Huggingface Trackio
Hugging FaceOfficialPublished by Hugging Face
- Skill
- Tool integration
- Data and AI
- Apache-2.0
- Medium risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
Trackio provides a Python API and CLI for tracking machine learning experiments. It supports local metric storage, real-time dashboard visualization, and synchronization with Hugging Face Spaces. Designed with a focus on autonomous workflows, it allows developers and LLM agents to log metrics, fire diagnostic alerts, and retrieve training data programmatically via JSON output.
What it does
- Logs training metrics and hyperparameters using a Python API.
- Provides real-time experiment visualization via local or remote Hugging Face Space dashboards.
- Enables diagnostic alerting with severity levels (INFO, WARN, ERROR) and webhook support.
- Allows programmatic retrieval of metrics and alerts via CLI with JSON output.
- Supports automatic integration with TRL trainers.
- Syncs local experiment data to persistent remote storage.
When to use it
- When you need to track and visualize metrics during ML model training.
- When building autonomous agents that need to monitor training health and iterate on hyperparameters.
- When you require persistent dashboards for cloud-based training runs.
- When you need to integrate training diagnostics with external notification systems like Slack or Discord.
Advantages
- Lightweight and local-first design.
- API-compatible with existing experiment tracking tools.
- Enables autonomous experiment iteration through structured alerts.
- Supports both local and cloud-based monitoring workflows.
- Provides structured JSON output for easy integration with automation scripts.
Usage examples
- “How do I log training metrics with Trackio?”
- Initialize the run with trackio.init(), use trackio.log() to record metrics during your loop, and call trackio.finish() to save the data.
- “How can an agent monitor for training failures?”
- The agent can use trackio.alert() in the training script to flag issues, then poll for these alerts using the CLI command 'trackio list alerts --json'.
- “How do I sync my local training metrics to a Hugging Face Space?”
- Pass the space_id parameter to trackio.init() during initialization, or use trackio.sync() to upload an existing local project.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/huggingface/huggingface-trackio/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip huggingface-trackio.zip -d .agents/skills
Documentation by Hugging Face
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'sreport_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 alertThree severity levels:
INFO,WARN,ERRORAlerts 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 availabletrackio get project/run/metric— retrieve summaries and valuestrackio list alerts --project <name> --json— retrieve alertstrackio show— launch the dashboardtrackio 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 --jsonAutonomous 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 conditionsLaunch training — run the script in the background
Poll for alerts — use
trackio list alerts --project <name> --json --since <timestamp>to check for new alertsRead metrics — use
trackio get metric ...to inspect specific valuesIterate — 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"Files
- ATTRIBUTION.mdAdded by AutoGineer882 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md5.0 KiB
- references/alerts.md5.8 KiB
- references/logging_metrics.md5.4 KiB
- references/retrieving_metrics.md8.5 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs Python
Security
What our scanner noticed· Medium risk
- Possible data-exfiltration endpoints
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Attribution and licence
- Published by
- Hugging Face
- Publisher
- Hugging Face (Official)
- Source
- huggingface/skills · skills/huggingface-trackio · commit 25910b3
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Hugging Face.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download huggingface-trackio.zip