Path`, or `numpy.ndarray`, *optional*): + A path to a video file, or a numpy array. + If numpy array, should be of type `np.uint8` with RGB values in the range `[0, 255]`. + It is expected to have shape of either (frames, channels, height, width) or (batch, frames, channels, height, width). + For the latter, the videos will be tiled into a grid. + caption (`str`, *optional*): + A string caption for the video. + fps (`int`, *optional*): + Frames per second for the video. Only used when value is an ndarray. Default is `24`. + format (`Literal["gif", "mp4", "webm"]`, *optional*): + Video format ("gif", "mp4", or "webm"). Only used when value is an ndarray. Default is "gif". + """ + + TYPE = "trackio.video" + + def __init__( + self, + value: TrackioVideoSourceType, + caption: str | None = None, + fps: int | None = None, + format: TrackioVideoFormatType | None = None, + ): + super().__init__(value, caption) + + if not isinstance(self._value, TrackioVideoSourceType): + raise ValueError( + f"Invalid value type, expected {TrackioVideoSourceType}, got {type(self._value)}" + ) + if isinstance(self._value, np.ndarray): + if self._value.dtype != np.uint8: + raise ValueError( + f"Invalid value dtype, expected np.uint8, got {self._value.dtype}" + ) + if format is None: + format = "gif" + if fps is None: + fps = 24 + self._fps = fps + self._format = format + + @staticmethod + def _check_array_format(video: np.ndarray) -> None: + """Raise an error if the array is not in the expected format.""" + if not (video.ndim == 4 and video.shape[-1] == 3): + raise ValueError( + f"Expected RGB input shaped (F, H, W, 3), got {video.shape}. " + f"Input has {video.ndim} dimensions, expected 4." + ) + if video.dtype != np.uint8: + raise TypeError( + f"Expected dtype=uint8, got {video.dtype}. " + "Please convert your video data to uint8 format." + ) + + @staticmethod + def write_video( + file_path: str | Path, video: np.ndarray, fps: float, codec: VideoCodec + ) -> None: + """RGB uint8 only, shape (F, H, W, 3).""" + check_ffmpeg_installed() + check_path(file_path) + + if codec not in {"h264", "vp9", "gif"}: + raise ValueError("Unsupported codec. Use h264, vp9, or gif.") + + arr = np.asarray(video) + TrackioVideo._check_array_format(arr) + + frames = np.ascontiguousarray(arr) + _, height, width, _ = frames.shape + out_path = str(file_path) + + cmd = [ + "ffmpeg", + "-y", + "-f", + "rawvideo", + "-s", + f"{width}x{height}", + "-pix_fmt", + "rgb24", + "-r", + str(fps), + "-i", + "-", + "-an", + ] + + if codec == "gif": + video_filter = "split[s0][s1];[s0]palettegen[p];[s1][p]paletteuse" + cmd += [ + "-vf", + video_filter, + "-loop", + "0", + ] + elif codec == "h264": + cmd += [ + "-vcodec", + "libx264", + "-pix_fmt", + "yuv420p", + "-movflags", + "+faststart", + ] + elif codec == "vp9": + bpp = 0.08 + bps = int(width * height * fps * bpp) + if bps >= 1_000_000: + bitrate = f"{round(bps / 1_000_000)}M" + elif bps >= 1_000: + bitrate = f"{round(bps / 1_000)}k" + else: + bitrate = str(max(bps, 1)) + cmd += [ + "-vcodec", + "libvpx-vp9", + "-b:v", + bitrate, + "-pix_fmt", + "yuv420p", + ] + cmd += [out_path] + proc = subprocess.Popen(cmd, stdin=subprocess.PIPE, stderr=subprocess.PIPE) + try: + for frame in frames: + proc.stdin.write(frame.tobytes()) + finally: + if proc.stdin: + proc.stdin.close() + stderr = ( + proc.stderr.read().decode("utf-8", errors="ignore") + if proc.stderr + else "" + ) + ret = proc.wait() + if ret != 0: + raise RuntimeError(f"ffmpeg failed with code {ret}\n{stderr}") + + @property + def _codec(self) -> str: + match self._format: + case "gif": + return "gif" + case "mp4": + return "h264" + case "webm": + return "vp9" + case _: + raise ValueError(f"Unsupported format: {self._format}") + + def _save_media(self, file_path: Path): + if isinstance(self._value, np.ndarray): + video = TrackioVideo._process_ndarray(self._value) + TrackioVideo.write_video(file_path, video, fps=self._fps, codec=self._codec) + elif isinstance(self._value, str | Path): + if os.path.isfile(self._value): + shutil.copy(self._value, file_path) + else: + raise ValueError(f"File not found: {self._value}") + + @staticmethod + def _process_ndarray(value: np.ndarray) -> np.ndarray: + # Verify value is either 4D (single video) or 5D array (batched videos). + # Expected format: (frames, channels, height, width) or (batch, frames, channels, height, width) + if value.ndim < 4: + raise ValueError( + "Video requires at least 4 dimensions (frames, channels, height, width)" + ) + if value.ndim > 5: + raise ValueError( + "Videos can have at most 5 dimensions (batch, frames, channels, height, width)" + ) + if value.ndim == 4: + # Reshape to 5D with single batch: (1, frames, channels, height, width) + value = value[np.newaxis, ...] + + value = TrackioVideo._tile_batched_videos(value) + return value + + @staticmethod + def _tile_batched_videos(video: np.ndarray) -> np.ndarray: + """ + Tiles a batch of videos into a grid of videos. + + Input format: (batch, frames, channels, height, width) - original FCHW format + Output format: (frames, total_height, total_width, channels) + """ + batch_size, frames, channels, height, width = video.shape + + next_pow2 = 1 << (batch_size - 1).bit_length() + if batch_size != next_pow2: + pad_len = next_pow2 - batch_size + pad_shape = (pad_len, frames, channels, height, width) + padding = np.zeros(pad_shape, dtype=video.dtype) + video = np.concatenate((video, padding), axis=0) + batch_size = next_pow2 + + n_rows = 1 << ((batch_size.bit_length() - 1) // 2) + n_cols = batch_size // n_rows + + # Reshape to grid layout: (n_rows, n_cols, frames, channels, height, width) + video = video.reshape(n_rows, n_cols, frames, channels, height, width) + + # Rearrange dimensions to (frames, total_height, total_width, channels) + video = video.transpose(2, 0, 4, 1, 5, 3) + video = video.reshape(frames, n_rows * height, n_cols * width, channels) + return video diff --git a/trackio/package.json b/trackio/package.json new file mode 100644 index 0000000000000000000000000000000000000000..6952b925ad44aae5607260f2dfb30ef2b4c72c59 --- /dev/null +++ b/trackio/package.json @@ -0,0 +1,6 @@ +{ + "name": "trackio", + "version": "0.23.0", + "description": "", + "python": "true" +} diff --git a/trackio/py.typed b/trackio/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/trackio/remote_client.py b/trackio/remote_client.py new file mode 100644 index 0000000000000000000000000000000000000000..03e15b8e8798c9a4d0597265b98e9e51959cfc3e --- /dev/null +++ b/trackio/remote_client.py @@ -0,0 +1,242 @@ +from __future__ import annotations + +from pathlib import Path +from typing import Any +from urllib.parse import urljoin, urlparse + +import httpx +from gradio_client import Client as GradioClient +from huggingface_hub.utils import build_hf_headers + +from trackio.utils import parse_trackio_server_url + +HTTP_API_VERSION = 1 + +WRITE_TOKEN_HEADER = "x-trackio-write-token" + + +def _normalize_src(src: str) -> str: + return src if src.endswith("/") else src + "/" + + +def _space_id_to_url(space_id: str) -> str: + namespace, name = space_id.split("/", 1) + subdomain = f"{namespace}-{name}".lower().replace("_", "-").replace(".", "-") + return f"https://{subdomain}.hf.space/" + + +def _host_is_hf_space(url: str) -> bool: + p = urlparse(url) + h = (p.hostname or "").lower() + return h.endswith(".hf.space") + + +def _resolve_src_url(src: str) -> str: + if src.startswith(("http://", "https://")): + base, _ = parse_trackio_server_url(src) + return _normalize_src(base) + if "/" in src: + return _space_id_to_url(src) + raise ValueError( + f"Could not resolve Trackio remote source '{src}'. " + "Pass a full Space id like 'user/space' or a URL." + ) + + +def _is_local_file_data(value: Any) -> bool: + return ( + isinstance(value, dict) + and "path" in value + and isinstance(value["path"], str) + and value.get("meta", {}).get("_type") == "gradio.FileData" + and Path(value["path"]).exists() + ) + + +def _merge_client_headers( + hf_token: str | None, write_token: str | None +) -> dict[str, str]: + headers: dict[str, str] = {} + if hf_token: + headers.update(build_hf_headers(token=hf_token)) + if write_token: + headers[WRITE_TOKEN_HEADER] = write_token + return headers + + +class _TrackioHTTPClient: + def __init__( + self, + src: str, + hf_token: str | None = None, + write_token: str | None = None, + httpx_kwargs: dict[str, Any] | None = None, + ) -> None: + self.src = _resolve_src_url(src) + self.httpx_kwargs = dict(httpx_kwargs or {}) + self.httpx_kwargs.setdefault("timeout", 60) + extra = self.httpx_kwargs.pop("headers", None) + h = _merge_client_headers(hf_token, write_token) + if isinstance(extra, dict): + h.update({str(k): str(v) for k, v in extra.items()}) + self.headers = h + + def _upload_file(self, file_data: dict[str, Any]) -> dict[str, Any]: + path = Path(file_data["path"]) + with path.open("rb") as f: + resp = httpx.post( + urljoin(self.src, "api/upload"), + headers=self.headers, + files={"files": (path.name, f)}, + **self.httpx_kwargs, + ) + resp.raise_for_status() + uploaded_path = resp.json()["paths"][0] + return { + **file_data, + "path": uploaded_path, + "orig_name": file_data.get("orig_name", path.name), + } + + def _prepare_value(self, value: Any) -> Any: + if _is_local_file_data(value): + return self._upload_file(value) + if isinstance(value, list): + return [self._prepare_value(item) for item in value] + if isinstance(value, tuple): + return [self._prepare_value(item) for item in value] + if isinstance(value, dict): + return {k: self._prepare_value(v) for k, v in value.items()} + return value + + def predict(self, *args, api_name: str, **kwargs) -> Any: + api_name = api_name.lstrip("/") + payload = { + "args": self._prepare_value(list(args)), + "kwargs": self._prepare_value(kwargs), + } + resp = httpx.post( + urljoin(self.src, f"api/{api_name}"), + headers=self.headers, + json=payload, + **self.httpx_kwargs, + ) + if resp.status_code == 404: + raise RuntimeError( + f"Space '{self.src}' does not support '/{api_name}'. Redeploy with `trackio sync`." + ) + resp.raise_for_status() + body = resp.json() + if body.get("error") is not None: + raise RuntimeError(body["error"]) + return body.get("data") + + +class _TrackioGradioCompatClient: + def __init__( + self, + src: str, + hf_token: str | None = None, + write_token: str | None = None, + httpx_kwargs: dict[str, Any] | None = None, + verbose: bool = False, + ) -> None: + kwargs: dict[str, Any] = {"verbose": verbose} + if hf_token: + kwargs["hf_token"] = hf_token + merged = dict(httpx_kwargs or {}) + h = _merge_client_headers( + hf_token if hf_token else None, + write_token, + ) + extra = merged.pop("headers", None) + if isinstance(extra, dict): + h.update({str(k): str(v) for k, v in extra.items()}) + if h: + merged["headers"] = h + if merged: + kwargs["httpx_kwargs"] = merged + self._client = GradioClient(src, **kwargs) + + def predict(self, *args, api_name: str, **kwargs) -> Any: + try: + return self._client.predict(*args, api_name=api_name, **kwargs) + except Exception as e: + if "API Not Found" in str(e) or "api_name" in str(e): + raise RuntimeError( + f"Space '{self._client.src}' does not support '{api_name}'. " + "Redeploy with `trackio sync`." + ) from e + raise + + +def _supports_http_api( + src: str, + hf_token: str | None = None, + write_token: str | None = None, + httpx_kwargs: dict[str, Any] | None = None, +) -> bool: + url = _resolve_src_url(src) + headers = _merge_client_headers(hf_token, write_token) + kwargs = dict(httpx_kwargs or {}) + kwargs.setdefault("timeout", 10) + try: + resp = httpx.get(urljoin(url, "version"), headers=headers, **kwargs) + if not resp.is_success: + return False + data = resp.json() + return data.get("api_version") == HTTP_API_VERSION + except Exception: + return False + + +class RemoteClient: + def __init__( + self, + space: str, + hf_token: str | None = None, + write_token: str | None = None, + httpx_kwargs: dict[str, Any] | None = None, + verbose: bool = False, + ) -> None: + self._space = space + src_for_resolve = space + hf_effective = hf_token + wt_effective = write_token + if space.startswith(("http://", "https://")): + base, url_tok = parse_trackio_server_url(space) + src_for_resolve = base + if wt_effective is None: + wt_effective = url_tok + if not _host_is_hf_space(_normalize_src(base)): + hf_effective = None + try: + if _supports_http_api( + src_for_resolve, + hf_token=hf_effective, + write_token=wt_effective, + httpx_kwargs=httpx_kwargs, + ): + self._client = _TrackioHTTPClient( + src_for_resolve, + hf_token=hf_effective, + write_token=wt_effective, + httpx_kwargs=httpx_kwargs, + ) + else: + self._client = _TrackioGradioCompatClient( + src_for_resolve, + hf_token=hf_effective, + write_token=wt_effective, + httpx_kwargs=httpx_kwargs, + verbose=verbose, + ) + except ValueError: + raise + except Exception as e: + raise ConnectionError( + f"Could not connect to Space '{space}'. Is it running?\n{e}" + ) from e + + def predict(self, *args, api_name: str, **kwargs) -> Any: + return self._client.predict(*args, api_name=api_name, **kwargs) diff --git a/trackio/run.py b/trackio/run.py new file mode 100644 index 0000000000000000000000000000000000000000..624533e9bdef363b55e7c1811119fc83491c00ab --- /dev/null +++ b/trackio/run.py @@ -0,0 +1,1017 @@ +import os +import shutil +import threading +import uuid +from datetime import datetime, timezone +from pathlib import Path +from typing import Any + +import huggingface_hub +from gradio_client import handle_file + +from trackio import utils +from trackio.alerts import ( + AlertLevel, + format_alert_terminal, + resolve_webhook_min_level, + send_webhook, + should_send_webhook, +) +from trackio.apple_gpu import AppleGpuMonitor, apple_gpu_available +from trackio.gpu import GpuMonitor, gpu_available +from trackio.histogram import Histogram +from trackio.markdown import Markdown +from trackio.media import TrackioMedia, get_project_media_path +from trackio.remote_client import RemoteClient +from trackio.sqlite_storage import SQLiteStorage +from trackio.table import Table +from trackio.typehints import AlertEntry, LogEntry, SystemLogEntry, UploadEntry +from trackio.utils import MEDIA_DIR, _emit_nonfatal_warning, _get_default_namespace + +BATCH_SEND_INTERVAL = 0.5 +MAX_BACKOFF = 30 + + +class Run: + def __init__( + self, + url: str | None, + project: str, + client: Any | None, + name: str | None = None, + run_id: str | None = None, + group: str | None = None, + config: dict | None = None, + space_id: str | None = None, + server_base_url: str | None = None, + write_token: str | None = None, + existing_runs: list[str] | None = None, + initial_last_step: int | None = None, + auto_log_gpu: bool = False, + gpu_log_interval: float = 10.0, + webhook_url: str | None = None, + webhook_min_level: AlertLevel | str | None = None, + ): + """ + Initialize a Run for logging metrics to Trackio. + + Args: + url: The URL or Space id of the Trackio server. + project: The name of the project to log metrics to. + client: A pre-configured Trackio-compatible client instance, or None to + create one automatically in a background thread with retry logic. + Passing None is recommended for normal usage. Passing a client + is useful for testing (e.g., injecting a mock client). + name: The name of this run. If None, a readable name like + "brave-sunset-0" is auto-generated. If space_id is provided, + generates a "username-timestamp" format instead. + group: Optional group name to organize related runs together. + config: A dictionary of configuration/hyperparameters for this run. + Keys starting with '_' are reserved for internal use. + space_id: The HF Space ID if logging to a Space (e.g., "user/space"). + If provided, media files will be uploaded to the Space. + existing_runs: Optional pre-fetched run names for this project. Used to + avoid redundant storage or remote lookups during init. + initial_last_step: Optional pre-fetched last step for a resumed run. + auto_log_gpu: Whether to automatically log GPU metrics (utilization, + memory, temperature) at regular intervals. + gpu_log_interval: The interval in seconds between GPU metric logs. + Only used when auto_log_gpu is True. + webhook_url: A webhook URL to POST alert payloads to. Supports + Slack and Discord webhook URLs natively. Can also be set via + the TRACKIO_WEBHOOK_URL environment variable. + webhook_min_level: Minimum alert level that should trigger webhook + delivery. For example, `AlertLevel.WARN` sends only WARN and + ERROR alerts to webhook destinations. Can also be set via + `TRACKIO_WEBHOOK_MIN_LEVEL`. + """ + self.url = url + self.project = project + self._client_lock = threading.Lock() + self._warning_lock = threading.Lock() + self._warned_failures: set[str] = set() + self._local_sender_thread: threading.Thread | None = None + self._client_thread = None + self._client = client + self._space_id = space_id + self._server_base_url = server_base_url + self._write_token = write_token + self._remote_storage_key = space_id or server_base_url + self.id = run_id or uuid.uuid4().hex + self._existing_runs = existing_runs + self._initial_last_step = initial_last_step + if name is not None: + self.name = name + else: + try: + self.name = utils.generate_readable_name( + self._safe_get_existing_runs(), self._space_id + ) + except Exception as e: + self._warn_once( + "init-run-name", + f"trackio.init() could not generate a run name: {e}. Falling back to a random name.", + ) + self.name = f"trackio-run-{uuid.uuid4().hex[:8]}" + self.group = group + try: + self.config = utils.to_json_safe(config or {}) + except Exception as e: + self._warn_once( + "init-config", + f"trackio.init() failed to serialize the run config: {e}. Continuing without config.", + ) + self.config = {} + + if isinstance(self.config, dict): + for key in self.config: + if key.startswith("_"): + raise ValueError( + f"Config key '{key}' is reserved (keys starting with '_' are reserved for internal use)" + ) + + self.config["_Username"] = self._get_username() + self.config["_Created"] = datetime.now(timezone.utc).isoformat() + self.config["_Group"] = self.group + + self._queued_logs: list[LogEntry] = [] + self._queued_system_logs: list[SystemLogEntry] = [] + self._queued_uploads: list[UploadEntry] = [] + self._queued_alerts: list[AlertEntry] = [] + self._stop_flag = threading.Event() + self._config_logged = False + max_step = self._safe_get_max_step_for_run() + self._next_step = 0 if max_step is None else max_step + 1 + self._has_local_buffer = False + + self._is_local = space_id is None and server_base_url is None + self._webhook_url = webhook_url or os.environ.get("TRACKIO_WEBHOOK_URL") + self._webhook_min_level = resolve_webhook_min_level( + webhook_min_level or os.environ.get("TRACKIO_WEBHOOK_MIN_LEVEL") + ) + + if self._is_local: + self._start_background_thread( + "_local_sender_thread", + self._local_batch_sender, + warning_key="local-sender-thread", + description="local Trackio logging thread", + ) + else: + self._start_background_thread( + "_client_thread", + self._init_client_background, + warning_key="remote-sender-thread", + description="remote Trackio logging thread", + ) + + self._gpu_monitor: "GpuMonitor | AppleGpuMonitor | None" = None + if auto_log_gpu: + try: + if gpu_available(): + self._gpu_monitor = GpuMonitor(self, interval=gpu_log_interval) + self._gpu_monitor.start() + elif apple_gpu_available(): + self._gpu_monitor = AppleGpuMonitor(self, interval=gpu_log_interval) + self._gpu_monitor.start() + except Exception as e: + self._warn_once( + "gpu-monitor", + f"trackio.init() failed to start automatic GPU logging: {e}. Continuing without system metric auto-logging.", + ) + + def _hf_token_for_remote(self) -> str | None: + return huggingface_hub.utils.get_token() if self._space_id else None + + def _get_username(self) -> str | None: + try: + return _get_default_namespace() + except Exception: + return None + + def _warn_once(self, key: str, message: str) -> None: + with self._warning_lock: + if key in self._warned_failures: + return + self._warned_failures.add(key) + _emit_nonfatal_warning(message) + + def _safe_get_existing_runs(self) -> list[str]: + if self._existing_runs is not None: + return self._existing_runs + try: + return SQLiteStorage.get_runs(self.project) + except Exception as e: + self._warn_once( + "init-existing-runs", + f"trackio.init() could not inspect existing runs for project '{self.project}': {e}. Continuing without prior-run metadata.", + ) + return [] + + def _safe_get_max_step_for_run(self) -> int | None: + if self._initial_last_step is not None: + return self._initial_last_step + try: + return SQLiteStorage.get_max_step_for_run( + self.project, self.name, run_id=self.id + ) + except Exception as e: + self._warn_once( + "init-max-step", + f"trackio.init() could not recover the previous step for run '{self.name}': {e}. Continuing from step 0.", + ) + return None + + def _start_background_thread( + self, + attr_name: str, + target, + *, + warning_key: str, + description: str, + ) -> bool: + try: + thread = threading.Thread(target=target, daemon=True) + setattr(self, attr_name, thread) + thread.start() + return True + except Exception as e: + setattr(self, attr_name, None) + self._warn_once( + warning_key, + f"trackio failed to start the {description}: {e}. Logging will continue in degraded mode.", + ) + return False + + def _thread_is_alive(self, attr_name: str) -> bool: + thread = getattr(self, attr_name, None) + return isinstance(thread, threading.Thread) and thread.is_alive() + + def _flush_queues_inline(self) -> None: + if self._is_local: + if self._queued_logs: + logs_to_send = self._queued_logs.copy() + self._queued_logs.clear() + self._write_logs_to_sqlite(logs_to_send) + + if self._queued_system_logs: + system_logs_to_send = self._queued_system_logs.copy() + self._queued_system_logs.clear() + self._write_system_logs_to_sqlite(system_logs_to_send) + + if self._queued_alerts: + alerts_to_send = self._queued_alerts.copy() + self._queued_alerts.clear() + self._write_alerts_to_sqlite(alerts_to_send) + return + + if self._queued_logs: + logs_to_send = self._queued_logs.copy() + self._queued_logs.clear() + self._persist_logs_locally(logs_to_send) + + if self._queued_system_logs: + system_logs_to_send = self._queued_system_logs.copy() + self._queued_system_logs.clear() + self._persist_system_logs_locally(system_logs_to_send) + + if self._queued_uploads: + uploads_to_send = self._queued_uploads.copy() + self._queued_uploads.clear() + self._persist_uploads_locally(uploads_to_send) + + if self._queued_alerts: + alerts_to_send = self._queued_alerts.copy() + self._queued_alerts.clear() + self._write_alerts_to_sqlite(alerts_to_send) + + def _local_batch_sender(self): + while ( + not self._stop_flag.is_set() + or len(self._queued_logs) > 0 + or len(self._queued_system_logs) > 0 + or len(self._queued_alerts) > 0 + ): + if not self._stop_flag.is_set(): + self._stop_flag.wait(timeout=BATCH_SEND_INTERVAL) + + try: + with self._client_lock: + if self._queued_logs: + logs_to_send = self._queued_logs.copy() + self._queued_logs.clear() + self._write_logs_to_sqlite(logs_to_send) + + if self._queued_system_logs: + system_logs_to_send = self._queued_system_logs.copy() + self._queued_system_logs.clear() + self._write_system_logs_to_sqlite(system_logs_to_send) + + if self._queued_alerts: + alerts_to_send = self._queued_alerts.copy() + self._queued_alerts.clear() + self._write_alerts_to_sqlite(alerts_to_send) + except Exception as e: + self._warn_once( + "local-sender-loop", + f"trackio's local logging thread hit an internal error: {e}. User code will continue, but some Trackio data may be dropped.", + ) + + def _write_logs_to_sqlite(self, logs: list[LogEntry]): + try: + logs_by_run: dict[tuple, dict] = {} + for entry in logs: + key = (entry["project"], entry["run"], entry.get("run_id")) + if key not in logs_by_run: + logs_by_run[key] = { + "metrics": [], + "steps": [], + "log_ids": [], + "config": None, + } + logs_by_run[key]["metrics"].append(entry["metrics"]) + logs_by_run[key]["steps"].append(entry.get("step")) + logs_by_run[key]["log_ids"].append(entry.get("log_id")) + if entry.get("config") and logs_by_run[key]["config"] is None: + logs_by_run[key]["config"] = entry["config"] + + for (project, run, run_id), data in logs_by_run.items(): + has_log_ids = any(lid is not None for lid in data["log_ids"]) + SQLiteStorage.bulk_log( + project=project, + run=run, + run_id=run_id, + metrics_list=data["metrics"], + steps=data["steps"], + config=data["config"], + log_ids=data["log_ids"] if has_log_ids else None, + ) + except Exception as e: + self._warn_once( + "write-logs-to-sqlite", + f"trackio failed to flush metric logs for run '{self.name}': {e}. User code will continue, but this batch could not be persisted.", + ) + + def _write_system_logs_to_sqlite(self, logs: list[SystemLogEntry]): + try: + logs_by_run: dict[tuple, dict] = {} + for entry in logs: + key = (entry["project"], entry["run"], entry.get("run_id")) + if key not in logs_by_run: + logs_by_run[key] = {"metrics": [], "timestamps": [], "log_ids": []} + logs_by_run[key]["metrics"].append(entry["metrics"]) + logs_by_run[key]["timestamps"].append(entry.get("timestamp")) + logs_by_run[key]["log_ids"].append(entry.get("log_id")) + + for (project, run, run_id), data in logs_by_run.items(): + has_log_ids = any(lid is not None for lid in data["log_ids"]) + SQLiteStorage.bulk_log_system( + project=project, + run=run, + run_id=run_id, + metrics_list=data["metrics"], + timestamps=data["timestamps"], + log_ids=data["log_ids"] if has_log_ids else None, + ) + except Exception as e: + self._warn_once( + "write-system-logs-to-sqlite", + f"trackio failed to flush system logs for run '{self.name}': {e}. User code will continue, but this batch could not be persisted.", + ) + + def _write_alerts_to_sqlite(self, alerts: list[AlertEntry]): + try: + alerts_by_run: dict[tuple, dict] = {} + for entry in alerts: + key = (entry["project"], entry["run"], entry.get("run_id")) + if key not in alerts_by_run: + alerts_by_run[key] = { + "titles": [], + "texts": [], + "levels": [], + "steps": [], + "timestamps": [], + "alert_ids": [], + } + alerts_by_run[key]["titles"].append(entry["title"]) + alerts_by_run[key]["texts"].append(entry.get("text")) + alerts_by_run[key]["levels"].append(entry["level"]) + alerts_by_run[key]["steps"].append(entry.get("step")) + alerts_by_run[key]["timestamps"].append(entry.get("timestamp")) + alerts_by_run[key]["alert_ids"].append(entry.get("alert_id")) + + for (project, run, run_id), data in alerts_by_run.items(): + has_alert_ids = any(aid is not None for aid in data["alert_ids"]) + SQLiteStorage.bulk_alert( + project=project, + run=run, + run_id=run_id, + titles=data["titles"], + texts=data["texts"], + levels=data["levels"], + steps=data["steps"], + timestamps=data["timestamps"], + alert_ids=data["alert_ids"] if has_alert_ids else None, + ) + except Exception as e: + self._warn_once( + "write-alerts-to-sqlite", + f"trackio failed to flush alerts for run '{self.name}': {e}. User code will continue, but this batch could not be persisted.", + ) + + def _batch_sender(self): + consecutive_failures = 0 + while ( + not self._stop_flag.is_set() + or len(self._queued_logs) > 0 + or len(self._queued_system_logs) > 0 + or len(self._queued_uploads) > 0 + or len(self._queued_alerts) > 0 + or self._has_local_buffer + ): + if not self._stop_flag.is_set(): + if consecutive_failures: + sleep_time = min( + BATCH_SEND_INTERVAL * (2**consecutive_failures), MAX_BACKOFF + ) + else: + sleep_time = BATCH_SEND_INTERVAL + self._stop_flag.wait(timeout=sleep_time) + elif self._has_local_buffer: + self._stop_flag.wait(timeout=BATCH_SEND_INTERVAL) + + try: + with self._client_lock: + if self._client is None: + if self._stop_flag.is_set(): + if self._queued_logs: + self._persist_logs_locally(self._queued_logs) + self._queued_logs.clear() + if self._queued_system_logs: + self._persist_system_logs_locally( + self._queued_system_logs + ) + self._queued_system_logs.clear() + if self._queued_uploads: + self._persist_uploads_locally(self._queued_uploads) + self._queued_uploads.clear() + if self._queued_alerts: + self._write_alerts_to_sqlite(self._queued_alerts) + self._queued_alerts.clear() + return + + failed = False + + if self._queued_logs: + logs_to_send = self._queued_logs.copy() + self._queued_logs.clear() + try: + self._client.predict( + api_name="/bulk_log", + logs=logs_to_send, + hf_token=self._hf_token_for_remote(), + ) + except Exception: + self._persist_logs_locally(logs_to_send) + failed = True + + if self._queued_system_logs: + system_logs_to_send = self._queued_system_logs.copy() + self._queued_system_logs.clear() + try: + self._client.predict( + api_name="/bulk_log_system", + logs=system_logs_to_send, + hf_token=self._hf_token_for_remote(), + ) + except Exception: + self._persist_system_logs_locally(system_logs_to_send) + failed = True + + if self._queued_uploads: + uploads_to_send = self._queued_uploads.copy() + self._queued_uploads.clear() + try: + self._client.predict( + api_name="/bulk_upload_media", + uploads=uploads_to_send, + hf_token=self._hf_token_for_remote(), + ) + except Exception: + self._persist_uploads_locally(uploads_to_send) + failed = True + + if self._queued_alerts: + alerts_to_send = self._queued_alerts.copy() + self._queued_alerts.clear() + try: + self._client.predict( + api_name="/bulk_alert", + alerts=alerts_to_send, + hf_token=self._hf_token_for_remote(), + ) + except Exception: + self._write_alerts_to_sqlite(alerts_to_send) + failed = True + + if failed: + consecutive_failures += 1 + else: + consecutive_failures = 0 + if self._has_local_buffer: + self._flush_local_buffer() + except Exception as e: + consecutive_failures += 1 + self._warn_once( + "remote-sender-loop", + f"trackio's remote logging thread hit an internal error: {e}. User code will continue while Trackio retries in the background.", + ) + + def _persist_logs_locally(self, logs: list[LogEntry]): + if not self._remote_storage_key: + return + try: + logs_by_run: dict[tuple, dict] = {} + for entry in logs: + key = (entry["project"], entry["run"], entry.get("run_id")) + if key not in logs_by_run: + logs_by_run[key] = { + "metrics": [], + "steps": [], + "log_ids": [], + "config": None, + } + logs_by_run[key]["metrics"].append(entry["metrics"]) + logs_by_run[key]["steps"].append(entry.get("step")) + logs_by_run[key]["log_ids"].append(entry.get("log_id")) + if entry.get("config") and logs_by_run[key]["config"] is None: + logs_by_run[key]["config"] = entry["config"] + + for (project, run, run_id), data in logs_by_run.items(): + SQLiteStorage.bulk_log( + project=project, + run=run, + run_id=run_id, + metrics_list=data["metrics"], + steps=data["steps"], + log_ids=data["log_ids"], + config=data["config"], + space_id=self._remote_storage_key, + ) + self._has_local_buffer = True + except Exception as e: + self._warn_once( + "persist-logs-locally", + f"trackio could not persist failed remote metric logs locally for run '{self.name}': {e}. User code will continue, but this batch could be lost.", + ) + + def _persist_system_logs_locally(self, logs: list[SystemLogEntry]): + if not self._remote_storage_key: + return + try: + logs_by_run: dict[tuple, dict] = {} + for entry in logs: + key = (entry["project"], entry["run"], entry.get("run_id")) + if key not in logs_by_run: + logs_by_run[key] = {"metrics": [], "timestamps": [], "log_ids": []} + logs_by_run[key]["metrics"].append(entry["metrics"]) + logs_by_run[key]["timestamps"].append(entry.get("timestamp")) + logs_by_run[key]["log_ids"].append(entry.get("log_id")) + + for (project, run, run_id), data in logs_by_run.items(): + SQLiteStorage.bulk_log_system( + project=project, + run=run, + run_id=run_id, + metrics_list=data["metrics"], + timestamps=data["timestamps"], + log_ids=data["log_ids"], + space_id=self._remote_storage_key, + ) + self._has_local_buffer = True + except Exception as e: + self._warn_once( + "persist-system-logs-locally", + f"trackio could not persist failed remote system logs locally for run '{self.name}': {e}. User code will continue, but this batch could be lost.", + ) + + def _persist_uploads_locally(self, uploads: list[UploadEntry]): + if not self._remote_storage_key: + return + try: + for entry in uploads: + file_data = entry.get("uploaded_file") + file_path = "" + if isinstance(file_data, dict): + file_path = file_data.get("path", "") + elif hasattr(file_data, "path"): + file_path = str(file_data.path) + else: + file_path = str(file_data) + SQLiteStorage.add_pending_upload( + project=entry["project"], + space_id=self._remote_storage_key, + run_id=entry.get("run_id"), + run_name=entry.get("run"), + step=entry.get("step"), + file_path=file_path, + relative_path=entry.get("relative_path"), + ) + self._has_local_buffer = True + except Exception as e: + self._warn_once( + "persist-uploads-locally", + f"trackio could not persist failed remote file uploads locally for run '{self.name}': {e}. User code will continue, but some artifacts could be lost.", + ) + + def _flush_local_buffer(self): + try: + buffered_logs = SQLiteStorage.get_pending_logs(self.project) + if buffered_logs: + self._client.predict( + api_name="/bulk_log", + logs=buffered_logs["logs"], + hf_token=self._hf_token_for_remote(), + ) + SQLiteStorage.clear_pending_logs(self.project, buffered_logs["ids"]) + + buffered_sys = SQLiteStorage.get_pending_system_logs(self.project) + if buffered_sys: + self._client.predict( + api_name="/bulk_log_system", + logs=buffered_sys["logs"], + hf_token=self._hf_token_for_remote(), + ) + SQLiteStorage.clear_pending_system_logs( + self.project, buffered_sys["ids"] + ) + + buffered_uploads = SQLiteStorage.get_pending_uploads(self.project) + if buffered_uploads: + upload_entries = [] + for u in buffered_uploads["uploads"]: + fp = u["file_path"] + if Path(fp).exists(): + upload_entries.append( + { + "project": u["project"], + "run": u["run"], + "run_id": u.get("run_id"), + "step": u["step"], + "relative_path": u["relative_path"], + "uploaded_file": handle_file(fp), + } + ) + if upload_entries: + self._client.predict( + api_name="/bulk_upload_media", + uploads=upload_entries, + hf_token=self._hf_token_for_remote(), + ) + SQLiteStorage.clear_pending_uploads( + self.project, buffered_uploads["ids"] + ) + + self._has_local_buffer = False + except Exception as e: + self._warn_once( + "flush-local-buffer", + f"trackio could not flush buffered remote data for run '{self.name}': {e}. It will retry later if possible.", + ) + + def _init_client_background(self): + if self._client is None: + fib = utils.fibo() + for sleep_coefficient in fib: + if self._stop_flag.is_set(): + break + try: + if self._server_base_url is not None: + client = RemoteClient( + self._server_base_url, + hf_token=None, + write_token=self._write_token, + verbose=False, + ) + else: + client = RemoteClient( + self.url, + hf_token=huggingface_hub.utils.get_token(), + verbose=False, + ) + + with self._client_lock: + self._client = client + break + except Exception: + pass + sleep_time = min(0.1 * sleep_coefficient, MAX_BACKOFF) + self._stop_flag.wait(timeout=sleep_time) + + self._batch_sender() + + def _queue_upload( + self, + file_path, + step: int | None, + relative_path: str | None = None, + use_run_name: bool = True, + ): + try: + if self._is_local: + self._save_upload_locally(file_path, step, relative_path, use_run_name) + else: + upload_entry: UploadEntry = { + "project": self.project, + "run": self.name if use_run_name else None, + "run_id": self.id if use_run_name else None, + "step": step, + "relative_path": relative_path, + "uploaded_file": handle_file(file_path), + } + with self._client_lock: + self._queued_uploads.append(upload_entry) + self._ensure_sender_alive() + if not self._thread_is_alive("_client_thread"): + self._flush_queues_inline() + except Exception as e: + self._warn_once( + "queue-upload", + f"trackio could not queue the artifact '{file_path}' for run '{self.name}': {e}. User code will continue, but this artifact could be missing.", + ) + + def _save_upload_locally( + self, + file_path, + step: int | None, + relative_path: str | None = None, + use_run_name: bool = True, + ): + media_path = get_project_media_path( + project=self.project, + run=self.name if use_run_name else None, + step=step, + relative_path=relative_path, + ) + src = Path(file_path) + if src.exists() and str(src.resolve()) != str(Path(media_path).resolve()): + shutil.copy(str(src), str(media_path)) + + def _process_media(self, value: TrackioMedia, step: int | None) -> dict: + value._save(self.project, self.name, step if step is not None else 0) + if self._space_id or self._server_base_url: + self._queue_upload(value._get_absolute_file_path(), step) + return value._to_dict() + + def _scan_and_queue_media_uploads(self, table_dict: dict, step: int | None): + if not self._space_id and not self._server_base_url: + return + + table_data = table_dict.get("_value", []) + for row in table_data: + for value in row.values(): + if isinstance(value, dict) and value.get("_type") in [ + "trackio.image", + "trackio.video", + "trackio.audio", + ]: + file_path = value.get("file_path") + if file_path: + absolute_path = MEDIA_DIR / file_path + self._queue_upload(absolute_path, step) + elif isinstance(value, list): + for item in value: + if isinstance(item, dict) and item.get("_type") in [ + "trackio.image", + "trackio.video", + "trackio.audio", + ]: + file_path = item.get("file_path") + if file_path: + absolute_path = MEDIA_DIR / file_path + self._queue_upload(absolute_path, step) + + def _ensure_sender_alive(self): + if self._is_local: + if ( + not self._thread_is_alive("_local_sender_thread") + and not self._stop_flag.is_set() + ): + self._start_background_thread( + "_local_sender_thread", + self._local_batch_sender, + warning_key="local-sender-thread-restart", + description="local Trackio logging thread", + ) + else: + if ( + not self._thread_is_alive("_client_thread") + and not self._stop_flag.is_set() + ): + self._start_background_thread( + "_client_thread", + self._init_client_background, + warning_key="remote-sender-thread-restart", + description="remote Trackio logging thread", + ) + + def log(self, metrics: dict, step: int | None = None): + try: + renamed_keys = [] + new_metrics = {} + + for k, v in metrics.items(): + if k in utils.RESERVED_KEYS or k.startswith("__"): + new_key = f"__{k}" + renamed_keys.append(k) + new_metrics[new_key] = v + else: + new_metrics[k] = v + + if renamed_keys: + _emit_nonfatal_warning( + f"Reserved keys renamed: {renamed_keys} → '__{{key}}'" + ) + + metrics = new_metrics + for key, value in metrics.items(): + if isinstance(value, Table): + metrics[key] = value._to_dict( + project=self.project, run=self.name, step=step + ) + self._scan_and_queue_media_uploads(metrics[key], step) + elif isinstance(value, Histogram): + metrics[key] = value._to_dict() + elif isinstance(value, Markdown): + metrics[key] = value._to_dict() + elif isinstance(value, TrackioMedia): + metrics[key] = self._process_media(value, step) + metrics = utils.serialize_values(metrics) + + if step is None: + step = self._next_step + self._next_step = max(self._next_step, step + 1) + + config_to_log = None + if not self._config_logged and self.config: + config_to_log = utils.to_json_safe(self.config) + self._config_logged = True + + log_entry: LogEntry = { + "project": self.project, + "run": self.name, + "run_id": self.id, + "metrics": metrics, + "step": step, + "config": config_to_log, + "log_id": uuid.uuid4().hex, + } + + with self._client_lock: + self._queued_logs.append(log_entry) + self._ensure_sender_alive() + if not self._thread_is_alive( + "_local_sender_thread" if self._is_local else "_client_thread" + ): + self._flush_queues_inline() + except Exception as e: + _emit_nonfatal_warning(f"trackio.log() failed to process metrics: {e}") + + def alert( + self, + title: str, + text: str | None = None, + level: AlertLevel = AlertLevel.WARN, + step: int | None = None, + webhook_url: str | None = None, + ): + try: + if step is None: + step = max(self._next_step - 1, 0) + timestamp = datetime.now(timezone.utc).isoformat() + + print(format_alert_terminal(level, title, text, step)) + + alert_entry: AlertEntry = { + "project": self.project, + "run": self.name, + "run_id": self.id, + "title": title, + "text": text, + "level": level.value, + "step": step, + "timestamp": timestamp, + "alert_id": uuid.uuid4().hex, + } + + with self._client_lock: + self._queued_alerts.append(alert_entry) + self._ensure_sender_alive() + if not self._thread_is_alive( + "_local_sender_thread" if self._is_local else "_client_thread" + ): + self._flush_queues_inline() + + url = webhook_url or self._webhook_url + if url and should_send_webhook(level, self._webhook_min_level): + t = threading.Thread( + target=send_webhook, + args=( + url, + level, + title, + text, + self.project, + self.name, + step, + timestamp, + ), + daemon=True, + ) + t.start() + except Exception as e: + _emit_nonfatal_warning(f"trackio.alert() failed: {e}") + + def log_system(self, metrics: dict): + try: + metrics = utils.serialize_values(metrics) + timestamp = datetime.now(timezone.utc).isoformat() + + system_log_entry: SystemLogEntry = { + "project": self.project, + "run": self.name, + "run_id": self.id, + "metrics": metrics, + "timestamp": timestamp, + "log_id": uuid.uuid4().hex, + } + + with self._client_lock: + self._queued_system_logs.append(system_log_entry) + self._ensure_sender_alive() + if not self._thread_is_alive( + "_local_sender_thread" if self._is_local else "_client_thread" + ): + self._flush_queues_inline() + except Exception as e: + _emit_nonfatal_warning(f"trackio.log_system() failed: {e}") + + def finish(self): + try: + if self._gpu_monitor is not None: + try: + self._gpu_monitor.stop() + except Exception as e: + self._warn_once( + "finish-gpu-monitor", + f"trackio.finish() could not stop automatic GPU logging cleanly: {e}.", + ) + + self._stop_flag.set() + + if self._is_local: + if self._local_sender_thread is not None: + print("* Run finished. Uploading logs to Trackio (please wait...)") + self._local_sender_thread.join(timeout=30) + if self._local_sender_thread.is_alive(): + _emit_nonfatal_warning( + "Could not flush all logs within 30s. Some data may be buffered locally." + ) + else: + with self._client_lock: + self._flush_queues_inline() + else: + if self._client_thread is not None: + print( + "* Run finished. Uploading logs to the remote Trackio server (please wait...)" + ) + self._client_thread.join(timeout=30) + if self._client_thread.is_alive(): + _emit_nonfatal_warning( + "Could not flush all logs within 30s. Some data may be buffered locally." + ) + else: + with self._client_lock: + self._flush_queues_inline() + + try: + has_pending = SQLiteStorage.has_pending_data(self.project) + except Exception as e: + self._warn_once( + "finish-pending-data", + f"trackio.finish() could not inspect pending buffered logs for project '{self.project}': {e}.", + ) + has_pending = False + + if has_pending: + if self._space_id is not None: + retry = f'trackio.init(project="{self.project}", space_id="{self._space_id}")' + else: + retry = f'trackio.init(project="{self.project}", server_url="{self._server_base_url}")' + _emit_nonfatal_warning( + f"* Some logs could not be sent to the remote server (it may still be starting up). " + f"They have been saved locally and will be sent automatically next time you call: " + f"{retry}" + ) + except Exception as e: + _emit_nonfatal_warning(f"trackio.finish() failed: {e}") diff --git a/trackio/server.py b/trackio/server.py new file mode 100644 index 0000000000000000000000000000000000000000..48000b3c706008f6bc37f34a1a93c25cf86e676c --- /dev/null +++ b/trackio/server.py @@ -0,0 +1,929 @@ +"""The main API layer for the Trackio UI.""" + +import base64 +import logging +import os +import re +import secrets +import shutil +import sqlite3 +import threading +import time +import warnings +from collections import deque +from functools import lru_cache +from typing import Any +from urllib.parse import urlencode + +import httpx +import huggingface_hub as hf +from starlette.requests import Request +from starlette.responses import RedirectResponse +from starlette.routing import Route + +import trackio.utils as utils +from trackio.asgi_app import ( + consume_uploaded_temp_file, + create_trackio_starlette_app, +) +from trackio.exceptions import TrackioAPIError +from trackio.media import get_project_media_path +from trackio.sqlite_storage import SQLiteStorage +from trackio.typehints import AlertEntry, LogEntry, SystemLogEntry, UploadEntry +from trackio.utils import on_spaces + +HfApi = hf.HfApi() + +logger = logging.getLogger("trackio") + +_write_queue: deque[tuple[str, Any]] = deque() +_flush_thread: threading.Thread | None = None +_flush_lock = threading.Lock() +_FLUSH_INTERVAL = 2.0 +_MAX_RETRIES = 30 + +_LOGS_BATCH_MAX_RUNS = 64 +_LOGS_BATCH_MAX_POINTS = 10_000 + + +def _normalize_logs_batch_runs(runs: Any) -> list[dict[str, Any]]: + if not isinstance(runs, list): + raise TrackioAPIError("runs must be a list") + if len(runs) > _LOGS_BATCH_MAX_RUNS: + raise TrackioAPIError( + f"runs cannot contain more than {_LOGS_BATCH_MAX_RUNS} entries" + ) + out: list[dict[str, Any]] = [] + for i, r in enumerate(runs): + if not isinstance(r, dict): + raise TrackioAPIError(f"runs[{i}] must be an object") + out.append({"run": r.get("run"), "run_id": r.get("run_id")}) + return out + + +def _normalize_logs_batch_max_points(max_points: Any) -> int | None: + if max_points is None: + return 1500 + if isinstance(max_points, bool): + raise TrackioAPIError("max_points must be a number or null") + if isinstance(max_points, float): + if not max_points.is_integer(): + raise TrackioAPIError("max_points must be a whole number") + max_points = int(max_points) + if not isinstance(max_points, int): + raise TrackioAPIError("max_points must be an integer or null") + if max_points < 1: + return 1500 + return min(max_points, _LOGS_BATCH_MAX_POINTS) + + +def _enqueue_write(kind: str, payload: Any) -> None: + _write_queue.append((kind, payload)) + _ensure_flush_thread() + + +def _ensure_flush_thread() -> None: + global _flush_thread + with _flush_lock: + if _flush_thread is not None and _flush_thread.is_alive(): + return + _flush_thread = threading.Thread(target=_flush_loop, daemon=True) + _flush_thread.start() + + +def _flush_loop() -> None: + retries = 0 + while _write_queue and retries < _MAX_RETRIES: + kind, payload = _write_queue[0] + try: + if kind == "bulk_log": + SQLiteStorage.bulk_log(**payload) + elif kind == "bulk_log_system": + SQLiteStorage.bulk_log_system(**payload) + elif kind == "bulk_alert": + SQLiteStorage.bulk_alert(**payload) + _write_queue.popleft() + retries = 0 + except sqlite3.OperationalError as e: + msg = str(e).lower() + if "disk i/o error" in msg or "readonly" in msg: + retries += 1 + logger.warning( + "write queue: flush failed (%s), retry %d/%d", + e, + retries, + _MAX_RETRIES, + ) + time.sleep(min(_FLUSH_INTERVAL * retries, 15.0)) + else: + logger.error("write queue: non-retryable error (%s), dropping entry", e) + _write_queue.popleft() + retries = 0 + if _write_queue: + logger.error( + "write queue: giving up after %d retries, %d entries dropped", + _MAX_RETRIES, + len(_write_queue), + ) + _write_queue.clear() + + +write_token = secrets.token_urlsafe(32) + +OAUTH_CALLBACK_PATH = "/login/callback" +OAUTH_START_PATH = "/oauth/hf/start" + + +def _hf_access_token(request: Request) -> str | None: + session_id = None + try: + session_id = request.headers.get("x-trackio-oauth-session") + except (AttributeError, TypeError): + pass + if session_id and session_id in _oauth_sessions: + token, created = _oauth_sessions[session_id] + if time.monotonic() - created <= _OAUTH_SESSION_TTL: + return token + del _oauth_sessions[session_id] + cookie_header = "" + try: + cookie_header = request.headers.get("cookie", "") + except (AttributeError, TypeError): + pass + if cookie_header: + for cookie in cookie_header.split(";"): + parts = cookie.strip().split("=", 1) + if len(parts) == 2 and parts[0] == "trackio_hf_access_token": + return parts[1] or None + return None + + +def _oauth_redirect_uri(request: Request) -> str: + space_host = os.getenv("SPACE_HOST") + if space_host: + space_host = space_host.split(",")[0] + return f"https://{space_host}{OAUTH_CALLBACK_PATH}" + return str(request.base_url).rstrip("/") + OAUTH_CALLBACK_PATH + + +_OAUTH_STATE_TTL = 86400 +_OAUTH_SESSION_TTL = 86400 * 30 +_pending_oauth_states: dict[str, float] = {} +_oauth_sessions: dict[str, tuple[str, float]] = {} + + +def _evict_expired_oauth(): + now = time.monotonic() + expired_states = [ + k for k, t in _pending_oauth_states.items() if now - t > _OAUTH_STATE_TTL + ] + for k in expired_states: + del _pending_oauth_states[k] + expired_sessions = [ + k for k, (_, t) in _oauth_sessions.items() if now - t > _OAUTH_SESSION_TTL + ] + for k in expired_sessions: + del _oauth_sessions[k] + + +def oauth_hf_start(request: Request): + client_id = os.getenv("OAUTH_CLIENT_ID") + if not client_id: + return RedirectResponse(url="/", status_code=302) + _evict_expired_oauth() + state = secrets.token_urlsafe(32) + _pending_oauth_states[state] = time.monotonic() + redirect_uri = _oauth_redirect_uri(request) + scope = os.getenv("OAUTH_SCOPES", "openid profile").strip() + url = "https://huggingface.co/oauth/authorize?" + urlencode( + { + "client_id": client_id, + "redirect_uri": redirect_uri, + "response_type": "code", + "scope": scope, + "state": state, + } + ) + return RedirectResponse(url=url, status_code=302) + + +def oauth_hf_callback(request: Request): + client_id = os.getenv("OAUTH_CLIENT_ID") + client_secret = os.getenv("OAUTH_CLIENT_SECRET") + err = "/?oauth_error=1" + if not client_id or not client_secret: + return RedirectResponse(url=err, status_code=302) + got_state = request.query_params.get("state") + code = request.query_params.get("code") + if not got_state or got_state not in _pending_oauth_states or not code: + return RedirectResponse(url=err, status_code=302) + state_created = _pending_oauth_states.pop(got_state) + if time.monotonic() - state_created > _OAUTH_STATE_TTL: + return RedirectResponse(url=err, status_code=302) + redirect_uri = _oauth_redirect_uri(request) + auth_b64 = base64.b64encode(f"{client_id}:{client_secret}".encode()).decode() + try: + with httpx.Client() as client: + token_resp = client.post( + "https://huggingface.co/oauth/token", + headers={"Authorization": f"Basic {auth_b64}"}, + data={ + "grant_type": "authorization_code", + "code": code, + "redirect_uri": redirect_uri, + "client_id": client_id, + }, + ) + token_resp.raise_for_status() + access_token = token_resp.json()["access_token"] + except Exception: + return RedirectResponse(url=err, status_code=302) + session_id = secrets.token_urlsafe(32) + _oauth_sessions[session_id] = (access_token, time.monotonic()) + _on_spaces = on_spaces() + resp = RedirectResponse(url=f"/?oauth_session={session_id}", status_code=302) + resp.set_cookie( + key="trackio_hf_access_token", + value=access_token, + httponly=True, + samesite="none" if _on_spaces else "lax", + max_age=86400 * 30, + path="/", + secure=_on_spaces, + ) + return resp + + +def oauth_logout(request: Request): + _on_spaces = on_spaces() + resp = RedirectResponse(url="/", status_code=302) + resp.delete_cookie( + "trackio_hf_access_token", + path="/", + samesite="none" if _on_spaces else "lax", + secure=_on_spaces, + ) + return resp + + +@lru_cache(maxsize=32) +def check_hf_token_has_write_access(hf_token: str | None) -> None: + """ + Checks if the provided hf_token has write access to the space. If it does not + have write access, a PermissionError is raised. Otherwise, the function returns None. + + The function is cached in two separate caches to avoid unnecessary API calls to /whoami-v2 which is heavily rate-limited: + - A cache of the whoami response for the hf_token using .whoami(token=hf_token, cache=True). + - This entire function is cached using @lru_cache(maxsize=32). + """ + if on_spaces(): + if hf_token is None: + raise PermissionError( + "Expected a HF_TOKEN to be provided when logging to a Space" + ) + space_token = os.getenv("HF_TOKEN") + if space_token and hf_token == space_token: + # If the HF_TOKEN is the same as the space token, we can assume that the user has write access. + # This avoids unnecessary API calls to /whoami-v2 which is heavily rate-limited. + return + who = HfApi.whoami(token=hf_token, cache=True) + owner_name = os.getenv("SPACE_AUTHOR_NAME") + repo_name = os.getenv("SPACE_REPO_NAME") + orgs = [o["name"] for o in who["orgs"]] + if owner_name != who["name"] and owner_name not in orgs: + raise PermissionError( + "Expected the provided hf_token to be the user owner of the space, or be a member of the org owner of the space" + ) + access_token = who["auth"]["accessToken"] + if access_token["role"] == "fineGrained": + matched = False + for item in access_token["fineGrained"]["scoped"]: + if ( + item["entity"]["type"] == "space" + and item["entity"]["name"] == f"{owner_name}/{repo_name}" + and "repo.write" in item["permissions"] + ): + matched = True + break + if ( + ( + item["entity"]["type"] == "user" + or item["entity"]["type"] == "org" + ) + and item["entity"]["name"] == owner_name + and "repo.write" in item["permissions"] + ): + matched = True + break + if not matched: + raise PermissionError( + "Expected the provided hf_token with fine grained permissions to provide write access to the space" + ) + elif access_token["role"] != "write": + raise PermissionError( + "Expected the provided hf_token to provide write permissions" + ) + + +_oauth_write_cache: dict[str, tuple[bool, float]] = {} +_OAUTH_WRITE_CACHE_TTL = 300 + + +def check_oauth_token_has_write_access(oauth_token: str | None) -> None: + if not on_spaces(): + return + if oauth_token is None: + raise PermissionError( + "Expected an oauth to be provided when logging to a Space" + ) + now = time.monotonic() + cached = _oauth_write_cache.get(oauth_token) + if cached is not None: + allowed, ts = cached + if now - ts < _OAUTH_WRITE_CACHE_TTL: + if not allowed: + raise PermissionError( + "Expected the oauth token to be the user owner of the space, or be a member of the org owner of the space" + ) + return + who = HfApi.whoami(oauth_token, cache=True) + user_name = who["name"] + owner_name = os.getenv("SPACE_AUTHOR_NAME") + if user_name == owner_name: + _oauth_write_cache[oauth_token] = (True, now) + return + for org in who["orgs"]: + if org["name"] == owner_name and org["roleInOrg"] == "write": + _oauth_write_cache[oauth_token] = (True, now) + return + _oauth_write_cache[oauth_token] = (False, now) + raise PermissionError( + "Expected the oauth token to be the user owner of the space, or be a member of the org owner of the space" + ) + + +def check_write_access(request: Request, token: str) -> bool: + expected_token = token or "" + hdr = request.headers.get("x-trackio-write-token") + if hdr is not None: + return secrets.compare_digest(hdr, expected_token) + cookies = request.headers.get("cookie", "") + if cookies: + for cookie in cookies.split(";"): + parts = cookie.strip().split("=", 1) + if len(parts) == 2 and parts[0] == "trackio_write_token": + return secrets.compare_digest(parts[1], expected_token) + if hasattr(request, "query_params") and request.query_params: + qp = request.query_params.get("write_token") + return secrets.compare_digest(qp or "", expected_token) + return False + + +def assert_can_write_metrics(request: Request, hf_token: str | None) -> None: + if on_spaces(): + check_hf_token_has_write_access(hf_token) + else: + if check_write_access(request, write_token): + return + raise TrackioAPIError( + "A write_token is required to log metrics or upload to this server. " + "Use the write-access URL from trackio.show(), set TRACKIO_WRITE_TOKEN, " + "or send header X-Trackio-Write-Token." + ) + + +def assert_can_mutate_runs(request: Request) -> None: + if not on_spaces(): + if check_write_access(request, write_token): + return + raise TrackioAPIError( + "A write_token is required to delete or rename runs. " + "Open the dashboard using the link that includes the write_token query parameter." + ) + hf_tok = _hf_access_token(request) + if hf_tok is not None: + try: + check_oauth_token_has_write_access(hf_tok) + except PermissionError as e: + raise TrackioAPIError(str(e)) from e + return + if check_write_access(request, write_token): + return + raise TrackioAPIError( + "Sign in with Hugging Face to delete or rename runs. You need write access to this Space, " + "or open the dashboard using a link that includes the write_token query parameter." + ) + + +def get_run_mutation_status(request: Request) -> dict[str, Any]: + if not on_spaces(): + if check_write_access(request, write_token): + return {"spaces": False, "allowed": True, "auth": "local"} + return {"spaces": False, "allowed": False, "auth": "none"} + hf_tok = _hf_access_token(request) + if hf_tok is not None: + try: + check_oauth_token_has_write_access(hf_tok) + return {"spaces": True, "allowed": True, "auth": "oauth"} + except PermissionError: + return {"spaces": True, "allowed": False, "auth": "oauth_insufficient"} + if check_write_access(request, write_token): + return {"spaces": True, "allowed": True, "auth": "write_token"} + return {"spaces": True, "allowed": False, "auth": "none"} + + +def upload_db_to_space( + request: Request, + project: str, + uploaded_db: dict, + hf_token: str | None, +) -> None: + assert_can_write_metrics(request, hf_token) + uploaded_path = consume_uploaded_temp_file(request, uploaded_db) + db_project_path = SQLiteStorage.get_project_db_path(project) + os.makedirs(os.path.dirname(db_project_path), exist_ok=True) + shutil.copy(uploaded_path, db_project_path) + + +def bulk_upload_media( + request: Request, + uploads: list[UploadEntry], + hf_token: str | None, +) -> None: + assert_can_write_metrics(request, hf_token) + for upload in uploads: + uploaded_path = consume_uploaded_temp_file(request, upload["uploaded_file"]) + media_path = get_project_media_path( + project=upload["project"], + run=upload["run"], + step=upload["step"], + relative_path=upload["relative_path"], + ) + shutil.copy(uploaded_path, media_path) + + +def log( + request: Request, + project: str, + run: str, + metrics: dict[str, Any], + step: int | None, + hf_token: str | None, + run_id: str | None = None, +) -> None: + assert_can_write_metrics(request, hf_token) + SQLiteStorage.log( + project=project, run=run, run_id=run_id, metrics=metrics, step=step + ) + + +def bulk_log( + request: Request, + logs: list[LogEntry], + hf_token: str | None, +) -> None: + assert_can_write_metrics(request, hf_token) + + logs_by_run = {} + for log_entry in logs: + key = (log_entry["project"], log_entry["run"], log_entry.get("run_id")) + if key not in logs_by_run: + logs_by_run[key] = { + "metrics": [], + "steps": [], + "log_ids": [], + "config": None, + } + logs_by_run[key]["metrics"].append(log_entry["metrics"]) + logs_by_run[key]["steps"].append(log_entry.get("step")) + logs_by_run[key]["log_ids"].append(log_entry.get("log_id")) + if log_entry.get("config") and logs_by_run[key]["config"] is None: + logs_by_run[key]["config"] = log_entry["config"] + + for (project, run, run_id), data in logs_by_run.items(): + has_log_ids = any(lid is not None for lid in data["log_ids"]) + payload = dict( + project=project, + run=run, + run_id=run_id, + metrics_list=data["metrics"], + steps=data["steps"], + config=data["config"], + log_ids=data["log_ids"] if has_log_ids else None, + ) + try: + SQLiteStorage.bulk_log(**payload) + except sqlite3.OperationalError: + _enqueue_write("bulk_log", payload) + + +def bulk_log_system( + request: Request, + logs: list[SystemLogEntry], + hf_token: str | None, +) -> None: + assert_can_write_metrics(request, hf_token) + + logs_by_run = {} + for log_entry in logs: + key = (log_entry["project"], log_entry["run"], log_entry.get("run_id")) + if key not in logs_by_run: + logs_by_run[key] = {"metrics": [], "timestamps": [], "log_ids": []} + logs_by_run[key]["metrics"].append(log_entry["metrics"]) + logs_by_run[key]["timestamps"].append(log_entry.get("timestamp")) + logs_by_run[key]["log_ids"].append(log_entry.get("log_id")) + + for (project, run, run_id), data in logs_by_run.items(): + has_log_ids = any(lid is not None for lid in data["log_ids"]) + payload = dict( + project=project, + run=run, + run_id=run_id, + metrics_list=data["metrics"], + timestamps=data["timestamps"], + log_ids=data["log_ids"] if has_log_ids else None, + ) + try: + SQLiteStorage.bulk_log_system(**payload) + except sqlite3.OperationalError: + _enqueue_write("bulk_log_system", payload) + + +def bulk_alert( + request: Request, + alerts: list[AlertEntry], + hf_token: str | None, +) -> None: + assert_can_write_metrics(request, hf_token) + + alerts_by_run: dict[tuple, dict] = {} + for entry in alerts: + key = (entry["project"], entry["run"], entry.get("run_id")) + if key not in alerts_by_run: + alerts_by_run[key] = { + "titles": [], + "texts": [], + "levels": [], + "steps": [], + "timestamps": [], + "alert_ids": [], + } + alerts_by_run[key]["titles"].append(entry["title"]) + alerts_by_run[key]["texts"].append(entry.get("text")) + alerts_by_run[key]["levels"].append(entry["level"]) + alerts_by_run[key]["steps"].append(entry.get("step")) + alerts_by_run[key]["timestamps"].append(entry.get("timestamp")) + alerts_by_run[key]["alert_ids"].append(entry.get("alert_id")) + + for (project, run, run_id), data in alerts_by_run.items(): + has_alert_ids = any(aid is not None for aid in data["alert_ids"]) + payload = dict( + project=project, + run=run, + run_id=run_id, + titles=data["titles"], + texts=data["texts"], + levels=data["levels"], + steps=data["steps"], + timestamps=data["timestamps"], + alert_ids=data["alert_ids"] if has_alert_ids else None, + ) + try: + SQLiteStorage.bulk_alert(**payload) + except sqlite3.OperationalError: + _enqueue_write("bulk_alert", payload) + + +def get_alerts( + project: str, + run: str | None = None, + run_id: str | None = None, + level: str | None = None, + since: str | None = None, +) -> list[dict[str, Any]]: + return SQLiteStorage.get_alerts( + project, run_name=run, run_id=run_id, level=level, since=since + ) + + +def get_metric_values( + project: str, + run: str, + metric_name: str, + step: int | None = None, + around_step: int | None = None, + at_time: str | None = None, + window: int | None = None, +) -> list[dict[str, Any]]: + return SQLiteStorage.get_metric_values( + project, + run, + metric_name, + step=step, + around_step=around_step, + at_time=at_time, + window=window, + ) + + +def get_runs_for_project(project: str) -> list[dict[str, Any]]: + return SQLiteStorage.get_run_records(project) + + +def get_metrics_for_run( + project: str, run: str | None = None, run_id: str | None = None +) -> list[str]: + return SQLiteStorage.get_all_metrics_for_run(project, run, run_id=run_id) + + +def filter_metrics_by_regex(metrics: list[str], filter_pattern: str) -> list[str]: + if not filter_pattern.strip(): + return metrics + try: + pattern = re.compile(filter_pattern, re.IGNORECASE) + return [metric for metric in metrics if pattern.search(metric)] + except re.error: + return [ + metric for metric in metrics if filter_pattern.lower() in metric.lower() + ] + + +def get_all_projects() -> list[str]: + return SQLiteStorage.get_projects() + + +def get_project_summary(project: str) -> dict[str, Any]: + runs = SQLiteStorage.get_run_records(project) + if not runs: + return {"project": project, "num_runs": 0, "runs": [], "last_activity": None} + + last_steps = SQLiteStorage.get_max_steps_for_runs(project) + + return { + "project": project, + "num_runs": len(runs), + "runs": runs, + "last_activity": max(last_steps.values()) if last_steps else None, + } + + +def get_run_summary( + project: str, run: str | None = None, run_id: str | None = None +) -> dict[str, Any]: + if run is None and run_id is not None: + record = next( + ( + record + for record in SQLiteStorage.get_run_records(project) + if record["id"] == run_id + ), + None, + ) + if record is not None: + run = record["name"] + + num_logs = SQLiteStorage.get_log_count(project, run, run_id=run_id) + if num_logs == 0: + return { + "project": project, + "run": run, + "run_id": run_id, + "num_logs": 0, + "metrics": [], + "config": None, + "last_step": None, + } + + metrics = SQLiteStorage.get_all_metrics_for_run(project, run, run_id=run_id) + config = SQLiteStorage.get_run_config(project, run, run_id=run_id) + last_step = SQLiteStorage.get_last_step(project, run, run_id=run_id) + + return { + "project": project, + "run": run, + "run_id": run_id, + "num_logs": num_logs, + "metrics": metrics, + "config": config, + "last_step": last_step, + } + + +def get_system_metrics_for_run( + project: str, run: str | None = None, run_id: str | None = None +) -> list[str]: + return SQLiteStorage.get_all_system_metrics_for_run(project, run, run_id=run_id) + + +def get_system_logs( + project: str, run: str | None = None, run_id: str | None = None +) -> list[dict[str, Any]]: + return SQLiteStorage.get_system_logs(project, run, run_id=run_id, max_points=1500) + + +def get_system_logs_batch( + project: str, + runs: list[dict[str, Any]], + max_points: int | None = 1500, +) -> list[dict[str, Any]]: + runs_clean = _normalize_logs_batch_runs(runs) + mp = _normalize_logs_batch_max_points(max_points) + return SQLiteStorage.get_system_logs_batch(project, runs_clean, max_points=mp) + + +def get_snapshot( + project: str, + run: str | None = None, + run_id: str | None = None, + step: int | None = None, + around_step: int | None = None, + at_time: str | None = None, + window: int | None = None, +) -> dict[str, Any]: + return SQLiteStorage.get_snapshot( + project, + run, + run_id=run_id, + step=step, + around_step=around_step, + at_time=at_time, + window=window, + ) + + +def get_logs( + project: str, run: str | None = None, run_id: str | None = None +) -> list[dict[str, Any]]: + return SQLiteStorage.get_logs(project, run, max_points=1500, run_id=run_id) + + +def get_logs_batch( + project: str, + runs: list[dict[str, Any]], + max_points: int | None = 1500, +) -> list[dict[str, Any]]: + runs_clean = _normalize_logs_batch_runs(runs) + mp = _normalize_logs_batch_max_points(max_points) + return SQLiteStorage.get_logs_batch(project, runs_clean, max_points=mp) + + +def query_project(project: str, query: str) -> dict[str, Any]: + return SQLiteStorage.query_project(project, query) + + +def get_settings() -> dict[str, Any]: + return { + "logo_urls": utils.get_logo_urls(), + "color_palette": utils.get_color_palette(), + "plot_order": [ + item.strip() + for item in os.environ.get("TRACKIO_PLOT_ORDER", "").split(",") + if item.strip() + ], + "table_truncate_length": int( + os.environ.get("TRACKIO_TABLE_TRUNCATE_LENGTH", "250") + ), + "media_dir": str(utils.MEDIA_DIR), + "space_id": os.getenv("SPACE_ID"), + } + + +def get_project_files(project: str) -> list[dict[str, Any]]: + files_dir = utils.MEDIA_DIR / project / "files" + if not files_dir.exists(): + return [] + results = [] + for file_path in sorted(files_dir.rglob("*")): + if file_path.is_file(): + relative = file_path.relative_to(files_dir) + results.append( + { + "name": str(relative), + "path": str(file_path), + "size": file_path.stat().st_size, + } + ) + return results + + +def delete_run( + request: Request, + project: str, + run: str | None = None, + run_id: str | None = None, +) -> bool: + assert_can_mutate_runs(request) + return SQLiteStorage.delete_run(project, run, run_id=run_id) + + +def rename_run( + request: Request, + project: str, + old_name: str, + new_name: str, + run_id: str | None = None, +) -> bool: + assert_can_mutate_runs(request) + SQLiteStorage.rename_run(project, old_name, new_name, run_id=run_id) + return True + + +def force_sync() -> bool: + if os.environ.get("TRACKIO_BUCKET_ID"): + return True + SQLiteStorage._dataset_import_attempted = True + SQLiteStorage.export_to_parquet() + scheduler = SQLiteStorage.get_scheduler() + scheduler.trigger().result() + return True + + +CSS = "" +HEAD = "" + + +def _api_registry() -> dict[str, Any]: + return { + "get_run_mutation_status": get_run_mutation_status, + "upload_db_to_space": upload_db_to_space, + "bulk_upload_media": bulk_upload_media, + "log": log, + "bulk_log": bulk_log, + "bulk_log_system": bulk_log_system, + "bulk_alert": bulk_alert, + "get_alerts": get_alerts, + "get_metric_values": get_metric_values, + "get_runs_for_project": get_runs_for_project, + "get_metrics_for_run": get_metrics_for_run, + "get_all_projects": get_all_projects, + "get_project_summary": get_project_summary, + "get_run_summary": get_run_summary, + "get_system_metrics_for_run": get_system_metrics_for_run, + "get_system_logs": get_system_logs, + "get_system_logs_batch": get_system_logs_batch, + "get_snapshot": get_snapshot, + "get_logs": get_logs, + "get_logs_batch": get_logs_batch, + "query_project": query_project, + "get_settings": get_settings, + "get_project_files": get_project_files, + "delete_run": delete_run, + "rename_run": rename_run, + "force_sync": force_sync, + } + + +class TrackioDashboardApp: + def __init__(self, starlette_app, uvicorn_server: Any, write_token: str) -> None: + self.app = starlette_app + self._uvicorn_server = uvicorn_server + self.write_token = write_token + + def close(self, verbose: bool = True) -> None: + if self._uvicorn_server is not None: + self._uvicorn_server.should_exit = True + + +def build_starlette_app_only(mcp_server: bool = False) -> tuple[Any, str]: + oauth_routes = [ + Route(OAUTH_START_PATH, oauth_hf_start, methods=["GET"]), + Route(OAUTH_CALLBACK_PATH, oauth_hf_callback, methods=["GET"]), + Route("/oauth/logout", oauth_logout, methods=["GET"]), + ] + mcp_lifespan = None + mcp_routes: list[Any] = [] + mcp_enabled = False + if mcp_server: + try: + from trackio.mcp_setup import create_mcp_integration # noqa: PLC0415 + + mcp_routes, mcp_lifespan = create_mcp_integration() + mcp_enabled = True + except ImportError: + warnings.warn( + "MCP support requested, but the optional `mcp` package is not installed. " + "Install `trackio[mcp]` to expose `/mcp`.", + UserWarning, + stacklevel=2, + ) + starlette_app = create_trackio_starlette_app( + oauth_routes, + _api_registry(), + extra_routes=mcp_routes, + mcp_lifespan=mcp_lifespan, + mcp_enabled=mcp_enabled, + allowed_file_roots=[ + utils.MEDIA_DIR, + utils.TRACKIO_DIR, + utils.TRACKIO_LOGO_DIR, + ], + ) + from trackio.frontend_server import mount_frontend # noqa: PLC0415 + + mount_frontend(starlette_app) + return starlette_app, write_token + + +def make_trackio_server(mcp_server: bool = False) -> TrackioDashboardApp: + app, wt = build_starlette_app_only(mcp_server=mcp_server) + return TrackioDashboardApp(app, None, wt) diff --git a/trackio/sqlite_storage.py b/trackio/sqlite_storage.py new file mode 100644 index 0000000000000000000000000000000000000000..140a9a8e95ca15579c77bd70b96bb30ff911d162 --- /dev/null +++ b/trackio/sqlite_storage.py @@ -0,0 +1,3192 @@ +import atexit +import json as json_mod +import os +import shutil +import sqlite3 +import time +import uuid +from collections.abc import Iterator +from contextlib import contextmanager +from datetime import datetime, timezone +from pathlib import Path +from threading import Lock +from typing import Any + +try: + import fcntl +except ImportError: + fcntl = None + +try: + import msvcrt as _msvcrt +except ImportError: + _msvcrt = None + +import huggingface_hub as hf +import orjson + +from trackio.commit_scheduler import CommitScheduler +from trackio.dummy_commit_scheduler import DummyCommitScheduler +from trackio.utils import ( + MEDIA_DIR, + TRACKIO_DIR, + deserialize_values, + get_color_palette, + on_spaces, + serialize_values, +) + +DB_EXT = ".db" + +_JOURNAL_MODE_WHITELIST = frozenset( + {"wal", "delete", "truncate", "persist", "memory", "off"} +) +_READ_ONLY_QUERY_PREFIXES = ("select", "with", "pragma") +_QUERY_MAX_ROWS = 10_000 +_READ_ONLY_PRAGMAS = frozenset( + {"table_info", "table_xinfo", "index_list", "index_info", "index_xinfo"} +) + + +def _configure_sqlite_pragmas(conn: sqlite3.Connection) -> None: + override = os.environ.get("TRACKIO_SQLITE_JOURNAL_MODE", "").strip().lower() + if override in _JOURNAL_MODE_WHITELIST: + journal = override.upper() + elif on_spaces(): + journal = "DELETE" + else: + journal = "WAL" + conn.execute(f"PRAGMA journal_mode = {journal}") + conn.execute("PRAGMA synchronous = NORMAL") + conn.execute("PRAGMA temp_store = MEMORY") + conn.execute("PRAGMA cache_size = -20000") + if on_spaces(): + conn.execute("PRAGMA locking_mode = EXCLUSIVE") + + +_persistent_connections: dict[str, sqlite3.Connection] = {} +_persistent_lock = Lock() +_db_access_locks: dict[str, Lock] = {} + + +def _get_db_access_lock(db_path: Path) -> Lock: + key = str(db_path) + with _persistent_lock: + if key not in _db_access_locks: + _db_access_locks[key] = Lock() + return _db_access_locks[key] + + +def _get_or_create_persistent_conn( + db_path: Path, timeout: float = 30.0 +) -> sqlite3.Connection: + key = str(db_path) + with _persistent_lock: + conn = _persistent_connections.get(key) + if conn is not None: + try: + conn.execute("SELECT 1") + return conn + except sqlite3.Error: + try: + conn.close() + except sqlite3.Error: + pass + _persistent_connections.pop(key, None) + conn = sqlite3.connect(str(db_path), timeout=timeout, check_same_thread=False) + _configure_sqlite_pragmas(conn) + conn.execute("SELECT 1") + _persistent_connections[key] = conn + return conn + + +def _close_all_persistent_connections() -> None: + with _persistent_lock: + for conn in _persistent_connections.values(): + try: + conn.close() + except sqlite3.Error: + pass + _persistent_connections.clear() + + +atexit.register(_close_all_persistent_connections) + + +class ProcessLock: + """Lock used to coordinate database access. + + Normally uses file-based locking for cross-process coordination. When running + on a bucket-mounted filesystem where file locks are unreliable, + falls back to an in-memory threading Lock (single-process only).""" + + _thread_locks: dict[str, Lock] = {} + _meta_lock = Lock() + + def __init__(self, lockfile_path: Path): + self.lockfile_path = lockfile_path + self.lockfile = None + self._use_thread_lock = on_spaces() + if self._use_thread_lock: + key = str(lockfile_path) + with ProcessLock._meta_lock: + if key not in ProcessLock._thread_locks: + ProcessLock._thread_locks[key] = Lock() + self._thread_lock = ProcessLock._thread_locks[key] + + def __enter__(self): + if self._use_thread_lock: + self._thread_lock.acquire() + return self + if fcntl is None and _msvcrt is None: + return self + self.lockfile_path.parent.mkdir(parents=True, exist_ok=True) + self.lockfile = open(self.lockfile_path, "w") + + max_retries = 100 + for attempt in range(max_retries): + try: + if fcntl is not None: + fcntl.flock(self.lockfile.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB) + else: + _msvcrt.locking(self.lockfile.fileno(), _msvcrt.LK_NBLCK, 1) + return self + except (IOError, OSError): + if attempt < max_retries - 1: + time.sleep(0.1) + else: + raise IOError("Could not acquire database lock after 10 seconds") + + def __exit__(self, exc_type, exc_val, exc_tb): + if self._use_thread_lock: + self._thread_lock.release() + return + if self.lockfile: + try: + if fcntl is not None: + fcntl.flock(self.lockfile.fileno(), fcntl.LOCK_UN) + elif _msvcrt is not None: + _msvcrt.locking(self.lockfile.fileno(), _msvcrt.LK_UNLCK, 1) + except (IOError, OSError): + pass + self.lockfile.close() + + +_LOGS_READ_CACHE: dict[tuple[Any, ...], tuple[int, list[dict[str, Any]]]] = {} +_LOGS_READ_CACHE_LOCK = Lock() +_LOGS_READ_CACHE_MAX_KEYS = 512 +_LOGS_READ_CACHE_MAX_ROWS_PER_ENTRY = 4000 + + +def _spaces_logs_read_cache_enabled() -> bool: + if not on_spaces(): + return False + v = os.environ.get("TRACKIO_DISABLE_LOGS_CACHE", "").strip().lower() + return v not in ("1", "true", "yes") + + +def _sqlite_db_invalidation_mtime_ns(db_path: Path) -> int | None: + try: + m = db_path.stat().st_mtime_ns + except OSError: + return None + wal_path = db_path.with_name(db_path.name + "-wal") + if wal_path.is_file(): + try: + m = max(m, wal_path.stat().st_mtime_ns) + except OSError: + pass + return m + + +def _logs_read_cache_key( + project: str, + run: str | None, + run_id: str | None, + max_points: int | None, +) -> tuple[Any, ...]: + return ( + project, + run or "", + run_id or "", + max_points if max_points is not None else -1, + ) + + +def _logs_read_cache_get( + db_path: Path, key: tuple[Any, ...] +) -> list[dict[str, Any]] | None: + if not _spaces_logs_read_cache_enabled(): + return None + mtime_ns = _sqlite_db_invalidation_mtime_ns(db_path) + if mtime_ns is None: + return None + with _LOGS_READ_CACHE_LOCK: + item = _LOGS_READ_CACHE.get(key) + if item is None: + return None + cached_mtime, logs = item + if cached_mtime != mtime_ns: + del _LOGS_READ_CACHE[key] + return None + return [{**d} for d in logs] + + +def _logs_read_cache_put( + db_path: Path, key: tuple[Any, ...], logs: list[dict[str, Any]] +) -> None: + if not _spaces_logs_read_cache_enabled(): + return + if len(logs) > _LOGS_READ_CACHE_MAX_ROWS_PER_ENTRY: + return + mtime_ns = _sqlite_db_invalidation_mtime_ns(db_path) + if mtime_ns is None: + return + snapshot = [{**d} for d in logs] + with _LOGS_READ_CACHE_LOCK: + while len(_LOGS_READ_CACHE) >= _LOGS_READ_CACHE_MAX_KEYS: + _LOGS_READ_CACHE.pop(next(iter(_LOGS_READ_CACHE))) + _LOGS_READ_CACHE[key] = (mtime_ns, snapshot) + + +_SYSTEM_LOGS_READ_CACHE: dict[tuple[Any, ...], tuple[int, list[dict[str, Any]]]] = {} + + +def _system_logs_read_cache_key( + project: str, + run: str | None, + run_id: str | None, + max_points: int | None = None, +) -> tuple[Any, ...]: + return ( + "system_logs", + project, + run or "", + run_id or "", + max_points if max_points is not None else -1, + ) + + +def _system_logs_read_cache_get( + db_path: Path, key: tuple[Any, ...] +) -> list[dict[str, Any]] | None: + if not _spaces_logs_read_cache_enabled(): + return None + mtime_ns = _sqlite_db_invalidation_mtime_ns(db_path) + if mtime_ns is None: + return None + with _LOGS_READ_CACHE_LOCK: + item = _SYSTEM_LOGS_READ_CACHE.get(key) + if item is None: + return None + cached_mtime, logs = item + if cached_mtime != mtime_ns: + del _SYSTEM_LOGS_READ_CACHE[key] + return None + return [{**d} for d in logs] + + +def _system_logs_read_cache_put( + db_path: Path, key: tuple[Any, ...], logs: list[dict[str, Any]] +) -> None: + if not _spaces_logs_read_cache_enabled(): + return + if len(logs) > _LOGS_READ_CACHE_MAX_ROWS_PER_ENTRY: + return + mtime_ns = _sqlite_db_invalidation_mtime_ns(db_path) + if mtime_ns is None: + return + snapshot = [{**d} for d in logs] + with _LOGS_READ_CACHE_LOCK: + while len(_SYSTEM_LOGS_READ_CACHE) >= _LOGS_READ_CACHE_MAX_KEYS: + _SYSTEM_LOGS_READ_CACHE.pop(next(iter(_SYSTEM_LOGS_READ_CACHE))) + _SYSTEM_LOGS_READ_CACHE[key] = (mtime_ns, snapshot) + + +class SQLiteStorage: + _dataset_import_attempted = False + _current_scheduler: CommitScheduler | DummyCommitScheduler | None = None + _scheduler_lock = Lock() + + @staticmethod + @contextmanager + def _get_connection( + db_path: Path, + *, + timeout: float = 30.0, + configure_pragmas: bool = True, + row_factory=sqlite3.Row, + ) -> Iterator[sqlite3.Connection]: + if on_spaces(): + # On Spaces, all callers share a single persistent connection + # that is pragma-configured at creation time. The `configure_pragmas` + # flag is intentionally ignored here — the pragmas (journal mode, + # synchronous, locking mode) don't affect query semantics. + access_lock = _get_db_access_lock(db_path) + access_lock.acquire() + try: + conn = _get_or_create_persistent_conn(db_path, timeout=timeout) + conn.row_factory = row_factory + with conn: + yield conn + finally: + access_lock.release() + else: + conn = sqlite3.connect(str(db_path), timeout=timeout) + try: + if configure_pragmas: + _configure_sqlite_pragmas(conn) + if row_factory is not None: + conn.row_factory = row_factory + with conn: + yield conn + finally: + conn.close() + + @staticmethod + def _get_process_lock(project: str) -> ProcessLock: + lockfile_path = TRACKIO_DIR / f"{project}.lock" + return ProcessLock(lockfile_path) + + @staticmethod + def get_project_db_filename(project: str) -> str: + """Get the database filename for a specific project.""" + safe_project_name = "".join( + c for c in project if c.isalnum() or c in ("-", "_") + ).rstrip() + if not safe_project_name: + safe_project_name = "default" + return f"{safe_project_name}{DB_EXT}" + + @staticmethod + def get_project_db_path(project: str) -> Path: + """Get the database path for a specific project.""" + filename = SQLiteStorage.get_project_db_filename(project) + return TRACKIO_DIR / filename + + @staticmethod + def init_db(project: str) -> Path: + """ + Initialize the SQLite database with required tables. + Returns the database path. + """ + SQLiteStorage._ensure_hub_loaded() + db_path = SQLiteStorage.get_project_db_path(project) + db_path.parent.mkdir(parents=True, exist_ok=True) + with SQLiteStorage._get_process_lock(project): + with SQLiteStorage._get_connection(db_path, row_factory=None) as conn: + cursor = conn.cursor() + cursor.execute( + """ + CREATE TABLE IF NOT EXISTS metrics ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + run_id TEXT NOT NULL, + timestamp TEXT NOT NULL, + run_name TEXT NOT NULL, + step INTEGER NOT NULL, + metrics TEXT NOT NULL + ) + """ + ) + cursor.execute( + """ + CREATE TABLE IF NOT EXISTS configs ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + run_id TEXT NOT NULL, + run_name TEXT NOT NULL, + config TEXT NOT NULL, + created_at TEXT NOT NULL, + UNIQUE(run_id) + ) + """ + ) + cursor.execute( + """ + CREATE TABLE IF NOT EXISTS system_metrics ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + run_id TEXT NOT NULL, + timestamp TEXT NOT NULL, + run_name TEXT NOT NULL, + metrics TEXT NOT NULL + ) + """ + ) + + cursor.execute( + """ + CREATE TABLE IF NOT EXISTS project_metadata ( + key TEXT PRIMARY KEY, + value TEXT NOT NULL + ) + """ + ) + + cursor.execute( + """ + CREATE TABLE IF NOT EXISTS pending_uploads ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + space_id TEXT NOT NULL, + run_id TEXT, + run_name TEXT, + step INTEGER, + file_path TEXT NOT NULL, + relative_path TEXT, + created_at TEXT NOT NULL + ) + """ + ) + + cursor.execute( + """ + CREATE TABLE IF NOT EXISTS alerts ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + run_id TEXT NOT NULL, + timestamp TEXT NOT NULL, + run_name TEXT NOT NULL, + title TEXT NOT NULL, + text TEXT, + level TEXT NOT NULL DEFAULT 'warn', + step INTEGER, + alert_id TEXT + ) + """ + ) + metrics_cols = SQLiteStorage._table_columns(conn, "metrics") + metrics_run_key = "run_id" if "run_id" in metrics_cols else "run_name" + cursor.execute( + f""" + CREATE INDEX IF NOT EXISTS idx_metrics_run_step + ON metrics({metrics_run_key}, step) + """ + ) + cursor.execute( + f""" + CREATE INDEX IF NOT EXISTS idx_metrics_run_timestamp + ON metrics({metrics_run_key}, timestamp) + """ + ) + cursor.execute( + """ + CREATE INDEX IF NOT EXISTS idx_configs_run_name + ON configs(run_name) + """ + ) + system_cols = SQLiteStorage._table_columns(conn, "system_metrics") + system_run_key = "run_id" if "run_id" in system_cols else "run_name" + cursor.execute( + f""" + CREATE INDEX IF NOT EXISTS idx_system_metrics_run_timestamp + ON system_metrics({system_run_key}, timestamp) + """ + ) + alerts_cols = SQLiteStorage._table_columns(conn, "alerts") + alerts_run_key = "run_id" if "run_id" in alerts_cols else "run_name" + cursor.execute( + f""" + CREATE INDEX IF NOT EXISTS idx_alerts_run + ON alerts({alerts_run_key}) + """ + ) + cursor.execute( + """ + CREATE INDEX IF NOT EXISTS idx_alerts_timestamp + ON alerts(timestamp) + """ + ) + cursor.execute( + """ + CREATE UNIQUE INDEX IF NOT EXISTS idx_alerts_alert_id + ON alerts(alert_id) WHERE alert_id IS NOT NULL + """ + ) + + for table in ("metrics", "system_metrics"): + for col in ("log_id TEXT", "space_id TEXT"): + try: + cursor.execute(f"ALTER TABLE {table} ADD COLUMN {col}") + except sqlite3.OperationalError: + pass + cursor.execute( + f"""CREATE UNIQUE INDEX IF NOT EXISTS idx_{table}_log_id + ON {table}(log_id) WHERE log_id IS NOT NULL""" + ) + cursor.execute( + f"""CREATE INDEX IF NOT EXISTS idx_{table}_pending + ON {table}(space_id) WHERE space_id IS NOT NULL""" + ) + + conn.commit() + return db_path + + @staticmethod + def _require_pyarrow(): + try: + import pyarrow as pa + import pyarrow.parquet as pq + except ImportError as e: + raise ImportError( + "Parquet import/export requires `trackio[spaces]`." + ) from e + return pa, pq + + @staticmethod + def _table_columns(conn: sqlite3.Connection, table: str) -> set[str]: + cursor = conn.cursor() + try: + cursor.execute(f"PRAGMA table_info({table})") + except sqlite3.OperationalError: + return set() + return {row[1] for row in cursor.fetchall()} + + @staticmethod + def _supports_run_ids(conn: sqlite3.Connection, table: str = "metrics") -> bool: + return "run_id" in SQLiteStorage._table_columns(conn, table) + + @staticmethod + def _resolve_run_identity( + conn: sqlite3.Connection, + run_name: str | None = None, + run_id: str | None = None, + *, + table: str = "metrics", + ) -> tuple[str, str] | None: + supports_run_ids = SQLiteStorage._supports_run_ids(conn, table) + if supports_run_ids: + if run_id is not None: + return ("run_id", run_id) + if run_name is None: + return None + source_table = ( + table + if "timestamp" in SQLiteStorage._table_columns(conn, table) + else "metrics" + ) + cursor = conn.cursor() + cursor.execute( + f""" + SELECT run_id + FROM {source_table} + WHERE run_name = ? + GROUP BY run_id + ORDER BY MIN(timestamp) DESC + LIMIT 1 + """, + (run_name,), + ) + row = cursor.fetchone() + if row is None: + return None + return ("run_id", row[0]) + + resolved = run_name if run_name is not None else run_id + if resolved is None: + return None + return ("run_name", resolved) + + @staticmethod + def get_run_records(project: str) -> list[dict[str, str | None]]: + SQLiteStorage._ensure_hub_loaded() + db_path = SQLiteStorage.get_project_db_path(project) + if not db_path.exists(): + return [] + + try: + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + if SQLiteStorage._supports_run_ids(conn): + cursor.execute( + """ + SELECT run_id, run_name, MIN(timestamp) as created_at + FROM metrics + GROUP BY run_id, run_name + ORDER BY created_at ASC + """ + ) + return [ + { + "id": row["run_id"], + "name": row["run_name"], + "created_at": row["created_at"], + } + for row in cursor.fetchall() + ] + + cursor.execute( + """ + SELECT run_name, MIN(timestamp) as created_at + FROM metrics + GROUP BY run_name + ORDER BY created_at ASC + """ + ) + return [ + { + "id": row["run_name"], + "name": row["run_name"], + "created_at": row["created_at"], + } + for row in cursor.fetchall() + ] + except sqlite3.OperationalError as e: + if "no such table: metrics" in str(e): + return [] + raise + + @staticmethod + def get_latest_run_record_by_name( + project: str, run_name: str + ) -> dict[str, str | None] | None: + db_path = SQLiteStorage.get_project_db_path(project) + if not db_path.exists(): + return None + + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + if SQLiteStorage._supports_run_ids(conn): + cursor.execute( + """ + SELECT run_id, run_name, MIN(timestamp) as created_at + FROM metrics + WHERE run_name = ? + GROUP BY run_id, run_name + ORDER BY created_at DESC + LIMIT 1 + """, + (run_name,), + ) + row = cursor.fetchone() + if row is None: + return None + return { + "id": row["run_id"], + "name": row["run_name"], + "created_at": row["created_at"], + } + + cursor.execute( + """ + SELECT run_name, MIN(timestamp) as created_at + FROM metrics + WHERE run_name = ? + GROUP BY run_name + ORDER BY created_at DESC + LIMIT 1 + """, + (run_name,), + ) + row = cursor.fetchone() + if row is None: + return None + return { + "id": row["run_name"], + "name": row["run_name"], + "created_at": row["created_at"], + } + + @staticmethod + def _read_table_rows(db_path: Path, table: str) -> list[dict[str, object]]: + try: + with SQLiteStorage._get_connection( + db_path, timeout=5.0, configure_pragmas=False + ) as conn: + cursor = conn.cursor() + cursor.execute(f"SELECT * FROM {table}") + return [dict(row) for row in cursor.fetchall()] + except Exception: + return [] + + @staticmethod + def _decode_json_blob(value: object) -> dict[str, object]: + if value is None: + return {} + if isinstance(value, memoryview): + value = value.tobytes() + return deserialize_values(orjson.loads(value)) + + @staticmethod + def _flatten_json_rows( + rows: list[dict[str, object]], json_col: str + ) -> list[dict[str, object]]: + flattened_rows = [] + for row in rows: + flat_row = {key: value for key, value in row.items() if key != json_col} + expanded = SQLiteStorage._decode_json_blob(row.get(json_col)) + for key, value in expanded.items(): + if key not in flat_row: + flat_row[key] = value + flattened_rows.append(flat_row) + return flattened_rows + + @staticmethod + def _write_parquet_rows(parquet_path: Path, rows: list[dict[str, object]]) -> None: + if not rows: + return + pa, pq = SQLiteStorage._require_pyarrow() + column_names: list[str] = [] + for row in rows: + for key in row: + if key not in column_names: + column_names.append(key) + normalized_rows = [{key: row.get(key) for key in column_names} for row in rows] + table = pa.Table.from_pylist(normalized_rows) + write_kwargs = { + "write_page_index": True, + "use_content_defined_chunking": True, + } + try: + pq.write_table(table, parquet_path, **write_kwargs) + except TypeError: + pq.write_table(table, parquet_path) + + @staticmethod + def _read_parquet_rows(parquet_path: Path) -> list[dict[str, object]]: + _, pq = SQLiteStorage._require_pyarrow() + return pq.read_table(parquet_path).to_pylist() + + @staticmethod + def _normalize_json_column_value(value: object) -> object: + if value is None: + return orjson.dumps({}) + if isinstance(value, memoryview): + return value.tobytes() + if isinstance(value, (bytes, bytearray, str)): + return value + return orjson.dumps(serialize_values(value)) + + @staticmethod + def _rows_to_sql_table_rows( + rows: list[dict[str, object]], + *, + json_col: str, + structural_cols: list[str], + ) -> list[dict[str, object]]: + sql_rows = [] + for row in rows: + sql_row = {col: row.get(col) for col in structural_cols} + if json_col in row: + sql_row[json_col] = SQLiteStorage._normalize_json_column_value( + row.get(json_col) + ) + else: + payload = { + key: value + for key, value in row.items() + if key not in structural_cols and key != json_col + } + sql_row[json_col] = orjson.dumps(serialize_values(payload)) + sql_rows.append(sql_row) + return sql_rows + + @staticmethod + def _replace_table_rows( + db_path: Path, + table: str, + rows: list[dict[str, object]], + columns: list[str], + ) -> None: + with SQLiteStorage._get_connection( + db_path, configure_pragmas=False, row_factory=None + ) as conn: + cursor = conn.cursor() + cursor.execute(f"DELETE FROM {table}") + if rows: + placeholders = ", ".join(["?"] * len(columns)) + cursor.executemany( + f"INSERT INTO {table} ({', '.join(columns)}) VALUES ({placeholders})", + [[row.get(column) for column in columns] for row in rows], + ) + conn.commit() + + @staticmethod + def _flatten_and_write_parquet( + db_path: Path, table: str, json_col: str, parquet_path: Path + ) -> None: + if ( + parquet_path.exists() + and db_path.stat().st_mtime <= parquet_path.stat().st_mtime + ): + return + rows = SQLiteStorage._read_table_rows(db_path, table) + if not rows: + return + flat_rows = SQLiteStorage._flatten_json_rows(rows, json_col) + SQLiteStorage._write_parquet_rows(parquet_path, flat_rows) + + @staticmethod + def export_to_parquet(): + """ + Exports all projects' DB files as Parquet under the same path but with extension ".parquet". + Also exports system_metrics to separate parquet files with "_system.parquet" suffix. + Also exports configs to separate parquet files with "_configs.parquet" suffix. + """ + if not SQLiteStorage._dataset_import_attempted: + return + if not TRACKIO_DIR.exists(): + return + + all_paths = os.listdir(TRACKIO_DIR) + db_names = [f for f in all_paths if f.endswith(DB_EXT)] + for db_name in db_names: + db_path = TRACKIO_DIR / db_name + SQLiteStorage._flatten_and_write_parquet( + db_path, "metrics", "metrics", db_path.with_suffix(".parquet") + ) + SQLiteStorage._flatten_and_write_parquet( + db_path, + "system_metrics", + "metrics", + TRACKIO_DIR / (db_path.stem + "_system.parquet"), + ) + SQLiteStorage._flatten_and_write_parquet( + db_path, + "configs", + "config", + TRACKIO_DIR / (db_path.stem + "_configs.parquet"), + ) + + @staticmethod + def export_for_static_space( + project: str, output_dir: Path, db_path_override: Path | None = None + ) -> None: + """ + Exports a single project's data as Parquet + JSON files for static Space deployment. + + Args: + project: The project name. + output_dir: Directory to write the exported files to. + db_path_override: If provided, read from this DB file instead of the + default local project path. Useful when exporting from a downloaded + remote database. + """ + db_path = db_path_override or SQLiteStorage.get_project_db_path(project) + if not db_path.exists(): + raise FileNotFoundError(f"No database found for project '{project}'") + + output_dir.mkdir(parents=True, exist_ok=True) + aux_dir = output_dir / "aux" + aux_dir.mkdir(parents=True, exist_ok=True) + + metrics_rows = SQLiteStorage._read_table_rows(db_path, "metrics") + if metrics_rows: + flat = SQLiteStorage._flatten_json_rows(metrics_rows, "metrics") + SQLiteStorage._write_parquet_rows(output_dir / "metrics.parquet", flat) + + sys_rows = SQLiteStorage._read_table_rows(db_path, "system_metrics") + if sys_rows: + flat = SQLiteStorage._flatten_json_rows(sys_rows, "metrics") + SQLiteStorage._write_parquet_rows(aux_dir / "system_metrics.parquet", flat) + + configs_rows = SQLiteStorage._read_table_rows(db_path, "configs") + if configs_rows: + flat = SQLiteStorage._flatten_json_rows(configs_rows, "config") + SQLiteStorage._write_parquet_rows(aux_dir / "configs.parquet", flat) + + try: + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + if SQLiteStorage._supports_run_ids(conn): + cursor.execute( + """SELECT run_id, run_name, MIN(timestamp) as created_at, + MAX(step) as last_step, COUNT(*) as log_count + FROM metrics + GROUP BY run_id, run_name + ORDER BY created_at ASC""" + ) + rows = cursor.fetchall() + runs_meta = [ + { + "id": row["run_id"], + "name": row["run_name"], + "created_at": row["created_at"], + "last_step": row["last_step"], + "log_count": row["log_count"], + } + for row in rows + ] + else: + cursor.execute( + """SELECT run_name, MIN(timestamp) as created_at, + MAX(step) as last_step, COUNT(*) as log_count + FROM metrics GROUP BY run_name ORDER BY created_at ASC""" + ) + rows = cursor.fetchall() + runs_meta = [ + { + "id": row["run_name"], + "name": row["run_name"], + "created_at": row["created_at"], + "last_step": row["last_step"], + "log_count": row["log_count"], + } + for row in rows + ] + except sqlite3.OperationalError: + runs_meta = [] + with open(output_dir / "runs.json", "w") as f: + json_mod.dump(runs_meta, f) + + settings = { + "color_palette": get_color_palette(), + "plot_order": [ + item.strip() + for item in os.environ.get("TRACKIO_PLOT_ORDER", "").split(",") + if item.strip() + ], + } + with open(output_dir / "settings.json", "w") as f: + json_mod.dump(settings, f) + + @staticmethod + def _cleanup_wal_sidecars(db_path: Path) -> None: + """Remove leftover -wal/-shm files for a DB basename (prevents disk I/O errors).""" + for suffix in ("-wal", "-shm"): + sidecar = Path(str(db_path) + suffix) + try: + if sidecar.exists(): + sidecar.unlink() + except Exception: + pass + + @staticmethod + def import_from_parquet(): + """ + Imports to all DB files that have matching files under the same path but with extension ".parquet". + Also imports system_metrics from "_system.parquet" files. + Also imports configs from "_configs.parquet" files. + """ + if not TRACKIO_DIR.exists(): + return + + all_paths = os.listdir(TRACKIO_DIR) + parquet_names = [ + f + for f in all_paths + if f.endswith(".parquet") + and not f.endswith("_system.parquet") + and not f.endswith("_configs.parquet") + ] + imported_projects = {Path(name).stem for name in parquet_names} + for pq_name in parquet_names: + parquet_path = TRACKIO_DIR / pq_name + db_path = parquet_path.with_suffix(DB_EXT) + + SQLiteStorage._cleanup_wal_sidecars(db_path) + + rows = SQLiteStorage._read_parquet_rows(parquet_path) + project = db_path.stem + SQLiteStorage.init_db(project) + metrics_rows = SQLiteStorage._rows_to_sql_table_rows( + rows, + json_col="metrics", + structural_cols=[ + "id", + "run_id", + "timestamp", + "run_name", + "step", + "log_id", + "space_id", + ], + ) + SQLiteStorage._replace_table_rows( + db_path, + "metrics", + metrics_rows, + [ + "id", + "run_id", + "timestamp", + "run_name", + "step", + "metrics", + "log_id", + "space_id", + ], + ) + + system_parquet_names = [f for f in all_paths if f.endswith("_system.parquet")] + for pq_name in system_parquet_names: + parquet_path = TRACKIO_DIR / pq_name + db_name = pq_name.replace("_system.parquet", DB_EXT) + db_path = TRACKIO_DIR / db_name + project_name = db_path.stem + if project_name not in imported_projects and not db_path.exists(): + continue + + rows = SQLiteStorage._read_parquet_rows(parquet_path) + SQLiteStorage.init_db(project_name) + system_rows = SQLiteStorage._rows_to_sql_table_rows( + rows, + json_col="metrics", + structural_cols=[ + "id", + "run_id", + "timestamp", + "run_name", + "log_id", + "space_id", + ], + ) + SQLiteStorage._replace_table_rows( + db_path, + "system_metrics", + system_rows, + [ + "id", + "run_id", + "timestamp", + "run_name", + "metrics", + "log_id", + "space_id", + ], + ) + + configs_parquet_names = [f for f in all_paths if f.endswith("_configs.parquet")] + for pq_name in configs_parquet_names: + parquet_path = TRACKIO_DIR / pq_name + db_name = pq_name.replace("_configs.parquet", DB_EXT) + db_path = TRACKIO_DIR / db_name + project_name = db_path.stem + if project_name not in imported_projects and not db_path.exists(): + continue + + rows = SQLiteStorage._read_parquet_rows(parquet_path) + SQLiteStorage.init_db(project_name) + config_rows = SQLiteStorage._rows_to_sql_table_rows( + rows, + json_col="config", + structural_cols=["id", "run_id", "run_name", "created_at"], + ) + SQLiteStorage._replace_table_rows( + db_path, + "configs", + config_rows, + ["id", "run_id", "run_name", "config", "created_at"], + ) + + @staticmethod + def get_scheduler(): + """ + Get the scheduler for the database based on the environment variables. + This applies to both local and Spaces. + """ + with SQLiteStorage._scheduler_lock: + if SQLiteStorage._current_scheduler is not None: + return SQLiteStorage._current_scheduler + hf_token = os.environ.get("HF_TOKEN") + dataset_id = os.environ.get("TRACKIO_DATASET_ID") + space_repo_name = os.environ.get("SPACE_REPO_NAME") + if dataset_id is not None and space_repo_name is not None: + scheduler = CommitScheduler( + repo_id=dataset_id, + repo_type="dataset", + folder_path=TRACKIO_DIR, + private=True, + allow_patterns=[ + "*.parquet", + "*_system.parquet", + "*_configs.parquet", + "media/**/*", + ], + squash_history=True, + token=hf_token, + on_before_commit=SQLiteStorage.export_to_parquet, + ) + else: + scheduler = DummyCommitScheduler() + SQLiteStorage._current_scheduler = scheduler + return scheduler + + @staticmethod + def log( + project: str, + run: str, + metrics: dict, + step: int | None = None, + run_id: str | None = None, + ): + """ + Safely log metrics to the database. Before logging, this method will ensure the database exists + and is set up with the correct tables. It also uses a cross-process lock to prevent + database locking errors when multiple processes access the same database. + + This method is not used in the latest versions of Trackio (replaced by bulk_log) but + is kept for backwards compatibility for users who are connecting to a newer version of + a Trackio Spaces dashboard with an older version of Trackio installed locally. + """ + db_path = SQLiteStorage.init_db(project) + with SQLiteStorage._get_process_lock(project): + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + supports_run_ids = SQLiteStorage._supports_run_ids(conn) + resolved_run_id = run_id or run + run_col = "run_id" if supports_run_ids else "run_name" + cursor.execute( + f""" + SELECT MAX(step) + FROM metrics + WHERE {run_col} = ? + """, + (resolved_run_id if supports_run_ids else run,), + ) + last_step = cursor.fetchone()[0] + current_step = ( + 0 + if step is None and last_step is None + else (step if step is not None else last_step + 1) + ) + current_timestamp = datetime.now(timezone.utc).isoformat() + if supports_run_ids: + cursor.execute( + """ + INSERT INTO metrics + (timestamp, run_id, run_name, step, metrics) + VALUES (?, ?, ?, ?, ?) + """, + ( + current_timestamp, + resolved_run_id, + run, + current_step, + orjson.dumps(serialize_values(metrics)), + ), + ) + else: + cursor.execute( + """ + INSERT INTO metrics + (timestamp, run_name, step, metrics) + VALUES (?, ?, ?, ?) + """, + ( + current_timestamp, + run, + current_step, + orjson.dumps(serialize_values(metrics)), + ), + ) + conn.commit() + + @staticmethod + def bulk_log( + project: str, + run: str, + metrics_list: list[dict], + steps: list[int] | None = None, + timestamps: list[str] | None = None, + config: dict | None = None, + log_ids: list[str] | None = None, + space_id: str | None = None, + run_id: str | None = None, + ): + """ + Safely log bulk metrics to the database. Before logging, this method will ensure the database exists + and is set up with the correct tables. It also uses a cross-process lock to prevent + database locking errors when multiple processes access the same database. + """ + if not metrics_list: + return + + if timestamps is None: + timestamps = [datetime.now(timezone.utc).isoformat()] * len(metrics_list) + + db_path = SQLiteStorage.init_db(project) + with SQLiteStorage._get_process_lock(project): + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + supports_run_ids = SQLiteStorage._supports_run_ids(conn) + resolved_run_id = run_id or run + + if steps is None: + steps = list(range(len(metrics_list))) + elif any(s is None for s in steps): + run_col = "run_id" if supports_run_ids else "run_name" + cursor.execute( + f"SELECT MAX(step) FROM metrics WHERE {run_col} = ?", + (resolved_run_id if supports_run_ids else run,), + ) + last_step = cursor.fetchone()[0] + current_step = 0 if last_step is None else last_step + 1 + processed_steps = [] + for step in steps: + if step is None: + processed_steps.append(current_step) + current_step += 1 + else: + processed_steps.append(step) + steps = processed_steps + + if len(metrics_list) != len(steps) or len(metrics_list) != len( + timestamps + ): + raise ValueError( + "metrics_list, steps, and timestamps must have the same length" + ) + + data = [] + for i, metrics in enumerate(metrics_list): + lid = log_ids[i] if log_ids else None + if supports_run_ids: + data.append( + ( + timestamps[i], + resolved_run_id, + run, + steps[i], + orjson.dumps(serialize_values(metrics)), + lid, + space_id, + ) + ) + else: + data.append( + ( + timestamps[i], + run, + steps[i], + orjson.dumps(serialize_values(metrics)), + lid, + space_id, + ) + ) + + if supports_run_ids: + cursor.executemany( + """ + INSERT OR IGNORE INTO metrics + (timestamp, run_id, run_name, step, metrics, log_id, space_id) + VALUES (?, ?, ?, ?, ?, ?, ?) + """, + data, + ) + else: + cursor.executemany( + """ + INSERT OR IGNORE INTO metrics + (timestamp, run_name, step, metrics, log_id, space_id) + VALUES (?, ?, ?, ?, ?, ?) + """, + data, + ) + + if config: + current_timestamp = datetime.now(timezone.utc).isoformat() + if "run_id" in SQLiteStorage._table_columns(conn, "configs"): + cursor.execute( + """ + INSERT OR REPLACE INTO configs + (run_id, run_name, config, created_at) + VALUES (?, ?, ?, ?) + """, + ( + resolved_run_id, + run, + orjson.dumps(serialize_values(config)), + current_timestamp, + ), + ) + else: + cursor.execute( + """ + INSERT OR REPLACE INTO configs + (run_name, config, created_at) + VALUES (?, ?, ?) + """, + ( + run, + orjson.dumps(serialize_values(config)), + current_timestamp, + ), + ) + + conn.commit() + + @staticmethod + def bulk_log_system( + project: str, + run: str, + metrics_list: list[dict], + timestamps: list[str] | None = None, + log_ids: list[str] | None = None, + space_id: str | None = None, + run_id: str | None = None, + ): + """ + Log system metrics (GPU, etc.) to the database without step numbers. + These metrics use timestamps for the x-axis instead of steps. + """ + if not metrics_list: + return + + if timestamps is None: + timestamps = [datetime.now(timezone.utc).isoformat()] * len(metrics_list) + + if len(metrics_list) != len(timestamps): + raise ValueError("metrics_list and timestamps must have the same length") + + db_path = SQLiteStorage.init_db(project) + with SQLiteStorage._get_process_lock(project): + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + supports_run_ids = SQLiteStorage._supports_run_ids( + conn, "system_metrics" + ) + resolved_run_id = run_id or run + data = [] + for i, metrics in enumerate(metrics_list): + lid = log_ids[i] if log_ids else None + if supports_run_ids: + data.append( + ( + timestamps[i], + resolved_run_id, + run, + orjson.dumps(serialize_values(metrics)), + lid, + space_id, + ) + ) + else: + data.append( + ( + timestamps[i], + run, + orjson.dumps(serialize_values(metrics)), + lid, + space_id, + ) + ) + + if supports_run_ids: + cursor.executemany( + """ + INSERT OR IGNORE INTO system_metrics + (timestamp, run_id, run_name, metrics, log_id, space_id) + VALUES (?, ?, ?, ?, ?, ?) + """, + data, + ) + else: + cursor.executemany( + """ + INSERT OR IGNORE INTO system_metrics + (timestamp, run_name, metrics, log_id, space_id) + VALUES (?, ?, ?, ?, ?) + """, + data, + ) + conn.commit() + + @staticmethod + def bulk_alert( + project: str, + run: str, + titles: list[str], + texts: list[str | None], + levels: list[str], + steps: list[int | None], + timestamps: list[str] | None = None, + alert_ids: list[str] | None = None, + run_id: str | None = None, + ): + if not titles: + return + + if timestamps is None: + timestamps = [datetime.now(timezone.utc).isoformat()] * len(titles) + + db_path = SQLiteStorage.init_db(project) + with SQLiteStorage._get_process_lock(project): + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + supports_run_ids = SQLiteStorage._supports_run_ids(conn, "alerts") + resolved_run_id = run_id or run + data = [] + for i in range(len(titles)): + aid = alert_ids[i] if alert_ids else None + if supports_run_ids: + data.append( + ( + resolved_run_id, + timestamps[i], + run, + titles[i], + texts[i], + levels[i], + steps[i], + aid, + ) + ) + else: + data.append( + ( + timestamps[i], + run, + titles[i], + texts[i], + levels[i], + steps[i], + aid, + ) + ) + + if supports_run_ids: + cursor.executemany( + """ + INSERT OR IGNORE INTO alerts + (run_id, timestamp, run_name, title, text, level, step, alert_id) + VALUES (?, ?, ?, ?, ?, ?, ?, ?) + """, + data, + ) + else: + cursor.executemany( + """ + INSERT OR IGNORE INTO alerts + (timestamp, run_name, title, text, level, step, alert_id) + VALUES (?, ?, ?, ?, ?, ?, ?) + """, + data, + ) + conn.commit() + + @staticmethod + def get_alerts( + project: str, + run_name: str | None = None, + run_id: str | None = None, + level: str | None = None, + since: str | None = None, + ) -> list[dict]: + db_path = SQLiteStorage.get_project_db_path(project) + if not db_path.exists(): + return [] + + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + try: + query = ( + "SELECT timestamp, run_name, title, text, level, step FROM alerts" + ) + conditions = [] + params = [] + run_identity = SQLiteStorage._resolve_run_identity( + conn, run_name=run_name, run_id=run_id, table="alerts" + ) + if run_identity is not None: + conditions.append(f"{run_identity[0]} = ?") + params.append(run_identity[1]) + elif run_name is not None or run_id is not None: + return [] + if level is not None: + conditions.append("level = ?") + params.append(level) + if since is not None: + conditions.append("timestamp > ?") + params.append(since) + if conditions: + query += " WHERE " + " AND ".join(conditions) + query += " ORDER BY timestamp DESC" + cursor.execute(query, params) + + rows = cursor.fetchall() + return [ + { + "timestamp": row["timestamp"], + "run": row["run_name"], + "title": row["title"], + "text": row["text"], + "level": row["level"], + "step": row["step"], + } + for row in rows + ] + except sqlite3.OperationalError as e: + if "no such table: alerts" in str(e): + return [] + raise + + @staticmethod + def get_alert_count(project: str) -> int: + db_path = SQLiteStorage.get_project_db_path(project) + if not db_path.exists(): + return 0 + + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + try: + cursor.execute("SELECT COUNT(*) FROM alerts") + return cursor.fetchone()[0] + except sqlite3.OperationalError: + return 0 + + @staticmethod + def _fetch_system_logs_with_cursor( + cursor: sqlite3.Cursor, + run_identity: tuple[str, Any], + max_points: int | None = None, + ) -> list[dict[str, Any]]: + cursor.execute( + f""" + SELECT timestamp, metrics + FROM system_metrics + WHERE {run_identity[0]} = ? + ORDER BY timestamp + """, + (run_identity[1],), + ) + rows = cursor.fetchall() + rows = SQLiteStorage._subsample_metric_rows(rows, max_points) + results = [] + for row in rows: + metrics = orjson.loads(row["metrics"]) + metrics = deserialize_values(metrics) + metrics["timestamp"] = row["timestamp"] + results.append(metrics) + return results + + @staticmethod + def get_system_logs( + project: str, + run: str | None = None, + run_id: str | None = None, + max_points: int | None = None, + ) -> list[dict]: + """Retrieve system metrics for a specific run. Returns metrics with timestamps (no steps).""" + db_path = SQLiteStorage.get_project_db_path(project) + if not db_path.exists(): + return [] + + cache_key = _system_logs_read_cache_key(project, run, run_id, max_points) + cached = _system_logs_read_cache_get(db_path, cache_key) + if cached is not None: + return cached + + try: + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + run_identity = SQLiteStorage._resolve_run_identity( + conn, run_name=run, run_id=run_id, table="system_metrics" + ) + if run_identity is None: + logs: list[dict[str, Any]] = [] + else: + logs = SQLiteStorage._fetch_system_logs_with_cursor( + cursor, run_identity, max_points + ) + except sqlite3.OperationalError as e: + if "no such table: system_metrics" in str(e): + return [] + raise + + _system_logs_read_cache_put(db_path, cache_key, logs) + return [{**d} for d in logs] + + @staticmethod + def get_system_logs_batch( + project: str, + runs: list[dict[str, Any]] | None = None, + max_points: int | None = None, + ) -> list[dict[str, Any]]: + if not runs: + return [] + db_path = SQLiteStorage.get_project_db_path(project) + if not db_path.exists(): + return [ + { + "run": r.get("run"), + "run_id": r.get("run_id"), + "logs": [], + } + for r in runs + ] + + out: list[dict[str, Any]] = [] + try: + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + for r in runs: + run = r.get("run") + run_id = r.get("run_id") + cache_key = _system_logs_read_cache_key( + project, run, run_id, max_points + ) + cached = _system_logs_read_cache_get(db_path, cache_key) + if cached is not None: + out.append( + { + "run": run, + "run_id": run_id, + "logs": cached, + } + ) + continue + run_identity = SQLiteStorage._resolve_run_identity( + conn, run_name=run, run_id=run_id, table="system_metrics" + ) + if run_identity is None: + logs = [] + else: + logs = SQLiteStorage._fetch_system_logs_with_cursor( + cursor, run_identity, max_points + ) + _system_logs_read_cache_put(db_path, cache_key, logs) + out.append( + { + "run": run, + "run_id": run_id, + "logs": [{**d} for d in logs], + } + ) + except sqlite3.OperationalError as e: + if "no such table: system_metrics" in str(e): + return [ + { + "run": r.get("run"), + "run_id": r.get("run_id"), + "logs": [], + } + for r in runs + ] + raise + + return out + + @staticmethod + def get_all_system_metrics_for_run( + project: str, run: str | None = None, run_id: str | None = None + ) -> list[str]: + """Get all system metric names for a specific project/run.""" + return SQLiteStorage._get_metric_names( + project, + run, + "system_metrics", + exclude_keys={"timestamp"}, + run_id=run_id, + ) + + @staticmethod + def has_system_metrics(project: str) -> bool: + """Check if a project has any system metrics logged.""" + db_path = SQLiteStorage.get_project_db_path(project) + if not db_path.exists(): + return False + + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + try: + cursor.execute("SELECT COUNT(*) FROM system_metrics LIMIT 1") + count = cursor.fetchone()[0] + return count > 0 + except sqlite3.OperationalError: + return False + + @staticmethod + def get_log_count( + project: str, run: str | None = None, run_id: str | None = None + ) -> int: + SQLiteStorage._ensure_hub_loaded() + db_path = SQLiteStorage.get_project_db_path(project) + if not db_path.exists(): + return 0 + try: + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + run_identity = SQLiteStorage._resolve_run_identity( + conn, run_name=run, run_id=run_id + ) + if run_identity is None: + return 0 + cursor.execute( + f"SELECT COUNT(*) FROM metrics WHERE {run_identity[0]} = ?", + (run_identity[1],), + ) + return cursor.fetchone()[0] + except sqlite3.OperationalError as e: + if "no such table: metrics" in str(e): + return 0 + raise + + @staticmethod + def get_last_step( + project: str, run: str | None = None, run_id: str | None = None + ) -> int | None: + db_path = SQLiteStorage.get_project_db_path(project) + if not db_path.exists(): + return None + try: + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + run_identity = SQLiteStorage._resolve_run_identity( + conn, run_name=run, run_id=run_id + ) + if run_identity is None: + return None + cursor.execute( + f"SELECT MAX(step) FROM metrics WHERE {run_identity[0]} = ?", + (run_identity[1],), + ) + row = cursor.fetchone() + return row[0] if row and row[0] is not None else None + except sqlite3.OperationalError as e: + if "no such table: metrics" in str(e): + return None + raise + + @staticmethod + def _subsample_metric_rows(rows: list[Any], max_points: int | None) -> list[Any]: + if max_points is None or max_points < 1: + return rows + if len(rows) <= max_points: + return rows + step = len(rows) / max_points + indices = {int(i * step) for i in range(max_points)} + indices.add(len(rows) - 1) + return [rows[i] for i in sorted(indices)] + + @staticmethod + def _metric_rows_to_log_dicts(rows: list[Any]) -> list[dict[str, Any]]: + results = [] + for row in rows: + metrics = orjson.loads(row["metrics"]) + metrics = deserialize_values(metrics) + metrics["timestamp"] = row["timestamp"] + metrics["step"] = row["step"] + results.append(metrics) + return results + + @staticmethod + def _fetch_metric_logs_with_cursor( + cursor: sqlite3.Cursor, + run_identity: tuple[str, Any], + max_points: int | None, + ) -> list[dict[str, Any]]: + cursor.execute( + f""" + SELECT timestamp, step, metrics + FROM metrics + WHERE {run_identity[0]} = ? + ORDER BY timestamp + """, + (run_identity[1],), + ) + rows = cursor.fetchall() + rows = SQLiteStorage._subsample_metric_rows(rows, max_points) + return SQLiteStorage._metric_rows_to_log_dicts(rows) + + @staticmethod + def get_logs( + project: str, + run: str | None = None, + max_points: int | None = None, + run_id: str | None = None, + ) -> list[dict]: + """Retrieve logs for a specific run. Logs include the step count (int) and the timestamp (datetime object).""" + db_path = SQLiteStorage.get_project_db_path(project) + if not db_path.exists(): + return [] + + cache_key = _logs_read_cache_key(project, run, run_id, max_points) + cached = _logs_read_cache_get(db_path, cache_key) + if cached is not None: + return cached + + try: + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + run_identity = SQLiteStorage._resolve_run_identity( + conn, run_name=run, run_id=run_id + ) + if run_identity is None: + logs: list[dict[str, Any]] = [] + else: + logs = SQLiteStorage._fetch_metric_logs_with_cursor( + cursor, run_identity, max_points + ) + except sqlite3.OperationalError as e: + if "no such table: metrics" in str(e): + return [] + raise + + _logs_read_cache_put(db_path, cache_key, logs) + return [{**d} for d in logs] + + @staticmethod + def get_logs_batch( + project: str, + runs: list[dict[str, Any]] | None = None, + max_points: int | None = None, + ) -> list[dict[str, Any]]: + if not runs: + return [] + db_path = SQLiteStorage.get_project_db_path(project) + if not db_path.exists(): + return [ + { + "run": r.get("run"), + "run_id": r.get("run_id"), + "logs": [], + } + for r in runs + ] + + out: list[dict[str, Any]] = [] + try: + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + for r in runs: + run = r.get("run") + run_id = r.get("run_id") + cache_key = _logs_read_cache_key(project, run, run_id, max_points) + cached = _logs_read_cache_get(db_path, cache_key) + if cached is not None: + out.append( + { + "run": run, + "run_id": run_id, + "logs": cached, + } + ) + continue + run_identity = SQLiteStorage._resolve_run_identity( + conn, run_name=run, run_id=run_id + ) + if run_identity is None: + logs = [] + else: + logs = SQLiteStorage._fetch_metric_logs_with_cursor( + cursor, run_identity, max_points + ) + _logs_read_cache_put(db_path, cache_key, logs) + out.append( + { + "run": run, + "run_id": run_id, + "logs": [{**d} for d in logs], + } + ) + except sqlite3.OperationalError as e: + if "no such table: metrics" in str(e): + return [ + { + "run": r.get("run"), + "run_id": r.get("run_id"), + "logs": [], + } + for r in runs + ] + raise + + return out + + @staticmethod + def load_from_dataset(): + bucket_id = os.environ.get("TRACKIO_BUCKET_ID") + if bucket_id is not None: + if not SQLiteStorage._dataset_import_attempted: + from trackio.bucket_storage import download_bucket_to_trackio_dir + + try: + download_bucket_to_trackio_dir(bucket_id) + except Exception: + pass + SQLiteStorage._dataset_import_attempted = True + return + dataset_id = os.environ.get("TRACKIO_DATASET_ID") + space_repo_name = os.environ.get("SPACE_REPO_NAME") + if dataset_id is not None and space_repo_name is not None: + hfapi = hf.HfApi() + updated = False + if not TRACKIO_DIR.exists(): + TRACKIO_DIR.mkdir(parents=True, exist_ok=True) + with SQLiteStorage.get_scheduler().lock: + try: + files = hfapi.list_repo_files(dataset_id, repo_type="dataset") + for file in files: + # Download parquet and media assets + if not (file.endswith(".parquet") or file.startswith("media/")): + continue + if (TRACKIO_DIR / file).exists(): + continue + hf.hf_hub_download( + dataset_id, file, repo_type="dataset", local_dir=TRACKIO_DIR + ) + updated = True + except hf.errors.EntryNotFoundError: + pass + except hf.errors.RepositoryNotFoundError: + pass + if updated: + SQLiteStorage.import_from_parquet() + SQLiteStorage._dataset_import_attempted = True + + @staticmethod + def _ensure_hub_loaded(): + if not SQLiteStorage._dataset_import_attempted: + SQLiteStorage.load_from_dataset() + + @staticmethod + def get_projects() -> list[str]: + """ + Get list of all projects by scanning the database files in the trackio directory. + """ + SQLiteStorage._ensure_hub_loaded() + + projects: set[str] = set() + if not TRACKIO_DIR.exists(): + return [] + + for db_file in TRACKIO_DIR.glob(f"*{DB_EXT}"): + project_name = db_file.stem + projects.add(project_name) + return sorted(projects) + + @staticmethod + def get_runs(project: str) -> list[str]: + """Get list of all runs for a project, ordered by creation time.""" + return [record["name"] for record in SQLiteStorage.get_run_records(project)] + + @staticmethod + def _validate_read_only_query(query: str) -> str: + normalized = query.strip().rstrip(";").strip() + if not normalized: + raise ValueError("Query cannot be empty.") + if not normalized.lower().startswith(_READ_ONLY_QUERY_PREFIXES): + raise ValueError( + "Only read-only SELECT, WITH, and safe PRAGMA queries are supported." + ) + return normalized + + @staticmethod + def _query_authorizer( + action_code: int, + arg1: str | None, + arg2: str | None, + db_name: str | None, + source: str | None, + ) -> int: + del arg2, db_name, source + if action_code in { + sqlite3.SQLITE_SELECT, + sqlite3.SQLITE_READ, + sqlite3.SQLITE_FUNCTION, + }: + return sqlite3.SQLITE_OK + pragma_code = getattr(sqlite3, "SQLITE_PRAGMA", None) + if action_code == pragma_code: + pragma_name = (arg1 or "").lower() + if pragma_name in _READ_ONLY_PRAGMAS: + return sqlite3.SQLITE_OK + return sqlite3.SQLITE_DENY + + @staticmethod + def _normalize_query_value(value: Any) -> Any: + if isinstance(value, (bytes, bytearray, memoryview)): + return bytes(value).hex() + return value + + @staticmethod + def query_project( + project: str, query: str, max_rows: int = _QUERY_MAX_ROWS + ) -> dict[str, Any]: + SQLiteStorage._ensure_hub_loaded() + db_path = SQLiteStorage.get_project_db_path(project) + if not db_path.exists(): + raise FileNotFoundError(f"Project '{project}' not found.") + + normalized_query = SQLiteStorage._validate_read_only_query(query) + with SQLiteStorage._get_connection(db_path) as conn: + conn.set_authorizer(SQLiteStorage._query_authorizer) + try: + cursor = conn.cursor() + cursor.execute(normalized_query) + description = cursor.description or [] + columns = [column[0] for column in description] + fetched = cursor.fetchmany(max_rows + 1) + if len(fetched) > max_rows: + raise ValueError( + f"Query returned more than {max_rows} rows. " + "Refine the query or add a LIMIT clause." + ) + rows = [ + { + column: SQLiteStorage._normalize_query_value(row[column]) + for column in columns + } + for row in fetched + ] + except sqlite3.DatabaseError as e: + raise ValueError(str(e)) from e + finally: + conn.set_authorizer(None) + + return { + "project": project, + "query": normalized_query, + "columns": columns, + "rows": rows, + "row_count": len(rows), + } + + @staticmethod + def get_max_steps_for_runs(project: str) -> dict[str, int]: + """Get the maximum step for each run in a project.""" + db_path = SQLiteStorage.get_project_db_path(project) + if not db_path.exists(): + return {} + + try: + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + if SQLiteStorage._supports_run_ids(conn): + cursor.execute( + """ + SELECT run_name, run_id, MAX(step) as max_step + FROM metrics + GROUP BY run_id, run_name + """ + ) + results = {} + for row in cursor.fetchall(): + results[row["run_id"]] = row["max_step"] + return results + + cursor.execute( + """ + SELECT run_name, MAX(step) as max_step + FROM metrics + GROUP BY run_name + """ + ) + + results = {} + for row in cursor.fetchall(): + results[row["run_name"]] = row["max_step"] + + return results + except sqlite3.OperationalError as e: + if "no such table: metrics" in str(e): + return {} + raise + + @staticmethod + def get_max_step_for_run( + project: str, run: str | None = None, run_id: str | None = None + ) -> int | None: + """Get the maximum step for a specific run, or None if no logs exist.""" + db_path = SQLiteStorage.get_project_db_path(project) + if not db_path.exists(): + return None + + try: + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + run_identity = SQLiteStorage._resolve_run_identity( + conn, run_name=run, run_id=run_id + ) + if run_identity is None: + return None + cursor.execute( + f"SELECT MAX(step) FROM metrics WHERE {run_identity[0]} = ?", + (run_identity[1],), + ) + result = cursor.fetchone()[0] + return result + except sqlite3.OperationalError as e: + if "no such table: metrics" in str(e): + return None + raise + + @staticmethod + def get_run_config( + project: str, run: str | None = None, run_id: str | None = None + ) -> dict | None: + """Get configuration for a specific run.""" + db_path = SQLiteStorage.get_project_db_path(project) + if not db_path.exists(): + return None + + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + try: + run_identity = SQLiteStorage._resolve_run_identity( + conn, run_name=run, run_id=run_id, table="metrics" + ) + if run_identity is None: + return None + config_col = ( + "run_id" + if "run_id" in SQLiteStorage._table_columns(conn, "configs") + else "run_name" + ) + cursor.execute( + f""" + SELECT config FROM configs WHERE {config_col} = ? + """, + (run_identity[1],), + ) + + row = cursor.fetchone() + if row: + config = orjson.loads(row["config"]) + return deserialize_values(config) + return None + except sqlite3.OperationalError as e: + if "no such table: configs" in str(e): + return None + raise + + @staticmethod + def delete_run(project: str, run: str, run_id: str | None = None) -> bool: + """Delete a run from the database (metrics, config, and system_metrics).""" + db_path = SQLiteStorage.get_project_db_path(project) + if not db_path.exists(): + return False + + with SQLiteStorage._get_process_lock(project): + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + try: + run_identity = SQLiteStorage._resolve_run_identity( + conn, run_name=run, run_id=run_id + ) + if run_identity is None: + return False + cursor.execute( + f"DELETE FROM metrics WHERE {run_identity[0]} = ?", + (run_identity[1],), + ) + config_identity = SQLiteStorage._resolve_run_identity( + conn, run_name=run, run_id=run_id, table="configs" + ) + if config_identity is not None: + cursor.execute( + f"DELETE FROM configs WHERE {config_identity[0]} = ?", + (config_identity[1],), + ) + try: + cursor.execute( + f"DELETE FROM system_metrics WHERE {run_identity[0]} = ?", + (run_identity[1],), + ) + except sqlite3.OperationalError: + pass + try: + cursor.execute( + f"DELETE FROM alerts WHERE {run_identity[0]} = ?", + (run_identity[1],), + ) + except sqlite3.OperationalError: + pass + conn.commit() + return True + except sqlite3.Error: + return False + + @staticmethod + def _update_media_paths(obj, old_prefix, new_prefix): + """Update media file paths in nested data structures.""" + if isinstance(obj, dict): + if obj.get("_type") in [ + "trackio.image", + "trackio.video", + "trackio.audio", + ]: + old_path = obj.get("file_path", "") + if isinstance(old_path, str): + normalized_path = old_path.replace("\\", "/") + if normalized_path.startswith(old_prefix): + new_path = normalized_path.replace(old_prefix, new_prefix, 1) + return {**obj, "file_path": new_path} + return { + key: SQLiteStorage._update_media_paths(value, old_prefix, new_prefix) + for key, value in obj.items() + } + elif isinstance(obj, list): + return [ + SQLiteStorage._update_media_paths(item, old_prefix, new_prefix) + for item in obj + ] + return obj + + @staticmethod + def _rewrite_metrics_rows( + metrics_rows, new_run_name, old_prefix, new_prefix, include_run_id=False + ): + """Deserialize metrics rows, update media paths, and reserialize.""" + result = [] + for row in metrics_rows: + metrics_data = orjson.loads(row["metrics"]) + metrics_deserialized = deserialize_values(metrics_data) + updated = SQLiteStorage._update_media_paths( + metrics_deserialized, old_prefix, new_prefix + ) + values = ( + row["timestamp"], + new_run_name, + row["step"], + orjson.dumps(serialize_values(updated)), + ) + if include_run_id: + values = values + (row["run_id"],) + result.append(values) + return result + + @staticmethod + def _move_media_dir(source: Path, target: Path): + """Move a media directory from source to target.""" + if source.exists(): + target.parent.mkdir(parents=True, exist_ok=True) + if target.exists(): + shutil.rmtree(target) + shutil.move(str(source), str(target)) + + @staticmethod + def rename_run( + project: str, old_name: str, new_name: str, run_id: str | None = None + ) -> None: + """Rename a run within the same project. + + Raises: + ValueError: If the new name is empty, the old run doesn't exist, + or a run with the new name already exists. + RuntimeError: If the database operation fails. + """ + if not new_name or not new_name.strip(): + raise ValueError("New run name cannot be empty") + + new_name = new_name.strip() + + db_path = SQLiteStorage.get_project_db_path(project) + if not db_path.exists(): + raise ValueError(f"Project '{project}' does not exist") + + with SQLiteStorage._get_process_lock(project): + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + supports_run_ids = SQLiteStorage._supports_run_ids(conn) + run_identity = SQLiteStorage._resolve_run_identity( + conn, run_name=old_name, run_id=run_id + ) + if run_identity is None: + raise ValueError( + f"Run '{old_name}' does not exist in project '{project}'" + ) + + run_col, run_value = run_identity + + if not supports_run_ids: + cursor.execute( + "SELECT COUNT(*) FROM metrics WHERE run_name = ?", (new_name,) + ) + if cursor.fetchone()[0] > 0: + raise ValueError( + f"A run named '{new_name}' already exists in project '{project}'" + ) + + try: + select_cols = ( + "run_id, timestamp, step, metrics" + if supports_run_ids + else "timestamp, step, metrics" + ) + cursor.execute( + f"SELECT {select_cols} FROM metrics WHERE {run_col} = ?", + (run_value,), + ) + metrics_rows = cursor.fetchall() + + old_prefix = f"{project}/{old_name}/" + new_prefix = f"{project}/{new_name}/" + + updated_rows = [] + for row in metrics_rows: + metrics_data = orjson.loads(row["metrics"]) + metrics_deserialized = deserialize_values(metrics_data) + updated = SQLiteStorage._update_media_paths( + metrics_deserialized, old_prefix, new_prefix + ) + if supports_run_ids: + updated_rows.append( + ( + row["run_id"], + row["timestamp"], + new_name, + row["step"], + orjson.dumps(serialize_values(updated)), + ) + ) + else: + updated_rows.append( + ( + row["timestamp"], + new_name, + row["step"], + orjson.dumps(serialize_values(updated)), + ) + ) + + cursor.execute( + f"DELETE FROM metrics WHERE {run_col} = ?", (run_value,) + ) + if supports_run_ids: + cursor.executemany( + "INSERT INTO metrics (run_id, timestamp, run_name, step, metrics) VALUES (?, ?, ?, ?, ?)", + updated_rows, + ) + else: + cursor.executemany( + "INSERT INTO metrics (timestamp, run_name, step, metrics) VALUES (?, ?, ?, ?)", + updated_rows, + ) + + config_col = ( + "run_id" + if "run_id" in SQLiteStorage._table_columns(conn, "configs") + else "run_name" + ) + cursor.execute( + f"UPDATE configs SET run_name = ? WHERE {config_col} = ?", + (new_name, run_value), + ) + + try: + cursor.execute( + f"UPDATE system_metrics SET run_name = ? WHERE {run_col} = ?", + (new_name, run_value), + ) + except sqlite3.OperationalError: + pass + + try: + cursor.execute( + f"UPDATE alerts SET run_name = ? WHERE {run_col} = ?", + (new_name, run_value), + ) + except sqlite3.OperationalError: + pass + + conn.commit() + + SQLiteStorage._move_media_dir( + MEDIA_DIR / project / old_name, + MEDIA_DIR / project / new_name, + ) + except sqlite3.Error as e: + raise RuntimeError( + f"Database error while renaming run '{old_name}' to '{new_name}': {e}" + ) from e + + @staticmethod + def move_run(project: str, run: str, new_project: str) -> bool: + """Move a run from one project to another.""" + source_db_path = SQLiteStorage.get_project_db_path(project) + if not source_db_path.exists(): + return False + + target_db_path = SQLiteStorage.init_db(new_project) + + with SQLiteStorage._get_process_lock(project): + with SQLiteStorage._get_process_lock(new_project): + with SQLiteStorage._get_connection(source_db_path) as source_conn: + source_cursor = source_conn.cursor() + + metrics_has_run_id = SQLiteStorage._supports_run_ids( + source_conn, "metrics" + ) + configs_has_run_id = SQLiteStorage._supports_run_ids( + source_conn, "configs" + ) + system_has_run_id = SQLiteStorage._supports_run_ids( + source_conn, "system_metrics" + ) + alerts_has_run_id = SQLiteStorage._supports_run_ids( + source_conn, "alerts" + ) + + metrics_select = ( + "SELECT timestamp, step, metrics" + + (", run_id" if metrics_has_run_id else "") + + " FROM metrics WHERE run_name = ?" + ) + source_cursor.execute(metrics_select, (run,)) + metrics_rows = source_cursor.fetchall() + + configs_select = ( + "SELECT config, created_at" + + (", run_id" if configs_has_run_id else "") + + " FROM configs WHERE run_name = ?" + ) + source_cursor.execute(configs_select, (run,)) + config_row = source_cursor.fetchone() + + try: + system_select = ( + "SELECT timestamp, metrics" + + (", run_id" if system_has_run_id else "") + + " FROM system_metrics WHERE run_name = ?" + ) + source_cursor.execute(system_select, (run,)) + system_metrics_rows = source_cursor.fetchall() + except sqlite3.OperationalError: + system_metrics_rows = [] + + try: + alerts_select = ( + "SELECT timestamp, title, text, level, step, alert_id" + + (", run_id" if alerts_has_run_id else "") + + " FROM alerts WHERE run_name = ?" + ) + source_cursor.execute(alerts_select, (run,)) + alert_rows = source_cursor.fetchall() + except sqlite3.OperationalError: + alert_rows = [] + + if not metrics_rows and not config_row and not system_metrics_rows: + return False + + with SQLiteStorage._get_connection(target_db_path) as target_conn: + target_cursor = target_conn.cursor() + + old_prefix = f"{project}/{run}/" + new_prefix = f"{new_project}/{run}/" + target_metrics_run_id = SQLiteStorage._supports_run_ids( + target_conn, "metrics" + ) + target_configs_run_id = SQLiteStorage._supports_run_ids( + target_conn, "configs" + ) + target_system_run_id = SQLiteStorage._supports_run_ids( + target_conn, "system_metrics" + ) + target_alerts_run_id = SQLiteStorage._supports_run_ids( + target_conn, "alerts" + ) + + needs_generated_run_id = ( + target_metrics_run_id + or target_configs_run_id + or target_system_run_id + or target_alerts_run_id + ) and not ( + metrics_has_run_id + or configs_has_run_id + or system_has_run_id + or alerts_has_run_id + ) + generated_run_id = ( + uuid.uuid4().hex if needs_generated_run_id else None + ) + + use_metrics_run_id = ( + metrics_has_run_id and target_metrics_run_id + ) + updated_rows = SQLiteStorage._rewrite_metrics_rows( + metrics_rows, + run, + old_prefix, + new_prefix, + include_run_id=use_metrics_run_id, + ) + + if use_metrics_run_id: + target_cursor.executemany( + "INSERT INTO metrics (timestamp, run_name, step, metrics, run_id) VALUES (?, ?, ?, ?, ?)", + updated_rows, + ) + elif target_metrics_run_id and generated_run_id is not None: + target_cursor.executemany( + "INSERT INTO metrics (timestamp, run_name, step, metrics, run_id) VALUES (?, ?, ?, ?, ?)", + [row + (generated_run_id,) for row in updated_rows], + ) + else: + target_cursor.executemany( + "INSERT INTO metrics (timestamp, run_name, step, metrics) VALUES (?, ?, ?, ?)", + updated_rows, + ) + + if config_row: + if ( + configs_has_run_id + and target_configs_run_id + and "run_id" in config_row.keys() + ): + target_cursor.execute( + """ + INSERT OR REPLACE INTO configs (run_name, config, created_at, run_id) + VALUES (?, ?, ?, ?) + """, + ( + run, + config_row["config"], + config_row["created_at"], + config_row["run_id"], + ), + ) + elif target_configs_run_id and generated_run_id is not None: + target_cursor.execute( + """ + INSERT OR REPLACE INTO configs (run_name, config, created_at, run_id) + VALUES (?, ?, ?, ?) + """, + ( + run, + config_row["config"], + config_row["created_at"], + generated_run_id, + ), + ) + else: + target_cursor.execute( + """ + INSERT OR REPLACE INTO configs (run_name, config, created_at) + VALUES (?, ?, ?) + """, + ( + run, + config_row["config"], + config_row["created_at"], + ), + ) + + for row in system_metrics_rows: + try: + if ( + system_has_run_id + and target_system_run_id + and "run_id" in row.keys() + ): + target_cursor.execute( + """ + INSERT INTO system_metrics (timestamp, run_name, metrics, run_id) + VALUES (?, ?, ?, ?) + """, + ( + row["timestamp"], + run, + row["metrics"], + row["run_id"], + ), + ) + elif ( + target_system_run_id + and generated_run_id is not None + ): + target_cursor.execute( + """ + INSERT INTO system_metrics (timestamp, run_name, metrics, run_id) + VALUES (?, ?, ?, ?) + """, + ( + row["timestamp"], + run, + row["metrics"], + generated_run_id, + ), + ) + else: + target_cursor.execute( + """ + INSERT INTO system_metrics (timestamp, run_name, metrics) + VALUES (?, ?, ?) + """, + (row["timestamp"], run, row["metrics"]), + ) + except sqlite3.OperationalError: + pass + + for row in alert_rows: + try: + if ( + alerts_has_run_id + and target_alerts_run_id + and "run_id" in row.keys() + ): + target_cursor.execute( + """ + INSERT OR IGNORE INTO alerts (timestamp, run_name, title, text, level, step, alert_id, run_id) + VALUES (?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + row["timestamp"], + run, + row["title"], + row["text"], + row["level"], + row["step"], + row["alert_id"], + row["run_id"], + ), + ) + elif ( + target_alerts_run_id + and generated_run_id is not None + ): + target_cursor.execute( + """ + INSERT OR IGNORE INTO alerts (timestamp, run_name, title, text, level, step, alert_id, run_id) + VALUES (?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + row["timestamp"], + run, + row["title"], + row["text"], + row["level"], + row["step"], + row["alert_id"], + generated_run_id, + ), + ) + else: + target_cursor.execute( + """ + INSERT OR IGNORE INTO alerts (timestamp, run_name, title, text, level, step, alert_id) + VALUES (?, ?, ?, ?, ?, ?, ?) + """, + ( + row["timestamp"], + run, + row["title"], + row["text"], + row["level"], + row["step"], + row["alert_id"], + ), + ) + except sqlite3.OperationalError: + pass + + target_conn.commit() + + SQLiteStorage._move_media_dir( + MEDIA_DIR / project / run, + MEDIA_DIR / new_project / run, + ) + + source_cursor.execute( + "DELETE FROM metrics WHERE run_name = ?", (run,) + ) + source_cursor.execute( + "DELETE FROM configs WHERE run_name = ?", (run,) + ) + try: + source_cursor.execute( + "DELETE FROM system_metrics WHERE run_name = ?", (run,) + ) + except sqlite3.OperationalError: + pass + try: + source_cursor.execute( + "DELETE FROM alerts WHERE run_name = ?", (run,) + ) + except sqlite3.OperationalError: + pass + source_conn.commit() + + return True + + @staticmethod + def get_all_run_configs(project: str) -> dict[str, dict]: + """Get configurations for all runs in a project.""" + db_path = SQLiteStorage.get_project_db_path(project) + if not db_path.exists(): + return {} + + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + try: + cursor.execute( + """ + SELECT run_name, config FROM configs + """ + ) + + results = {} + for row in cursor.fetchall(): + config = orjson.loads(row["config"]) + results[row["run_name"]] = deserialize_values(config) + return results + except sqlite3.OperationalError as e: + if "no such table: configs" in str(e): + return {} + raise + + @staticmethod + def get_metric_values( + project: str, + run: str, + metric_name: str, + step: int | None = None, + around_step: int | None = None, + at_time: str | None = None, + window: int | float | None = None, + ) -> list[dict]: + """Get values for a specific metric in a project/run with optional filtering. + + Filtering modes: + - step: return the single row at exactly this step + - around_step + window: return rows where step is in [around_step - window, around_step + window] + - at_time + window: return rows within ±window seconds of the ISO timestamp + - No filters: return all rows + """ + db_path = SQLiteStorage.get_project_db_path(project) + if not db_path.exists(): + return [] + + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + query = "SELECT timestamp, step, metrics FROM metrics WHERE run_name = ?" + params: list = [run] + + if step is not None: + query += " AND step = ?" + params.append(step) + elif around_step is not None and window is not None: + query += " AND step >= ? AND step <= ?" + params.extend([around_step - int(window), around_step + int(window)]) + elif at_time is not None and window is not None: + query += ( + " AND timestamp >= datetime(?, '-' || ? || ' seconds')" + " AND timestamp <= datetime(?, '+' || ? || ' seconds')" + ) + params.extend([at_time, int(window), at_time, int(window)]) + + query += " ORDER BY timestamp" + cursor.execute(query, params) + + rows = cursor.fetchall() + results = [] + for row in rows: + metrics = orjson.loads(row["metrics"]) + metrics = deserialize_values(metrics) + if metric_name in metrics: + results.append( + { + "timestamp": row["timestamp"], + "step": row["step"], + "value": metrics[metric_name], + } + ) + return results + + @staticmethod + def get_snapshot( + project: str, + run: str | None = None, + step: int | None = None, + around_step: int | None = None, + at_time: str | None = None, + window: int | float | None = None, + run_id: str | None = None, + ) -> dict[str, list[dict]]: + """Get all metrics at/around a point in time or step. + + Returns a dict mapping metric names to lists of {timestamp, step, value}. + """ + db_path = SQLiteStorage.get_project_db_path(project) + if not db_path.exists(): + return {} + + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + run_identity = SQLiteStorage._resolve_run_identity( + conn, run_name=run, run_id=run_id, table="metrics" + ) + if run_identity is None: + return {} + query = f"SELECT timestamp, step, metrics FROM metrics WHERE {run_identity[0]} = ?" + params: list = [run_identity[1]] + + if step is not None: + query += " AND step = ?" + params.append(step) + elif around_step is not None and window is not None: + query += " AND step >= ? AND step <= ?" + params.extend([around_step - int(window), around_step + int(window)]) + elif at_time is not None and window is not None: + query += ( + " AND timestamp >= datetime(?, '-' || ? || ' seconds')" + " AND timestamp <= datetime(?, '+' || ? || ' seconds')" + ) + params.extend([at_time, int(window), at_time, int(window)]) + + query += " ORDER BY timestamp" + cursor.execute(query, params) + + result: dict[str, list[dict]] = {} + for row in cursor.fetchall(): + metrics = orjson.loads(row["metrics"]) + metrics = deserialize_values(metrics) + for key, value in metrics.items(): + if key not in result: + result[key] = [] + result[key].append( + { + "timestamp": row["timestamp"], + "step": row["step"], + "value": value, + } + ) + return result + + @staticmethod + def get_all_metrics_for_run( + project: str, run: str | None = None, run_id: str | None = None + ) -> list[str]: + """Get all metric names for a specific project/run.""" + return SQLiteStorage._get_metric_names( + project, + run, + "metrics", + exclude_keys={"timestamp", "step"}, + run_id=run_id, + ) + + @staticmethod + def _get_metric_names( + project: str, + run: str | None, + table: str, + exclude_keys: set[str], + run_id: str | None = None, + ) -> list[str]: + db_path = SQLiteStorage.get_project_db_path(project) + if not db_path.exists(): + return [] + + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + try: + run_identity = SQLiteStorage._resolve_run_identity( + conn, run_name=run, run_id=run_id, table=table + ) + if run_identity is None: + return [] + cursor.execute( + f""" + SELECT metrics + FROM {table} + WHERE {run_identity[0]} = ? + ORDER BY timestamp + """, + (run_identity[1],), + ) + + rows = cursor.fetchall() + all_metrics = set() + for row in rows: + metrics = orjson.loads(row["metrics"]) + metrics = deserialize_values(metrics) + for key in metrics.keys(): + if key not in exclude_keys: + all_metrics.add(key) + return sorted(list(all_metrics)) + except sqlite3.OperationalError as e: + if f"no such table: {table}" in str(e): + return [] + raise + + @staticmethod + def set_project_metadata(project: str, key: str, value: str) -> None: + db_path = SQLiteStorage.init_db(project) + with SQLiteStorage._get_process_lock(project): + with SQLiteStorage._get_connection(db_path) as conn: + conn.execute( + "INSERT OR REPLACE INTO project_metadata (key, value) VALUES (?, ?)", + (key, value), + ) + conn.commit() + + @staticmethod + def get_project_metadata(project: str, key: str) -> str | None: + db_path = SQLiteStorage.get_project_db_path(project) + if not db_path.exists(): + return None + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + try: + cursor.execute( + "SELECT value FROM project_metadata WHERE key = ?", (key,) + ) + row = cursor.fetchone() + return row[0] if row else None + except sqlite3.OperationalError: + return None + + @staticmethod + def get_space_id(project: str) -> str | None: + return SQLiteStorage.get_project_metadata(project, "space_id") + + @staticmethod + def has_pending_data(project: str) -> bool: + db_path = SQLiteStorage.get_project_db_path(project) + if not db_path.exists(): + return False + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + try: + cursor.execute( + "SELECT EXISTS(SELECT 1 FROM metrics WHERE space_id IS NOT NULL LIMIT 1)" + ) + if cursor.fetchone()[0]: + return True + except sqlite3.OperationalError: + pass + try: + cursor.execute( + "SELECT EXISTS(SELECT 1 FROM system_metrics WHERE space_id IS NOT NULL LIMIT 1)" + ) + if cursor.fetchone()[0]: + return True + except sqlite3.OperationalError: + pass + try: + cursor.execute("SELECT EXISTS(SELECT 1 FROM pending_uploads LIMIT 1)") + if cursor.fetchone()[0]: + return True + except sqlite3.OperationalError: + pass + return False + + @staticmethod + def get_pending_logs(project: str) -> dict | None: + return SQLiteStorage._get_pending( + project, "metrics", extra_fields=["step"], include_config=True + ) + + @staticmethod + def clear_pending_logs(project: str, metric_ids: list[int]) -> None: + SQLiteStorage._clear_pending(project, "metrics", metric_ids) + + @staticmethod + def get_pending_system_logs(project: str) -> dict | None: + return SQLiteStorage._get_pending(project, "system_metrics") + + @staticmethod + def _get_pending( + project: str, + table: str, + extra_fields: list[str] | None = None, + include_config: bool = False, + ) -> dict | None: + db_path = SQLiteStorage.get_project_db_path(project) + if not db_path.exists(): + return None + extra_cols = ", ".join(extra_fields) + ", " if extra_fields else "" + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + try: + run_id_col = ( + "run_id, " if SQLiteStorage._supports_run_ids(conn, table) else "" + ) + cursor.execute( + f"""SELECT id, timestamp, {run_id_col}run_name, {extra_cols}metrics, log_id, space_id + FROM {table} WHERE space_id IS NOT NULL""" + ) + except sqlite3.OperationalError: + return None + rows = cursor.fetchall() + if not rows: + return None + logs = [] + ids = [] + for row in rows: + metrics = deserialize_values(orjson.loads(row["metrics"])) + entry = { + "project": project, + "run": row["run_name"], + "run_id": row["run_name"], + "metrics": metrics, + "timestamp": row["timestamp"], + "log_id": row["log_id"], + } + if "run_id" in row.keys(): + entry["run_id"] = row["run_id"] + for field in extra_fields or []: + entry[field] = row[field] + if include_config: + entry["config"] = None + logs.append(entry) + ids.append(row["id"]) + return {"logs": logs, "ids": ids, "space_id": rows[0]["space_id"]} + + @staticmethod + def clear_pending_system_logs(project: str, metric_ids: list[int]) -> None: + SQLiteStorage._clear_pending(project, "system_metrics", metric_ids) + + @staticmethod + def _clear_pending(project: str, table: str, ids: list[int]) -> None: + if not ids: + return + db_path = SQLiteStorage.get_project_db_path(project) + if not db_path.exists(): + return + with SQLiteStorage._get_process_lock(project): + with SQLiteStorage._get_connection(db_path) as conn: + placeholders = ",".join("?" * len(ids)) + conn.execute( + f"UPDATE {table} SET space_id = NULL WHERE id IN ({placeholders})", + ids, + ) + conn.commit() + + @staticmethod + def get_pending_uploads(project: str) -> dict | None: + db_path = SQLiteStorage.get_project_db_path(project) + if not db_path.exists(): + return None + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + try: + columns = SQLiteStorage._table_columns(conn, "pending_uploads") + run_id_col = "run_id, " if "run_id" in columns else "" + cursor.execute( + f"""SELECT id, space_id, {run_id_col}run_name, step, file_path, relative_path + FROM pending_uploads""" + ) + except sqlite3.OperationalError: + return None + rows = cursor.fetchall() + if not rows: + return None + uploads = [] + ids = [] + for row in rows: + uploads.append( + { + "project": project, + "run": row["run_name"], + "run_id": ( + row["run_id"] if "run_id" in row.keys() else row["run_name"] + ) + or row["run_name"], + "step": row["step"], + "file_path": row["file_path"], + "relative_path": row["relative_path"], + } + ) + ids.append(row["id"]) + return {"uploads": uploads, "ids": ids, "space_id": rows[0]["space_id"]} + + @staticmethod + def clear_pending_uploads(project: str, upload_ids: list[int]) -> None: + if not upload_ids: + return + db_path = SQLiteStorage.get_project_db_path(project) + if not db_path.exists(): + return + with SQLiteStorage._get_process_lock(project): + with SQLiteStorage._get_connection(db_path) as conn: + placeholders = ",".join("?" * len(upload_ids)) + conn.execute( + f"DELETE FROM pending_uploads WHERE id IN ({placeholders})", + upload_ids, + ) + conn.commit() + + @staticmethod + def add_pending_upload( + project: str, + space_id: str, + run_id: str | None, + run_name: str | None, + step: int | None, + file_path: str, + relative_path: str | None, + ) -> None: + db_path = SQLiteStorage.init_db(project) + with SQLiteStorage._get_process_lock(project): + with SQLiteStorage._get_connection(db_path) as conn: + if SQLiteStorage._supports_run_ids(conn, "pending_uploads"): + conn.execute( + """INSERT INTO pending_uploads + (space_id, run_id, run_name, step, file_path, relative_path, created_at) + VALUES (?, ?, ?, ?, ?, ?, ?)""", + ( + space_id, + run_id, + run_name, + step, + file_path, + relative_path, + datetime.now(timezone.utc).isoformat(), + ), + ) + else: + conn.execute( + """INSERT INTO pending_uploads + (space_id, run_name, step, file_path, relative_path, created_at) + VALUES (?, ?, ?, ?, ?, ?)""", + ( + space_id, + run_name, + step, + file_path, + relative_path, + datetime.now(timezone.utc).isoformat(), + ), + ) + conn.commit() + + @staticmethod + def get_all_logs_for_sync(project: str) -> list[dict]: + return SQLiteStorage._get_all_for_sync( + project, + "metrics", + order_by="run_name, step", + extra_fields=["step"], + include_config=True, + ) + + @staticmethod + def get_all_system_logs_for_sync(project: str) -> list[dict]: + return SQLiteStorage._get_all_for_sync( + project, "system_metrics", order_by="run_name, timestamp" + ) + + @staticmethod + def _get_all_for_sync( + project: str, + table: str, + order_by: str, + extra_fields: list[str] | None = None, + include_config: bool = False, + ) -> list[dict]: + db_path = SQLiteStorage.get_project_db_path(project) + if not db_path.exists(): + return [] + extra_cols = ", ".join(extra_fields) + ", " if extra_fields else "" + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + try: + run_id_col = ( + "run_id, " if SQLiteStorage._supports_run_ids(conn, table) else "" + ) + cursor.execute( + f"""SELECT timestamp, {run_id_col}run_name, {extra_cols}metrics, log_id + FROM {table} ORDER BY {order_by}""" + ) + except sqlite3.OperationalError: + return [] + rows = cursor.fetchall() + results = [] + for row in rows: + metrics = deserialize_values(orjson.loads(row["metrics"])) + entry = { + "project": project, + "run": row["run_name"], + "run_id": row["run_name"], + "metrics": metrics, + "timestamp": row["timestamp"], + "log_id": row["log_id"], + } + if "run_id" in row.keys(): + entry["run_id"] = row["run_id"] + for field in extra_fields or []: + entry[field] = row[field] + if include_config: + entry["config"] = None + results.append(entry) + return results diff --git a/trackio/table.py b/trackio/table.py new file mode 100644 index 0000000000000000000000000000000000000000..6f693a816bec971cc2d2b0f603a9cda051118f9e --- /dev/null +++ b/trackio/table.py @@ -0,0 +1,201 @@ +import os +from typing import Any, Literal +from urllib.parse import quote + +from trackio.media.media import TrackioMedia +from trackio.utils import MEDIA_DIR + + +class Table: + """ + Initializes a Table object. + + Tables can be used to log tabular data including images, numbers, and text. + + Args: + columns (`list[str]`, *optional*): + Names of the columns in the table. Optional if `data` is provided. Not + expected if `dataframe` is provided. Currently ignored. + data (`list[list[Any]]`, *optional*): + 2D row-oriented array of values. Each value can be a number, a string + (treated as Markdown and truncated if too long), or a `Trackio.Image` or + list of `Trackio.Image` objects. + dataframe (`pandas.DataFrame`, *optional*): + DataFrame used to create the table. When set, `data` and `columns` + arguments are ignored. + rows (`list[list[Any]]`, *optional*): + Currently ignored. + optional (`bool` or `list[bool]`, *optional*, defaults to `True`): + Currently ignored. + allow_mixed_types (`bool`, *optional*, defaults to `False`): + Currently ignored. + log_mode: (`Literal["IMMUTABLE", "MUTABLE", "INCREMENTAL"]` or `None`, *optional*, defaults to `"IMMUTABLE"`): + Currently ignored. + """ + + TYPE = "trackio.table" + + def __init__( + self, + columns: list[str] | None = None, + data: list[list[Any]] | None = None, + dataframe: Any | None = None, + rows: list[list[Any]] | None = None, + optional: bool | list[bool] = True, + allow_mixed_types: bool = False, + log_mode: Literal["IMMUTABLE", "MUTABLE", "INCREMENTAL"] | None = "IMMUTABLE", + ): + # TODO: implement support for columns, dtype, optional, allow_mixed_types, and log_mode. + # for now (like `rows`) they are included for API compat but don't do anything. + self.data = self._normalize_rows( + columns=columns, data=data, dataframe=dataframe + ) + + @staticmethod + def _normalize_rows( + columns: list[str] | None, + data: list[list[Any]] | None, + dataframe: Any | None, + ) -> list[dict[str, Any]]: + if dataframe is not None: + try: + records = dataframe.to_dict(orient="records") + except Exception as e: + raise TypeError( + "The `dataframe` argument must support `to_dict(orient='records')`." + ) from e + return [dict(row) for row in records] + + if data is None: + return [] + + if data and isinstance(data[0], dict): + return [dict(row) for row in data] + + normalized_rows: list[dict[str, Any]] = [] + for row in data: + row_dict: dict[str, Any] = {} + if columns is None: + for idx, value in enumerate(row): + row_dict[idx] = value + else: + for idx, column in enumerate(columns): + row_dict[column] = row[idx] if idx < len(row) else None + for idx in range(len(columns), len(row)): + row_dict[idx] = row[idx] + normalized_rows.append(row_dict) + return normalized_rows + + def _has_media_objects(self, rows: list[dict[str, Any]]) -> bool: + """Check if rows contain any TrackioMedia objects or lists of TrackioMedia objects.""" + for row in rows: + for value in row.values(): + if isinstance(value, TrackioMedia): + return True + if ( + isinstance(value, list) + and len(value) > 0 + and isinstance(value[0], TrackioMedia) + ): + return True + return False + + def _process_data(self, project: str, run: str, step: int = 0): + """Convert rows to dict format, processing any TrackioMedia objects if present.""" + if not self._has_media_objects(self.data): + return [dict(row) for row in self.data] + + processed_rows = [dict(row) for row in self.data] + for row in processed_rows: + for key, value in list(row.items()): + if isinstance(value, TrackioMedia): + value._save(project, run, step) + row[key] = value._to_dict() + if ( + isinstance(value, list) + and len(value) > 0 + and isinstance(value[0], TrackioMedia) + ): + [v._save(project, run, step) for v in value] + row[key] = [v._to_dict() for v in value] + + return processed_rows + + @staticmethod + def to_display_format(table_data: list[dict]) -> list[dict]: + """ + Converts stored table data to display format for UI rendering. + + Note: + This does not use the `self.data` attribute, but instead uses the + `table_data` parameter, which is what the UI receives. + + Args: + table_data (`list[dict]`): + List of dictionaries representing table rows (from stored `_value`). + + Returns: + `list[dict]`: Table data with images converted to markdown syntax and long + text truncated. + """ + truncate_length = int(os.getenv("TRACKIO_TABLE_TRUNCATE_LENGTH", "250")) + + def convert_image_to_markdown(image_data: dict) -> str: + relative_path = image_data.get("file_path", "") + caption = image_data.get("caption", "") + absolute_path = MEDIA_DIR / relative_path + return ( + f'{caption}' + ) + + processed_data = [] + for row in table_data: + processed_row = {} + for key, value in row.items(): + if isinstance(value, dict) and value.get("_type") == "trackio.image": + processed_row[key] = convert_image_to_markdown(value) + elif ( + isinstance(value, list) + and len(value) > 0 + and isinstance(value[0], dict) + and value[0].get("_type") == "trackio.image" + ): + # This assumes that if the first item is an image, all items are images. Ok for now since we don't support mixed types in a single cell. + processed_row[key] = ( + '
        ' + + "".join([convert_image_to_markdown(item) for item in value]) + + "
        " + ) + elif isinstance(value, str) and len(value) > truncate_length: + truncated = value[:truncate_length] + full_text = value.replace("<", "<").replace(">", ">") + processed_row[key] = ( + f'
        ' + f'{truncated}…(truncated, click to expand)' + f'
        ' + f'
        {full_text}
        ' + f"
        " + f"
        " + ) + else: + processed_row[key] = value + processed_data.append(processed_row) + return processed_data + + def _to_dict(self, project: str, run: str, step: int = 0): + """ + Converts the table to a dictionary representation. + + Args: + project (`str`): + Project name for saving media files. + run (`str`): + Run name for saving media files. + step (`int`, *optional*, defaults to `0`): + Step number for saving media files. + """ + data = self._process_data(project, run, step) + return { + "_type": self.TYPE, + "_value": data, + } diff --git a/trackio/typehints.py b/trackio/typehints.py new file mode 100644 index 0000000000000000000000000000000000000000..e65cd86cf40386a6526e0e71a4eeac9587641f4e --- /dev/null +++ b/trackio/typehints.py @@ -0,0 +1,43 @@ +from typing import Any, TypedDict + +from gradio_client import FileData + + +class LogEntry(TypedDict, total=False): + project: str + run: str + run_id: str | None + metrics: dict[str, Any] + step: int | None + config: dict[str, Any] | None + log_id: str | None + + +class SystemLogEntry(TypedDict, total=False): + project: str + run: str + run_id: str | None + metrics: dict[str, Any] + timestamp: str + log_id: str | None + + +class AlertEntry(TypedDict, total=False): + project: str + run: str + run_id: str | None + title: str + text: str | None + level: str + step: int | None + timestamp: str + alert_id: str | None + + +class UploadEntry(TypedDict): + project: str + run: str | None + run_id: str | None + step: int | None + relative_path: str | None + uploaded_file: FileData diff --git a/trackio/utils.py b/trackio/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..1af2c3be1beb5497030ed440a05eb9d8df124737 --- /dev/null +++ b/trackio/utils.py @@ -0,0 +1,1022 @@ +import math +import os +import re +import secrets +import time +import warnings +from datetime import datetime, timezone +from pathlib import Path +from typing import TYPE_CHECKING, Any +from urllib.parse import quote, urlencode + +import huggingface_hub +import numpy as np +from huggingface_hub.constants import HF_HOME + +if TYPE_CHECKING: + from trackio.commit_scheduler import CommitScheduler + from trackio.dummy_commit_scheduler import DummyCommitScheduler + +RESERVED_KEYS = ["project", "run", "timestamp", "step", "time", "metrics"] + +TRACKIO_LOGO_DIR = Path(__file__).parent / "assets" + + +def _emit_nonfatal_warning(message: str, *args, **kwargs) -> None: + try: + warnings.warn(message, *args, **kwargs) + except Exception: + print(f"* Trackio warning: {message}") + + +def get_logo_urls() -> dict[str, str]: + """Get logo URLs from environment variables or use defaults.""" + light_url = os.environ.get( + "TRACKIO_LOGO_LIGHT_URL", + f"/file?path={quote(str(TRACKIO_LOGO_DIR / 'trackio_logo_type_light_transparent.png'))}", + ) + dark_url = os.environ.get( + "TRACKIO_LOGO_DARK_URL", + f"/file?path={quote(str(TRACKIO_LOGO_DIR / 'trackio_logo_type_dark_transparent.png'))}", + ) + return {"light": light_url, "dark": dark_url} + + +def order_metrics_by_plot_preference(metrics: list[str]) -> tuple[list[str], dict]: + """ + Order metrics based on TRACKIO_PLOT_ORDER environment variable and group them. + + Args: + metrics: List of metric names to order and group + + Returns: + Tuple of (ordered_group_names, grouped_metrics_dict) + """ + plot_order_env = os.environ.get("TRACKIO_PLOT_ORDER", "") + if not plot_order_env.strip(): + plot_order = [] + else: + plot_order = [ + item.strip() for item in plot_order_env.split(",") if item.strip() + ] + + def get_metric_priority(metric: str) -> tuple[int, int, str]: + if not plot_order: + return (float("inf"), float("inf"), metric) + + group_prefix = metric.split("/")[0] if "/" in metric else "charts" + no_match_priority = len(plot_order) + + group_priority = no_match_priority + for i, pattern in enumerate(plot_order): + pattern_group = pattern.split("/")[0] if "/" in pattern else "charts" + if pattern_group == group_prefix: + group_priority = i + break + + within_group_priority = no_match_priority + for i, pattern in enumerate(plot_order): + if pattern == metric: + within_group_priority = i + break + elif pattern.endswith("/*") and within_group_priority == no_match_priority: + pattern_prefix = pattern[:-2] + if metric.startswith(pattern_prefix + "/"): + within_group_priority = i + len(plot_order) + + return (group_priority, within_group_priority, metric) + + result = {} + for metric in metrics: + if "/" not in metric: + if "charts" not in result: + result["charts"] = {"direct_metrics": [], "subgroups": {}} + result["charts"]["direct_metrics"].append(metric) + else: + parts = metric.split("/") + main_prefix = parts[0] + if main_prefix not in result: + result[main_prefix] = {"direct_metrics": [], "subgroups": {}} + if len(parts) == 2: + result[main_prefix]["direct_metrics"].append(metric) + else: + subprefix = parts[1] + if subprefix not in result[main_prefix]["subgroups"]: + result[main_prefix]["subgroups"][subprefix] = [] + result[main_prefix]["subgroups"][subprefix].append(metric) + + for group_data in result.values(): + group_data["direct_metrics"].sort(key=get_metric_priority) + for subgroup_name in group_data["subgroups"]: + group_data["subgroups"][subgroup_name].sort(key=get_metric_priority) + + if "charts" in result and not result["charts"]["direct_metrics"]: + del result["charts"] + + def get_group_priority(group_name: str) -> tuple[int, str]: + if not plot_order: + return (float("inf"), group_name) + + min_priority = len(plot_order) + for i, pattern in enumerate(plot_order): + pattern_group = pattern.split("/")[0] if "/" in pattern else "charts" + if pattern_group == group_name: + min_priority = min(min_priority, i) + return (min_priority, group_name) + + ordered_groups = sorted(result.keys(), key=get_group_priority) + + return ordered_groups, result + + +def on_spaces() -> bool: + return os.environ.get("SYSTEM") == "spaces" + + +def resolve_space_id_and_server_url( + space_id: str | None, server_url: str | None +) -> tuple[str | None, str | None]: + space_id = space_id or os.environ.get("TRACKIO_SPACE_ID") + server_url = server_url or os.environ.get("TRACKIO_SERVER_URL") + if space_id is not None: + server_url = None + return space_id, server_url + + +def parse_trackio_server_url(url: str) -> tuple[str, str | None]: + from urllib.parse import parse_qsl, urlencode, urlparse, urlunparse + + p = urlparse(url.strip()) + if p.scheme not in ("http", "https"): + return url, None + pairs = parse_qsl(p.query, keep_blank_values=True) + write_token: str | None = None + rest: list[tuple[str, str]] = [] + for k, v in pairs: + if k == "write_token": + write_token = v + else: + rest.append((k, v)) + new_query = urlencode(rest) + rebuilt = urlunparse((p.scheme, p.netloc, p.path, p.params, new_query, p.fragment)) + return rebuilt, write_token + + +def _get_trackio_dir() -> Path: + if os.environ.get("TRACKIO_DIR"): + return Path(os.environ.get("TRACKIO_DIR")) + return Path(HF_HOME) / "trackio" + + +TRACKIO_DIR = _get_trackio_dir() +MEDIA_DIR = TRACKIO_DIR / "media" + + +def get_or_create_project_hash(project: str) -> str: + hash_path = TRACKIO_DIR / f"{project}.hash" + if hash_path.exists(): + return hash_path.read_text().strip() + hash_value = secrets.token_urlsafe(8) + TRACKIO_DIR.mkdir(parents=True, exist_ok=True) + hash_path.write_text(hash_value) + return hash_value + + +def generate_readable_name(used_names: list[str], space_id: str | None = None) -> str: + """ + Generates a random, readable name like "dainty-sunset-0". + If space_id is provided, generates username-timestamp format instead. + """ + if space_id is not None: + username = _get_default_namespace() + timestamp = int(time.time()) + return f"{username}-{timestamp}" + adjectives = [ + "dainty", + "brave", + "calm", + "eager", + "fancy", + "gentle", + "happy", + "jolly", + "kind", + "lively", + "merry", + "nice", + "proud", + "quick", + "hugging", + "silly", + "tidy", + "witty", + "zealous", + "bright", + "shy", + "bold", + "clever", + "daring", + "elegant", + "faithful", + "graceful", + "honest", + "inventive", + "jovial", + "keen", + "lucky", + "modest", + "noble", + "optimistic", + "patient", + "quirky", + "resourceful", + "sincere", + "thoughtful", + "upbeat", + "valiant", + "warm", + "youthful", + "zesty", + "adventurous", + "breezy", + "cheerful", + "delightful", + "energetic", + "fearless", + "glad", + "hopeful", + "imaginative", + "joyful", + "kindly", + "luminous", + "mysterious", + "neat", + "outgoing", + "playful", + "radiant", + "spirited", + "tranquil", + "unique", + "vivid", + "wise", + "zany", + "artful", + "bubbly", + "charming", + "dazzling", + "earnest", + "festive", + "gentlemanly", + "hearty", + "intrepid", + "jubilant", + "knightly", + "lively", + "magnetic", + "nimble", + "orderly", + "peaceful", + "quick-witted", + "robust", + "sturdy", + "trusty", + "upstanding", + "vibrant", + "whimsical", + ] + nouns = [ + "sunset", + "forest", + "river", + "mountain", + "breeze", + "meadow", + "ocean", + "valley", + "sky", + "field", + "cloud", + "star", + "rain", + "leaf", + "stone", + "flower", + "bird", + "tree", + "wave", + "trail", + "island", + "desert", + "hill", + "lake", + "pond", + "grove", + "canyon", + "reef", + "bay", + "peak", + "glade", + "marsh", + "cliff", + "dune", + "spring", + "brook", + "cave", + "plain", + "ridge", + "wood", + "blossom", + "petal", + "root", + "branch", + "seed", + "acorn", + "pine", + "willow", + "cedar", + "elm", + "falcon", + "eagle", + "sparrow", + "robin", + "owl", + "finch", + "heron", + "crane", + "duck", + "swan", + "fox", + "wolf", + "bear", + "deer", + "moose", + "otter", + "beaver", + "lynx", + "hare", + "badger", + "butterfly", + "bee", + "ant", + "beetle", + "dragonfly", + "firefly", + "ladybug", + "moth", + "spider", + "worm", + "coral", + "kelp", + "shell", + "pebble", + "face", + "boulder", + "cobble", + "sand", + "wavelet", + "tide", + "current", + "mist", + ] + number = 0 + name = f"{adjectives[0]}-{nouns[0]}-{number}" + while name in used_names: + number += 1 + adjective = adjectives[number % len(adjectives)] + noun = nouns[number % len(nouns)] + name = f"{adjective}-{noun}-{number}" + return name + + +def is_in_notebook(): + """ + Detect if code is running in a notebook environment (Jupyter, Colab, etc.). + """ + try: + from IPython import get_ipython + + if get_ipython() is not None: + return get_ipython().__class__.__name__ in [ + "ZMQInteractiveShell", # Jupyter notebook/lab + "Shell", # IPython terminal + ] or "google.colab" in str(get_ipython()) + except ImportError: + pass + return False + + +def block_main_thread_until_keyboard_interrupt(): + try: + while True: + time.sleep(0.1) + except (KeyboardInterrupt, OSError): + print("Keyboard interruption in main thread... closing dashboard.") + + +def simplify_column_names(columns: list[str]) -> dict[str, str]: + """ + Simplifies column names to first 10 alphanumeric or "/" characters with unique suffixes. + + Args: + columns: List of original column names + + Returns: + Dictionary mapping original column names to simplified names + """ + simplified_names = {} + used_names = set() + + for col in columns: + alphanumeric = re.sub(r"[^a-zA-Z0-9/]", "", col) + base_name = alphanumeric[:10] if alphanumeric else f"col_{len(used_names)}" + + final_name = base_name + suffix = 1 + while final_name in used_names: + final_name = f"{base_name}_{suffix}" + suffix += 1 + + simplified_names[col] = final_name + used_names.add(final_name) + + return simplified_names + + +def print_dashboard_instructions(project: str) -> None: + """ + Prints instructions for viewing the Trackio dashboard. + + Args: + project: The name of the project to show dashboard for. + """ + ORANGE = "\033[38;5;208m" + BOLD = "\033[1m" + RESET = "\033[0m" + + print("* View dashboard by running in your terminal:") + print(f'{BOLD}{ORANGE}trackio show --project "{project}"{RESET}') + print(f'* or by running in Python: trackio.show(project="{project}")') + + +def print_write_token_instructions(full_url: str) -> None: + print() + print(f"* Trackio dashboard opened in browser with write access at: {full_url}") + print( + "* Only share this write_token with trusted users, as it allows them to write logs, " + "rename/delete runs, and connect MCP tools." + ) + + +def preprocess_space_and_dataset_ids( + space_id: str | None, + dataset_id: str | None, + bucket_id: str | None = None, +) -> tuple[str | None, str | None, str | None]: + """ + Preprocesses the Space and Bucket names to ensure they are valid + "username/name" format. When space_id is provided and bucket_id is not + explicitly set, auto-generates a bucket_id. + """ + if space_id is not None and "/" not in space_id: + username = _get_default_namespace() + space_id = f"{username}/{space_id}" + if dataset_id is not None: + warnings.warn( + "`dataset_id` is deprecated. Use `bucket_id` instead.", + DeprecationWarning, + stacklevel=3, + ) + if dataset_id is not None and "/" not in dataset_id: + username = _get_default_namespace() + dataset_id = f"{username}/{dataset_id}" + if bucket_id is not None and "/" not in bucket_id: + username = _get_default_namespace() + bucket_id = f"{username}/{bucket_id}" + if space_id is not None and dataset_id is None and bucket_id is None: + bucket_id = f"{space_id}-bucket" + return space_id, dataset_id, bucket_id + + +def fibo(): + """Generator for Fibonacci backoff: 1, 1, 2, 3, 5, 8, ...""" + a, b = 1, 1 + while True: + yield a + a, b = b, a + b + + +def format_timestamp(timestamp_str): + """Convert ISO timestamp to human-readable format like '3 minutes ago'.""" + if not timestamp_str or is_missing_value(timestamp_str): + return "Unknown" + + try: + created_time = datetime.fromisoformat(timestamp_str.replace("Z", "+00:00")) + if created_time.tzinfo is None: + created_time = created_time.replace(tzinfo=timezone.utc) + + now = datetime.now(timezone.utc) + diff = now - created_time + + seconds = int(diff.total_seconds()) + if seconds < 60: + return "Just now" + elif seconds < 3600: + minutes = seconds // 60 + return f"{minutes} minute{'s' if minutes != 1 else ''} ago" + elif seconds < 86400: + hours = seconds // 3600 + return f"{hours} hour{'s' if hours != 1 else ''} ago" + else: + days = seconds // 86400 + return f"{days} day{'s' if days != 1 else ''} ago" + except Exception: + return "Unknown" + + +DEFAULT_COLOR_PALETTE = [ + "#A8769B", + "#E89957", + "#3B82F6", + "#10B981", + "#EF4444", + "#8B5CF6", + "#14B8A6", + "#F59E0B", + "#EC4899", + "#06B6D4", +] + + +def get_color_palette() -> list[str]: + """Get the color palette from environment variable or use default.""" + env_palette = os.environ.get("TRACKIO_COLOR_PALETTE") + if env_palette: + return [color.strip() for color in env_palette.split(",")] + return DEFAULT_COLOR_PALETTE + + +def get_color_mapping( + runs: list[str], smoothing: bool, color_palette: list[str] | None = None +) -> dict[str, str]: + """Generate color mapping for runs, with transparency for original data when smoothing is enabled.""" + if color_palette is None: + color_palette = get_color_palette() + + color_map = {} + + for i, run in enumerate(runs): + base_color = color_palette[i % len(color_palette)] + + if smoothing: + color_map[run] = base_color + "4D" + color_map[f"{run}_smoothed"] = base_color + else: + color_map[run] = base_color + + return color_map + + +def is_missing_value(value: object) -> bool: + if value is None: + return True + if isinstance(value, str): + return False + try: + return bool(math.isnan(value)) + except (TypeError, ValueError): + return False + + +def _to_records_with_columns( + data: object, +) -> tuple[list[dict[str, Any]], list[str], Any]: + if hasattr(data, "to_dict"): + try: + records = data.to_dict(orient="records") + except Exception: + pass + else: + columns = [str(column) for column in getattr(data, "columns", [])] + return [dict(row) for row in records], columns, data.__class__ + + if isinstance(data, list): + records = [dict(row) for row in data] + columns = list(records[0].keys()) if records else [] + return records, columns, None + + raise TypeError( + "downsample() expects a list of row dictionaries or a dataframe-like object." + ) + + +def _restore_records_shape( + records: list[dict[str, Any]], + columns: list[str], + dataframe_class: Any, +) -> Any: + if dataframe_class is None or not hasattr(dataframe_class, "from_records"): + return records + return dataframe_class.from_records(records, columns=columns) + + +def downsample( + data: object, + x: str, + y: str, + color: str | None, + x_lim: tuple[float | None, float | None] | None = None, +) -> tuple[Any, tuple[float, float] | None]: + """ + Downsample the dataframe to reduce the number of points plotted. + Also updates the x-axis limits to the data min/max if either of the x-axis limits are None. + + Args: + df: The dataframe to downsample. + x: The column name to use for the x-axis. + y: The column name to use for the y-axis. + color: The column name to use for the color. + x_lim: The x-axis limits to use. + + Returns: + A tuple containing the downsampled dataframe and the updated x-axis limits. + """ + rows, columns, dataframe_class = _to_records_with_columns(data) + + if not rows: + if x_lim is not None: + x_lim = (x_lim[0] or 0, x_lim[1] or 0) + return _restore_records_shape([], columns, dataframe_class), x_lim + + columns_to_keep = [x, y] + if color is not None and any(color in row for row in rows): + columns_to_keep.append(color) + filtered_rows = [ + {column: row.get(column) for column in columns_to_keep} for row in rows + ] + + data_x_values = [row[x] for row in filtered_rows] + data_x_min = min(data_x_values) + data_x_max = max(data_x_values) + + if x_lim is not None: + x_min, x_max = x_lim + if x_min is None: + x_min = data_x_min + if x_max is None: + x_max = data_x_max + updated_x_lim = (x_min, x_max) + else: + updated_x_lim = None + + n_bins = 100 + + groups: dict[Any, list[tuple[int, dict[str, Any]]]] = {} + if color is not None and color in columns_to_keep: + for idx, row in enumerate(filtered_rows): + groups.setdefault(row.get(color), []).append((idx, row)) + else: + groups[None] = list(enumerate(filtered_rows)) + + downsampled_indices: list[int] = [] + + for group_rows in groups.values(): + if not group_rows: + continue + + group_rows = sorted(group_rows, key=lambda item: item[1][x]) + + if updated_x_lim is not None: + x_min, x_max = updated_x_lim + before_point = [item for item in group_rows if item[1][x] < x_min] + after_point = [item for item in group_rows if item[1][x] > x_max] + group_rows = [item for item in group_rows if x_min <= item[1][x] <= x_max] + else: + before_point = after_point = None + x_min = group_rows[0][1][x] + x_max = group_rows[-1][1][x] + + if before_point: + downsampled_indices.append(before_point[-1][0]) + if after_point: + downsampled_indices.append(after_point[0][0]) + + if not group_rows: + continue + + if x_min == x_max: + min_y_idx = min(group_rows, key=lambda item: item[1][y])[0] + max_y_idx = max(group_rows, key=lambda item: item[1][y])[0] + if min_y_idx != max_y_idx: + downsampled_indices.extend([min_y_idx, max_y_idx]) + else: + downsampled_indices.append(min_y_idx) + continue + + if len(group_rows) < 500: + downsampled_indices.extend(idx for idx, _ in group_rows) + continue + + bins = np.linspace(x_min, x_max, n_bins + 1) + binned_rows: dict[int, list[tuple[int, dict[str, Any]]]] = {} + for idx, row in group_rows: + bin_idx = int( + np.clip(np.digitize(row[x], bins, right=False) - 1, 0, n_bins - 1) + ) + binned_rows.setdefault(bin_idx, []).append((idx, row)) + + for bin_rows in binned_rows.values(): + if not bin_rows: + continue + + min_y_idx = min(bin_rows, key=lambda item: item[1][y])[0] + max_y_idx = max(bin_rows, key=lambda item: item[1][y])[0] + + downsampled_indices.append(min_y_idx) + if min_y_idx != max_y_idx: + downsampled_indices.append(max_y_idx) + + unique_indices = sorted(set(downsampled_indices)) + selected_rows = [filtered_rows[idx] for idx in unique_indices] + + if color is not None and color in columns_to_keep: + grouped_rows: dict[Any, list[dict[str, Any]]] = {} + group_order: list[Any] = [] + for row in selected_rows: + group_key = row.get(color) + if group_key not in grouped_rows: + grouped_rows[group_key] = [] + group_order.append(group_key) + grouped_rows[group_key].append(row) + downsampled_rows = [] + for group_key in group_order: + downsampled_rows.extend( + sorted(grouped_rows[group_key], key=lambda row: row[x]) + ) + else: + downsampled_rows = sorted(selected_rows, key=lambda row: row[x]) + + return ( + _restore_records_shape(downsampled_rows, columns_to_keep, dataframe_class), + updated_x_lim, + ) + + +def sort_metrics_by_prefix(metrics: list[str]) -> list[str]: + """ + Sort metrics by grouping prefixes together for dropdown/list display. + Metrics without prefixes come first, then grouped by prefix. + + Args: + metrics: List of metric names + + Returns: + List of metric names sorted by prefix + + Example: + Input: ["train/loss", "loss", "train/acc", "val/loss"] + Output: ["loss", "train/acc", "train/loss", "val/loss"] + """ + groups = group_metrics_by_prefix(metrics) + result = [] + + if "charts" in groups: + result.extend(groups["charts"]) + + for group_name in sorted(groups.keys()): + if group_name != "charts": + result.extend(groups[group_name]) + + return result + + +def group_metrics_by_prefix(metrics: list[str]) -> dict[str, list[str]]: + """ + Group metrics by their prefix. Metrics without prefix go to 'charts' group. + + Args: + metrics: List of metric names + + Returns: + Dictionary with prefix names as keys and lists of metrics as values + + Example: + Input: ["loss", "accuracy", "train/loss", "train/acc", "val/loss"] + Output: { + "charts": ["loss", "accuracy"], + "train": ["train/loss", "train/acc"], + "val": ["val/loss"] + } + """ + no_prefix = [] + with_prefix = [] + + for metric in metrics: + if "/" in metric: + with_prefix.append(metric) + else: + no_prefix.append(metric) + + no_prefix.sort() + + prefix_groups = {} + for metric in with_prefix: + prefix = metric.split("/")[0] + if prefix not in prefix_groups: + prefix_groups[prefix] = [] + prefix_groups[prefix].append(metric) + + for prefix in prefix_groups: + prefix_groups[prefix].sort() + + groups = {} + if no_prefix: + groups["charts"] = no_prefix + + for prefix in sorted(prefix_groups.keys()): + groups[prefix] = prefix_groups[prefix] + + return groups + + +def get_sync_status(scheduler: "CommitScheduler | DummyCommitScheduler") -> int | None: + """Get the sync status from the CommitScheduler in an integer number of minutes, or None if not synced yet.""" + if getattr( + scheduler, "last_push_time", None + ): # DummyCommitScheduler doesn't have last_push_time + time_diff = time.time() - scheduler.last_push_time + return int(time_diff / 60) + else: + return None + + +def generate_share_url( + project: str, + metrics: str, + selected_runs: list = None, + hide_headers: bool = False, +) -> str: + """Generate the shareable Space URL based on current settings.""" + space_host = os.environ.get("SPACE_HOST", "") + if not space_host: + return "" + + params: dict[str, str] = {} + + if project: + params["project"] = project + + if metrics and metrics.strip(): + params["metrics"] = metrics + + if selected_runs: + params["runs"] = ",".join(selected_runs) + + if hide_headers: + params["accordion"] = "hidden" + params["sidebar"] = "hidden" + params["navbar"] = "hidden" + + query_string = urlencode(params) + return f"https://{space_host}?{query_string}" + + +def generate_embed_code( + project: str, + metrics: str, + selected_runs: list = None, + hide_headers: bool = False, +) -> str: + """Generate the embed iframe code based on current settings.""" + embed_url = generate_share_url(project, metrics, selected_runs, hide_headers) + if not embed_url: + return "" + + return f'' + + +def serialize_values(metrics): + """ + Serialize values to make them JSON-compliant. + + Converts: + - float('inf') -> "Infinity" + - float('-inf') -> "-Infinity" + - float('nan') -> "NaN" + + Example: + {"loss": float('inf'), "accuracy": 0.95} -> {"loss": "Infinity", "accuracy": 0.95} + """ + + def _serialize(value): + if isinstance(value, dict): + return {str(key): _serialize(item) for key, item in value.items()} + if isinstance(value, (list, tuple, set)): + return [_serialize(item) for item in value] + if isinstance(value, np.generic): + value = value.item() + if isinstance(value, bool | int): + return value + if isinstance(value, float): + if math.isinf(value): + return "Infinity" if value > 0 else "-Infinity" + if math.isnan(value): + return "NaN" + return float(value) + return value + + return _serialize(metrics) + + +def deserialize_values(metrics): + """ + Deserialize infinity and NaN string values back to their numeric forms. + Only handles top-level string values. + + Converts: + - "Infinity" -> float('inf') + - "-Infinity" -> float('-inf') + - "NaN" -> float('nan') + + Example: + {"loss": "Infinity", "accuracy": 0.95} -> {"loss": float('inf'), "accuracy": 0.95} + """ + if not isinstance(metrics, dict): + return metrics + + result = {} + for key, value in metrics.items(): + if value == "Infinity": + result[key] = float("inf") + elif value == "-Infinity": + result[key] = float("-inf") + elif value == "NaN": + result[key] = float("nan") + else: + result[key] = value + return result + + +def get_full_url( + base_url: str, project: str | None, write_token: str, footer: bool = True +) -> str: + params = [] + if project: + params.append(f"project={project}") + params.append(f"write_token={write_token}") + if not footer: + params.append("footer=false") + return base_url + "?" + "&".join(params) + + +def embed_url_in_notebook(url: str) -> None: + try: + from IPython.display import HTML, display + + embed_code = HTML( + f'
        ' + ) + display(embed_code) + except ImportError: + pass + + +def to_json_safe(obj): + if isinstance(obj, (str, int, float, bool, type(None))): + return obj + if isinstance(obj, np.generic): + return obj.item() + if isinstance(obj, dict): + return {str(k): to_json_safe(v) for k, v in obj.items()} + if isinstance(obj, (list, tuple, set)): + return [to_json_safe(v) for v in obj] + if hasattr(obj, "to_dict") and callable(obj.to_dict): + return to_json_safe(obj.to_dict()) + if hasattr(obj, "__dict__"): + return { + str(k): to_json_safe(v) + for k, v in vars(obj).items() + if not k.startswith("_") + } + return str(obj) + + +def get_space() -> str | None: + """ + Get the space ID ("user/space") if Trackio is running in a Space, or None if not. + """ + return os.environ.get("SPACE_ID") + + +def ordered_subset(items: list[str], subset: list[str] | None) -> list[str]: + subset_set = set(subset or []) + return [item for item in items if item in subset_set] + + +def _get_default_namespace() -> str: + """Get the default namespace (username). + + This function uses caching to avoid repeated API calls to /whoami-v2. + """ + token = huggingface_hub.get_token() + return huggingface_hub.whoami(token=token, cache=True)["name"]