huggingface-trackio

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Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation.

3installs

Sourcehuggingface/skills

Added on2026-03-28

NPX Install

npx skill4agent add huggingface/skills huggingface-trackio

SKILL.md Content

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Trackio - Experiment Tracking for ML Training

Trackio is an experiment tracking library for logging and visualizing ML training metrics. It syncs to Hugging Face Spaces for real-time monitoring dashboards.

Three Interfaces

Task	Interface	Reference
Logging metrics during training	Python API	references/logging_metrics.md
Firing alerts for training diagnostics	Python API	references/alerts.md
Retrieving metrics & alerts after/during training	CLI	references/retrieving_metrics.md

When to Use Each

Python API → Logging

Use

import trackio

in your training scripts to log metrics:

Initialize tracking with
```
trackio.init()
```
Log metrics with
```
trackio.log()
```
or use TRL's
```
report_to="trackio"
```
Finalize with
```
trackio.finish()
```

Key concept: For remote/cloud training, pass

space_id

— metrics sync to a Space dashboard so they persist after the instance terminates.

→ See references/logging_metrics.md for setup, TRL integration, and configuration options.

Python API → Alerts

Insert

trackio.alert()

calls in training code to flag important events — like inserting print statements for debugging, but structured and queryable:

trackio.alert(title="...", level=trackio.AlertLevel.WARN)

— fire an alert

Three severity levels:
```
INFO
```
,
```
WARN
```
,
```
ERROR
```
Alerts are printed to terminal, stored in the database, shown in the dashboard, and optionally sent to webhooks (Slack/Discord)

Key concept for LLM agents: Alerts are the primary mechanism for autonomous experiment iteration. An agent should insert alerts into training code for diagnostic conditions (loss spikes, NaN gradients, low accuracy, training stalls). Since alerts are printed to the terminal, an agent that is watching the training script's output will see them automatically. For background or detached runs, the agent can poll via CLI instead.

→ See references/alerts.md for the full alerts API, webhook setup, and autonomous agent workflows.

CLI → Retrieving

Use the

trackio

command to query logged metrics and alerts:

```
trackio list projects/runs/metrics
```
— discover what's available
```
trackio get project/run/metric
```
— retrieve summaries and values

trackio list alerts --project <name> --json

— retrieve alerts

```
trackio show
```
— launch the dashboard
```
trackio sync
```
— sync to HF Space

Key concept: Add

--json

for programmatic output suitable for automation and LLM agents.

→ See references/retrieving_metrics.md for all commands, workflows, and JSON output formats.

Minimal Logging Setup

python

import trackio

trackio.init(project="my-project", space_id="username/trackio")
trackio.log({"loss": 0.1, "accuracy": 0.9})
trackio.log({"loss": 0.09, "accuracy": 0.91})
trackio.finish()

Minimal Retrieval

bash

trackio list projects --json
trackio get metric --project my-project --run my-run --metric loss --json

Autonomous ML Experiment Workflow

When running experiments autonomously as an LLM agent, the recommended workflow is:

Set up training with alerts — insert
```
trackio.alert()
```
calls for diagnostic conditions
Launch training — run the script in the background

Poll for alerts — use

trackio list alerts --project <name> --json --since <timestamp>

to check for new alerts

Read metrics — use
```
trackio get metric ...
```
to inspect specific values
Iterate — based on alerts and metrics, stop the run, adjust hyperparameters, and launch a new run