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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,}