Weights & Biases

In Deepnote, Weights & Biases (W&B) plays a key role in helping developers track, visualize, and improve machine learning models. W&B is seamlessly integrated into the workflow, allowing users to follow the results of experiments, track hyperparameters, and visualize training metrics in real time. Its easy integration and comprehensive toolset make W&B a key component in any machine learning project.

Weights & Biases covers a wide range of resources in its Tutorials section for those interested in learning more about improving their machine learning workflows. For those working with PyTorch, there are dedicated PyTorch tutorials to help them get started.

##Key capabilities of Weights & Biases

Weights & Biases is a machine learning platform designed to accelerate the development and deployment of better models. The platform offers a lightweight, interactive toolkit that allows developers to:

• Track experiments: W&B captures and records data from experiments in real time, helping users track model performance over multiple iterations.

• Version and iterate datasets: Users can easily manage and version their datasets to ensure consistency across experiments.

• Evaluate model performance: W&B provides detailed visualizations of model metrics, allowing users to monitor accuracy, loss, and other performance indicators.

• Reproduce models: It ensures reproducibility by documenting configurations, environments, and code versions.

• Visualization of results: An intuitive interface allows rich visualizations to better understand model behavior.

• Spot Regressions: W&B helps identify underperforming areas of models, allowing developers to solve problems more efficiently

• Share findings: Users can easily share results and insights with colleagues or the broader community.

##Why choose Weights and Biases?

Compared to other experiment tracking platforms like Comet.ml, Weights & Biases stands out with its easy integration, powerful visualization tools, and comprehensive teaching support. W&B is lightweight and doesn't add overhead to existing workflows. Its robust feature set for version control, duplication, and sharing makes it a comprehensive tool for both individual developers and larger teams.

For more details on leveraging Weights & Biases for machine learning projects, the documentation provides comprehensive guides and tutorials.

##Using Weights & Biases in Deepnote

Wandb can be easily integrated into Deepnote by following these steps:

!pip install --upgrade wandb
!wandb login <your_wandb_token>

The W&B token can be generated by visiting the Weights & Biases login page. Once logged i, the token can be copied from your W&B account settings and used in Deepnote's terminal. The same steps can be completed directly in Deepnote's terminal, making set up easy.

For illustration, here's an example of using W&B in Deepnote: 2024 Olympic predictor: powered by PyTorch and W&B , where an ML model was trained using PyTorch.

###A few examples of how W&B can be used:

• Training with W&B: This code snippet runs a training loop, where the model predicts, computes loss, updates parameters, and adjusts the learning rate each epoch. Weights and Biases tracks the experiment configuration.

#Training loop with learning rate scheduling
num_epochs = wandb.config.epochs
for epoch in range(num_epochs):
    model.train()
    optimizer.zero_grad()
    outputs = model(X_train_tensor)
    loss = criterion(outputs, y_train_tensor)
    loss.backward()
    optimizer.step()
    scheduler.step()  

• Logging and monitoring loss: This code logs training loss to W&B for performance tracking and prints the loss every 10 epochs to monitor progress during training.

# Log training loss to W&B
wandb.log({"loss": loss.item()})

if (epoch + 1) % 10 == 0:
    print(f'Epoch [{epoch + 1}/{num_epochs}], Loss: {loss.item():.4f}')

• Logging test accuracy: In this code snippet the W&B logs the test accuracy after each epoch, enabling performance tracking on the test set.

# Log test accuracy to W&B
wandb.log({"test_accuracy": test_accuracy})

In the log of a specific notebook or in a personal workspace on the W&B website, various characteristics of concrete project can be seen. Below are two figures showing an example of the progress of 10 runs related to loss and accuracy.

##Conclusion

Weights & Biases is a powerful machine learning platform that simplifies experiment tracking, model evaluation and collaboration. Seamless integration with Deepnote provides a comprehensive solution for developers looking to build and refine models. Whether it's a through an simple setup process or powerful visualization tools, W&B helps users build better models faster.