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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,}