🔥 Combining Weights & Biases with PyTorch

Weights & Biases is a powerful tool for tracking and visualizing machine learning experiments, and integrating it with PyTorch can provide valuable insights into your model’s performance. In this tutorial, we will walk through the steps to integrate Weights & Biases with PyTorch for tracking your model training progress.

Step 1: Install the Weights & Biases library

The first step is to install the Weights & Biases library. You can do this by running the following command in your terminal:

pip install wandb

Step 2: Sign up for a Weights & Biases account

Before you can start using Weights & Biases, you need to sign up for an account on their website (https://www.wandb.com/). Once you have created an account, you will be able to access your API key which is necessary for authenticating your experiments.

Step 3: Initialize Weights & Biases in your PyTorch script

To start tracking your PyTorch experiments with Weights & Biases, you need to initialize it in your script. This can be done by importing the library and calling the wandb.init() function at the beginning of your script. Make sure to pass your API key and project name as arguments to the wandb.init() function.

import wandb

# Initialize Weights & Biases
wandb.init(project='your-project-name', entity='your-username')

Step 4: Log metrics during training

Once you have initialized Weights & Biases in your script, you can start logging metrics during training. This can be done by calling the wandb.log() function at the end of each training iteration, passing in the metrics you want to track as key-value pairs.

Here’s an example of logging the loss and accuracy during training:

for epoch in range(num_epochs):
    for batch_idx, (data, target) in enumerate(train_loader):
        # Train your model
        loss, accuracy = train(model, data, target)

        # Log metrics
        wandb.log({'loss': loss, 'accuracy': accuracy})

Step 5: Visualize results on the Weights & Biases dashboard

Once you have logged your metrics during training, you can visualize the results on the Weights & Biases dashboard. Simply go to the project page on the Weights & Biases website, and you will be able to see the metrics you logged during training displayed in various charts and graphs.

That’s it! You have successfully integrated Weights & Biases with PyTorch for tracking and visualizing your machine learning experiments. This can help you monitor the performance of your models, compare different experiments, and make informed decisions about your model training. Happy experimenting!