> ## Documentation Index
> Fetch the complete documentation index at: https://wb-21fd5541-docs-weave-byob-note.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

> Integrate W&B with Meta AI's MMF framework to track multimodal model training experiments and log metrics.

# MMF

The `WandbLogger` class in [Meta AI's MMF](https://github.com/facebookresearch/mmf) library will enable W\&B 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() 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,}
```