Buckets:

qgallouedec's picture
|
download
raw
4.12 kB
# Launching the Dashboard
## Launching a Local Dashboard
You can launch the dashboard by running:
```sh
trackio show
```
```py
import trackio
trackio.show()
```
## Loading a Specific Project
You can also provide an optional `project` name as the argument to load a specific project directly:
```sh
trackio show --project "my-project"
```
```py
import trackio
trackio.show(project="my-project")
```
## Using a Custom Frontend
You can replace the bundled dashboard with your own static frontend directory. The directory only needs an `index.html` file; your frontend can call the existing Trackio API under `/api/*`.
The intended workflow is:
1. Run `trackio show --frontend ./my-trackio-frontend`.
2. Ask your LLM to edit the files in that directory.
3. Keep the browser open while Trackio live reloads the frontend as those files change.
If the directory passed to `--frontend` does not exist, or exists but is empty, Trackio copies in the starter frontend automatically, prints that it did so, and then serves that directory. The starter is a complete plain-HTML/CSS/JS template: it calls the Trackio API, loads projects and runs, fetches metric values, and draws simple charts that you can replace with your own UI.
The currently available HTTP endpoints are:
- `POST /api/get_run_mutation_status`
- `POST /api/upload_db_to_space`
- `POST /api/bulk_upload_media`
- `POST /api/log`
- `POST /api/bulk_log`
- `POST /api/bulk_log_system`
- `POST /api/bulk_alert`
- `POST /api/get_alerts`
- `POST /api/get_metric_values`
- `POST /api/get_runs_for_project`
- `POST /api/get_metrics_for_run`
- `POST /api/get_all_projects`
- `POST /api/get_project_summary`
- `POST /api/get_run_summary`
- `POST /api/get_system_metrics_for_run`
- `POST /api/get_system_logs`
- `POST /api/get_system_logs_batch`
- `POST /api/get_snapshot`
- `POST /api/get_logs`
- `POST /api/get_logs_batch`
- `POST /api/get_traces`
- `POST /api/query_project`
- `POST /api/get_settings`
- `POST /api/get_project_files`
- `POST /api/delete_run`
- `POST /api/rename_run`
- `POST /api/force_sync`
- `POST /api/upload` for multipart file uploads used by media and file-related flows
For reading stored files returned by the API, Trackio also serves `GET /file?path=...`.
```sh
trackio show --frontend ./my-trackio-frontend
```
```py
import trackio
trackio.show(frontend_dir="./my-trackio-frontend")
```
If the provided frontend directory is non-empty but invalid, Trackio falls back to the shipped starter template.
## Setting a Persistent Default Frontend
If you want the same custom frontend to be used by `trackio show`, `trackio sync`, and deploy flows by default, save it in Trackio's persistent config:
```sh
trackio config set frontend ./my-trackio-frontend
```
Reset it with:
```sh
trackio config unset frontend
```
## Customizing Plot Colors
You can customize the color palette used for plot lines by providing a `color_palette` argument. This is useful if you want to match your organization's branding or have specific color preferences.
```sh
trackio show --color-palette "#FF0000,#00FF00,#0000FF"
```
```py
import trackio
trackio.show(color_palette=["#FF0000", "#00FF00", "#0000FF"])
```
The colors will be cycled through when displaying multiple runs. You can provide as many or as few colors as you like.
## Enabling Remote Access
By default, the dashboard binds to `127.0.0.1` (localhost), which means it can only be accessed from the same machine. To allow remote access from other machines on the network, use the `--host` option:
```sh
trackio show --host 0.0.0.0
```
```py
import trackio
trackio.show(host="0.0.0.0")
```
This is particularly useful when running Trackio on a remote server or in a containerized environment where you need to access the dashboard from a different machine.
## Launching a Dashboard in Jupyter Notebooks
You can also launch the dashboard directly within a Jupyter Notebook. Just use the same command as above:
```py
import trackio
trackio.show()
```
Check the [demo notebook](https://github.com/gradio-app/trackio/blob/main/examples/notebook_integration.ipynb).

Xet Storage Details

Size:
4.12 kB
·
Xet hash:
ae2a7a21b69f5726335d19c8f1641feaf863e269a79df4d1aa4ecd9ca537972e

Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.