Optimize Your Model: Hyperparameter Tuning with Weights & Biases Sweeps

In the world of machine learning, many mature algorithms have already been implemented in popular libraries, enabling us to build applications with only a few lines of code. However, finding the optimal hyperparameters remains a challenging task. With countless values to choose from, even a small tweak in a single hyperparameter can significantly impact model performance.

I’ve traditionally relied on random search or grid search to find the best hyperparameter combinations. These methods, though effective, can be labor-intensive and difficult to track manually. Writing loops to sweep through each preset value often results in messy, unmanageable code. Then, I discovered Weights & Biases (W&B), which provides a more efficient and organized way to conduct hyperparameter sweeps.

Step 1: Setting Up Weights & Biases

W&B integrates seamlessly with Colab notebooks, requiring only a few lines of code to connect to the cloud and initialize the program.

Setting up the tracking is easy. For example, I have a behavior cloning code with those hyper parameters:

    learning_rate = 1e-3
    batch_size = 32  # training batch size
    max_length = 50  # maximum path length for recursively generating next link until destination is reached
    max_iter_num = 500


... ...

avg_loss = epoch_loss / num_batches

... ...

torch.save(CNNMODEL.state_dict(), model_p)