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

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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

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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() 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,}