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.

Two Interfaces

Task	Interface	Reference
Logging metrics during training	Python API	references/logging_metrics.md
Retrieving metrics 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.

CLI → Retrieving

Use the

trackio

command to query logged metrics:

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

Task

Interface

Reference

Logging metrics during training

Python API

references/logging_metrics.md

Retrieving metrics 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.

CLI → Retrieving

Use the

trackio

command to query logged metrics:

trackio list projects/runs/metrics

— discover what's available

trackio get project/run/metric

— retrieve summaries and values

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

hugging-face-trackio

NPX Install

SKILL.md Content

Trackio - Experiment Tracking for ML Training

Two Interfaces

When to Use Each

Python API → Logging

CLI → Retrieving

Minimal Logging Setup

Minimal Retrieval

hugging-face-trackio

NPX Install

SKILL.md Content

Trackio - Experiment Tracking for ML Training

Two Interfaces

When to Use Each

Python API → Logging

CLI → Retrieving

Minimal Logging Setup

Minimal Retrieval