Huggingface Trackio Agent Skill

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.

6k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

10895

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/huggingface/skills --skill huggingface-trackio

What comes with it

20 233 bytes besides the instruction

references/alerts.md

references/logging_metrics.md

references/retrieving_metrics.md

The instruction itself

9 sections, as written by the author

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. Auto-created Spaces are public by default — pass private=True if the metrics should not be public.

→ 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

import trackio

# Spaces are PUBLIC by default (good for shareable dashboards);
# pass private=True if the metrics should not be public
trackio.init(project="my-project", space_id="username/trackio", private=True)
trackio.log({"loss": 0.1, "accuracy": 0.9})
trackio.log({"loss": 0.09, "accuracy": 0.91})
trackio.finish()

Minimal Retrieval

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

import trackio

trackio.init(project="my-project", config={"lr": 1e-4})

for step in range(num_steps):
    loss = train_step()
    trackio.log({"loss": loss, "step": step})

    if step > 100 and loss > 5.0:
        trackio.alert(
            title="Loss divergence",
            text=f"Loss {loss:.4f} still high after {step} steps",
            level=trackio.AlertLevel.ERROR,
        )
    if step > 0 and abs(loss) < 1e-8:
        trackio.alert(
            title="Vanishing loss",
            text="Loss near zero — possible gradient collapse",
            level=trackio.AlertLevel.WARN,
        )

trackio.finish()

Then poll from a separate terminal/process:

trackio list alerts --project my-project --json --since "2025-01-01T00:00:00"

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How to use it

Copy the folder

Take huggingface/huggingface-trackio from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.