Logging Metrics with Trackio

Trackio is a lightweight, free experiment tracking library from Hugging Face. It provides a wandb-compatible API for logging metrics with local-first design.

• GitHub: gradio-app/trackio
• Docs: huggingface.co/docs/trackio

Installation

pip install trackio
# or
uv pip install trackio

Core API

Basic Usage

import trackio

# Initialize a run
trackio.init(
    project="my-project",
    config={"learning_rate": 0.001, "epochs": 10}
)

# Log metrics during training
for epoch in range(10):
    loss = train_epoch()
    trackio.log({"loss": loss, "epoch": epoch})

# Finalize the run
trackio.finish()

Key Functions

trackio.init() Parameters

trackio.init(
    project="my-project",           # Project name (groups runs together)
    name="run-name",                # Optional: name for this specific run
    config={...},                   # Hyperparameters and config to log
    space_id="username/trackio",    # Optional: sync to HF Space for remote dashboard
    private=True,                   # Optional: make an auto-created Space private.
                                    #   Default: PUBLIC (unless your org defaults to private)
    bucket_id="username/my-bucket", # Optional: pin the HF Bucket used for metric storage.
                                    #   Default: auto-derived from space_id
    group="experiment-group",       # Optional: group related runs
)

Local vs Remote Dashboard

Local (Default)

By default, trackio stores metrics in a local SQLite database and runs the dashboard locally:

trackio.init(project="my-project")
# ... training ...
trackio.finish()

# Launch local dashboard
trackio.show()

Or from terminal:

trackio show --project my-project

Remote (HF Space)

Pass space_id to sync metrics to a Hugging Face Space for persistent, shareable dashboards:

trackio.init(
    project="my-project",
    space_id="username/trackio",  # Auto-creates Space if it doesn't exist
    private=True,                 # Spaces are PUBLIC by default; omit for a shareable dashboard
)

⚠️ For remote training (cloud GPUs, HF Jobs, etc.): Always use space_id since local storage is lost when the instance terminates. If the metrics should not be public, also pass private=True — an auto-created Space is public by default (unless your org's default is private); the flag is ignored if the Space already exists.

Sync Local to Remote

Sync existing local projects to a Space:

trackio.sync(project="my-project", space_id="username/my-experiments")

wandb Compatibility

Trackio is API-compatible with wandb. Drop-in replacement:

import trackio as wandb

wandb.init(project="my-project")
wandb.log({"loss": 0.5})
wandb.finish()

TRL Integration

When using TRL trainers, set report_to="trackio" for automatic metric logging:

from trl import SFTConfig, SFTTrainer
import trackio

trackio.init(
    project="sft-training",
    space_id="username/trackio",
    private=True,  # Spaces are public by default; omit for a shareable dashboard
    config={"model": "Qwen/Qwen2.5-0.5B", "dataset": "trl-lib/Capybara"}
)

config = SFTConfig(
    output_dir="./output",
    report_to="trackio",  # Automatic metric logging
    # ... other config
)

trainer = SFTTrainer(model=model, args=config, ...)
trainer.train()
trackio.finish()

What Gets Logged

With TRL/Transformers integration, trackio automatically captures:

• Training loss
• Learning rate
• Eval metrics
• Training throughput

For manual logging, log any numeric metrics:

trackio.log({
    "train_loss": 0.5,
    "train_accuracy": 0.85,
    "val_loss": 0.4,
    "val_accuracy": 0.88,
    "epoch": 1
})

Grouping Runs

Use group to organize related experiments in the dashboard sidebar:

# Group by experiment type
trackio.init(project="my-project", name="baseline-v1", group="baseline")
trackio.init(project="my-project", name="augmented-v1", group="augmented")

# Group by hyperparameter
trackio.init(project="hyperparam-sweep", name="lr-0.001", group="lr_0.001")
trackio.init(project="hyperparam-sweep", name="lr-0.01", group="lr_0.01")

Configuration Best Practices

Keep config minimal — only log what's useful for comparing runs:

trackio.init(
    project="qwen-sft-capybara",
    name="baseline-lr2e5",
    config={
        "model": "Qwen/Qwen2.5-0.5B",
        "dataset": "trl-lib/Capybara",
        "learning_rate": 2e-5,
        "num_epochs": 3,
        "batch_size": 8,
    }
)

Embedding Dashboards

Embed Space dashboards in websites with query parameters:

<iframe 
  src="https://username-trackio.hf.space/?project=my-project&metrics=train_loss,val_loss&sidebar=hidden" 
  style="width:1600px; height:500px; border:0;">
</iframe>

Query parameters:

• project: Filter to specific project
• metrics: Comma-separated metric names to show
• sidebar: hidden or collapsed
• smoothing: 0-20 (smoothing slider value)
• xmin, xmax: X-axis limits