MMF

The WandbLogger class in Meta AI's MMF library will enable Weights & Biases to log the training/validation metrics, system (GPU and CPU) metrics, model checkpoints and configuration parameters.

Current features

The following features are currently supported by the WandbLogger in MMF:

• Training & Validation metrics
• Learning Rate over time
• Model Checkpoint saving to W&B Artifacts
• GPU and CPU system metrics
• Training configuration parameters

Config parameters

The following options are available in MMF config to enable and customize the wandb logging:

training:
    wandb:
        enabled: true
        
        # An entity is a username or team name where you're sending runs.
        # By default it will log the run to your user account.
        entity: null
        
        # Project name to be used while logging the experiment with wandb
        project: mmf
        
        # Experiment/ run name to be used while logging the experiment
        # under the project with wandb. The default experiment name
        # is: ${training.experiment_name}
        name: ${training.experiment_name}
        
        # Turn on model checkpointing, saving checkpoints to W&B Artifacts
        log_model_checkpoint: true
        
        # Additional argument values that you want to pass to wandb.init(). 
        # Check out the documentation at https://docs.wandb.ai/ref/python/init
        # to see what arguments are available, such as:
        # job_type: 'train'
        # tags: ['tag1', 'tag2']
        
env:
    # To change the path to the directory where wandb metadata would be 
    # stored (Default: env.log_dir):
    wandb_logdir: ${env:MMF_WANDB_LOGDIR,}