trackio is a lightweight, free experiment tracking Python library built by Hugging Face 🤗.

• API compatible with wandb.init, wandb.log, and wandb.finish. Drop-in replacement: just

  import trackio as wandb

  and keep your existing logging code.

• Local-first design: dashboard runs locally by default. You can also host it on Spaces by specifying a space_id in trackio.init().

  • Persists logs in a Sqlite database locally (or, if you provide a space_id, in a private Hugging Face Dataset)
  • Visualize experiments with a Gradio dashboard locally (or, if you provide a space_id, on Hugging Face Spaces)

• Everything here, including hosting on Hugging Face, is free!

Trackio is designed to be lightweight (the core codebase is <5,000 lines of Python code), not fully-featured. It is designed in an extensible way and written entirely in Python so that developers can easily fork the repository and add functionality that they care about.

Installation

Trackio requires Python 3.10 or higher. Install with pip:

pip install trackio

or with uv:

uv pip install trackio

Usage

To get started, you can run a simple example that logs some fake training metrics:

import trackio
import random
import time

runs = 3
epochs = 8


for run in range(runs):
    trackio.init(
        project="my-project",
        config={"epochs": epochs, "learning_rate": 0.001, "batch_size": 64}
    )

    for epoch in range(epochs):
        train_loss = random.uniform(0.2, 1.0)
        train_acc = random.uniform(0.6, 0.95)

        val_loss = train_loss - random.uniform(0.01, 0.1)
        val_acc = train_acc + random.uniform(0.01, 0.05)

        trackio.log({
            "epoch": epoch,
            "train_loss": train_loss,
            "train_accuracy": train_acc,
            "val_loss": val_loss,
            "val_accuracy": val_acc
        })

        time.sleep(0.2)

trackio.finish()

Running the above will print to the terminal instructions on launching the dashboard.

The usage of trackio is designed to be identical to wandb in most cases, so you can easily switch between the two libraries.

import trackio as wandb

Dashboard

You can launch the dashboard by running in your terminal:

trackio show

or, in Python:

import trackio

trackio.show()

You can also provide an optional project name as the argument to load a specific project directly:

trackio show --project "my-project"

or, in Python:

import trackio 

trackio.show(project="my-project")

Deploying to Hugging Face Spaces

When calling trackio.init(), by default the service will run locally and store project data on the local machine.

But if you pass a space_id to init, like:

trackio.init(project="my-project", space_id="orgname/space_id")

or

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

it will use an existing or automatically deploy a new Hugging Face Space as needed. You should be logged in with the huggingface-cli locally and your token should have write permissions to create the Space.

Embedding a Trackio Dashboard

One of the reasons we created trackio was to make it easy to embed live dashboards on websites, blog posts, or anywhere else you can embed a website.

If you are hosting your Trackio dashboard on Spaces, then you can embed the url of that Space as an IFrame. You can even use query parameters to only specific projects and/or metrics, e.g.

<iframe src="https://abidlabs-trackio-1234.hf.space/?project=my-project&metrics=train_loss,train_accuracy&sidebar=hidden" style="width:1600px; height:500px; border:0;">

Supported query parameters:

• project: (string) Filter the dashboard to show only a specific project
• metrics: (comma-separated list) Filter the dashboard to show only specific metrics, e.g. train_loss,train_accuracy
• sidebar: (string: one of "hidden" or "collapsed"). If "hidden", then the sidebar will not be visible. If "collapsed", the sidebar will be in a collapsed state initially but the user will be able to open it. Otherwise, by default, the sidebar is shown in an open and visible state.
• xmin: (number) Set the initial minimum value for the x-axis limits across all metric plots.
• xmax: (number) Set the initial maximum value for the x-axis limits across all metric plots.
• smoothing: (number) Set the initial value of the smoothing slider (0-20, where 0 = no smoothing).

Examples

To get started and see basic examples of usage, see these files:

• Basic example of logging metrics locally
• Persisting metrics in a Hugging Face Dataset
• Deploying the dashboard to Spaces

Note: Trackio is in Beta (DB Schema May Change)

Note that Trackio is in pre-release right now and we may release breaking changes. In particular, the schema of the Trackio sqlite database may change, which may require migrating or deleting existing database files (located by default at: ~/.cache/huggingface/trackio).

Since Trackio is in beta, your feedback is welcome! Please create issues with bug reports or feature requests.

License

MIT License

Documentation

The complete documentation and API reference for each version of Trackio can be found at: https://huggingface.co/docs/trackio/index

Contribute

We welcome contributions to Trackio! Whether you're fixing bugs, adding features, or improving documentation, your contributions help make Trackio better for the entire machine learning community.

To start contributing, see our Contributing Guide.

Pronunciation

Trackio is pronounced TRACK-yo, as in "track yo' experiments"