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