SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

✨ update to use interlibrary links instead of Markdown (#18500)

Browse Source
This commit is contained in:
Steven Liu
2022-08-08 08:53:52 -07:00
committed by GitHub
parent ec8d26248f
commit 36b37990af
1 changed files with 4 additions and 4 deletions
Download Patch File Download Diff File Expand all files Collapse all files

8
docs/source/en/accelerate.mdx
View File

@@ -22,7 +22,7 @@ Get started by installing 🤗 Accelerate:
pip install accelerate pip install accelerate
``` ```
Then import and create an [`Accelerator`](https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator) object. `Accelerator` will automatically detect your type of distributed setup and initialize all the necessary components for training. You don't need to explicitly place your model on a device. Then import and create an [`~accelerate.Accelerator`] object. The [`~accelerate.Accelerator`] will automatically detect your type of distributed setup and initialize all the necessary components for training. You don't need to explicitly place your model on a device.
```py ```py
>>> from accelerate import Accelerator >>> from accelerate import Accelerator
@@ -32,7 +32,7 @@ Then import and create an [`Accelerator`](https://huggingface.co/docs/accelerate
## Prepare to accelerate ## Prepare to accelerate
The next step is to pass all the relevant training objects to the [`prepare`](https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.prepare) method. This includes your training and evaluation DataLoaders, a model and an optimizer: The next step is to pass all the relevant training objects to the [`~accelerate.Accelerator.prepare`] method. This includes your training and evaluation DataLoaders, a model and an optimizer:
```py ```py
>>> train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare( >>> train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare(
@@ -42,7 +42,7 @@ The next step is to pass all the relevant training objects to the [`prepare`](ht
## Backward ## Backward
The last addition is to replace the typical `loss.backward()` in your training loop with 🤗 Accelerate's [`backward`](https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.backward) method: The last addition is to replace the typical `loss.backward()` in your training loop with 🤗 Accelerate's [`~accelerate.Accelerator.backward`]method:
```py ```py
>>> for epoch in range(num_epochs): >>> for epoch in range(num_epochs):
@@ -121,7 +121,7 @@ accelerate launch train.py
### Train with a notebook ### Train with a notebook
🤗 Accelerate can also run in a notebook if you're planning on using Colaboratory's TPUs. Wrap all the code responsible for training in a function, and pass it to `notebook_launcher`: 🤗 Accelerate can also run in a notebook if you're planning on using Colaboratory's TPUs. Wrap all the code responsible for training in a function, and pass it to [`~accelerate.notebook_launcher`]:
```py ```py
>>> from accelerate import notebook_launcher >>> from accelerate import notebook_launcher