🏃‍♀️ Quickstart

Use Weights & Biases for machine learning experiment tracking, model checkpointing, and collaboration with your team. See the full Weights & Biases Documentation here

🤩 A shared dashboard for your experiments

With just a few lines of code, you'll get rich, interactive, shareable dashboards which you can see yourself here.

🔒 Data & Privacy

We take security very seriously, and our cloud-hosted dashboard uses industry standard best practices for encryption. If you're working with datasets that cannot leave your enterprise cluster, we have on-prem installations available.

It's also easy to download all your data and export it to other tools — like custom analysis in a Jupyter notebook. Here's more on our API.

Start by installing the library and logging in to your free account.

👟 Run an experiment

1️⃣. Start a new run and pass in hyperparameters to track

2️⃣. Log metrics from training or evaluation

3️⃣. Visualize results in the dashboard

You have now trained your first model using wandb! 👆 Click on the wandb link above to see your metrics

🥕 Simple Keras Classifier

Run this model to train a simple MNIST classifier, and click on the project page link to see your results stream in live to a W&B project. For a full guide on how to use Weights & Biases with Keras, see here

You have now trained your first model using wandb! 👆 Click on the wandb link above to see your metrics.

For a full guide on how to use Weights & Biases with Keras, see here

🔔 Try W&B Alerts

W&B Alerts allows you to send alerts, triggered from your Python code, to your Slack or email. There are 2 steps to follow the first time you'd like to send a Slack or email alert, triggered from your code:

1. Turn on Alerts in your W&B User Settings

2. Add wandb.alert() to your code:

wandb.alert(
    title="Low accuracy",
    text=f"Accuracy is below the acceptable threshold"
)

See the minimal example below to see how to use wandb.alert. You can find the full docs for W&B Alerts here

What's next 🚀 ?

The next tutorial you will learn how to do hyperparameter optimization using W&B Sweeps:

👉 Hyperparameters sweeps using PyTorch