Logging with Weights and Biases

When running fusilli, setting wandb_logging = True in train_and_save_models() enables logging training and validation behavior to Weights and Biases, facilitated by the wandb library.

Weights and Biases serves as a free tool for tracking machine learning experiments. To utilise Weights and Biases with Fusilli, creating an account and logging into it is necessary. Follow the instructions here for account creation.

Details on Fusilli’s WandB integration can be found in the function set_logger(). Essentially:

  1. When wandb_logging = True, Fusilli logs training and validation behavior to Weights and Biases. If wandb_logging = True, Fusilli plots loss curves using matplotlib and saves them locally as PNG files.

  2. If you set the argument project_name in train_and_save_models(), Fusilli creates a project in your WandB account with that string. If this project exists, Fusilli uses it; otherwise, it creates a new one. If the project name isn’t specified, Fusilli generates a project named "fusilli".

  3. Rerunning fusion models with different parameters groups these runs by the fusion model’s name.

  4. Each run, by default, is tagged with the fusion model’s modality type and fusion type. Additional tags can be added via extra_log_string_dict in train_and_save_models().

  5. With k-fold cross-validation, each fold is logged as a separate run grouped by the fusion model’s name and tagged with the current fold number.

For instance, consider specifying extra_log_string_dict in train_and_save_models() for running EdgeCorrGNN fusion model with dropout_prob as 0.2 and logging this to Weights and Biases:


For further information on model modifications in fusilli (e.g., altering dropout probability in EdgeCorrGNN), refer to Modify the Fusion Models.

# Importing data and fusion models etc.

fusion_model = EdgeCorrGNN

modification = {
    "EdgeCorrGNN": {
        "dropout_prob": 0.2

extra_string_for_wandb = {"dropout_prob": 0.2}

trained_model = train_and_save_models(

Upon training and inspecting Weights and Biases, the run will be labeled as EdgeCorrGNN_dropout_prob_0.2 and tagged with dropout_prob_0.2.

What if you’re not using Weights and Biases?

When not using Weights and Biases, Fusilli plots loss curves and saves them locally as PNG files. In this scenario, the WandB project name is replaced by user-specified tags in the PNG file name. For instance, running the same fusion model without using Weights and Biases will produce a PNG file named EdgeCorrGNN_dropout_prob_0.2.png, saved in a folder called “losses” in your local figures directory.