> ## Documentation Index
> Fetch the complete documentation index at: https://wb-21fd5541-css-tab-borders.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

This page describes how to use the `WandbLogger` class in [Meta AI's MMF](https://github.com/facebookresearch/mmf) library to track your multimodal model training with W\&B. Enabling `WandbLogger` lets you log training and validation metrics, system (GPU and CPU) metrics, model checkpoints, and configuration parameters, so you can monitor experiments and compare runs without adding custom logging code.

## Features

The `WandbLogger` in MMF supports the following features:

* Training and validation metrics
* Learning rate over time
* Model checkpoint saving to W\&B Artifacts
* GPU and CPU system metrics
* Training configuration parameters

## Configuration parameters

To turn on W\&B logging and customize how runs are tracked, set the following options in your MMF configuration:

```yaml theme={null}
training:
    wandb:
        enabled: true
        
        # An entity is a username or team name where you're sending runs.
        # By default, it logs 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 is 
    # stored (Default: env.log_dir):
    wandb_logdir: ${env:MMF_WANDB_LOGDIR,}
```