Quickstart

Get your script integrated quickly to see our experiment tracking and visualization features on your own project

Get started logging machine learning experiments in 3 quick steps.

1. Install Library

Install our library in an environment using Python 3.

pip install wandb

If you are training models in an automated environment where it's inconvenient to run shell commands, such as Google's CloudML, you should look at the documentation on Running in Automated Environments.

2. Create Account

Sign up for a free account in your shell or go to our sign up page.

wandb login

3. Modify your training script

Add a few lines to your script to log hyperparameters and metrics.

Weights and Biases is framework agnostic, but if you are using a common ML framework, you may find framework-specific examples even easier for getting started and in many cases we've build framework specific hooks to simplify the integration. You can find examples for Keras, TensorFlow, PyTorch, Fast.ai, Scikit-learn, XGBoost, Catalyst and Jax.

Initialize Wandb

Initialize wandb at the beginning of your script right after the imports.

# Inside my model training code
import wandb
wandb.init(project="my-project")

We automatically create the project for you if it doesn't exist. (See the wandb.init documentation for more initialization options.)

Declare Hyperparameters

It's easy to save hyperparameters with the wandb.config object.

wandb.config.dropout = 0.2
wandb.config.hidden_layer_size = 128

Log Metrics

Log metrics like loss or accuracy as your model trains or log more complicated things like histograms, graphs or images with wandb.log.

Then log a few metrics:

def my_train_loop():
for epoch in range(10):
loss = 0 # change as appropriate :)
wandb.log({'epoch': epoch, 'loss': loss})

Save Files

Anything saved in the wandb.run.dir directory will be uploaded to W&B and saved along with your run when it completes. This is especially convenient for saving the literal weights and biases in your model:

model.save(os.path.join(wandb.run.dir, "mymodel.h5"))

Great! Now run your script normally and we'll sync logs in a background process. Your terminal logs, metrics, and files will be synced to the cloud along with a record of your git state if you're running from a git repo.

If you're testing and want to disable wandb syncing, set the environment variable WANDB_MODE=dryrun