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..368bb81aaebe3bfb96a3ae9cb42ae02221f16720 --- /dev/null +++ b/trackio/CHANGELOG.md @@ -0,0 +1,232 @@ +# trackio + +## 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..8e540d4e7070ed63beb37cea004b773bd5066a55 --- /dev/null +++ b/trackio/__init__.py @@ -0,0 +1,750 @@ +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 Client, 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.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 + +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", +] + +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 init( + project: str, + name: str | None = None, + group: str | None = None, + space_id: 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. + 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 Gradio app and embeds it. For Space runs, + this embeds the Space URL. In Colab, the local dashboard will be accessible + via a public share URL (default Gradio behavior). + 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: + warnings.warn( + "* 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." + ) + + space_id = space_id or os.environ.get("TRACKIO_SPACE_ID") + bucket_id = bucket_id or os.environ.get("TRACKIO_BUCKET_ID") + 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 + ) + 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: + warnings.warn( + "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) + + url = context_vars.current_server.get() + + if space_id is not None: + if url is None: + url = space_id + context_vars.current_server.set(url) + context_vars.current_space_id.set(space_id) + + _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: + 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: + 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: + 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) + else: + 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) + context_vars.current_project.set(project) + + if resume == "must": + if name is None: + raise ValueError("Must provide a run name when resume='must'") + if name not in SQLiteStorage.get_runs(project): + raise ValueError(f"Run '{name}' does not exist in project '{project}'") + resumed = True + elif resume == "allow": + resumed = name is not None and name in SQLiteStorage.get_runs(project) + elif resume == "never": + if name is not None and name in SQLiteStorage.get_runs(project): + warnings.warn( + f"* Warning: resume='never' but a run '{name}' already exists in " + f"project '{project}'. Generating a new name and instead. If you want " + "to resume this run, call init() with resume='must' or resume='allow'." + ) + name = None + resumed = False + else: + raise ValueError("resume must be one of: 'must', 'allow', or 'never'") + + 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, + group=group, + config=config, + space_id=space_id, + 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: + SQLiteStorage.set_project_metadata(project, "space_id", space_id) + if SQLiteStorage.has_pending_data(project): + run._has_local_buffer = True + + 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: + show(project=project, open_browser=False, block_thread=False) + + 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: + warnings.warn( + "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().") + run.finish() + + +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) + ) + + 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: + client = Client(url, verbose=False, httpx_kwargs={"timeout": 90}) + client.predict( + api_name="/bulk_upload_media", + uploads=upload_entries, + hf_token=huggingface_hub.utils.get_token(), + ) + except Exception as e: + warnings.warn( + 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 enables MCP lifespan hooks when available. 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: + 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"* Trackio UI launched at: {dashboard_url}") + print(f"* API (gradio_client-compatible) at: {base_url}gradio_api/") + if open_browser: + webbrowser.open(dashboard_url) + block_thread = block_thread if block_thread is not None else True + else: + utils.embed_url_in_notebook(dashboard_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..801396543efdf24ba589a21b2cb3c6458f550899 --- /dev/null +++ b/trackio/api.py @@ -0,0 +1,87 @@ +from typing import Iterator + +from trackio.sqlite_storage import SQLiteStorage + + +class Run: + def __init__(self, project: str, name: str): + self.project = project + self.name = name + self._config = None + + @property + def id(self) -> str: + return self.name + + @property + def config(self) -> dict | None: + if self._config is None: + self._config = SQLiteStorage.get_run_config(self.project, self.name) + 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, level=level, since=since + ) + + def delete(self) -> bool: + return SQLiteStorage.delete_run(self.project, self.name) + + 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: + run_names = SQLiteStorage.get_runs(self.project) + self._runs = [Run(self.project, name) for name in run_names] + + 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..9f9a60961ed022965847444b75e405cd9ff94db1 --- /dev/null +++ b/trackio/asgi_app.py @@ -0,0 +1,175 @@ +from __future__ import annotations + +import asyncio +import inspect +import json +import secrets +import traceback +from collections.abc import Callable +from typing import Any + +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 + +API_PREFIX = "/gradio_api" + + +def _serialize_result(data: Any) -> str: + def default(o: Any): + if isinstance(o, (dict, list, str, int, float, bool)) or o is None: + return o + if hasattr(o, "item"): + try: + return o.item() + except Exception: + pass + return str(o) + + return json.dumps(data, default=default) + + +def _invoke_handler( + fn: Callable, + request: Request, + data: list[Any], +) -> Any: + sig = inspect.signature(fn) + params = list(sig.parameters.values()) + pos_args: list[Any] = [] + di = 0 + for p in params: + if p.name == "request": + pos_args.append(request) + else: + if di < len(data): + pos_args.append(data[di]) + else: + pos_args.append(None) + di += 1 + return fn(*pos_args) + + +class _EventStore: + def __init__(self) -> None: + self._results: dict[str, tuple[Any, bool, str | None]] = {} + + def store_result( + self, event_id: str, result: Any, is_error: bool, err_msg: str | None + ) -> None: + self._results[event_id] = (result, is_error, err_msg) + + def pop(self, event_id: str) -> tuple[Any, bool, str | None] | None: + return self._results.pop(event_id, None) + + +_event_store = _EventStore() + + +def build_gradio_compat_handlers( + api_registry: dict[str, Callable[..., Any]], +) -> tuple[Callable, Callable]: + async def call_post(request: Request) -> Response: + api_name = request.path_params["api_name"].lstrip("/") + fn = api_registry.get(api_name) + if fn is None: + return JSONResponse({"detail": f"Unknown API: {api_name}"}, status_code=404) + try: + body = await request.json() + except Exception: + body = {} + data = body.get("data") or [] + if not isinstance(data, list): + data = [data] + event_id = secrets.token_urlsafe(16) + try: + out = await asyncio.to_thread(_invoke_handler, fn, request, data) + _event_store.store_result(event_id, out, False, None) + except TrackioAPIError as e: + _event_store.store_result(event_id, None, True, str(e)) + except Exception: + tb = traceback.format_exc() + print(tb) + _event_store.store_result(event_id, None, True, tb) + return JSONResponse({"event_id": event_id}) + + async def call_get(request: Request) -> Response: + event_id = request.path_params["event_id"] + + async def sse_gen(): + for _ in range(500): + await asyncio.sleep(0.005) + got = _event_store.pop(event_id) + if got is not None: + result, is_err, err_msg = got + if is_err: + payload = json.dumps(err_msg) + yield f"event: error\ndata: {payload}\n\n" + else: + wrapped = [result] if not isinstance(result, list) else result + payload = _serialize_result(wrapped) + yield f"event: complete\ndata: {payload}\n\n" + return + err = json.dumps("timeout waiting for result") + yield f"event: error\ndata: {err}\n\n" + + return StreamingResponse(sse_gen(), media_type="text/event-stream") + + return call_post, call_get + + +async def gradio_file_handler(request: Request) -> Response: + from pathlib import Path + from urllib.parse import unquote + + rest = request.path_params.get("rest", "") + if not rest.startswith("file="): + return Response("Not found", status_code=404) + fs_path = unquote(rest[5:]) + fp = Path(fs_path) + if fp.is_file(): + return FileResponse(str(fp)) + return Response("Not found", status_code=404) + + +async def startup_events_handler(request: Request) -> Response: + return JSONResponse({"status": "ok"}) + + +def create_trackio_starlette_app( + oauth_routes: list[Route], + api_registry: dict[str, Callable[..., Any]], + mcp_lifespan: Any = None, +) -> Starlette: + call_post, call_get = build_gradio_compat_handlers(api_registry) + routes: list[Any] = list(oauth_routes) + routes.append( + Route( + API_PREFIX + "/call/{api_name:path}", + endpoint=call_post, + methods=["POST"], + ) + ) + routes.append( + Route( + API_PREFIX + "/call/{api_name:path}/{event_id}", + endpoint=call_get, + methods=["GET"], + ) + ) + routes.append( + Route( + API_PREFIX + "/startup-events", + endpoint=startup_events_handler, + methods=["GET"], + ) + ) + routes.append( + Route( + API_PREFIX + "/{rest:path}", endpoint=gradio_file_handler, methods=["GET"] + ) + ) + return Starlette(routes=routes, lifespan=mcp_lifespan) 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 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a/trackio/bucket_storage.py b/trackio/bucket_storage.py new file mode 100644 index 0000000000000000000000000000000000000000..9ba9b6b381a2b17021a113e868bcb99f87bd0792 --- /dev/null +++ b/trackio/bucket_storage.py @@ -0,0 +1,151 @@ +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}", + dest=str(TRACKIO_DIR.parent), + 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))], + ) + 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..6f7252b61b03691d9ce2f2a5a9449bb3ed3062dd --- /dev/null +++ b/trackio/cli.py @@ -0,0 +1,1272 @@ +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_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 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", + ) + + 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: + runs = remote.predict(args.project, api_name="/get_runs_for_project") + 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: + runs = remote.predict(args.project, api_name="/get_runs_for_project") + 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 == "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", + ] + + 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..55967a365096a0463e8c2d6b4fc21ba6679c5932 --- /dev/null +++ b/trackio/cli_helpers.py @@ -0,0 +1,158 @@ +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 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..5390161add6e88d83071880d181a76c3ba9d52c0 --- /dev/null +++ b/trackio/context_vars.py @@ -0,0 +1,18 @@ +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 +) diff --git a/trackio/deploy.py b/trackio/deploy.py new file mode 100644 index 0000000000000000000000000000000000000000..6afd797583297da1055e06272d218757dd042ce0 --- /dev/null +++ b/trackio/deploy.py @@ -0,0 +1,938 @@ +import importlib.metadata +import io +import json as json_mod +import os +import shutil +import sys +import tempfile +import threading +import time +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 Client, handle_file +from httpx import ReadTimeout +from huggingface_hub import Volume +from huggingface_hub.errors import 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.sqlite_storage import SQLiteStorage +from trackio.utils import ( + MEDIA_DIR, + get_or_create_project_hash, + 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_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", []) + ) + return "\n".join(deps + spaces_deps) + + +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 ( + os.getenv("SYSTEM") == "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 pandas and huggingface-hub are 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("{GRADIO_VERSION}", trackio.__version__) + 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 = f"trackio[spaces]=={trackio.__version__}" + + 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: + 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: + non_bucket = [ + v + for v in existing + if not (v.type == "bucket" and v.source == bucket_id) + ] + hf_api.set_space_volumes( + space_id, + non_bucket + + [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") + 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}" + ) + 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 Gradio Client to upload since we do not want to trigger a new build of + the Space, which would happen if we used `huggingface_hub.upload_file`. + + 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 = Client(space_id, verbose=False, 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) + + client = Client(space_id, verbose=False, httpx_kwargs={"timeout": 90}) + hf_token = huggingface_hub.utils.get_token() + + if pending_only: + pending_logs = SQLiteStorage.get_pending_logs(project) + if pending_logs: + logs = pending_logs["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: + 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 + ) + + 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 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 os.getenv("SYSTEM") == "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}'") + 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 + ) + + 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 + ) + 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: + volumes = _get_space_volumes(space_id) + for v in volumes: + if v.type == "bucket" and v.mount_path == "/data": + return v.source + 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}") + + 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 + ) + + 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-Cs1UkfOV.css b/trackio/frontend/dist/assets/index-Cs1UkfOV.css new file mode 100644 index 0000000000000000000000000000000000000000..619e21e53d8ef44e4324557d126e4e3af9e5023d --- /dev/null +++ b/trackio/frontend/dist/assets/index-Cs1UkfOV.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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We are using "${t}" scale instead.`}function jP(e,t,n){return`${n}-scale's "${t}" is dropped as it does not work with ${e} scale.`}function UP(e){return`The step for "${e}" is dropped because the ${e==="width"?"x":"y"} is continuous.`}function bge(e,t,n,r){return`Conflicting ${t.toString()} property "${e.toString()}" (${At(n)} and ${At(r)}). Using ${At(n)}.`}function xge(e,t,n,r){return`Conflicting ${t.toString()} property "${e.toString()}" (${At(n)} and ${At(r)}). Using the union of the two domains.`}function _ge(e){return`Setting the scale to be independent for "${e}" means we also have to set the guide (axis or legend) to be independent.`}function wge(e){return`Dropping sort property ${At(e)} as unioned domains only support boolean or op "count", "min", and "max".`}const BM="Domains that should be unioned has conflicting sort properties. Sort will be set to true.",Ege="Detected faceted independent scales that union domain of multiple fields from different data sources. 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kbe=["type","clipAngle","clipExtent","center","rotate","precision","reflectX","reflectY","coefficient","distance","fraction","lobes","parallel","radius","ratio","spacing","tilt"];class yj extends Fa{constructor(t,n,r,i){super({...n},{name:t}),this.specifiedProjection=n,this.size=r,this.data=i,this.merged=!1}get isFit(){return!!this.data}}function vj(e){e.component.projection=Gt(e)?Cbe(e):Dbe(e)}function Cbe(e){if(e.hasProjection){const t=zn(e.specifiedProjection),n=!(t&&(t.scale!=null||t.translate!=null)),r=n?[e.getSizeSignalRef("width"),e.getSizeSignalRef("height")]:void 0,i=n?$be(e):void 0,s=new yj(e.projectionName(!0),{...zn(e.config.projection),...t},r,i);return s.get("type")||s.set("type","equalEarth",!1),s}}function $be(e){const t=[],{encoding:n}=e;for(const r of[[Is,Ls],[Yi,Ps]])(yn(n[r[0]])||yn(n[r[1]]))&&t.push({signal:e.getName(`geojson_${t.length}`)});return 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i=Gt(e)?e.axis(n)??e.legend(n)??{}:{},s=pe(t,{expr:"datum"}),o=pe(t,{expr:"datum",binSuffix:"end"});return{formulaAs:pe(t,{binSuffix:"range",forAs:!0}),formula:zg(s,o,i.format,i.formatType,r)}}return{}}function bj(e,t){return`${FP(e)}_${t}`}function Rbe(e,t){return{signal:e.getName(`${t}_bins`),extentSignal:e.getName(`${t}_extent`)}}function K3(e,t,n){const r=eb(n,void 0)??{},i=bj(r,t);return e.getName(`${i}_bins`)}function Tbe(e){return"as"in e}function T4(e,t,n){let r,i;Tbe(e)?r=me(e.as)?[e.as,`${e.as}_end`]:[e.as[0],e.as[1]]:r=[pe(e,{forAs:!0}),pe(e,{binSuffix:"end",forAs:!0})];const s={...eb(t,void 0)},o=bj(s,e.field),{signal:a,extentSignal:u}=Rbe(n,o);if(qv(s.extent)){const c=s.extent;i=Zz(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 po extends _t{clone(){return new po(null,Be(this.bins))}constructor(t,n){super(t),this.bins=n}static makeFromEncoding(t,n){const r=n.reduceFieldDef((i,s,o)=>{if(Dr(s)&&Mt(s.bin)){const{key:a,binComponent:u}=T4(s,s.bin,n);i[a]={...u,...i[a],...Mbe(n,s,o,n.config)}}return i},{});return Et(r)?null:new po(t,r)}static makeFromTransform(t,n,r){const{key:i,binComponent:s}=T4(n,n.bin,r);return new po(t,{[i]:s})}merge(t,n){for(const r of ie(t.bins))r in this.bins?(n(t.bins[r].signal,this.bins[r].signal),this.bins[r].as=oo([...this.bins[r].as,...t.bins[r].as],rt)):this.bins[r]=t.bins[r];for(const r of t.children)t.removeChild(r),r.parent=this;t.remove()}producedFields(){return new Set(Yn(this.bins).map(t=>t.as).flat(2))}dependentFields(){return new Set(Yn(this.bins).map(t=>t.field))}hash(){return`Bin ${rt(this.bins)}`}assemble(){return Yn(this.bins).flatMap(t=>{const n=[],[r,...i]=t.as,{extent:s,...o}=t.bin,a={type:"bin",field:Gi(t.field),as:r,signal:t.signal,...qv(s)?{extent:null}:{extent:s},...t.span?{span:{signal:`span(${t.span})`}}:{},...o};!s&&t.extentSignal&&(n.push({type:"extent",field:Gi(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:pe({field:r[l]},{expr:"datum"}),as:u[l]});return t.formula&&n.push({type:"formula",expr:t.formula,as:t.formulaAs}),n})}}function Nbe(e,t,n,r){var s;const i=Gt(r)?r.encoding[Lo(t)]:void 0;if(Dr(n)&&Gt(r)&&EB(n,i,r.markDef,r.config)){e.add(pe(n,{})),e.add(pe(n,{suffix:"end"}));const{mark:o,markDef:a,config:u}=r,l=Eu({fieldDef:n,markDef:a,config:u});Ip(o)&&l!==.5&&ln(t)&&(e.add(pe(n,{suffix:ab})),e.add(pe(n,{suffix:ub}))),n.bin&&Wg(n,t)&&e.add(pe(n,{binSuffix:"range"}))}else if(xP(t)){const o=bP(t);e.add(r.getName(o))}else e.add(pe(n));return Fc(n)&&d0e((s=n.scale)==null?void 0:s.range)&&e.add(n.scale.range.field),e}function Obe(e,t){for(const n of ie(t)){const r=t[n];for(const i of ie(r))n in e?e[n][i]=new Set([...e[n][i]??[],...r[i]]):e[n]={[i]:r[i]}}}class _s extends _t{clone(){return new _s(null,new Set(this.dimensions),Be(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([pe(o,{forAs:!0})]);else{if(va(u)||Wu(u)){const c=va(u)?"argmin":"argmax",f=u[c];i[f]??(i[f]={}),i[f][c]=new Set([pe({op:c,field:f},{forAs:!0})])}else i[l]??(i[l]={}),i[l][u]=new Set([pe(o,{forAs:!0})]);Io(a)&&n.scaleDomain(a)==="unaggregated"&&(i[l]??(i[l]={}),i[l].min=new Set([pe({field:l,aggregate:"min"},{forAs:!0})]),i[l].max=new Set([pe({field:l,aggregate:"max"},{forAs:!0})]))}else Nbe(s,a,o,n)}),s.size+ie(i).length===0)?null:new _s(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||pe(o,{forAs:!0})])):(s[u]??(s[u]={}),(r=s[u])[a]??(r[a]=new Set),s[u][a].add(l||pe(o,{forAs:!0}))))}for(const o of n.groupby??[])i.add(o);return i.size+ie(s).length===0?null:new _s(t,i,s)}merge(t){return fP(this.dimensions,t.dimensions)?(Obe(this.measures,t.measures),!0):(Ige("different dimensions, cannot merge"),!1)}addDimensions(t){t.forEach(this.dimensions.add,this.dimensions)}dependentFields(){return new Set([...this.dimensions,...ie(this.measures)])}producedFields(){const t=new Set;for(const n of ie(this.measures))for(const r of ie(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 ${rt({dimensions:this.dimensions,measures:this.measures})}`}assemble(){const t=[],n=[],r=[];for(const s of ie(this.measures))for(const o of ie(this.measures[s]))for(const a of this.measures[s][o])r.push(a),t.push(o),n.push(s==="*"?null:Gi(s));return{type:"aggregate",groupby:[...this.dimensions].map(Gi),ops:t,fields:n,as:r}}}class Ud extends _t{constructor(t,n,r,i){super(t),this.model=n,this.name=r,this.data=i;for(const s of Oi){const o=n.facet[s];if(o){const{bin:a,sort:u}=o;this[s]={name:n.getName(`${s}_domain`),fields:[pe(o),...Mt(a)?[pe(o,{binSuffix:"end"})]:[]],...fa(u)?{sortField:u}:ee(u)?{sortIndexField:hd(o,s)}:{}}}}this.childModel=n.child}hash(){let t="Facet";for(const n of Oi)this[n]&&(t+=` ${n.charAt(0)}:${rt(this[n])}`);return t}get fields(){var n;const t=[];for(const r of Oi)(n=this[r])!=null&&n.fields&&t.push(...this[r].fields);return t}dependentFields(){const t=new Set(this.fields);for(const n of Oi)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 Ca){const r=this.childModel.component.scales[n];if(r&&!r.merged){const i=r.get("type"),s=r.get("range");if(Tn(i)&&kc(s)){const o=yb(this.childModel,n),a=e$(o);a?t[n]=a:ae(ZC(n))}}}return t}assembleRowColumnHeaderData(t,n,r){const i={row:"y",column:"x",facet:void 0}[t],s=[],o=[],a=[];i&&r&&r[i]&&(n?(s.push(`distinct_${r[i]}`),o.push("max")):(s.push(r[i]),o.push("distinct")),a.push(`distinct_${r[i]}`));const{sortField:u,sortIndexField:l}=this[t];if(u){const{op:c=Zv,field:f}=u;s.push(f),o.push(c),a.push(pe(u,{forAs:!0}))}else l&&(s.push(l),o.push("max"),a.push(l));return{name:this[t].name,source:n??this.data,transform:[{type:"aggregate",groupby:this[t].fields,...s.length?{fields:s,ops:o,as:a}:{}}]}}assembleFacetHeaderData(t){var u;const{columns:n}=this.model.layout,{layoutHeaders:r}=this.model.component,i=[],s={};for(const l of G3){for(const c of Y3){const f=(r[l]&&r[l][c])??[];for(const d of f)if(((u=d.axes)==null?void 0:u.length)>0){s[l]=!0;break}}if(s[l]){const c=`length(data("${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}_${this.row.name}`;const a=[].concat(r.x??[],r.y??[]),u=a.map(()=>"distinct");t.push({name:n,source:this.data,transform:[{type:"aggregate",groupby:this.fields,fields:a,ops:u}]})}for(const a of[ca,la])this[a]&&t.push(this.assembleRowColumnHeaderData(a,n,r));if(o){const a=this.assembleFacetHeaderData(r);a&&t.push(...a)}return t}}function N4(e){return e.startsWith("'")&&e.endsWith("'")||e.startsWith('"')&&e.endsWith('"')?e.slice(1,-1):e}function Lbe(e,t){const n=zC(e);if(t==="number")return`toNumber(${n})`;if(t==="boolean")return`toBoolean(${n})`;if(t==="string")return`toString(${n})`;if(t==="date")return`toDate(${n})`;if(t==="flatten")return n;if(t.startsWith("date:")){const r=N4(t.slice(5,t.length));return`timeParse(${n},'${r}')`}else if(t.startsWith("utc:")){const r=N4(t.slice(4,t.length));return`utcParse(${n},'${r}')`}else return ae(Gpe(t)),null}function Ibe(e){const t={};return wm(e.filter,n=>{if(eB(n)){let r=null;r3(n)?r=li(n.equal):s3(n)?r=li(n.lte):i3(n)?r=li(n.lt):o3(n)?r=li(n.gt):a3(n)?r=li(n.gte):u3(n)?r=n.range[0]:l3(n)&&(r=(n.oneOf??n.in)[0]),r&&(Cc(r)?t[n.field]="date":ft(r)?t[n.field]="number":me(r)&&(t[n.field]="string")),n.timeUnit&&(t[n.field]="date")}}),t}function Pbe(e){const t={};function n(r){ld(r)?t[r.field]="date":r.type==="quantitative"&&xpe(r.aggregate)?t[r.field]="number":rd(r.field)>1?r.field in t||(t[r.field]="flatten"):Fc(r)&&fa(r.sort)&&rd(r.sort.field)>1&&(r.sort.field in t||(t[r.sort.field]="flatten"))}if((Gt(e)||mi(e))&&e.forEachFieldDef((r,i)=>{if(Dr(r))n(r);else{const s=Ac(i),o=e.fieldDef(s);n({...r,type:o.type})}}),Gt(e)){const{mark:r,markDef:i,encoding:s}=e;if(Hu(r)&&!e.encoding.order){const o=i.orient==="horizontal"?"y":"x",a=s[o];ue(a)&&a.type==="quantitative"&&!(a.field in t)&&(t[a.field]="number")}}return t}function Bbe(e){const t={};if(Gt(e)&&e.component.selection)for(const n of ie(e.component.selection)){const r=e.component.selection[n];for(const i of r.project.items)!i.channel&&rd(i.field)>1&&(t[i.field]="flatten")}return t}class or extends _t{clone(){return new or(null,Be(this._parse))}constructor(t,n){super(t),this._parse=n}hash(){return`Parse ${rt(this._parse)}`}static makeExplicit(t,n,r){var o;let i={};const s=n.data;return!lu(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 ie(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]:ae(IM(a,r[a],u.value)))}for(const a of ie(n)){const u=i.get(a);u!==void 0&&(u===n[a]?delete n[a]:ae(IM(a,n[a],u)))}const s=new Fa(n,r);i.copyAll(s);const o={};for(const a of ie(s.combine())){const u=s.get(a);u!==null&&(o[a]=u)}return ie(o).length===0||i.parseNothing?null:new or(t,o)}get parse(){return this._parse}merge(t){this._parse={...this._parse,...t.parse},t.remove()}assembleFormatParse(){const t={};for(const n of ie(this._parse)){const r=this._parse[n];rd(n)===1&&(t[n]=r)}return t}producedFields(){return new Set(ie(this._parse))}dependentFields(){return new Set(ie(this._parse))}assembleTransforms(t=!1){return ie(this._parse).filter(n=>t?rd(n)>1:!0).map(n=>{const r=Lbe(n,this._parse[n]);return r?{type:"formula",expr:r,as:Id(n)}:null}).filter(n=>n!==null)}}class Su extends _t{clone(){return new Su(null)}constructor(t){super(t)}dependentFields(){return new Set}producedFields(){return new Set([Ds])}hash(){return"Identifier"}assemble(){return{type:"identifier",as:Ds}}}class Kg extends _t{clone(){return new Kg(null,this.params)}constructor(t,n){super(t),this.params=n}dependentFields(){return new Set}producedFields(){}hash(){return`Graticule ${rt(this.params)}`}assemble(){return{type:"graticule",...this.params===!0?{}:this.params}}}class Zg extends _t{clone(){return new Zg(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 ${rt(this.params)}`}assemble(){return{type:"sequence",...this.params}}}class hc extends _t{constructor(t){super(null),t??(t={name:"source"});let n;if(lu(t)||(n=t.format?{...Hr(t.format,["parse"])}:{}),Pp(t))this._data={values:t.values};else if(cd(t)){if(this._data={url:t.url},!n.type){let r=/(?:\.([^.]+))?$/.exec(t.url)[1];Qe(["json","csv","tsv","dsv","topojson"],r)||(r="json"),n.type=r}}else yz(t)?this._data={values:[{type:"Sphere"}]}:(gz(t)||lu(t))&&(this._data={});this._generator=lu(t),t.name&&(this._name=t.name),n&&!Et(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 O4=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},zbe=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)},qh;function Z3(e){return e instanceof hc||e instanceof Kg||e instanceof Zg}class J3{constructor(){qh.set(this,void 0),O4(this,qh,!1,"f")}setModified(){O4(this,qh,!0,"f")}get modifiedFlag(){return zbe(this,qh,"f")}}qh=new WeakMap;class Mc extends J3{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 Q3 extends J3{optimize(t){this.run(t);for(const n of t.children)this.optimize(n);return this.modifiedFlag}}class jbe extends Q3{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 Ube extends Q3{constructor(t){super(),this.requiresSelectionId=t&&U3(t)}run(t){t instanceof Su&&(this.requiresSelectionId&&(Z3(t.parent)||t.parent instanceof _s||t.parent instanceof or)||(this.setModified(),t.remove()))}}class qbe extends J3{optimize(t){return this.run(t,new Set),this.modifiedFlag}run(t,n){let r=new Set;t instanceof fo&&(r=t.producedFields(),PC(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 Wbe extends Q3{constructor(){super()}run(t){t instanceof Gr&&!t.isRequired()&&(this.setModified(),t.remove())}}class Hbe extends Mc{run(t){if(!Z3(t)&&!(t.numChildren()>1)){for(const n of t.children)if(n instanceof or)if(t instanceof or)this.setModified(),t.merge(n);else{if(BC(t.producedFields(),n.dependentFields()))continue;this.setModified(),n.swapWithParent()}}}}class Gbe extends Mc{run(t){const n=[...t.children],r=t.children.filter(i=>i instanceof or);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 ie(a))u in i?i[u]!==a[u]&&s.add(u):i[u]=a[u]}for(const o of s)delete i[o];if(!Et(i)){this.setModified();const o=new or(t,i);for(const a of n){if(a instanceof or)for(const u of ie(i))delete a.parse[u];t.removeChild(a),a.parent=o,a instanceof or&&ie(a.parse).length===0&&a.remove()}}}}}class Ybe extends Mc{run(t){t instanceof Gr||t.numChildren()>0||t instanceof Ud||t instanceof hc||(this.setModified(),t.remove())}}class Vbe extends Mc{run(t){const n=t.children.filter(i=>i instanceof fo),r=n.pop();for(const i of n)this.setModified(),r.merge(i)}}class Xbe extends Mc{run(t){const n=t.children.filter(i=>i instanceof _s),r={};for(const i of n){const s=rt(i.groupBy);s in r||(r[s]=[]),r[s].push(i)}for(const i of ie(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 Kbe extends Mc{constructor(t){super(),this.model=t}run(t){const n=!(Z3(t)||t instanceof jd||t instanceof or||t instanceof Su),r=[],i=[];for(const s of t.children)s instanceof po&&(n&&!BC(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 po?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 Zbe extends Mc{run(t){const n=[...t.children];if(!nd(n,o=>o instanceof Gr)||t.numChildren()<=1)return;const i=[];let s;for(const o of n)if(o instanceof Gr){let a=o;for(;a.numChildren()===1;){const[u]=a.children;if(u instanceof Gr)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 Rc extends _t{clone(){return new Rc(null,Be(this.transform))}constructor(t,n){super(t),this.transform=n}addDimensions(t){this.transform.groupby=oo(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??pe(t)}hash(){return`JoinAggregateTransform ${rt(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 gd extends _t{clone(){return new gd(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=Io(f)&&n.getScaleComponent(f);if(d){const h=d.get("type"),{aggregate:p}=c,g=m3({scaleChannel:f,markDef:s,config:i,scaleType:h,isCountAggregate:Uv(p)});g!=="show"&&g!=="always-valid"&&(l[c.field]=c)}return l},{});return ie(u).length?new gd(t,u):null}dependentFields(){return new Set(ie(this.filter))}producedFields(){return new Set}hash(){return`FilterInvalid ${rt(this.filter)}`}assemble(){const t=ie(this.filter).reduce((n,r)=>{const i=this.filter[r],s=pe(i,{expr:"datum"});return i!==null&&(i.type==="temporal"?n.push(`(isDate(${s}) || (${fE(s)}))`):i.type==="quantitative"&&n.push(fE(s))),n},[]);return t.length>0?{type:"filter",expr:t.join(" && ")}:null}}function fE(e){return`isValid(${e}) && isFinite(+${e})`}function Jbe(e){return e.stack.stackBy.reduce((t,n)=>{const r=n.fieldDef,i=pe(r);return i&&t.push(i),t},[])}function Qbe(e){return ee(e)&&e.every(t=>me(t))&&e.length>1}class ha extends _t{clone(){return new ha(null,Be(this._stack))}constructor(t,n){super(t),this._stack=n}static makeFromTransform(t,n){const{stack:r,groupby:i,as:s,offset:o="zero"}=n,a=[],u=[];if(n.sort!==void 0)for(const f of n.sort)a.push(f.field),u.push(un(f.order,"ascending"));const l={field:a,order:u};let c;return Qbe(s)?c=s:me(s)?c=[s,`${s}_end`]:c=[`${n.stack}_start`,`${n.stack}_end`],new ha(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 Fs(p)}).filter(h=>!!h),c=Jbe(n),f=n.encoding.order;let d;if(ee(f)||ue(f))d=NP(f);else{const h=AB(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 ha(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 ${rt(this._stack)}`}getGroupbyFields(){const{dimensionFieldDefs:t,impute:n,groupby:r}=this._stack;return t.length>0?t.map(i=>i.bin?n?[pe(i,{binSuffix:"mid"})]:[pe(i,{}),pe(i,{binSuffix:"end"})]:[pe(i)]).flat():r??[]}assemble(){const 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0:i.unit;return t==="temporal"||r?o2e(e,t,r):[e]}function Oa(e,t,n,r){const{encoding:i,markDef:s,mark:o,config:a,stack:u}=n,l=yn(i[r]),{type:c}=l,f=l.timeUnit,d=q1e({invalid:wo("invalid",s,a),isPath:Hu(o)});if(f0e(t)){const g=Oa(e,void 0,n,r),m=dx(t.unionWith,c,f);return Xs([...m,...g.value])}else{if(Ce(t))return Xs([t]);if(t&&t!=="unaggregated"&&!aB(t))return Xs(dx(t,c,f))}if(u&&r===u.fieldChannel){if(u.offset==="normalize")return ii([[0,1]]);const g=n.requestDataName(d);return ii([{data:g,field:n.vgField(r,{suffix:"start"})},{data:g,field:n.vgField(r,{suffix:"end"})}])}const h=Io(r)&&ue(l)?u2e(n,r,e):void 0;if(Po(l)){const g=dx([l.datum],c,f);return ii(g)}const p=l;if(t==="unaggregated"){const{field:g}=l;return ii([{data:n.requestDataName(d),field:pe({field:g,aggregate:"min"})},{data:n.requestDataName(d),field:pe({field:g,aggregate:"max"})}])}else if(Mt(p.bin)){if(Tn(e))return ii(e==="bin-ordinal"?[]:[{data:Op(h)?n.requestDataName(d):n.requestDataName(Vt.Raw),field:n.vgField(r,Wg(p,r)?{binSuffix:"range"}:{}),sort:h===!0||!ge(h)?{field:n.vgField(r,{}),op:"min"}:h}]);{const{bin:g}=p;if(Mt(g)){const m=K3(n,p.field,g);return ii([new En(()=>{const y=n.getSignalName(m);return`[${y}.start, ${y}.stop]`})])}else return ii([{data:n.requestDataName(d),field:n.vgField(r,{})}])}}else if(p.timeUnit&&Qe(["time","utc"],e)){const g=i[Lo(r)];if(EB(p,g,s,a)){const m=n.requestDataName(d),y=Eu({fieldDef:p,fieldDef2:g,markDef:s,config:a}),v=Ip(o)&&y!==.5&&ln(r);return ii([{data:m,field:n.vgField(r,v?{suffix:ab}:{})},{data:m,field:n.vgField(r,{suffix:v?ub:"end"})}])}}return ii(h?[{data:Op(h)?n.requestDataName(d):n.requestDataName(Vt.Raw),field:n.vgField(r),sort:h}]:[{data:n.requestDataName(d),field:n.vgField(r)}])}function hx(e,t){const{op:n,field:r,order:i}=e;return{op:n??(t?"sum":Zv),...r?{field:Gi(r)}:{},...i?{order:i}:{}}}function a2e(e,t){var a;const 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e.explicit&&t.explicit&&ae(xge(n,r,e.value,t.value)),{explicit:e.explicit,value:[...e.value,...t.value]}}function l2e(e){const t=oo(e.map(o=>{if(sa(o)){const{sort:a,...u}=o;return u}return o}),rt),n=oo(e.map(o=>{if(sa(o)){const a=o.sort;return a!==void 0&&!Op(a)&&("op"in a&&a.op==="count"&&delete a.field,a.order==="ascending"&&delete a.order),a}}).filter(o=>o!==void 0),rt);if(t.length===0)return;if(t.length===1){const o=e[0];if(sa(o)&&n.length>0){let a=n[0];if(n.length>1){ae(BM);const u=n.filter(l=>ge(l)&&"op"in l&&l.op!=="min");n.every(l=>ge(l)&&"op"in l)&&u.length===1?a=u[0]:a=!0}else if(ge(a)&&"field"in a){const u=a.field;o.field===u&&(a=a.order?{order:a.order}:!0)}return{...o,sort:a}}return o}const r=oo(n.map(o=>Op(o)||!("op"in o)||me(o.op)&&be(vpe,o.op)?o:(ae(wge(o)),!0)),rt);let i;r.length===1?i=r[0]:r.length>1&&(ae(BM),i=!0);const s=oo(e.map(o=>sa(o)?o.data:null),o=>o);return s.length===1&&s[0]!==null?{data:s[0],fields:t.map(a=>a.field),...i?{sort:i}:{}}:{fields:t,...i?{sort:i}:{}}}function e$(e){if(sa(e)&&me(e.field))return e.field;if(Epe(e)){let t;for(const n of e.fields)if(sa(n)&&me(n.field)){if(!t)t=n.field;else if(t!==n.field)return ae(Ege),t}return ae(Age),t}else if(Ape(e)){ae(Sge);const t=e.fields[0];return me(t)?t:void 0}}function yb(e,t){const r=e.component.scales[t].get("domains").map(i=>(sa(i)&&(i.data=e.lookupDataSource(i.data)),i));return l2e(r)}function wj(e){return Wd(e)||t$(e)?e.children.reduce((t,n)=>t.concat(wj(n)),P4(e)):P4(e)}function P4(e){return ie(e.component.scales).reduce((t,n)=>{const r=e.component.scales[n];if(r.merged)return t;const i=r.combine(),{name:s,type:o,selectionExtent:a,domains:u,range:l,reverse:c,...f}=i,d=c2e(i.range,s,n,e),h=yb(e,n),p=a?eye(e,a,r,h):null;return t.push({name:s,type:o,...h?{domain:h}:{},...p?{domainRaw:p}:{},range:d,...c!==void 0?{reverse:c}:{},...f}),t},[])}function c2e(e,t,n,r){if(ln(n)){if(kc(e))return{step:{signal:`${t}_step`}}}else if(ge(e)&&sa(e))return{...e,data:r.lookupDataSource(e.data)};return e}class Ej extends Fa{constructor(t,n){super({},{name:t}),this.merged=!1,this.setWithExplicit("type",n)}domainHasZero(){const t=this.get("type");if(Qe([jn.LOG,jn.TIME,jn.UTC],t))return"definitely-not";const n=this.get("zero");if(n===!0||n===void 0&&Qe([jn.LINEAR,jn.SQRT,jn.POW],t))return"definitely";const r=this.get("domains");if(r.length>0){let i=!1,s=!1,o=!1;for(const a of r){if(ee(a)){const u=a[0],l=a[a.length-1];if(ft(u)&&ft(l))if(u<=0&&l>=0){i=!0;continue}else{s=!0;continue}}o=!0}if(i)return"definitely";if(s&&!o)return"definitely-not"}return"maybe"}}const f2e=["range","scheme"];function d2e(e){const t=e.component.scales;for(const n of VC){const r=t[n];if(!r)continue;const i=h2e(n,e);r.setWithExplicit("range",i)}}function B4(e,t){const 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o=e===qt||e==="xOffset"?"width":"height",a=r[o];if(Ao(a)){if(ln(e))if(Tn(s)){const f=Sj(a,t,e);if(f)return Xs({step:f})}else ae(UP(o));else if(Ig(e)){const f=e===Pu?"x":"y";if(t.getScaleComponent(f).get("type")==="band"){const p=kj(a,s);if(p)return Xs(p)}}}const{rangeMin:u,rangeMax:l}=n,c=g2e(e,t);return(u!==void 0||l!==void 0)&&Yw(s,"rangeMin")&&ee(c)&&c.length===2?Xs([u??c[0],l??c[1]]):ii(c)}function p2e(e){return c0e(e)?{scheme:e.name,...Hr(e,["name"])}:{scheme:e}}function Aj(e,t,n,{center:r}={}){const i=Jr(e),s=t.getName(i),o=t.getSignalName.bind(t);return e===On&&Cs(n)?r?[En.fromName(a=>`${o(a)}/2`,s),En.fromName(a=>`-${o(a)}/2`,s)]:[En.fromName(o,s),0]:r?[En.fromName(a=>`-${o(a)}/2`,s),En.fromName(a=>`${o(a)}/2`,s)]:[0,En.fromName(o,s)]}function g2e(e,t){const{size:n,config:r,mark:i,encoding:s}=t,{type:o}=yn(s[e]),u=t.getScaleComponent(e).get("type"),{domain:l,domainMid:c}=t.specifiedScales[e];switch(e){case qt:case On:{if(Qe(["point","band"],u)){const f=Cj(e,n,r.view);if(Ao(f))return{step:Sj(f,t,e)}}return Aj(e,t,u)}case Pu:case Pd:return m2e(e,t,u);case Sa:{const f=b2e(i,r),d=x2e(i,n,t,r);return od(u)?v2e(f,d,y2e(u,r,l,e)):[f,d]}case Xi:return[0,Math.PI*2];case Ec:return[0,360];case Os:return[0,new En(()=>{const f=t.getSignalName(mi(t.parent)?"child_width":"width"),d=t.getSignalName(mi(t.parent)?"child_height":"height");return`min(${f},${d})/2`})];case Bu:return{step:1e3/r.scale.framesPerSecond};case Uu:return[r.scale.minStrokeWidth,r.scale.maxStrokeWidth];case qu:return[[1,0],[4,2],[2,1],[1,1],[1,2,4,2]];case Zr:return"symbol";case Kr:case No:case Oo:return u==="ordinal"?o==="nominal"?"category":"ordinal":c!==void 0?"diverging":i==="rect"||i==="geoshape"?"heatmap":"ramp";case ka:case zu:case ju:return[r.scale.minOpacity,r.scale.maxOpacity]}}function Sj(e,t,n){const{encoding:r}=t,i=t.getScaleComponent(n),s=WC(n),o=r[s];if(tz({step:e,offsetIsDiscrete:Ue(o)&&nB(o.type)})==="offset"&&OB(r,s)){const u=t.getScaleComponent(s);let c=`domain('${t.scaleName(s)}').length`;if(u.get("type")==="band"){const d=u.get("paddingInner")??u.get("padding")??0,h=u.get("paddingOuter")??u.get("padding")??0;c=`bandspace(${c}, ${d}, ${h})`}const f=i.get("paddingInner")??i.get("padding");return{signal:`${e.step} * ${c} / (1-${$pe(f)})`}}else return e.step}function kj(e,t){if(tz({step:e,offsetIsDiscrete:Tn(t)})==="offset")return{step:e.step}}function m2e(e,t,n){const r=e===Pu?"x":"y",i=t.getScaleComponent(r);if(!i)return Aj(r,t,n,{center:!0});const s=i.get("type"),o=t.scaleName(r),{markDef:a,config:u}=t;if(s==="band"){const l=Cj(r,t.size,t.config.view);if(Ao(l)){const c=kj(l,n);if(c)return c}return[0,{signal:`bandwidth('${o}')`}]}else{const l=t.encoding[r];if(ue(l)&&l.timeUnit){const c=JP(l.timeUnit,p=>`scale('${o}', ${p})`),f=t.config.scale.bandWithNestedOffsetPaddingInner,d=Eu({fieldDef:l,markDef:a,config:u})-.5,h=d!==0?` + ${d}`:"";if(f){const p=Ce(f)?`${f.signal}/2`+h:`${f/2+d}`,g=Ce(f)?`(1 - 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i={x:B4(n,"x"),y:B4(n,"y")};switch(e){case"bar":case"tick":{if(r.scale.maxBandSize!==void 0)return r.scale.maxBandSize;const s=j4(t,i,r.view);return ft(s)?s-1:new En(()=>`${s.signal} - 1`)}case"line":case"trail":case"rule":return r.scale.maxStrokeWidth;case"text":return r.scale.maxFontSize;case"point":case"square":case"circle":{if(r.scale.maxSize)return r.scale.maxSize;const s=j4(t,i,r.view);return ft(s)?Math.pow(z4*s,2):new En(()=>`pow(${z4} * ${s.signal}, 2)`)}}throw new Error(Wv("size",e))}function j4(e,t,n){const r=Ao(e.width)?e.width.step:Zw(n,"width"),i=Ao(e.height)?e.height.step:Zw(n,"height");return t.x||t.y?new En(()=>`min(${[t.x?t.x.signal:r,t.y?t.y.signal:i].join(", ")})`):Math.min(r,i)}function $j(e,t){Gt(e)?_2e(e,t):Dj(e,t)}function _2e(e,t){const n=e.component.scales,{config:r,encoding:i,markDef:s,specifiedScales:o}=e;for(const a of ie(n)){const u=o[a],l=n[a],c=e.getScaleComponent(a),f=yn(i[a]),d=u[t],h=c.get("type"),p=c.get("padding"),g=c.get("paddingInner"),m=Yw(h,t),y=uB(a,t);if(d!==void 0&&(m?y&&ae(y):ae(jP(h,t,a))),m&&y===void 0)if(d!==void 0){const v=f.timeUnit,b=f.type;switch(t){case"domainMax":case"domainMin":Cc(u[t])||b==="temporal"||v?l.set(t,{signal:tb(u[t],{type:b,timeUnit:v})},!0):l.set(t,u[t],!0);break;default:l.copyKeyFromObject(t,u)}}else{const v=oe(U4,t)?U4[t]({model:e,channel:a,fieldOrDatumDef:f,scaleType:h,scalePadding:p,scalePaddingInner:g,domain:u.domain,domainMin:u.domainMin,domainMax:u.domainMax,markDef:s,config:r,hasNestedOffsetScale:LB(i,a),hasSecondaryRangeChannel:!!i[Lo(a)]}):r.scale[t];v!==void 0&&l.set(t,v,!1)}}}const U4={bins:({model:e,fieldOrDatumDef:t})=>ue(t)?w2e(e,t):void 0,interpolate:({channel:e,fieldOrDatumDef:t})=>E2e(e,t.type),nice:({scaleType:e,channel:t,domain:n,domainMin:r,domainMax:i,fieldOrDatumDef:s})=>A2e(e,t,n,r,i,s),padding:({channel:e,scaleType:t,fieldOrDatumDef:n,markDef:r,config:i})=>S2e(e,t,i.scale,n,r,i.bar),paddingInner:({scalePadding:e,channel:t,markDef:n,scaleType:r,config:i,hasNestedOffsetScale:s})=>k2e(e,t,n.type,r,i.scale,s),paddingOuter:({scalePadding:e,channel:t,scaleType:n,scalePaddingInner:r,config:i,hasNestedOffsetScale:s})=>C2e(e,t,n,r,i.scale,s),reverse:({fieldOrDatumDef:e,scaleType:t,channel:n,config:r})=>{const i=ue(e)?e.sort:void 0;return $2e(t,i,n,r.scale)},zero:({channel:e,fieldOrDatumDef:t,domain:n,markDef:r,scaleType:i,config:s,hasSecondaryRangeChannel:o})=>F2e(e,t,n,r,i,s.scale,o)};function Fj(e){Gt(e)?d2e(e):Dj(e,"range")}function Dj(e,t){const n=e.component.scales;for(const r of e.children)t==="range"?Fj(r):$j(r,t);for(const r of ie(n)){let i;for(const s of e.children){const o=s.component.scales[r];if(o){const a=o.getWithExplicit(t);i=Au(i,a,t,"scale",pz((u,l)=>{switch(t){case"range":return u.step&&l.step?u.step-l.step:0}return 0}))}}n[r].setWithExplicit(t,i)}}function w2e(e,t){const n=t.bin;if(Mt(n)){const r=K3(e,t.field,n);return new En(()=>e.getSignalName(r))}else if(Pn(n)&&Sc(n)&&n.step!==void 0)return{step:n.step}}function E2e(e,t){if(Qe([Kr,No,Oo],e)&&t!=="nominal")return"hcl"}function A2e(e,t,n,r,i,s){var o;if(!((o=Fs(s))!=null&&o.bin||ee(n)||i!=null||r!=null||Qe([jn.TIME,jn.UTC],e)))return ln(t)?!0:void 0}function S2e(e,t,n,r,i,s){if(ln(e)){if(ao(t)){if(n.continuousPadding!==void 0)return n.continuousPadding;const{type:o,orient:a}=i;if(o==="bar"&&!(ue(r)&&(r.bin||r.timeUnit))&&(a==="vertical"&&e==="x"||a==="horizontal"&&e==="y"))return s.continuousBandSize}if(t===jn.POINT)return n.pointPadding}}function k2e(e,t,n,r,i,s=!1){if(e===void 0){if(ln(t)){const{bandPaddingInner:o,barBandPaddingInner:a,rectBandPaddingInner:u,tickBandPaddingInner:l,bandWithNestedOffsetPaddingInner:c}=i;return s?c:un(o,n==="bar"?a:n==="tick"?l:u)}else if(Ig(t)&&r===jn.BAND)return i.offsetBandPaddingInner}}function C2e(e,t,n,r,i,s=!1){if(e===void 0){if(ln(t)){const{bandPaddingOuter:o,bandWithNestedOffsetPaddingOuter:a}=i;if(s)return a;if(n===jn.BAND)return un(o,Ce(r)?{signal:`${r.signal}/2`}:r/2)}else if(Ig(t)){if(n===jn.POINT)return .5;if(n===jn.BAND)return i.offsetBandPaddingOuter}}}function $2e(e,t,n,r){if(n==="x"&&r.xReverse!==void 0)return Cs(e)&&t==="descending"?Ce(r.xReverse)?{signal:`!${r.xReverse.signal}`}:!r.xReverse:r.xReverse;if(Cs(e)&&t==="descending")return!0}function F2e(e,t,n,r,i,s,o){if(!!n&&n!=="unaggregated"&&Cs(i)){if(ee(n)){const u=n[0],l=n[n.length-1];if(ft(u)&&u<=0&&ft(l)&&l>=0)return!0}return!1}if(e==="size"&&t.type==="quantitative"&&!od(i))return!0;if(!(ue(t)&&t.bin)&&Qe([...Ca,...lpe],e)){const{orient:u,type:l}=r;return Qe(["bar","area","line","trail"],l)&&(u==="horizontal"&&e==="y"||u==="vertical"&&e==="x")?!1:Qe(["bar","area"],l)&&!o?!0:s==null?void 0:s.zero}return!1}function D2e(e,t,n,r,i=!1){const s=M2e(t,n,r,i),{type:o}=e;return Io(t)?o!==void 0?y0e(t,o)?ue(n)&&!m0e(o,n.type)?(ae(vge(o,s)),s):o:(ae(yge(t,o,s)),s):s:null}function M2e(e,t,n,r){var i;switch(t.type){case"nominal":case"ordinal":{if(gf(e)||J2(e)==="discrete")return e==="shape"&&t.type==="ordinal"&&ae(Q2(e,"ordinal")),"ordinal";if(Z2(e))return"band";if(ln(e)||Ig(e)){if(Qe(["rect","bar","image","rule","tick"],n.type)||r)return"band"}else if(n.type==="arc"&&e in YC)return"band";const s=n[Jr(e)];return ac(s)||ud(t)&&((i=t.axis)!=null&&i.tickBand)?"band":"point"}case"temporal":return gf(e)?"time":J2(e)==="discrete"?(ae(Q2(e,"temporal")),"ordinal"):ue(t)&&t.timeUnit&&Mn(t.timeUnit).utc?"utc":Z2(e)?"band":"time";case"quantitative":return gf(e)?ue(t)&&Mt(t.bin)?"bin-ordinal":"linear":J2(e)==="discrete"?(ae(Q2(e,"quantitative")),"ordinal"):Z2(e)?"band":"linear";case"geojson":return}throw new Error(BP(t.type))}function R2e(e,{ignoreRange:t}={}){Mj(e),_j(e);for(const n of g0e)$j(e,n);t||Fj(e)}function Mj(e){Gt(e)?e.component.scales=T2e(e):e.component.scales=O2e(e)}function T2e(e){const{encoding:t,mark:n,markDef:r}=e,i={};for(const s of VC){const o=yn(t[s]);if(o&&n===fB&&s===Zr&&o.type===Bd)continue;let a=o&&o.scale;if(o&&a!==null&&a!==!1){a??(a={});const u=LB(t,s),l=D2e(a,s,o,r,u);i[s]=new Ej(e.scaleName(`${s}`,!0),{value:l,explicit:a.type===l})}}return i}const N2e=pz((e,t)=>jM(e)-jM(t));function O2e(e){var t;const n=e.component.scales={},r={},i=e.component.resolve;for(const s of e.children){Mj(s);for(const o of ie(s.component.scales))if((t=i.scale)[o]??(t[o]=uj(o,e)),i.scale[o]==="shared"){const a=r[o],u=s.component.scales[o].getWithExplicit("type");a?s0e(a.value,u.value)?r[o]=Au(a,u,"type","scale",N2e):(i.scale[o]="independent",delete r[o]):r[o]=u}}for(const s of ie(r)){const o=e.scaleName(s,!0),a=r[s];n[s]=new Ej(o,a);for(const u of e.children){const l=u.component.scales[s];l&&(u.renameScale(l.get("name"),o),l.merged=!0)}}return n}class px{constructor(){this.nameMap={}}rename(t,n){this.nameMap[t]=n}has(t){return this.nameMap[t]!==void 0}get(t){for(;this.nameMap[t]&&t!==this.nameMap[t];)t=this.nameMap[t];return t}}function Gt(e){return(e==null?void 0:e.type)==="unit"}function mi(e){return(e==null?void 0:e.type)==="facet"}function t$(e){return(e==null?void 0:e.type)==="concat"}function Wd(e){return(e==null?void 0:e.type)==="layer"}class n${constructor(t,n,r,i,s,o,a){this.type=n,this.parent=r,this.config=s,this.parent=r,this.config=s,this.view=zn(a),this.name=t.name??i,this.title=za(t.title)?{text:t.title}:t.title?zn(t.title):void 0,this.scaleNameMap=r?r.scaleNameMap:new px,this.projectionNameMap=r?r.projectionNameMap:new px,this.signalNameMap=r?r.signalNameMap:new px,this.data=t.data,this.description=t.description,this.transforms=M1e(t.transform??[]),this.layout=n==="layer"||n==="unit"?{}:Ome(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:Jv(t)||(r==null?void 0:r.component.data.isFaceted)&&t.data===void 0},layoutSize:new Fa,layoutHeaders:{row:{},column:{},facet:{}},mark:null,resolve:{scale:{},axis:{},legend:{},...o?Be(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(){R2e(this)}parseProjection(){vj(this)}renameTopLevelLayoutSizeSignal(){this.getName("width")!=="width"&&this.renameSignal(this.getName("width"),"width"),this.getName("height")!=="height"&&this.renameSignal(this.getName("height"),"height")}parseLegends(){hj(this)}assembleEncodeFromView(t){const{style:n,...r}=t,i={};for(const s of ie(r)){const o=r[s];o!==void 0&&(i[s]=Ot(o))}return i}assembleGroupEncodeEntry(t){let n={};return this.view&&(n=this.assembleEncodeFromView(this.view)),!t&&(this.description&&(n.description=Ot(this.description)),this.type==="unit"||this.type==="layer")?{width:this.getSizeSignalRef("width"),height:this.getSizeSignalRef("height"),...n}:Et(n)?void 0:n}assembleLayout(){if(!this.layout)return;const{spacing:t,...n}=this.layout,{component:r,config:i}=this,s=tbe(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 Oi)t[r].title&&n.push(Xve(this,r));for(const r of G3)n=n.concat(Kve(this,r));return n}assembleAxes(){return Lve(this.component.axes,this.config)}assembleLegends(){return gj(this)}assembleProjections(){return Abe(this)}assembleTitle(){const{encoding:t,...n}=this.title??{},r={...DP(this.config.title).nonMarkTitleProperties,...n,...t?{encode:{update:t}}:{}};if(r.text)return Qe(["unit","layer"],this.type)?Qe(["middle",void 0],r.anchor)&&(r.frame??(r.frame="group")):r.anchor??(r.anchor="start"),Et(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||mi(this.parent)?wj(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 Ut((this.name?`${this.name}_`:"")+t)}getDataName(t){return this.getName(Vt[t].toLowerCase())}requestDataName(t){const n=this.getDataName(t),r=this.component.data.outputNodeRefCounts;return r[n]=(r[n]||0)+1,n}getSizeSignalRef(t){if(mi(this.parent)){const n=oj(t),r=jv(n),i=this.component.scales[r];if(i&&!i.merged){const s=i.get("type"),o=i.get("range");if(Tn(s)&&kc(o)){const a=i.get("name"),u=yb(this,r),l=e$(u);if(l){const c=pe({aggregate:"distinct",field:l},{expr:"datum"});return{signal:sj(a,i,c)}}else return ae(ZC(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(wP(t)&&Io(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(Tpe(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 Rj extends n${vgField(t,n={}){const r=this.fieldDef(t);if(r)return pe(r,n)}reduceFieldDef(t,n){return cme(this.getMapping(),(r,i,s)=>{const o=Fs(i);return o?t(r,o,s):r},n)}forEachFieldDef(t,n){S3(this.getMapping(),(r,i)=>{const s=Fs(r);s&&t(s,i)},n)}}class vb extends _t{clone(){return new vb(null,Be(this.transform))}constructor(t,n){super(t),this.transform=n,this.transform=Be(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 ${rt(this.transform)}`}assemble(){const{density:t,...n}=this.transform,r={type:"kde",field:t,...n};return r.resolve=this.transform.resolve,r}}class bb extends _t{clone(){return new bb(null,Be(this.transform))}constructor(t,n){super(t),this.transform=n,this.transform=Be(n)}dependentFields(){return new Set([this.transform.extent])}producedFields(){return new Set([])}hash(){return`ExtentTransform ${rt(this.transform)}`}assemble(){const{extent:t,param:n}=this.transform;return{type:"extent",field:t,signal:n}}}class xb extends _t{clone(){return new xb(this.parent,Be(this.transform))}constructor(t,n){super(t),this.transform=n,this.transform=Be(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 ${rt(this.transform)}`}assemble(){const{flatten:t,as:n}=this.transform;return{type:"flatten",fields:t,as:n}}}class _b extends _t{clone(){return new _b(null,Be(this.transform))}constructor(t,n){super(t),this.transform=n,this.transform=Be(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 ${rt(this.transform)}`}assemble(){const{fold:t,as:n}=this.transform;return{type:"fold",fields:t,as:n}}}class vf extends _t{clone(){return new vf(null,Be(this.fields),this.geojson,this.signal)}static parseAll(t,n){if(n.component.projection&&!n.component.projection.isFit)return t;let r=0;for(const i of[[Is,Ls],[Yi,Ps]]){const s=i.map(o=>{const a=yn(n.encoding[o]);return ue(a)?a.field:Po(a)?{expr:`${a.datum}`}:$s(a)?{expr:`${a.value}`}:void 0});(s[0]||s[1])&&(t=new vf(t,s,null,n.getName(`geojson_${r++}`)))}if(n.channelHasField(Zr)){const i=n.typedFieldDef(Zr);i.type===Bd&&(t=new vf(t,null,i.field,n.getName(`geojson_${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(me);return new Set([...this.geojson?[this.geojson]:[],...t])}producedFields(){return new Set}hash(){return`GeoJSON ${this.geojson} ${this.signal} ${rt(this.fields)}`}assemble(){return[...this.geojson?[{type:"filter",expr:`isValid(datum["${this.geojson}"])`}]:[],{type:"geojson",...this.fields?{fields:this.fields}:{},...this.geojson?{geojson:this.geojson}:{},signal:this.signal}]}}class jp extends _t{clone(){return new jp(null,this.projection,Be(this.fields),Be(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[[Is,Ls],[Yi,Ps]]){const i=r.map(o=>{const a=yn(n.encoding[o]);return ue(a)?a.field:Po(a)?{expr:`${a.datum}`}:$s(a)?{expr:`${a.value}`}:void 0}),s=r[0]===Yi?"2":"";(i[0]||i[1])&&(t=new jp(t,n.projectionName(),i,[n.getName(`x${s}`),n.getName(`y${s}`)]))}return t}dependentFields(){return new Set(this.fields.filter(me))}producedFields(){return new Set(this.as)}hash(){return`Geopoint ${this.projection} ${rt(this.fields)} ${rt(this.as)}`}assemble(){return{type:"geopoint",projection:this.projection,fields:this.fields,as:this.as}}}class Nl extends _t{clone(){return new Nl(null,Be(this.transform))}constructor(t,n){super(t),this.transform=n}dependentFields(){return new Set([this.transform.impute,this.transform.key,...this.transform.groupby??[]])}producedFields(){return new Set([this.transform.impute])}processSequence(t){const{start:n=0,stop:r,step:i}=t;return{signal:`sequence(${[n,r,...i?[i]:[]].join(",")})`}}static makeFromTransform(t,n){return new Nl(t,n)}static makeFromEncoding(t,n){const r=n.encoding,i=r.x,s=r.y;if(ue(i)&&ue(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=BB(n.mark,r);return new Nl(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 ${rt(this.transform)}`}assemble(){const{impute:t,key:n,keyvals:r,method:i,groupby:s,value:o,frame:a=[null,null]}=this.transform,u={type:"impute",field:t,key:n,...r?{keyvals:d1e(r)?this.processSequence(r):r}:{},method:"value",...s?{groupby:s}:{},value:!i||i==="value"?o:null};if(i&&i!=="value"){const l={type:"window",as:[`imputed_${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 wb extends _t{clone(){return new wb(null,Be(this.transform))}constructor(t,n){super(t),this.transform=n,this.transform=Be(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 ${rt(this.transform)}`}assemble(){const{loess:t,on:n,...r}=this.transform;return{type:"loess",x:n,y:t,...r}}}class Up extends _t{clone(){return new Up(null,Be(this.transform),this.secondary)}constructor(t,n,r){super(t),this.transform=n,this.secondary=r}static make(t,n,r,i){const s=n.component.data.sources,{from:o}=r;let a=null;if(h1e(o)){let u=Oj(o.data,s);u||(u=new hc(o.data),s.push(u));const l=n.getName(`lookup_${i}`);a=new Gr(u,l,Vt.Lookup,n.component.data.outputNodeRefCounts),n.component.data.outputNodes[l]=a}else if(p1e(o)){const u=o.param;r={as:u,...r};let l;try{l=n.getSelectionComponent(Ut(u),u)}catch{throw new Error(Ipe(u))}if(a=l.materialized,!a)throw new Error(Ppe(u))}return new Up(t,r,a.getSource())}dependentFields(){return new Set([this.transform.lookup])}producedFields(){return new Set(this.transform.as?ye(this.transform.as):this.transform.from.fields)}hash(){return`Lookup ${rt({transform:this.transform,secondary:this.secondary})}`}assemble(){let t;if(this.transform.from.fields)t={values:this.transform.from.fields,...this.transform.as?{as:ye(this.transform.as)}:{}};else{let n=this.transform.as;me(n)||(ae(Xpe),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 Eb extends _t{clone(){return new Eb(null,Be(this.transform))}constructor(t,n){super(t),this.transform=n,this.transform=Be(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 ${rt(this.transform)}`}assemble(){const{quantile:t,...n}=this.transform;return{type:"quantile",field:t,...n}}}class Ab extends _t{clone(){return new Ab(null,Be(this.transform))}constructor(t,n){super(t),this.transform=n,this.transform=Be(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 ${rt(this.transform)}`}assemble(){const{regression:t,on:n,...r}=this.transform;return{type:"regression",x:n,y:t,...r}}}class Sb extends _t{clone(){return new Sb(null,Be(this.transform))}constructor(t,n){super(t),this.transform=n}addDimensions(t){this.transform.groupby=oo((this.transform.groupby??[]).concat(t),n=>n)}producedFields(){}dependentFields(){return new Set([this.transform.pivot,this.transform.value,...this.transform.groupby??[]])}hash(){return`PivotTransform ${rt(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 kb extends _t{clone(){return new kb(null,Be(this.transform))}constructor(t,n){super(t),this.transform=n}dependentFields(){return new Set}producedFields(){return new Set}hash(){return`SampleTransform ${rt(this.transform)}`}assemble(){return{type:"sample",size:this.transform.sample}}}function Tj(e){let t=0;function n(r,i){if(r instanceof hc&&!r.isGenerator&&!cd(r.data)&&(e.push(i),i={name:null,source:i.name,transform:[]}),r instanceof or&&(r.parent instanceof hc&&!i.source?(i.format={...i.format,parse:r.assembleFormatParse()},i.transform.push(...r.assembleTransforms(!0))):i.transform.push(...r.assembleTransforms())),r instanceof Ud){i.name||(i.name=`data_${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 Kg||r instanceof Zg||r instanceof gd||r instanceof jd||r instanceof dd||r instanceof jp||r instanceof _s||r instanceof Up||r instanceof qd||r instanceof Rc||r instanceof _b||r instanceof xb||r instanceof vb||r instanceof wb||r instanceof Eb||r instanceof Ab||r instanceof Su||r instanceof kb||r instanceof Sb||r instanceof bb)&&i.transform.push(r.assemble()),(r instanceof po||r instanceof fo||r instanceof Nl||r instanceof ha||r instanceof vf)&&i.transform.push(...r.assemble()),r instanceof Gr&&(i.source&&i.transform.length===0?r.setSource(i.source):r.parent instanceof Gr?r.setSource(i.name):(i.name||(i.name=`data_${t++}`),r.setSource(i.name),r.numChildren()===1&&(e.push(i),i={name:null,source:i.name,transform:[]}))),r.numChildren()){case 0:r instanceof Gr&&(!i.source||i.transform.length>0)&&e.push(i);break;case 1:n(r.children[0],i);break;default:{i.name||(i.name=`data_${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 L2e(e){const t=[],n=Tj(t);for(const r of e.children)n(r,{source:e.name,name:null,transform:[]});return t}function I2e(e,t){const n=[],r=Tj(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 P2e(e){return e==="top"||e==="left"||Ce(e)?"header":"footer"}function B2e(e){for(const t of Oi)z2e(e,t);q4(e,"x"),q4(e,"y")}function z2e(e,t){var o;const{facet:n,config:r,child:i,component:s}=e;if(e.channelHasField(t)){const a=n[t],u=pd("title",null,r,t);let l=mf(a,r,{allowDisabling:!0,includeDefault:u===void 0||!!u});i.component.layoutHeaders[t].title&&(l=ee(l)?l.join(", "):l,l+=` / ${i.component.layoutHeaders[t].title}`,i.component.layoutHeaders[t].title=null);const c=pd("labelOrient",a.header,r,t),f=a.header!==null?un((o=a.header)==null?void 0:o.labels,r.header.labels,!0):!1,d=Qe(["bottom","right"],c)?"footer":"header";s.layoutHeaders[t]={title:a.header!==null?l:null,facetFieldDef:a,[d]:t==="facet"?[]:[Nj(e,t,f)]}}}function Nj(e,t,n){const r=t==="row"?"height":"width";return{labels:n,sizeSignal:e.child.component.layoutSize.get(r)?e.child.getSizeSignalRef(r):void 0,axes:[]}}function q4(e,t){const{child:n}=e;if(n.component.axes[t]){const{layoutHeaders:r,resolve:i}=e.component;if(i.axis[t]=X3(i,t),i.axis[t]==="shared"){const s=t==="x"?"column":"row",o=r[s];for(const a of n.component.axes[t]){const u=P2e(a.get("orient"));o[u]??(o[u]=[Nj(e,s,!1)]);const l=Uh(a,"main",e.config,{header:!0});l&&o[u][0].axes.push(l),a.mainExtracted=!0}}}}function j2e(e){r$(e),ay(e,"width"),ay(e,"height")}function U2e(e){r$(e);const t=e.layout.columns===1?"width":"childWidth",n=e.layout.columns===void 0?"height":"childHeight";ay(e,t),ay(e,n)}function r$(e){for(const t of e.children)t.parseLayoutSize()}function ay(e,t){const n=oj(t),r=jv(n),i=e.component.resolve,s=e.component.layoutSize;let o;for(const a of e.children){const u=a.component.layoutSize.getWithExplicit(n),l=i.scale[r]??uj(r,e);if(l==="independent"&&u.value==="step"){o=void 0;break}if(o){if(l==="independent"&&o.value!==u.value){o=void 0;break}o=Au(o,u,n,"")}else o=u}if(o){for(const a of e.children)e.renameSignal(a.getName(n),e.getName(t)),a.component.layoutSize.set(n,"merged",!1);s.setWithExplicit(t,o)}else s.setWithExplicit(t,{explicit:!1,value:void 0})}function q2e(e){const{size:t,component:n}=e;for(const r of Ca){const i=Jr(r);if(t[i]){const s=t[i];n.layoutSize.set(i,Ao(s)?"step":s,!0)}else{const s=W2e(e,i);n.layoutSize.set(i,s,!1)}}}function W2e(e,t){const n=t==="width"?"x":"y",r=e.config,i=e.getScaleComponent(n);if(i){const s=i.get("type"),o=i.get("range");if(Tn(s)){const a=ny(r.view,t);return kc(o)||Ao(a)?"step":a}else return Kw(r.view,t)}else{if(e.hasProjection||e.mark==="arc")return Kw(r.view,t);{const s=ny(r.view,t);return Ao(s)?s.step:s}}}function mE(e,t,n){return pe(t,{suffix:`by_${pe(e)}`,...n})}class lp extends Rj{constructor(t,n,r,i){super(t,"facet",n,r,i,t.resolve),this.child=u$(t.spec,this,this.getName("child"),void 0,i),this.children=[this.child],this.facet=this.initFacet(t.facet)}initFacet(t){if(!jg(t))return{facet:this.initFacetFieldDef(t,"facet")};const n=ie(t),r={};for(const i of n){if(![la,ca].includes(i)){ae(Wv(i,"facet"));break}const s=t[i];if(s.field===void 0){ae(Hw(s,i));break}r[i]=this.initFacetFieldDef(s,i)}return r}initFacetFieldDef(t,n){const r=A3(t,n);return r.header?r.header=zn(r.header):r.header===null&&(r.header=null),r}channelHasField(t){return oe(this.facet,t)}fieldDef(t){return this.facet[t]}parseData(){this.component.data=Cb(this),this.child.parseData()}parseLayoutSize(){r$(this)}parseSelections(){this.child.parseSelections(),this.component.selection=this.child.component.selection,Object.values(this.component.selection).some(t=>ho(t))&&QC(JC)}parseMarkGroup(){this.child.parseMarkGroup()}parseAxesAndHeaders(){this.child.parseAxesAndHeaders(),B2e(this)}assembleSelectionTopLevelSignals(t){return this.child.assembleSelectionTopLevelSignals(t)}assembleSignals(){return this.child.assembleSignals(),[]}assembleSelectionData(t){return this.child.assembleSelectionData(t)}getHeaderLayoutMixins(){const t={};for(const n of Oi)for(const r of Y3){const i=this.component.layoutHeaders[n],s=i[r],{facetFieldDef:o}=i;if(o){const a=pd("titleOrient",o.header,this.config,n);if(["right","bottom"].includes(a)){const u=gb(n,a);t.titleAnchor??(t.titleAnchor={}),t.titleAnchor[u]="end"}}if(s!=null&&s[0]){const a=n==="row"?"height":"width",u=r==="header"?"headerBand":"footerBand";n!=="facet"&&!this.child.component.layoutSize.get(a)&&(t[u]??(t[u]={}),t[u][n]=.5),i.title&&(t.offset??(t.offset={}),t.offset[n==="row"?"rowTitle":"columnTitle"]=10)}}return t}assembleDefaultLayout(){const{column:t,row:n}=this.facet,r=t?this.columnDistinctSignal():n?1:void 0;let i="all";return(!n&&this.component.resolve.scale.x==="independent"||!t&&this.component.resolve.scale.y==="independent")&&(i="none"),{...this.getHeaderLayoutMixins(),...r?{columns:r}:{},bounds:"full",align:i}}assembleLayoutSignals(){return this.child.assembleLayoutSignals()}columnDistinctSignal(){if(!(this.parent&&this.parent instanceof lp))return{signal:`length(data('${this.getName("column_domain")}'))`}}assembleGroupStyle(){}assembleGroup(t){return this.parent&&this.parent instanceof lp?{...this.channelHasField("column")?{encode:{update:{columns:{field:pe(this.facet.column,{prefix:"distinct"})}}}}:{},...super.assembleGroup(t)}:super.assembleGroup(t)}getCardinalityAggregateForChild(){const t=[],n=[],r=[];if(this.child instanceof lp){if(this.child.channelHasField("column")){const i=pe(this.child.facet.column);t.push(i),n.push("distinct"),r.push(`distinct_${i}`)}}else for(const i of Ca){const s=this.child.component.scales[i];if(s&&!s.merged){const o=s.get("type"),a=s.get("range");if(Tn(o)&&kc(a)){const u=yb(this.child,i),l=e$(u);l?(t.push(l),n.push("distinct"),r.push(`distinct_${l}`)):ae(ZC(i))}}}return{fields:t,ops:n,as:r}}assembleFacet(){const{name:t,data:n}=this.component.data.facetRoot,{row:r,column:i}=this.facet,{fields:s,ops:o,as:a}=this.getCardinalityAggregateForChild(),u=[];for(const c of Oi){const f=this.facet[c];if(f){u.push(pe(f));const{bin:d,sort:h}=f;if(Mt(d)&&u.push(pe(f,{binSuffix:"end"})),fa(h)){const{field:p,op:g=Zv}=h,m=mE(f,h);r&&i?(s.push(m),o.push("max"),a.push(m)):(s.push(p),o.push(g),a.push(m))}else if(ee(h)){const p=hd(f,c);s.push(p),o.push("max"),a.push(p)}}}const l=!!r&&!!i;return{name:t,data:n,groupby:u,...l||s.length>0?{aggregate:{...l?{cross:l}:{},...s.length?{fields:s,ops:o,as:a}:{}}}:{}}}facetSortFields(t){const{facet:n}=this,r=n[t];return 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c=(i=e.format)==null?void 0:i.feature;if((c||l)&&c!==l)continue;const f=(s=a.format)==null?void 0:s.mesh;if(!((u||f)&&u!==f)){if(Pp(e)&&Pp(a)){if(fi(e.values,a.values))return o}else if(cd(e)&&cd(a)){if(e.url===a.url)return o}else if(gz(e)&&e.name===o.dataName)return o}}return null}function G2e(e,t){if(e.data||!e.parent){if(e.data===null){const r=new hc({values:[]});return t.push(r),r}const n=Oj(e.data,t);if(n)return lu(e.data)||(n.data.format=cP({},e.data.format,n.data.format)),!n.hasName()&&e.data.name&&(n.dataName=e.data.name),n;{const r=new hc(e.data);return t.push(r),r}}else return e.parent.component.data.facetRoot?e.parent.component.data.facetRoot:e.parent.component.data.main}function Y2e(e,t,n){let r=0;for(const i of t.transforms){let s,o;if(A1e(i))o=e=new dd(e,i),s="derived";else if(L3(i)){const a=Ibe(i);o=e=or.makeWithAncestors(e,{},a,n)??e,e=new jd(e,t,i.filter)}else if(cz(i))o=e=po.makeFromTransform(e,i,t),s="number";else 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t=G2e(e,e.component.data.sources);const{outputNodes:n,outputNodeRefCounts:r}=e.component.data,i=e.data,o=!(i&&(lu(i)||cd(i)||Pp(i)))&&e.parent?e.parent.component.data.ancestorParse.clone():new U1e;lu(i)?(mz(i)?t=new Zg(t,i.sequence):I3(i)&&(t=new Kg(t,i.graticule)),o.parseNothing=!0):((m=i==null?void 0:i.format)==null?void 0:m.parse)===null&&(o.parseNothing=!0),t=or.makeExplicit(t,e,o)??t,t=new Su(t);const a=e.parent&&Wd(e.parent);(Gt(e)||mi(e))&&a&&(t=po.makeFromEncoding(t,e)??t),e.transforms.length>0&&(t=Y2e(t,e,o));const u=Bbe(e),l=Pbe(e);t=or.makeWithAncestors(t,{},{...u,...l},o)??t,Gt(e)&&(t=vf.parseAll(t,e),t=jp.parseAll(t,e)),(Gt(e)||mi(e))&&(a||(t=po.makeFromEncoding(t,e)??t),t=fo.makeFromEncoding(t,e)??t,t=dd.parseAllForSortIndex(t,e));const c=t=N0(Vt.Raw,e,t);if(Gt(e)){const y=_s.makeFromEncoding(t,e);y&&(t=y,U3(e)&&(t=new Su(t))),t=Nl.makeFromEncoding(t,e)??t,t=ha.makeFromEncoding(t,e)??t}let f,d;if(Gt(e)){const{markDef:y,mark:v,config:b}=e,x=wt("invalid",y,b),{marks:_,scales:A}=d=vz({invalid:x,isPath:Hu(v)});_!==A&&A==="include-invalid-values"&&(f=t=N0(Vt.PreFilterInvalid,e,t)),_==="exclude-invalid-values"&&(t=gd.make(t,e,d)??t)}const h=t=N0(Vt.Main,e,t);let p;if(Gt(e)&&d){const{marks:y,scales:v}=d;y==="include-invalid-values"&&v==="exclude-invalid-values"&&(t=gd.make(t,e,d)??t,p=t=N0(Vt.PostFilterInvalid,e,t))}Gt(e)&&Nve(e,h);let g=null;if(mi(e)){const y=e.getName("facet");t=H2e(t,e.facet)??t,g=new Ud(t,e,y,h.getSource()),n[y]=g}return{...e.component.data,outputNodes:n,outputNodeRefCounts:r,raw:c,main:h,facetRoot:g,ancestorParse:o,preFilterInvalid:f,postFilterInvalid:p}}function N0(e,t,n){const{outputNodes:r,outputNodeRefCounts:i}=t.component.data,s=t.getDataName(e),o=new Gr(n,s,e,i);return r[s]=o,o}class V2e extends n${constructor(t,n,r,i){var s,o,a,u;super(t,"concat",n,r,i,t.resolve),(((o=(s=t.resolve)==null?void 0:s.axis)==null?void 0:o.x)==="shared"||((u=(a=t.resolve)==null?void 0:a.axis)==null?void 0:u.y)==="shared")&&ae(Hpe),this.children=this.getChildren(t).map((l,c)=>u$(l,this,this.getName(`concat_${c}`),void 0,i))}parseData(){this.component.data=Cb(this);for(const t of this.children)t.parseData()}parseSelections(){this.component.selection={};for(const t of this.children){t.parseSelections();for(const n of ie(t.component.selection))this.component.selection[n]=t.component.selection[n]}Object.values(this.component.selection).some(t=>ho(t))&&QC(JC)}parseMarkGroup(){for(const t of this.children)t.parseMarkGroup()}parseAxesAndHeaders(){for(const t of this.children)t.parseAxesAndHeaders()}getChildren(t){return ib(t)?t.vconcat:N3(t)?t.hconcat:t.concat}parseLayoutSize(){U2e(this)}parseAxisGroup(){return null}assembleSelectionTopLevelSignals(t){return this.children.reduce((n,r)=>r.assembleSelectionTopLevelSignals(n),t)}assembleSignals(){return this.children.forEach(t=>t.assembleSignals()),[]}assembleLayoutSignals(){const t=V3(this);for(const n of this.children)t.push(...n.assembleLayoutSignals());return t}assembleSelectionData(t){return this.children.reduce((n,r)=>r.assembleSelectionData(n),t)}assembleMarks(){return this.children.map(t=>{const n=t.assembleTitle(),r=t.assembleGroupStyle(),i=t.assembleGroupEncodeEntry(!1);return{type:"group",name:t.getName("group"),...n?{title:n}:{},...r?{style:r}:{},...i?{encode:{update:i}}:{},...t.assembleGroup()}})}assembleGroupStyle(){}assembleDefaultLayout(){const t=this.layout.columns;return{...t!=null?{columns:t}:{},bounds:"full",align:"each"}}}function X2e(e){return e===!1||e===null}const K2e={disable:1,gridScale:1,scale:1,...TB,labelExpr:1,encode:1},Lj=ie(K2e);class i$ extends Fa{constructor(t={},n={},r=!1){super(),this.explicit=t,this.implicit=n,this.mainExtracted=r}clone(){return new i$(Be(this.explicit),Be(this.implicit),this.mainExtracted)}hasAxisPart(t){return t==="axis"?!0:t==="grid"||t==="title"?!!this.get(t):!X2e(this.get(t))}hasOrientSignalRef(){return Ce(this.explicit.orient)}}function Z2e(e,t,n){const{encoding:r,config:i}=e,s=yn(r[t])??yn(r[Lo(t)]),o=e.axis(t)||{},{format:a,formatType:u}=o;if(uc(u))return{text:bs({fieldOrDatumDef:s,field:"datum.value",format:a,formatType:u,config:i}),...n};if(a===void 0&&u===void 0&&i.customFormatTypes){if(ad(s)==="quantitative"){if(ud(s)&&s.stack==="normalize"&&i.normalizedNumberFormatType)return{text:bs({fieldOrDatumDef:s,field:"datum.value",format:i.normalizedNumberFormat,formatType:i.normalizedNumberFormatType,config:i}),...n};if(i.numberFormatType)return{text:bs({fieldOrDatumDef:s,field:"datum.value",format:i.numberFormat,formatType:i.numberFormatType,config:i}),...n}}if(ad(s)==="temporal"&&i.timeFormatType&&ue(s)&&!s.timeUnit)return{text:bs({fieldOrDatumDef:s,field:"datum.value",format:i.timeFormat,formatType:i.timeFormatType,config:i}),...n}}return n}function J2e(e){return Ca.reduce((t,n)=>(e.component.scales[n]&&(t[n]=[sxe(n,e)]),t),{})}const Q2e={bottom:"top",top:"bottom",left:"right",right:"left"};function exe(e){const{axes:t,resolve:n}=e.component,r={top:0,bottom:0,right:0,left:0};for(const i of e.children){i.parseAxesAndHeaders();for(const s of ie(i.component.axes))n.axis[s]=X3(e.component.resolve,s),n.axis[s]==="shared"&&(t[s]=txe(t[s],i.component.axes[s]),t[s]||(n.axis[s]="independent",delete t[s]))}for(const i of Ca){for(const s of e.children)if(s.component.axes[i]){if(n.axis[i]==="independent"){t[i]=(t[i]??[]).concat(s.component.axes[i]);for(const o of s.component.axes[i]){const{value:a,explicit:u}=o.getWithExplicit("orient");if(!Ce(a)){if(r[a]>0&&!u){const l=Q2e[a];r[a]>r[l]&&o.set("orient",l,!1)}r[a]++}}}delete s.component.axes[i]}if(n.axis[i]==="independent"&&t[i]&&t[i].length>1)for(const[s,o]of(t[i]||[]).entries())s>0&&o.get("grid")&&!o.explicit.grid&&(o.implicit.grid=!1)}}function txe(e,t){if(e){if(e.length!==t.length)return;const n=e.length;for(let r=0;rn.clone());return e}function nxe(e,t){for(const n of Lj){const r=Au(e.getWithExplicit(n),t.getWithExplicit(n),n,"axis",(i,s)=>{switch(n){case"title":return IP(i,s);case"gridScale":return{explicit:i.explicit,value:un(i.value,s.value)}}return ob(i,s,n,"axis")});e.setWithExplicit(n,r)}return e}function rxe(e,t,n,r,i){if(t==="disable")return n!==void 0;switch(n=n||{},t){case"titleAngle":case"labelAngle":return e===(Ce(n.labelAngle)?n.labelAngle:Lp(n.labelAngle));case"values":return!!n.values;case"encode":return!!n.encoding||!!n.labelAngle;case"title":if(e===tj(r,i))return!0}return e===n[t]}const ixe=new Set(["grid","translate","format","formatType","orient","labelExpr","tickCount","position","tickMinStep"]);function sxe(e,t){var y,v;let n=t.axis(e);const r=new i$,i=yn(t.encoding[e]),{mark:s,config:o}=t,a=(n==null?void 0:n.orient)||((y=o[e==="x"?"axisX":"axisY"])==null?void 0:y.orient)||((v=o.axis)==null?void 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oxe({encoding:e,size:t}){for(const n of Ca){const r=Jr(n);Ao(t[r])&&Qa(e[n])&&(delete t[r],ae(UP(r)))}return t}const axe={vgMark:"arc",encodeEntry:e=>({...Ki(e,{align:"ignore",baseline:"ignore",color:"include",size:"ignore",orient:"ignore",theta:"ignore"}),...Sr("x",e,{defaultPos:"mid"}),...Sr("y",e,{defaultPos:"mid"}),...ba(e,"radius"),...ba(e,"theta")})},uxe={vgMark:"area",encodeEntry:e=>({...Ki(e,{align:"ignore",baseline:"ignore",color:"include",orient:"include",size:"ignore",theta:"ignore"}),...ry("x",e,{defaultPos:"zeroOrMin",defaultPos2:"zeroOrMin",range:e.markDef.orient==="horizontal"}),...ry("y",e,{defaultPos:"zeroOrMin",defaultPos2:"zeroOrMin",range:e.markDef.orient==="vertical"}),...j3(e)})},lxe={vgMark:"rect",encodeEntry:e=>({...Ki(e,{align:"ignore",baseline:"ignore",color:"include",orient:"ignore",size:"ignore",theta:"ignore"}),...ba(e,"x"),...ba(e,"y")})},cxe={vgMark:"shape",encodeEntry:e=>({...Ki(e,{align:"ignore",baseline:"ignore",color:"include",size:"ignore",orient:"ignore",theta:"ignore"})}),postEncodingTransform:e=>{const{encoding:t}=e,n=t.shape;return[{type:"geoshape",projection:e.projectionName(),...n&&ue(n)&&n.type===Bd?{field:pe(n,{expr:"datum"})}:{}}]}},fxe={vgMark:"image",encodeEntry:e=>({...Ki(e,{align:"ignore",baseline:"ignore",color:"ignore",orient:"ignore",size:"ignore",theta:"ignore"}),...ba(e,"x"),...ba(e,"y"),...B3(e,"url")})},dxe={vgMark:"line",encodeEntry:e=>({...Ki(e,{align:"ignore",baseline:"ignore",color:"include",size:"ignore",orient:"ignore",theta:"ignore"}),...Sr("x",e,{defaultPos:"mid"}),...Sr("y",e,{defaultPos:"mid"}),...Un("size",e,{vgChannel:"strokeWidth"}),...j3(e)})},hxe={vgMark:"trail",encodeEntry:e=>({...Ki(e,{align:"ignore",baseline:"ignore",color:"include",size:"include",orient:"ignore",theta:"ignore"}),...Sr("x",e,{defaultPos:"mid"}),...Sr("y",e,{defaultPos:"mid"}),...Un("size",e),...j3(e)})};function s$(e,t){const{config:n}=e;return{...Ki(e,{align:"ignore",baseline:"ignore",color:"include",size:"include",orient:"ignore",theta:"ignore"}),...Sr("x",e,{defaultPos:"mid"}),...Sr("y",e,{defaultPos:"mid"}),...Un("size",e),...Un("angle",e),...pxe(e,n,t)}}function pxe(e,t,n){return n?{shape:{value:n}}:Un("shape",e)}const gxe={vgMark:"symbol",encodeEntry:e=>s$(e)},mxe={vgMark:"symbol",encodeEntry:e=>s$(e,"circle")},yxe={vgMark:"symbol",encodeEntry:e=>s$(e,"square")},vxe={vgMark:"rect",encodeEntry:e=>({...Ki(e,{align:"ignore",baseline:"ignore",color:"include",orient:"ignore",size:"ignore",theta:"ignore"}),...ba(e,"x"),...ba(e,"y")})},bxe={vgMark:"rule",encodeEntry:e=>{const{markDef:t}=e,n=t.orient;return!e.encoding.x&&!e.encoding.y&&!e.encoding.latitude&&!e.encoding.longitude?{}:{...Ki(e,{align:"ignore",baseline:"ignore",color:"include",orient:"ignore",size:"ignore",theta:"ignore"}),...ry("x",e,{defaultPos:n==="horizontal"?"zeroOrMax":"mid",defaultPos2:"zeroOrMin",range:n!=="vertical"}),...ry("y",e,{defaultPos:n==="vertical"?"zeroOrMax":"mid",defaultPos2:"zeroOrMin",range:n!=="horizontal"}),...Un("size",e,{vgChannel:"strokeWidth"})}}},xxe={vgMark:"text",encodeEntry:e=>{const{config:t,encoding:n}=e;return{...Ki(e,{align:"include",baseline:"include",color:"include",size:"ignore",orient:"ignore",theta:"include"}),...Sr("x",e,{defaultPos:"mid"}),...Sr("y",e,{defaultPos:"mid"}),...B3(e),...Un("size",e,{vgChannel:"fontSize"}),...Un("angle",e),...y4("align",_xe(e.markDef,n,t)),...y4("baseline",wxe(e.markDef,n,t)),...Sr("radius",e,{defaultPos:null}),...Sr("theta",e,{defaultPos:null})}}};function _xe(e,t,n){if(wt("align",e,n)===void 0)return"center"}function wxe(e,t,n){if(wt("baseline",e,n)===void 0)return"middle"}const Exe={vgMark:"rect",encodeEntry:e=>{const{config:t,markDef:n}=e,r=n.orient,i=r==="horizontal"?"x":"y",s=r==="horizontal"?"y":"x",o=r==="horizontal"?"height":"width";return{...Ki(e,{align:"ignore",baseline:"ignore",color:"include",orient:"ignore",size:"ignore",theta:"ignore"}),...ba(e,i),...Sr(s,e,{defaultPos:"mid",vgChannel:s==="y"?"yc":"xc"}),[o]:Ot(wt("thickness",n,t))}}},O0={arc:axe,area:uxe,bar:lxe,circle:mxe,geoshape:cxe,image:fxe,line:dxe,point:gxe,rect:vxe,rule:bxe,square:yxe,text:xxe,tick:Exe,trail:hxe};function Axe(e){if(Qe([Xv,Yv,_0e],e.mark)){const t=BB(e.mark,e.encoding);if(t.length>0)return Sxe(e,t)}else if(e.mark===Vv){const t=qw.some(n=>wt(n,e.markDef,e.config));if(e.stack&&!e.fieldDef("size")&&t)return kxe(e)}return o$(e)}const W4="faceted_path_";function Sxe(e,t){return[{name:e.getName("pathgroup"),type:"group",from:{facet:{name:W4+e.requestDataName(Vt.Main),data:e.requestDataName(Vt.Main),groupby:t}},encode:{update:{width:{field:{group:"width"}},height:{field:{group:"height"}}}},marks:o$(e,{fromPrefix:W4})}]}const H4="stack_group_";function kxe(e){var l;const[t]=o$(e,{fromPrefix:H4}),n=e.scaleName(e.stack.fieldChannel),r=(c={})=>e.vgField(e.stack.fieldChannel,c),i=(c,f)=>{const d=[r({prefix:"min",suffix:"start",expr:f}),r({prefix:"max",suffix:"start",expr:f}),r({prefix:"min",suffix:"end",expr:f}),r({prefix:"max",suffix:"end",expr:f})];return`${c}(${d.map(h=>`scale('${n}',${h})`).join(",")})`};let s,o;e.stack.fieldChannel==="x"?(s={...td(t.encode.update,["y","yc","y2","height",...qw]),x:{signal:i("min","datum")},x2:{signal:i("max","datum")},clip:{value:!0}},o={x:{field:{group:"x"},mult:-1},height:{field:{group:"height"}}},t.encode.update={...Hr(t.encode.update,["y","yc","y2"]),height:{field:{group:"height"}}}):(s={...td(t.encode.update,["x","xc","x2","width"]),y:{signal:i("min","datum")},y2:{signal:i("max","datum")},clip:{value:!0}},o={y:{field:{group:"y"},mult:-1},width:{field:{group:"width"}}},t.encode.update={...Hr(t.encode.update,["x","xc","x2"]),width:{field:{group:"width"}}});for(const c of qw){const f=wo(c,e.markDef,e.config);t.encode.update[c]?(s[c]=t.encode.update[c],delete t.encode.update[c]):f&&(s[c]=Ot(f)),f&&(t.encode.update[c]={value:0})}const a=[];if(((l=e.stack.groupbyChannels)==null?void 0:l.length)>0)for(const c of e.stack.groupbyChannels){const f=e.fieldDef(c),d=pe(f);d&&a.push(d),(f!=null&&f.bin||f!=null&&f.timeUnit)&&a.push(pe(f,{binSuffix:"end"}))}return s=["stroke","strokeWidth","strokeJoin","strokeCap","strokeDash","strokeDashOffset","strokeMiterLimit","strokeOpacity"].reduce((c,f)=>{if(t.encode.update[f])return{...c,[f]:t.encode.update[f]};{const d=wo(f,e.markDef,e.config);return d!==void 0?{...c,[f]:Ot(d)}:c}},s),s.stroke&&(s.strokeForeground={value:!0},s.strokeOffset={value:0}),[{type:"group",from:{facet:{data:e.requestDataName(Vt.Main),name:H4+e.requestDataName(Vt.Main),groupby:a,aggregate:{fields:[r({suffix:"start"}),r({suffix:"start"}),r({suffix:"end"}),r({suffix:"end"})],ops:["min","max","min","max"]}}},encode:{update:s},marks:[{type:"group",encode:{update:o},marks:[t]}]}]}function Cxe(e){const{encoding:t,stack:n,mark:r,markDef:i,config:s}=e,o=t.order;if(!(!ee(o)&&$s(o)&&jw(o.value)||!o&&jw(wt("order",i,s)))){if((ee(o)||ue(o))&&!n)return NP(o,{expr:"datum"});if(Hu(r)){const a=i.orient==="horizontal"?"y":"x",u=t[a];if(ue(u))return{field:a}}}}function o$(e,t={fromPrefix:""}){const{mark:n,markDef:r,encoding:i,config:s}=e,o=un(r.clip,$xe(e),Fxe(e)),a=RP(r),u=i.key,l=Cxe(e),c=Dxe(e),f=wt("aria",r,s),d=O0[n].postEncodingTransform?O0[n].postEncodingTransform(e):null;return[{name:e.getName("marks"),type:O0[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(Vt.Main)},encode:{update:O0[n].encodeEntry(e)},...d?{transform:d}:{}}]}function $xe(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 Fxe(e){const t=e.component.projection;return t&&!t.isFit?!0:void 0}function Dxe(e){if(!e.component.selection)return null;const t=ie(e.component.selection).length;let n=t,r=e.parent;for(;r&&n===0;)n=ie(r.component.selection).length,r=r.parent;return n?{interactive:t>0||e.mark==="geoshape"||!!e.encoding.tooltip||!!e.markDef.tooltip}:null}class Ij extends Rj{constructor(t,n,r,i={},s){super(t,"unit",n,r,s,void 0,KM(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+xz)),(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=Eo(t.mark)?{...t.mark}:{type:t.mark},a=o.type;o.filled===void 0&&(o.filled=i1e(o,s,{graticule:t.data&&I3(t.data)}));const 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      No System Metrics Available

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diff --git a/trackio/frontend/dist/index.html b/trackio/frontend/dist/index.html new file mode 100644 index 0000000000000000000000000000000000000000..f1f680e45356a35e0131b843ebe5cb900358f66e --- /dev/null +++ b/trackio/frontend/dist/index.html @@ -0,0 +1,14 @@ + + + + + + Trackio Dashboard + + + + + +
        + + 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..89df244cf7f630a5e87c14e8f6b4744f9476ebf3 --- /dev/null +++ b/trackio/frontend_server.py @@ -0,0 +1,63 @@ +"""Serves the built Svelte frontend alongside the Gradio 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", +) + + +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..dbe6d2c5d6d1eb1559ba1d3907d58e63b9237247 --- /dev/null +++ b/trackio/gpu.py @@ -0,0 +1,357 @@ +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 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) -> 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. + + Returns: + Dictionary of GPU metrics. Keys use logical device indices (gpu/0/, gpu/1/, etc.) + which correspond to CUDA device indices, not physical GPU indices. + """ + if not _init_nvml(): + return {} + + 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 logical_idx not in _energy_baseline: + _energy_baseline[logical_idx] = energy_mj + energy_consumed_mj = energy_mj - _energy_baseline[logical_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_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() + 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..96c54b266f5a8e5ab779ccee52b49ce859e62e43 --- /dev/null +++ b/trackio/imports.py @@ -0,0 +1,290 @@ +import os +from pathlib import Path + +import pandas as pd + +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}") + + df = pd.read_csv(csv_path) + if df.empty: + raise ValueError("CSV file is empty") + + column_mapping = utils.simplify_column_names(df.columns.tolist()) + df = df.rename(columns=column_mapping) + + step_column = None + for col in df.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 df.columns: + if column == step_column: + continue + if column == "timestamp": + continue + + try: + pd.to_numeric(df[column], errors="raise") + numeric_columns.append(column) + except (ValueError, TypeError): + continue + + for _, row in df.iterrows(): + metrics = {} + for column in numeric_columns: + value = row[column] + if bool(pd.notna(value)): + metrics[column] = float(value) + + if metrics: + metrics_list.append(metrics) + steps.append(int(row[step_column])) + + if "timestamp" in df.columns and bool(pd.notna(row["timestamp"])): + 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 bool( + pd.isna(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..c9650d392d20e1a3f3dbb76af96774b2305a28bc --- /dev/null +++ b/trackio/launch.py @@ -0,0 +1,211 @@ +from __future__ import annotations + +import os +import secrets +import socket +import threading +import time +import warnings +from typing import Any + +import httpx +import uvicorn +from uvicorn.config import Config + +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: + s = socket.socket() + s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) + s.bind((LOCALHOST_NAME, port)) + s.close() + 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]: + from pathlib import Path + + from trackio._vendor.networking import normalize_share_url, setup_tunnel + from trackio._vendor.tunneling import BINARY_PATH + + 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('/')}/gradio_api/" + try: + httpx.get(f"{local_api_url}startup-events", verify=ssl_verify, timeout=10) + except Exception as e: + raise RuntimeError( + f"Could not reach startup-events at {local_api_url}startup-events: {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: + from trackio._vendor.gradio_exceptions import ChecksumMismatchError + + 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()`.") + + if mcp_server and not quiet: + base = share_url or local_url.rstrip("/") + print(f"\n* MCP streamable HTTP: {base}/gradio_api/mcp/") + + return local_url, share_url, local_api_url, uv_server + + +def url_ok_local(local_url: str) -> bool: + from trackio._vendor.networking import url_ok + + return url_ok(local_url) diff --git a/trackio/launch_utils.py b/trackio/launch_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..b535eb421708278a48e2693c177d377e58a58289 --- /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 + + 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 + + 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..c5baf5e02a1f377251c96a4a129300be0eb78ba8 --- /dev/null +++ b/trackio/mcp_setup.py @@ -0,0 +1,6 @@ +from contextlib import asynccontextmanager + + +@asynccontextmanager +async def mcp_lifespan_context(app): + yield 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..3b98c4265c449d9d5262622a8d2a6e4c2b15f69f --- /dev/null +++ b/trackio/package.json @@ -0,0 +1,6 @@ +{ + "name": "trackio", + "version": "0.22.0", + "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..452302b6b0e5984d1e01c6960a2e221f8ce16233 --- /dev/null +++ b/trackio/remote_client.py @@ -0,0 +1,28 @@ +from __future__ import annotations + +from gradio_client import Client + + +class RemoteClient: + def __init__(self, space: str, hf_token: str | None = None): + self._space = space + kwargs: dict = {"verbose": False} + if hf_token: + kwargs["hf_token"] = hf_token + try: + self._client = Client(space, **kwargs) + except Exception as e: + raise ConnectionError( + f"Could not connect to Space '{space}'. Is it running?\n{e}" + ) + + def predict(self, *args, api_name: str): + try: + return self._client.predict(*args, api_name=api_name) + except Exception as e: + if "API Not Found" in str(e) or "api_name" in str(e): + raise RuntimeError( + f"Space '{self._space}' does not support '{api_name}'. " + "Redeploy with `trackio sync`." + ) + raise diff --git a/trackio/run.py b/trackio/run.py new file mode 100644 index 0000000000000000000000000000000000000000..6102dfa61f26a97652c6f56a89043cafb8bdc983 --- /dev/null +++ b/trackio/run.py @@ -0,0 +1,739 @@ +import os +import shutil +import threading +import uuid +import warnings +from datetime import datetime, timezone +from pathlib import Path + +import huggingface_hub +from gradio_client import Client, 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.sqlite_storage import SQLiteStorage +from trackio.table import Table +from trackio.typehints import AlertEntry, LogEntry, SystemLogEntry, UploadEntry +from trackio.utils import _get_default_namespace + +BATCH_SEND_INTERVAL = 0.5 +MAX_BACKOFF = 30 + + +class Run: + def __init__( + self, + url: str | None, + project: str, + client: Client | None, + name: str | None = None, + group: str | None = None, + config: dict | None = None, + space_id: str | 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 of the Trackio server (local Gradio app or HF Space). + project: The name of the project to log metrics to. + client: A pre-configured gradio_client.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. + 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._client_thread = None + self._client = client + self._space_id = space_id + self.name = name or utils.generate_readable_name( + SQLiteStorage.get_runs(project), space_id + ) + self.group = group + self.config = utils.to_json_safe(config or {}) + + 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 = SQLiteStorage.get_max_step_for_run(self.project, self.name) + 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 + 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._local_sender_thread = threading.Thread( + target=self._local_batch_sender + ) + self._local_sender_thread.daemon = True + self._local_sender_thread.start() + else: + self._client_thread = threading.Thread(target=self._init_client_background) + self._client_thread.daemon = True + self._client_thread.start() + + self._gpu_monitor: "GpuMonitor | AppleGpuMonitor | None" = None + if auto_log_gpu: + 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() + + def _get_username(self) -> str | None: + try: + return _get_default_namespace() + except Exception: + return None + + 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) + + 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) + + def _write_logs_to_sqlite(self, logs: list[LogEntry]): + logs_by_run: dict[tuple, dict] = {} + for entry in logs: + key = (entry["project"], entry["run"]) + 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), 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, + metrics_list=data["metrics"], + steps=data["steps"], + config=data["config"], + log_ids=data["log_ids"] if has_log_ids else None, + ) + + def _write_system_logs_to_sqlite(self, logs: list[SystemLogEntry]): + logs_by_run: dict[tuple, dict] = {} + for entry in logs: + key = (entry["project"], entry["run"]) + 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), 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, + metrics_list=data["metrics"], + timestamps=data["timestamps"], + log_ids=data["log_ids"] if has_log_ids else None, + ) + + def _write_alerts_to_sqlite(self, alerts: list[AlertEntry]): + alerts_by_run: dict[tuple, dict] = {} + for entry in alerts: + key = (entry["project"], entry["run"]) + 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), 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, + 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, + ) + + 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) + + 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=huggingface_hub.utils.get_token(), + ) + 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=huggingface_hub.utils.get_token(), + ) + 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=huggingface_hub.utils.get_token(), + ) + 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=huggingface_hub.utils.get_token(), + ) + 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() + + def _persist_logs_locally(self, logs: list[LogEntry]): + if not self._space_id: + return + logs_by_run: dict[tuple, dict] = {} + for entry in logs: + key = (entry["project"], entry["run"]) + 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), data in logs_by_run.items(): + SQLiteStorage.bulk_log( + project=project, + run=run, + metrics_list=data["metrics"], + steps=data["steps"], + log_ids=data["log_ids"], + config=data["config"], + space_id=self._space_id, + ) + self._has_local_buffer = True + + def _persist_system_logs_locally(self, logs: list[SystemLogEntry]): + if not self._space_id: + return + logs_by_run: dict[tuple, dict] = {} + for entry in logs: + key = (entry["project"], entry["run"]) + 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), data in logs_by_run.items(): + SQLiteStorage.bulk_log_system( + project=project, + run=run, + metrics_list=data["metrics"], + timestamps=data["timestamps"], + log_ids=data["log_ids"], + space_id=self._space_id, + ) + self._has_local_buffer = True + + def _persist_uploads_locally(self, uploads: list[UploadEntry]): + if not self._space_id: + return + 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._space_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 + + 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=huggingface_hub.utils.get_token(), + ) + 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=huggingface_hub.utils.get_token(), + ) + 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"], + "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=huggingface_hub.utils.get_token(), + ) + SQLiteStorage.clear_pending_uploads( + self.project, buffered_uploads["ids"] + ) + + self._has_local_buffer = False + except Exception: + pass + + 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: + client = Client(self.url, 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, + ): + 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, + "step": step, + "relative_path": relative_path, + "uploaded_file": handle_file(file_path), + } + with self._client_lock: + self._queued_uploads.append(upload_entry) + + 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: + 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: + 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: + from trackio.utils import MEDIA_DIR + + 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: + from trackio.utils import MEDIA_DIR + + absolute_path = MEDIA_DIR / file_path + self._queue_upload(absolute_path, step) + + def _ensure_sender_alive(self): + if self._is_local: + if ( + hasattr(self, "_local_sender_thread") + and not self._local_sender_thread.is_alive() + and not self._stop_flag.is_set() + ): + self._local_sender_thread = threading.Thread( + target=self._local_batch_sender + ) + self._local_sender_thread.daemon = True + self._local_sender_thread.start() + else: + if ( + self._client_thread is not None + and not self._client_thread.is_alive() + and not self._stop_flag.is_set() + ): + self._client_thread = threading.Thread( + target=self._init_client_background + ) + self._client_thread.daemon = True + self._client_thread.start() + + def log(self, metrics: dict, step: int | None = None): + 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: + warnings.warn(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, + "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() + + def alert( + self, + title: str, + text: str | None = None, + level: AlertLevel = AlertLevel.WARN, + step: int | None = None, + webhook_url: str | None = None, + ): + 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, + "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() + + 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() + + def log_system(self, metrics: dict): + metrics = utils.serialize_values(metrics) + timestamp = datetime.now(timezone.utc).isoformat() + + system_log_entry: SystemLogEntry = { + "project": self.project, + "run": self.name, + "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() + + def finish(self): + if self._gpu_monitor is not None: + self._gpu_monitor.stop() + + self._stop_flag.set() + + if self._is_local: + if hasattr(self, "_local_sender_thread"): + print("* Run finished. Uploading logs to Trackio (please wait...)") + self._local_sender_thread.join(timeout=30) + if self._local_sender_thread.is_alive(): + warnings.warn( + "Could not flush all logs within 30s. Some data may be buffered locally." + ) + else: + if self._client_thread is not None: + print( + "* Run finished. Uploading logs to Trackio Space (please wait...)" + ) + self._client_thread.join(timeout=30) + if self._client_thread.is_alive(): + warnings.warn( + "Could not flush all logs within 30s. Some data may be buffered locally." + ) + if SQLiteStorage.has_pending_data(self.project): + warnings.warn( + f"* Some logs could not be sent to the Space (it may still be starting up). " + f"They have been saved locally and will be sent automatically next time you call: " + f'trackio.init(project="{self.project}", space_id="{self._space_id}")' + ) diff --git a/trackio/server.py b/trackio/server.py new file mode 100644 index 0000000000000000000000000000000000000000..a8ca4b9d14b9894921c59206e1797d0ca4013370 --- /dev/null +++ b/trackio/server.py @@ -0,0 +1,769 @@ +"""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 +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 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 + +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 + + +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 = os.getenv("SYSTEM") == "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 = os.getenv("SYSTEM") == "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 os.getenv("SYSTEM") == "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 os.getenv("SYSTEM") == "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: + 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 parts[1] == token + if hasattr(request, "query_params") and request.query_params: + qp = request.query_params.get("write_token") + return qp == token + return False + + +def assert_can_mutate_runs(request: Request) -> None: + if os.getenv("SYSTEM") != "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 os.getenv("SYSTEM") != "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(project: str, uploaded_db: dict, hf_token: str | None) -> None: + check_hf_token_has_write_access(hf_token) + db_project_path = SQLiteStorage.get_project_db_path(project) + os.makedirs(os.path.dirname(db_project_path), exist_ok=True) + shutil.copy(uploaded_db["path"], db_project_path) + + +def bulk_upload_media(uploads: list[UploadEntry], hf_token: str | None) -> None: + check_hf_token_has_write_access(hf_token) + for upload in uploads: + media_path = get_project_media_path( + project=upload["project"], + run=upload["run"], + step=upload["step"], + relative_path=upload["relative_path"], + ) + shutil.copy(upload["uploaded_file"]["path"], media_path) + + +def log( + project: str, + run: str, + metrics: dict[str, Any], + step: int | None, + hf_token: str | None, +) -> None: + check_hf_token_has_write_access(hf_token) + SQLiteStorage.log(project=project, run=run, metrics=metrics, step=step) + + +def bulk_log( + logs: list[LogEntry], + hf_token: str | None, +) -> None: + check_hf_token_has_write_access(hf_token) + + logs_by_run = {} + for log_entry in logs: + key = (log_entry["project"], log_entry["run"]) + 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), 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, + 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( + logs: list[SystemLogEntry], + hf_token: str | None, +) -> None: + check_hf_token_has_write_access(hf_token) + + logs_by_run = {} + for log_entry in logs: + key = (log_entry["project"], log_entry["run"]) + 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), 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, + 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( + alerts: list[AlertEntry], + hf_token: str | None, +) -> None: + check_hf_token_has_write_access(hf_token) + + alerts_by_run: dict[tuple, dict] = {} + for entry in alerts: + key = (entry["project"], entry["run"]) + 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), 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, + 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, + level: str | None = None, + since: str | None = None, +) -> list[dict]: + return SQLiteStorage.get_alerts(project, run_name=run, 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]: + 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[str]: + return SQLiteStorage.get_runs(project) + + +def get_metrics_for_run(project: str, run: str) -> list[str]: + return SQLiteStorage.get_all_metrics_for_run(project, run) + + +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: + runs = SQLiteStorage.get_runs(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) -> dict: + num_logs = SQLiteStorage.get_log_count(project, run) + if num_logs == 0: + return { + "project": project, + "run": run, + "num_logs": 0, + "metrics": [], + "config": None, + "last_step": None, + } + + metrics = SQLiteStorage.get_all_metrics_for_run(project, run) + config = SQLiteStorage.get_run_config(project, run) + last_step = SQLiteStorage.get_last_step(project, run) + + return { + "project": project, + "run": run, + "num_logs": num_logs, + "metrics": metrics, + "config": config, + "last_step": last_step, + } + + +def get_system_metrics_for_run(project: str, run: str) -> list[str]: + return SQLiteStorage.get_all_system_metrics_for_run(project, run) + + +def get_system_logs(project: str, run: str) -> list[dict]: + return SQLiteStorage.get_system_logs(project, run) + + +def get_snapshot( + project: str, + run: str, + step: int | None = None, + around_step: int | None = None, + at_time: str | None = None, + window: int | None = None, +) -> dict: + return SQLiteStorage.get_snapshot( + project, run, step=step, around_step=around_step, at_time=at_time, window=window + ) + + +def get_logs(project: str, run: str) -> list[dict]: + return SQLiteStorage.get_logs(project, run, max_points=1500) + + +def get_settings() -> dict: + 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), + } + + +def get_project_files(project: str) -> list[dict]: + 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) -> bool: + assert_can_mutate_runs(request) + return SQLiteStorage.delete_run(project, run) + + +def rename_run( + request: Request, + project: str, + old_name: str, + new_name: str, +) -> bool: + assert_can_mutate_runs(request) + SQLiteStorage.rename_run(project, old_name, new_name) + 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_snapshot": get_snapshot, + "get_logs": get_logs, + "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 + if mcp_server: + try: + from trackio.mcp_setup import mcp_lifespan_context + + mcp_lifespan = mcp_lifespan_context + except ImportError: + pass + starlette_app = create_trackio_starlette_app( + oauth_routes, + _api_registry(), + mcp_lifespan=mcp_lifespan, + ) + from trackio.frontend_server import mount_frontend + + 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..c26dc4cb816d869914c9d4c3b41293fcca0c99ab --- /dev/null +++ b/trackio/sqlite_storage.py @@ -0,0 +1,1920 @@ +import json as json_mod +import os +import shutil +import sqlite3 +import time +from collections.abc import Iterator +from contextlib import contextmanager +from datetime import datetime, timezone +from pathlib import Path +from threading import Lock + +try: + import fcntl +except ImportError: + fcntl = None + +try: + import msvcrt as _msvcrt +except ImportError: + _msvcrt = None + +import huggingface_hub as hf +import orjson +import pandas as pd + +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, + serialize_values, +) + +DB_EXT = ".db" + +_JOURNAL_MODE_WHITELIST = frozenset( + {"wal", "delete", "truncate", "persist", "memory", "off"} +) + + +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 os.environ.get("SYSTEM") == "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") + + +class ProcessLock: + """A file-based lock that works across processes using fcntl (Unix) or msvcrt (Windows).""" + + def __init__(self, lockfile_path: Path): + self.lockfile_path = lockfile_path + self.lockfile = None + + def __enter__(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.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() + + +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]: + 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, + 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_name TEXT NOT NULL, + config TEXT NOT NULL, + created_at TEXT NOT NULL, + UNIQUE(run_name) + ) + """ + ) + cursor.execute( + """ + CREATE INDEX IF NOT EXISTS idx_metrics_run_step + ON metrics(run_name, step) + """ + ) + cursor.execute( + """ + CREATE INDEX IF NOT EXISTS idx_configs_run_name + ON configs(run_name) + """ + ) + cursor.execute( + """ + CREATE INDEX IF NOT EXISTS idx_metrics_run_timestamp + ON metrics(run_name, timestamp) + """ + ) + cursor.execute( + """ + CREATE TABLE IF NOT EXISTS system_metrics ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + timestamp TEXT NOT NULL, + run_name TEXT NOT NULL, + metrics TEXT NOT NULL + ) + """ + ) + cursor.execute( + """ + CREATE INDEX IF NOT EXISTS idx_system_metrics_run_timestamp + ON system_metrics(run_name, timestamp) + """ + ) + + 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_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, + 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 + ) + """ + ) + cursor.execute( + """ + CREATE INDEX IF NOT EXISTS idx_alerts_run + ON alerts(run_name) + """ + ) + 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 _flatten_json_column(df: pd.DataFrame, col: str) -> pd.DataFrame: + if df.empty: + return df + expanded = df[col].copy() + expanded = pd.DataFrame( + expanded.apply( + lambda x: deserialize_values(orjson.loads(x)) + ).values.tolist(), + index=df.index, + ) + df = df.drop(columns=[col]) + expanded = expanded.loc[:, ~expanded.columns.isin(df.columns)] + return pd.concat([df, expanded], axis=1) + + @staticmethod + def _read_table(db_path: Path, table: str) -> pd.DataFrame: + try: + with SQLiteStorage._get_connection( + db_path, timeout=5.0, configure_pragmas=False, row_factory=None + ) as conn: + return pd.read_sql(f"SELECT * FROM {table}", conn) + except Exception: + return pd.DataFrame() + + @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 + df = SQLiteStorage._read_table(db_path, table) + if df.empty: + return + df = SQLiteStorage._flatten_json_column(df, json_col) + df.to_parquet( + parquet_path, + write_page_index=True, + use_content_defined_chunking=True, + ) + + @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_df = SQLiteStorage._read_table(db_path, "metrics") + if not metrics_df.empty: + flat = SQLiteStorage._flatten_json_column(metrics_df.copy(), "metrics") + flat.to_parquet(output_dir / "metrics.parquet") + + sys_df = SQLiteStorage._read_table(db_path, "system_metrics") + if not sys_df.empty: + flat = SQLiteStorage._flatten_json_column(sys_df.copy(), "metrics") + flat.to_parquet(aux_dir / "system_metrics.parquet") + + configs_df = SQLiteStorage._read_table(db_path, "configs") + if not configs_df.empty: + flat = SQLiteStorage._flatten_json_column(configs_df.copy(), "config") + flat.to_parquet(aux_dir / "configs.parquet") + + try: + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + cursor.execute( + """SELECT run_name, MAX(step) as last_step, COUNT(*) as log_count + FROM metrics GROUP BY run_name ORDER BY MIN(timestamp) ASC""" + ) + rows = cursor.fetchall() + runs_meta = [ + { + "name": row["run_name"], + "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) + + df = pd.read_parquet(parquet_path) + if "metrics" not in df.columns: + metrics = df.copy() + structural_cols = [ + "id", + "timestamp", + "run_name", + "step", + "log_id", + "space_id", + ] + df = df[[c for c in structural_cols if c in df.columns]] + for col in structural_cols: + if col in metrics.columns: + del metrics[col] + metrics = orjson.loads(metrics.to_json(orient="records")) + df["metrics"] = [orjson.dumps(serialize_values(row)) for row in metrics] + + with SQLiteStorage._get_connection( + db_path, configure_pragmas=False, row_factory=None + ) as conn: + df.to_sql("metrics", conn, if_exists="replace", index=False) + conn.commit() + + 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 + + df = pd.read_parquet(parquet_path) + if "metrics" not in df.columns: + metrics = df.copy() + other_cols = ["id", "timestamp", "run_name"] + df = df[[c for c in other_cols if c in df.columns]] + for col in other_cols: + if col in metrics.columns: + del metrics[col] + metrics = orjson.loads(metrics.to_json(orient="records")) + df["metrics"] = [orjson.dumps(serialize_values(row)) for row in metrics] + + with SQLiteStorage._get_connection( + db_path, configure_pragmas=False, row_factory=None + ) as conn: + df.to_sql("system_metrics", conn, if_exists="replace", index=False) + conn.commit() + + 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 + + df = pd.read_parquet(parquet_path) + if "config" not in df.columns: + config_data = df.copy() + other_cols = ["id", "run_name", "created_at"] + df = df[[c for c in other_cols if c in df.columns]] + for col in other_cols: + if col in config_data.columns: + del config_data[col] + config_data = orjson.loads(config_data.to_json(orient="records")) + df["config"] = [ + orjson.dumps(serialize_values(row)) for row in config_data + ] + + with SQLiteStorage._get_connection( + db_path, configure_pragmas=False, row_factory=None + ) as conn: + df.to_sql("configs", conn, if_exists="replace", index=False) + conn.commit() + + @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): + """ + 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() + cursor.execute( + """ + SELECT MAX(step) + FROM metrics + WHERE run_name = ? + """, + (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() + 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, + ): + """ + 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() + + if steps is None: + steps = list(range(len(metrics_list))) + elif any(s is None for s in steps): + cursor.execute( + "SELECT MAX(step) FROM metrics WHERE run_name = ?", (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 + data.append( + ( + timestamps[i], + run, + steps[i], + orjson.dumps(serialize_values(metrics)), + lid, + space_id, + ) + ) + + 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() + 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, + ): + """ + 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() + data = [] + for i, metrics in enumerate(metrics_list): + lid = log_ids[i] if log_ids else None + data.append( + ( + timestamps[i], + run, + orjson.dumps(serialize_values(metrics)), + lid, + space_id, + ) + ) + + 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, + ): + 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() + data = [] + for i in range(len(titles)): + aid = alert_ids[i] if alert_ids else None + data.append( + ( + timestamps[i], + run, + titles[i], + texts[i], + levels[i], + steps[i], + aid, + ) + ) + + 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, + 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 = [] + if run_name is not None: + conditions.append("run_name = ?") + params.append(run_name) + 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 get_system_logs(project: str, run: str) -> 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 [] + + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + try: + cursor.execute( + """ + SELECT timestamp, metrics + FROM system_metrics + WHERE run_name = ? + ORDER BY timestamp + """, + (run,), + ) + + rows = cursor.fetchall() + results = [] + for row in rows: + metrics = orjson.loads(row["metrics"]) + metrics = deserialize_values(metrics) + metrics["timestamp"] = row["timestamp"] + results.append(metrics) + return results + except sqlite3.OperationalError as e: + if "no such table: system_metrics" in str(e): + return [] + raise + + @staticmethod + def get_all_system_metrics_for_run(project: str, run: str) -> list[str]: + """Get all system metric names for a specific project/run.""" + return SQLiteStorage._get_metric_names( + project, run, "system_metrics", exclude_keys={"timestamp"} + ) + + @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) -> 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() + cursor.execute( + "SELECT COUNT(*) FROM metrics WHERE run_name = ?", + (run,), + ) + 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) -> 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() + cursor.execute( + "SELECT MAX(step) FROM metrics WHERE run_name = ?", + (run,), + ) + 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 get_logs(project: str, run: str, max_points: int | 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 [] + + try: + with SQLiteStorage._get_connection(db_path) as conn: + cursor = conn.cursor() + cursor.execute( + """ + SELECT timestamp, step, metrics + FROM metrics + WHERE run_name = ? + ORDER BY timestamp + """, + (run,), + ) + + rows = cursor.fetchall() + if max_points is not None and len(rows) > max_points: + step = len(rows) / max_points + indices = {int(i * step) for i in range(max_points)} + indices.add(len(rows) - 1) + rows = [rows[i] for i in sorted(indices)] + + 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 + except sqlite3.OperationalError as e: + if "no such table: metrics" in str(e): + return [] + raise + + @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.""" + 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() + cursor.execute( + """ + SELECT run_name + FROM metrics + GROUP BY run_name + ORDER BY MIN(timestamp) ASC + """, + ) + return [row[0] for row in cursor.fetchall()] + except sqlite3.OperationalError as e: + if "no such table: metrics" in str(e): + return [] + raise + + @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() + 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) -> 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() + cursor.execute( + "SELECT MAX(step) FROM metrics WHERE run_name = ?", (run,) + ) + 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) -> 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: + cursor.execute( + """ + SELECT config FROM configs WHERE run_name = ? + """, + (run,), + ) + + 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) -> 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: + cursor.execute("DELETE FROM metrics WHERE run_name = ?", (run,)) + cursor.execute("DELETE FROM configs WHERE run_name = ?", (run,)) + try: + cursor.execute( + "DELETE FROM system_metrics WHERE run_name = ?", (run,) + ) + except sqlite3.OperationalError: + pass + try: + cursor.execute("DELETE FROM alerts WHERE run_name = ?", (run,)) + 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): + """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 + ) + result.append( + ( + row["timestamp"], + new_run_name, + row["step"], + orjson.dumps(serialize_values(updated)), + ) + ) + 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) -> 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() + + cursor.execute( + "SELECT COUNT(*) FROM metrics WHERE run_name = ?", (old_name,) + ) + if cursor.fetchone()[0] == 0: + raise ValueError( + f"Run '{old_name}' does not exist in project '{project}'" + ) + + 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: + cursor.execute( + "SELECT timestamp, step, metrics FROM metrics WHERE run_name = ?", + (old_name,), + ) + metrics_rows = cursor.fetchall() + + old_prefix = f"{project}/{old_name}/" + new_prefix = f"{project}/{new_name}/" + + updated_rows = SQLiteStorage._rewrite_metrics_rows( + metrics_rows, new_name, old_prefix, new_prefix + ) + + cursor.execute( + "DELETE FROM metrics WHERE run_name = ?", (old_name,) + ) + cursor.executemany( + "INSERT INTO metrics (timestamp, run_name, step, metrics) VALUES (?, ?, ?, ?)", + updated_rows, + ) + + cursor.execute( + "UPDATE configs SET run_name = ? WHERE run_name = ?", + (new_name, old_name), + ) + + try: + cursor.execute( + "UPDATE system_metrics SET run_name = ? WHERE run_name = ?", + (new_name, old_name), + ) + except sqlite3.OperationalError: + pass + + try: + cursor.execute( + "UPDATE alerts SET run_name = ? WHERE run_name = ?", + (new_name, old_name), + ) + 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() + + source_cursor.execute( + "SELECT timestamp, step, metrics FROM metrics WHERE run_name = ?", + (run,), + ) + metrics_rows = source_cursor.fetchall() + + source_cursor.execute( + "SELECT config, created_at FROM configs WHERE run_name = ?", + (run,), + ) + config_row = source_cursor.fetchone() + + try: + source_cursor.execute( + "SELECT timestamp, metrics FROM system_metrics WHERE run_name = ?", + (run,), + ) + system_metrics_rows = source_cursor.fetchall() + except sqlite3.OperationalError: + system_metrics_rows = [] + + try: + source_cursor.execute( + "SELECT timestamp, title, text, level, step, alert_id FROM alerts WHERE run_name = ?", + (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}/" + updated_rows = SQLiteStorage._rewrite_metrics_rows( + metrics_rows, run, old_prefix, new_prefix + ) + + target_cursor.executemany( + "INSERT INTO metrics (timestamp, run_name, step, metrics) VALUES (?, ?, ?, ?)", + updated_rows, + ) + + if config_row: + 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: + 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: + 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, + step: int | None = None, + around_step: int | None = None, + at_time: str | None = None, + window: int | float | 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() + 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) + + 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) -> list[str]: + """Get all metric names for a specific project/run.""" + return SQLiteStorage._get_metric_names( + project, run, "metrics", exclude_keys={"timestamp", "step"} + ) + + @staticmethod + def _get_metric_names( + project: str, run: str, table: str, exclude_keys: set[str] + ) -> 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: + cursor.execute( + f""" + SELECT metrics + FROM {table} + WHERE run_name = ? + ORDER BY timestamp + """, + (run,), + ) + + 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: + cursor.execute( + f"""SELECT id, timestamp, 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"], + "metrics": metrics, + "timestamp": row["timestamp"], + "log_id": row["log_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: + cursor.execute( + """SELECT id, space_id, 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"], + "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_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: + 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: + cursor.execute( + f"""SELECT timestamp, 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"], + "metrics": metrics, + "timestamp": row["timestamp"], + "log_id": row["log_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..272b0eb4c103a1efd87cf1a951db0e1a4b5eaaec --- /dev/null +++ b/trackio/table.py @@ -0,0 +1,173 @@ +import os +from typing import Any, Literal + +from pandas import DataFrame + +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: DataFrame | 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. + if dataframe is None: + self.data = DataFrame(data) if data is not None else DataFrame() + else: + self.data = dataframe + + def _has_media_objects(self, dataframe: DataFrame) -> bool: + """Check if dataframe contains any TrackioMedia objects or lists of TrackioMedia objects.""" + for col in dataframe.columns: + if dataframe[col].apply(lambda x: isinstance(x, TrackioMedia)).any(): + return True + if ( + dataframe[col] + .apply( + lambda x: ( + isinstance(x, list) + and len(x) > 0 + and isinstance(x[0], TrackioMedia) + ) + ) + .any() + ): + return True + return False + + def _process_data(self, project: str, run: str, step: int = 0): + """Convert dataframe to dict format, processing any TrackioMedia objects if present.""" + df = self.data + if not self._has_media_objects(df): + return df.to_dict(orient="records") + + processed_df = df.copy() + for col in processed_df.columns: + for idx in processed_df.index: + value = processed_df.at[idx, col] + if isinstance(value, TrackioMedia): + value._save(project, run, step) + processed_df.at[idx, col] = 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] + processed_df.at[idx, col] = [v._to_dict() for v in value] + + return processed_df.to_dict(orient="records") + + @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..274fb1d25831672e92c59d24ed86ffb31d862220 --- /dev/null +++ b/trackio/typehints.py @@ -0,0 +1,39 @@ +from typing import Any, TypedDict + +from gradio_client import FileData + + +class LogEntry(TypedDict, total=False): + project: str + run: str + 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 + metrics: dict[str, Any] + timestamp: str + log_id: str | None + + +class AlertEntry(TypedDict, total=False): + project: str + run: str + title: str + text: str | None + level: str + step: int | None + timestamp: str + alert_id: str | None + + +class UploadEntry(TypedDict): + project: str + run: 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..e92a85a8dc817a6742b808033600dcaedea7d6df --- /dev/null +++ b/trackio/utils.py @@ -0,0 +1,927 @@ +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 +from urllib.parse import urlencode + +import huggingface_hub +import numpy as np +import pandas as pd +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 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"/gradio_api/file={TRACKIO_LOGO_DIR}/trackio_logo_type_light_transparent.png", + ) + dark_url = os.environ.get( + "TRACKIO_LOGO_DARK_URL", + f"/gradio_api/file={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 persistent_storage_enabled() -> bool: + return ( + os.environ.get("PERSISTANT_STORAGE_ENABLED") == "true" + ) # typo in the name of the environment variable + + +def _get_trackio_dir() -> Path: + if persistent_storage_enabled(): + return Path("/data/trackio") + 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 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 pd.isna(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 downsample( + df: pd.DataFrame, + x: str, + y: str, + color: str | None, + x_lim: tuple[float | None, float | None] | None = None, +) -> tuple[pd.DataFrame, 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. + """ + if df.empty: + if x_lim is not None: + x_lim = (x_lim[0] or 0, x_lim[1] or 0) + return df, x_lim + + columns_to_keep = [x, y] + if color is not None and color in df.columns: + columns_to_keep.append(color) + df = df[columns_to_keep].copy() + + data_x_min = df[x].min() + data_x_max = df[x].max() + + 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 + + if color is not None and color in df.columns: + groups = df.groupby(color) + else: + groups = [(None, df)] + + downsampled_indices = [] + + for _, group_df in groups: + if group_df.empty: + continue + + group_df = group_df.sort_values(x) + + if updated_x_lim is not None: + x_min, x_max = updated_x_lim + before_point = group_df[group_df[x] < x_min].tail(1) + after_point = group_df[group_df[x] > x_max].head(1) + group_df = group_df[(group_df[x] >= x_min) & (group_df[x] <= x_max)] + else: + before_point = after_point = None + x_min = group_df[x].min() + x_max = group_df[x].max() + + if before_point is not None and not before_point.empty: + downsampled_indices.extend(before_point.index.tolist()) + if after_point is not None and not after_point.empty: + downsampled_indices.extend(after_point.index.tolist()) + + if group_df.empty: + continue + + if x_min == x_max: + min_y_idx = group_df[y].idxmin() + max_y_idx = group_df[y].idxmax() + 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_df) < 500: + downsampled_indices.extend(group_df.index.tolist()) + continue + + bins = np.linspace(x_min, x_max, n_bins + 1) + group_df["bin"] = pd.cut( + group_df[x], bins=bins, labels=False, include_lowest=True + ) + + for bin_idx in group_df["bin"].dropna().unique(): + bin_data = group_df[group_df["bin"] == bin_idx] + if bin_data.empty: + continue + + min_y_idx = bin_data[y].idxmin() + max_y_idx = bin_data[y].idxmax() + + downsampled_indices.append(min_y_idx) + if min_y_idx != max_y_idx: + downsampled_indices.append(max_y_idx) + + unique_indices = list(set(downsampled_indices)) + + downsampled_df = df.loc[unique_indices].copy() + + if color is not None: + downsampled_df = ( + downsampled_df.groupby(color, sort=False)[downsampled_df.columns] + .apply(lambda group: group.sort_values(x)) + .reset_index(drop=True) + ) + else: + downsampled_df = downsampled_df.sort_values(x).reset_index(drop=True) + + downsampled_df = downsampled_df.drop(columns=["bin"], errors="ignore") + + return downsampled_df, 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 infinity and NaN values in metrics dict to make it JSON-compliant. + Only handles top-level float values. + + Converts: + - float('inf') -> "Infinity" + - float('-inf') -> "-Infinity" + - float('nan') -> "NaN" + + Example: + {"loss": float('inf'), "accuracy": 0.95} -> {"loss": "Infinity", "accuracy": 0.95} + """ + if not isinstance(metrics, dict): + return metrics + + result = {} + for key, value in metrics.items(): + if isinstance(value, float): + if math.isinf(value): + result[key] = "Infinity" if value > 0 else "-Infinity" + elif math.isnan(value): + result[key] = "NaN" + else: + result[key] = value + elif isinstance(value, np.floating): + float_val = float(value) + if math.isinf(float_val): + result[key] = "Infinity" if float_val > 0 else "-Infinity" + elif math.isnan(float_val): + result[key] = "NaN" + else: + result[key] = float_val + else: + result[key] = value + return result + + +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"]