[docs] Doc sprint (#3099)

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docs/source/_toctree.yml

@@ -16,7 +16,7 @@
   - local: basic_tutorials/tpu
     title: TPU training
   - local: basic_tutorials/launch
-    title: Launching distributed code
+    title: Launching Accelerate scripts
   - local: basic_tutorials/notebook
     title: Launching distributed training from Jupyter Notebooks
   title: Tutorials
@@ -34,7 +34,7 @@
     - local: usage_guides/profiler
       title: Profiler
     - local: usage_guides/checkpoint
-      title: Save and load training states
+      title: Checkpointing
     - local: basic_tutorials/troubleshooting
       title: Troubleshoot
     - local: usage_guides/training_zoo
@@ -53,7 +53,7 @@
     - local: usage_guides/ddp_comm_hook
       title: DDP Communication Hooks
     - local: usage_guides/fsdp
-      title: Fully Sharded Data Parallelism
+      title: Fully Sharded Data Parallel
     - local: usage_guides/megatron_lm
       title: Megatron-LM
     - local: usage_guides/sagemaker
@@ -73,7 +73,7 @@
   title: How to guides
 - sections:
   - local: concept_guides/internal_mechanism
-    title: 🤗 Accelerate's internal mechanism
+    title: Accelerate's internal mechanism
   - local: concept_guides/big_model_inference
     title: Loading big models into memory
   - local: concept_guides/performance
@@ -85,39 +85,39 @@
   - local: concept_guides/fsdp_and_deepspeed
     title: FSDP vs DeepSpeed
   - local: concept_guides/low_precision_training
-    title: How training in low-precision environments is possible (FP8)
+    title: Low precision training methods
   - local: concept_guides/training_tpu
-    title: TPU best practices
+    title: Training on TPUs
   title: Concepts and fundamentals
 - sections: 
   - local: package_reference/accelerator
     title: Accelerator
   - local: package_reference/state
-    title: Stateful configuration classes
+    title: Stateful classes
   - local: package_reference/cli
     title: The Command Line
   - local: package_reference/torch_wrappers
-    title: Torch wrapper classes
+    title: DataLoaders, Optimizers, Schedulers
   - local: package_reference/tracking
     title: Experiment trackers
   - local: package_reference/launchers
-    title: Distributed launchers
+    title: Launchers
   - local: package_reference/deepspeed
     title: DeepSpeed utilities
   - local: package_reference/logging
     title: Logging
   - local: package_reference/big_modeling
     title: Working with large models
   - local: package_reference/inference
-    title: Distributed inference with big models
+    title: Pipeline parallelism
   - local: package_reference/kwargs
     title: Kwargs handlers
   - local: package_reference/fp8
-    title: FP8 Functionality
+    title: FP8
   - local: package_reference/utilities
     title: Utility functions and classes
   - local: package_reference/megatron_lm
-    title: Megatron-LM Utilities
+    title: Megatron-LM utilities
   - local: package_reference/fsdp
-    title: Fully Sharded Data Parallelism Utilities
+    title: Fully Sharded Data Parallel utilities
   title: "Reference"

docs/source/basic_tutorials/install.md

@@ -13,31 +13,29 @@ specific language governing permissions and limitations under the License.
 rendered properly in your Markdown viewer.
 -->
 
-# Installation and Configuration
+# Installation
 
-Before you start, you will need to setup your environment, install the appropriate packages, and configure 🤗 Accelerate. 🤗 Accelerate is tested on **Python 3.8+**.
+Before you start, you will need to setup your environment, install the appropriate packages, and configure Accelerate. Accelerate is tested on **Python 3.8+**.
 
-## Installing 🤗 Accelerate
+Accelerate is available on pypi and conda, as well as on GitHub. Details to install from each are below:
 
-🤗 Accelerate is available on pypi and conda, as well as on GitHub. Details to install from each are below:
+## pip
 
-### pip 
-
-To install 🤗 Accelerate from pypi, perform:
+To install Accelerate from pypi, perform:
 
 ```bash
 pip install accelerate
 ```
 
-### conda
+## conda
 
-🤗 Accelerate can also be installed with conda with:
+Accelerate can also be installed with conda with:
 
 ```bash
 conda install -c conda-forge accelerate
 ```
 
-### Source
+## Source
 
 New features are added every day that haven't been released yet. To try them out yourself, install
 from the GitHub repository:
@@ -56,9 +54,9 @@ cd accelerate
 pip install -e .
 ```
 
-## Configuring 🤗 Accelerate
+## Configuration
 
-After installing, you need to configure 🤗 Accelerate for how the current system is setup for training. 
+After installing, you need to configure Accelerate for how the current system is setup for training. 
 To do so run the following and answer the questions prompted to you:
 
 ```bash
@@ -70,7 +68,8 @@ To write a barebones configuration that doesn't include options such as DeepSpee
 ```bash
 python -c "from accelerate.utils import write_basic_config; write_basic_config(mixed_precision='fp16')"
 ```
-🤗 Accelerate will automatically utilize the maximum number of GPUs available and set the mixed precision mode.
+
+Accelerate will automatically utilize the maximum number of GPUs available and set the mixed precision mode.
 
 To check that your configuration looks fine, run:
 
@@ -99,4 +98,4 @@ An example output is shown below, which describes two GPUs on a single machine w
         - main_training_function: main
         - deepspeed_config: {}
         - fsdp_config: {}
-```
+```

docs/source/basic_tutorials/launch.md

@@ -13,9 +13,9 @@ specific language governing permissions and limitations under the License.
 rendered properly in your Markdown viewer.
 -->
 
-# Launching your 🤗 Accelerate scripts
+# Launching Accelerate scripts
 
-In the previous tutorial, you were introduced to how to modify your current training script to use 🤗 Accelerate.
+In the previous tutorial, you were introduced to how to modify your current training script to use Accelerate.
 The final version of that code is shown below:
 
 ```python
@@ -69,14 +69,14 @@ Next, you need to launch it with `accelerate launch`.
 <Tip warning={true}>
 
   It's recommended you run `accelerate config` before using `accelerate launch` to configure your environment to your liking. 
-  Otherwise 🤗 Accelerate will use very basic defaults depending on your system setup.
+  Otherwise Accelerate will use very basic defaults depending on your system setup.
 
 </Tip>
 
 
 ## Using accelerate launch
 
-🤗 Accelerate has a special CLI command to help you launch your code in your system through `accelerate launch`.
+Accelerate has a special CLI command to help you launch your code in your system through `accelerate launch`.
 This command wraps around all of the different commands needed to launch your script on various platforms, without you having to remember what each of them is.
 
 <Tip>
@@ -101,7 +101,7 @@ CUDA_VISIBLE_DEVICES="0" accelerate launch {script_name.py} --arg1 --arg2 ...
 ```
 
 You can also use `accelerate launch` without performing `accelerate config` first, but you may need to manually pass in the right configuration parameters.
-In this case, 🤗 Accelerate will make some hyperparameter decisions for you, e.g., if GPUs are available, it will use all of them by default without the mixed precision.
+In this case, Accelerate will make some hyperparameter decisions for you, e.g., if GPUs are available, it will use all of them by default without the mixed precision.
 Here is how you would use all GPUs and train with mixed precision disabled:
 
 ```bash
@@ -129,7 +129,7 @@ accelerate launch -h
 
 <Tip>
 
-  Even if you are not using 🤗 Accelerate in your code, you can still use the launcher for starting your scripts!
+  Even if you are not using Accelerate in your code, you can still use the launcher for starting your scripts!
 
 </Tip>
 
@@ -178,7 +178,7 @@ accelerate launch {script_name.py} {--arg1} {--arg2} ...
 ## Custom Configurations
 
 As briefly mentioned earlier, `accelerate launch` should be mostly used through combining set configurations 
-made with the `accelerate config` command. These configs are saved to a `default_config.yaml` file in your cache folder for 🤗 Accelerate. 
+made with the `accelerate config` command. These configs are saved to a `default_config.yaml` file in your cache folder for Accelerate. 
 This cache folder is located at (with decreasing order of priority):
 
 - The content of your environment variable `HF_HOME` suffixed with `accelerate`.
@@ -211,7 +211,7 @@ accelerate launch --config_file {path/to/config/my_config_file.yaml} {script_nam
 ```
 
 ## Multi-node training
-Multi-node training with 🤗Accelerate is similar to [multi-node training with torchrun](https://pytorch.org/tutorials/intermediate/ddp_series_multinode.html). The simplest way to launch a multi-node training run is to do the following:
+Multi-node training with Accelerate is similar to [multi-node training with torchrun](https://pytorch.org/tutorials/intermediate/ddp_series_multinode.html). The simplest way to launch a multi-node training run is to do the following:
 
 - Copy your codebase and data to all nodes. (or place them on a shared filesystem)
 - Setup your python packages on all nodes.

docs/source/basic_tutorials/migration.md

@@ -220,8 +220,5 @@ To further customize where and how states are saved through [`~Accelerator.save_
 
 Any other stateful items to be stored should be registered with the [`~Accelerator.register_for_checkpointing`] method so they can be saved and loaded. Every object passed to this method to be stored must have a `load_state_dict` and `state_dict` function.
 
-<Note>
-
-If you have [`torchdata>=0.8.0`](https://github.com/pytorch/data/tree/main) installed, you can additionally pass `use_stateful_dataloader=True` into your [`~utils.DataLoaderConfiguration`]. This extends Accelerate's DataLoader classes with a `load_state_dict` and `state_dict` function, and makes it so `Accelerator.save_state` and `Accelerator.load_state` also track how far into the training dataset it has read when persisting the model.
-
-</Note>
+> [!TIP]
+> If you have [`torchdata>=0.8.0`](https://github.com/pytorch/data/tree/main) installed, you can additionally pass `use_stateful_dataloader=True` into your [`~utils.DataLoaderConfiguration`]. This extends Accelerate's DataLoader classes with a `load_state_dict` and `state_dict` function, and makes it so `Accelerator.save_state` and `Accelerator.load_state` also track how far into the training dataset it has read when persisting the model.

docs/source/basic_tutorials/notebook.md

@@ -13,7 +13,7 @@ specific language governing permissions and limitations under the License.
 rendered properly in your Markdown viewer.
 -->
 
-# Launching Multi-GPU Training from a Jupyter Environment
+# Launching distributed training from Jupyter Notebooks
 
 This tutorial teaches you how to fine tune a computer vision model with 🤗 Accelerate from a Jupyter Notebook on a distributed system.
 You will also learn how to setup a few requirements needed for ensuring your environment is configured properly, your data has been prepared properly, and finally how to launch training.
@@ -26,13 +26,13 @@ You will also learn how to setup a few requirements needed for ensuring your env
 
 ## Configuring the Environment
 
-Before any training can be performed, a 🤗 Accelerate config file must exist in the system. Usually this can be done by running the following in a terminal and answering the prompts:
+Before any training can be performed, a Accelerate config file must exist in the system. Usually this can be done by running the following in a terminal and answering the prompts:
 
 ```bash
 accelerate config
 ```
 
-However, if general defaults are fine and you are *not* running on a TPU, 🤗Accelerate has a utility to quickly write your GPU configuration into a config file via [`utils.write_basic_config`].
+However, if general defaults are fine and you are *not* running on a TPU, Accelerate has a utility to quickly write your GPU configuration into a config file via [`utils.write_basic_config`].
 
 The following code will restart Jupyter after writing the configuration, as CUDA code was called to perform this. 
 
@@ -454,7 +454,7 @@ epoch 4: 94.71
 
 And that's it!
 
-Please note that [`notebook_launcher`] ignores the 🤗 Accelerate config file, to launch based on the config use:
+Please note that [`notebook_launcher`] ignores the Accelerate config file, to launch based on the config use:
 
 ```bash
 accelerate launch

docs/source/basic_tutorials/overview.md

@@ -15,10 +15,10 @@ rendered properly in your Markdown viewer.
 
 # Overview
 
-Welcome to the 🤗 Accelerate tutorials! These introductory guides will help catch you up to speed on working with 🤗 Accelerate.
+Welcome to the Accelerate tutorials! These introductory guides will help catch you up to speed on working with Accelerate.
 You'll learn how to modify your code to have it work with the API seamlessly, how to launch your script properly,
 and more!
 
 These tutorials assume some basic knowledge of Python and familiarity with the PyTorch framework.
 
-If you have any questions about 🤗 Accelerate, feel free to join and ask the community on our [forum](https://discuss.huggingface.co/c/accelerate/18).
+If you have any questions about Accelerate, feel free to join and ask the community on our [forum](https://discuss.huggingface.co/c/accelerate/18).

docs/source/basic_tutorials/troubleshooting.md

@@ -204,8 +204,8 @@ Vastly different GPUs within the same setup can lead to performance bottlenecks.
 
 If none of the solutions and advice here helped resolve your issue, you can always reach out to the community and Accelerate team for help.
 
-- Ask for help on the Hugging Face forums by posting your question in the [🤗 Accelerate category](https://discuss.huggingface.co/c/accelerate/18). Make sure to write a descriptive post with relevant context about your setup and reproducible code to maximize the likelihood that your problem is solved!
+- Ask for help on the Hugging Face forums by posting your question in the [Accelerate category](https://discuss.huggingface.co/c/accelerate/18). Make sure to write a descriptive post with relevant context about your setup and reproducible code to maximize the likelihood that your problem is solved!
 
 - Post a question on [Discord](http://hf.co/join/discord), and let the team and the community help you.
 
-- Create an Issue on the 🤗 Accelerate [GitHub repository](https://github.com/huggingface/accelerate/issues) if you think you've found a bug related to the library. Include context regarding the bug and details about your distributed setup to help us better figure out what's wrong and how we can fix it.
+- Create an Issue on the Accelerate [GitHub repository](https://github.com/huggingface/accelerate/issues) if you think you've found a bug related to the library. Include context regarding the bug and details about your distributed setup to help us better figure out what's wrong and how we can fix it.