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

> Training and inference at scale made simple, efficient, and adaptable

# Hugging Face Accelerate

Hugging Face Accelerate is a library that enables the same PyTorch code to run across any distributed configuration, to simplify model training and inference at scale.

Accelerate includes a W\&B Tracker, which this page shows how to use to log metrics, configuration, and artifacts from distributed training runs to W\&B. For more information, see [Accelerate Trackers in Hugging Face](https://huggingface.co/docs/accelerate/main/en/usage_guides/tracking).

## Start logging with Accelerate

This section shows how to configure Accelerate to log experiment data to W\&B during training. To get started with Accelerate and W\&B, follow this pseudocode:

```python theme={null}
from accelerate import Accelerator

# Tell the Accelerator object to log with wandb
accelerator = Accelerator(log_with="wandb")

# Initialise your wandb run, passing wandb parameters and any config information
accelerator.init_trackers(
    project_name="my_project", 
    config={"dropout": 0.1, "learning_rate": 1e-2},
    init_kwargs={"wandb": {"entity": "my-wandb-team"}}
    )

...

# Log to wandb by calling accelerator.log(); step is optional
accelerator.log({"train_loss": 1.12, "valid_loss": 0.8}, step=global_step)


# Make sure that the wandb tracker finishes correctly
accelerator.end_training()
```

In more detail:

1. Pass `log_with="wandb"` when you initialize the `Accelerator` class.
2. Call the [`init_trackers`](https://huggingface.co/docs/accelerate/main/en/package_reference/accelerator#accelerate.Accelerator.init_trackers) method and pass it:
   * A project name through `project_name`.
   * Any parameters you want to pass to [`wandb.init()`](/models/ref/python/functions/init) through a nested dict to `init_kwargs`.
   * Any other experiment config information you want to log to your wandb run, through `config`.
3. Use the `wandb.Run.log()` method to log to W\&B. The `step` argument is optional.
4. Call `.end_training()` when training finishes.

## Access the W\&B tracker

Once Accelerate logs to W\&B, you may want direct access to the underlying W\&B run object to log artifacts, custom charts, or other data that the tracker doesn't expose. To access the W\&B tracker, use the `Accelerator.get_tracker()` method. Pass in the string corresponding to a tracker's `.name` attribute, which returns the tracker on the `main` process.

```python theme={null}
wandb_tracker = accelerator.get_tracker("wandb")

```

From there, you can interact with the `wandb` run object as usual:

```python theme={null}
wandb_tracker.log_artifact(some_artifact_to_log)
```

<Warning>
  Trackers built in Accelerate automatically execute on the correct process, so if a tracker only needs to run on the main process it does so automatically.

  To remove Accelerate's wrapping entirely, you can achieve the same outcome with:

  ```python theme={null}
  wandb_tracker = accelerator.get_tracker("wandb", unwrap=True)
  with accelerator.on_main_process:
      wandb_tracker.log_artifact(some_artifact_to_log)
  ```
</Warning>

## Accelerate articles

For a deeper walkthrough of using Accelerate with W\&B, see the following article.

<details>
  <summary>HuggingFace Accelerate Super Charged With W\&B</summary>

  This article looks at what HuggingFace Accelerate offers and how to perform distributed training and evaluation while logging results to W\&B.

  Read the [Hugging Face Accelerate Super Charged with W\&B report](https://wandb.ai/gladiator/HF%20Accelerate%20+%20W\&B/reports/Hugging-Face-Accelerate-Super-Charged-with-Weights-Biases--VmlldzoyNzk3MDUx?utm_source=docs\&utm_medium=docs\&utm_campaign=accelerate-docs).
</details>

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