Hugging Face Trackio

Track and visualize ML training experiments with real-time dashboards synced to Hugging Face Spaces.

Prerequisites

•trackio package (pip install trackio)
•HF_TOKEN for Space syncing

Instructions

Two Interfaces

Task	Interface
Logging metrics during training	Python API
Retrieving metrics after/during	CLI

Python API: Logging

python

import trackio

# Initialize tracking
trackio.init(project="my-project", space_id="username/trackio")

# Log metrics
trackio.log({"loss": 0.1, "accuracy": 0.9})
trackio.log({"loss": 0.09, "accuracy": 0.91})

# Finalize
trackio.finish()

With TRL Integration

python

from trl import SFTConfig

config = SFTConfig(
    report_to="trackio",
    project="my-project",
    run_name="sft-experiment-1",
    # ... other config
)

CLI: Retrieving Metrics

bash

# List projects
trackio list projects --json

# List runs in project
trackio list runs --project my-project --json

# Get specific metric
trackio get metric --project my-project --run my-run --metric loss --json

# Launch dashboard
trackio show

# Sync to HF Space
trackio sync --space-id username/trackio

Key Concepts

For remote/cloud training: Pass space_id so metrics sync to a Space dashboard and persist after the instance terminates.

For local training: Metrics stored locally, use trackio show to view dashboard.

JSON Output

Add --json flag for programmatic output:

bash

trackio list projects --json
trackio get metric --project my-project --run run-1 --metric loss --json

Notes

•Metrics sync to HF Spaces for persistence
•Use run_name for descriptive experiment names
•Group related runs under the same project

Source: huggingface/skills