trl/references/trackio_guide.md

Trackio Integration for TRL Training

Trackio is a local-first experiment tracking library that provides real-time metrics visualization via a Gradio dashboard.

⚠️ IMPORTANT: Trackio is local-first, which means:

• It runs a dashboard on the machine where training happens
• For Jobs training, sync to a Hugging Face Space to view metrics
• Without a Space, metrics are only accessible during the job (then lost)

Setting Up Trackio for Jobs

Step 1: Add trackio dependency

# /// script
# dependencies = [
#     "trl>=0.12.0",
#     "trackio",  # Required!
# ]
# ///

Step 2: Create a Trackio Space (one-time setup)

Option A: Let Trackio auto-create (Recommended) Pass a space_id to trackio.init() and Trackio will automatically create the Space if it doesn't exist.

Option B: Create manually

• Create Space via Hub UI at https://huggingface.co/new-space
• Select Gradio SDK
• OR use command: huggingface-cli repo create my-trackio-dashboard --type space --space_sdk gradio

Step 3: Initialize Trackio with space_id

import trackio

trackio.init(
    project="my-training",
    space_id="username/my-trackio-dashboard",  # CRITICAL for Jobs!
    config={
        "model": "Qwen/Qwen2.5-0.5B",
        "dataset": "trl-lib/Capybara",
        "learning_rate": 2e-5,
    }
)

Step 4: Configure TRL to use Trackio

SFTConfig(
    report_to="trackio",
    # ... other config
)

Step 5: Finish tracking

trainer.train()
trackio.finish()  # Ensures final metrics are synced

What Trackio Tracks

Trackio automatically logs:

• ✅ Training loss
• ✅ Learning rate
• ✅ GPU utilization
• ✅ Memory usage
• ✅ Training throughput
• ✅ Custom metrics

How It Works with Jobs

1. Training runs → Metrics logged to local SQLite DB
2. Every 5 minutes → Trackio syncs DB to HF Dataset (Parquet)
3. Space dashboard → Reads from Dataset, displays metrics in real-time
4. Job completes → Final sync ensures all metrics persisted

Viewing the Dashboard

After starting training:

1. Navigate to the Space: https://huggingface.co/spaces/username/my-trackio-dashboard
2. The Gradio dashboard shows all tracked experiments
3. Filter by project, compare runs, view charts with smoothing

Alternative: TensorBoard (Simpler for Jobs)

For simpler setup without needing a Space:

SFTConfig(
    report_to="tensorboard",  # Logs saved with model to Hub
)

TensorBoard logs are automatically saved with the model and viewable via TensorBoard locally after downloading.

Recommendation

• Trackio: Best for real-time monitoring during long training runs
• TensorBoard: Best for post-training analysis, simpler setup
• Weights & Biases: Best for team collaboration, requires account