Quickstart Guide

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:

Shell

Python

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

Shell

Python

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")

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.

Update on GitHub

Trackio

Quickstart Guide

Dashboard

Deploying to Hugging Face Spaces