style: apply /style-guide pass to models/integrations

models/integrations/accelerate.mdx

@@ -1,15 +1,16 @@
 ---
-description: Training and inference at scale made simple, efficient and adaptable
+description: Training and inference at scale made simple, efficient, and adaptable
 title: Hugging Face Accelerate
+keywords: ["multi-GPU", "mixed precision", "DeepSpeed", "FSDP"]
 ---
 
 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 we show how to use below. You can also read more about [Accelerate Trackers in Hugging Face](https://huggingface.co/docs/accelerate/main/en/usage_guides/tracking).
+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
 
-To get started with Accelerate and W&B you can follow the pseudocode below:
+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
 from accelerate import Accelerator
@@ -34,33 +35,33 @@ accelerator.log({"train_loss": 1.12, "valid_loss": 0.8}, step=global_step)
 accelerator.end_training()
 ```
 
-Explaining more, you need to:
-1. Pass `log_with="wandb"` when initialising the Accelerator class
+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 via `project_name`
-- any parameters you want to pass to [`wandb.init()`](/models/ref/python/functions/init) via a nested dict to `init_kwargs`
-- any other experiment config information you want to log to your wandb run, via `config`
-3. Use the `wandb.Run.log()` method to log to Weigths & Biases; the `step` argument is optional
-4. Call `.end_training()` when finished training
+   - A project name via `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
 
-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.
+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
 wandb_tracker = accelerator.get_tracker("wandb")
 
 ```
-From there you can interact with wandb’s run object like normal:
+From there, you can interact with the `wandb` run object as usual:
 
 ```python
 wandb_tracker.log_artifact(some_artifact_to_log)
 ```
 
 <Warning>
-Trackers built in Accelerate will automatically execute on the correct process, so if a tracker is only meant to be ran on the main process it will do so automatically.
+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.
 
-If you want to truly remove Accelerate’s wrapping entirely, you can achieve the same outcome with:
+To remove Accelerate's wrapping entirely, you can achieve the same outcome with:
 
 ```python
 wandb_tracker = accelerator.get_tracker("wandb", unwrap=True)
@@ -70,13 +71,14 @@ with accelerator.on_main_process:
 </Warning>
 
 ## Accelerate articles
-Below is an Accelerate article you may enjoy
+
+For a deeper walkthrough of using Accelerate with W&B, see the following article.
 
 <details>
 
 <summary>HuggingFace Accelerate Super Charged With W&B</summary>
 
-* In this article, we'll look at what HuggingFace Accelerate has to offer and how simple it is to perform distributed training and evaluation, while logging results to W&B.
+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>