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 a 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