Huggingface accelerate

Login Signup. An Introduction to HuggingFace's Accelerate Library In this article, huggingface accelerate, we dive into the internal workings of the Accelerate library from HuggingFace, to answer "could Accelerate really be this easy? Aman Arora. As someone who first spent around huggingface accelerate day implementing Distributed Data Parallel DDP in PyTorch and then spent around 5 mins doing the same thing using HuggingFace's new Accelerate library, I was intrigued and amazed by the simplicity of the package.

There are many ways to launch and run your code depending on your training environment torchrun , DeepSpeed , etc. Accelerate offers a unified interface for launching and training on different distributed setups, allowing you to focus on your PyTorch training code instead of the intricacies of adapting your code to these different setups. Accelerate automatically selects the appropriate configuration values for any given distributed training framework DeepSpeed, FSDP, etc. But in most cases, you should always run accelerate config first to help Accelerate learn about your training setup. This file stores the configuration for your training environment, which helps Accelerate correctly launch your training script based on your machine. Once your environment is setup, launch your training script with accelerate launch!

Huggingface accelerate

Each distributed training framework has their own way of doing things which can require writing a lot of custom code to adapt it to your PyTorch training code and training environment. Accelerate offers a friendly way to interface with these distributed training frameworks without having to learn the specific details of each one. Accelerate takes care of those details for you, so you can focus on the training code and scale it to any distributed training environment. The Accelerator is the main class for adapting your code to work with Accelerate. This class also provides access to many of the necessary methods for enabling your PyTorch code to work in any distributed training environment and for managing and executing processes across devices. The Accelerator also knows which device to move your PyTorch objects to, so it is recommended to let Accelerate handle this for you. Next, you need to prepare your PyTorch objects model, optimizer, scheduler, etc. Accelerate only prepares objects that inherit from their respective PyTorch classes such as torch. Put everything together and your new Accelerate training loop should now look like this! Accelerate offers additional features - like gradient accumulation, gradient clipping, mixed precision training and more - you can add to your script to improve your training run.

PathLike — The name of the folder to save all relevant weights and states.

This is the most memory-intensive solution, as it requires each GPU to keep a full copy of the model in memory at a given time. Normally when doing this, users send the model to a specific device to load it from the CPU, and then move each prompt to a different device. A basic pipeline using the diffusers library might look something like so:. Followed then by performing inference based on the specific prompt:. One will notice how we have to check the rank to know what prompt to send, which can be a bit tedious. To learn more, check out the relevant section in the Quick Tour. Can it manage it?

As you can see in this example, by adding 5-lines to any standard PyTorch training script you can now run on any kind of single or distributed node setting single CPU, single GPU, multi-GPUs and TPUs as well as with or without mixed precision fp8, fp16, bf In particular, the same code can then be run without modification on your local machine for debugging or your training environment. Want to learn more? Check out the documentation or have a look at our examples. No need to remember how to use torch. On your machine s just run:. This will generate a config file that will be used automatically to properly set the default options when doing. You can also directly pass in the arguments you would to torchrun as arguments to accelerate launch if you wish to not run accelerate config. To learn more, check the CLI documentation available here.

Huggingface accelerate

With the latest release of PyTorch 2. With this release we are excited to announce support for pipeline-parallel inference by integrating PyTorch's PiPPy framework so no need to use Megatron or DeepSpeed! This is still under heavy development, however the inference side is stable enough that we are ready for a release. Read more about it in our docs and check out the example zoo. Full Changelog : v0. It is the default backend of choice. Read more in the docs here.

Kannasuku

A decorator that will run the decorated function on a given local process index only. No need to remember how to use torch. Faster examples with accelerated inference. Now, moving on to the DataLoaders - this is where most of the work needs to be done. This is very easy to do with the gather method. To learn more, check out the Launch distributed code tutorial for more information about launching your scripts. To illustrate how you can use this with Accelerate, we have created an example zoo showcasing a number of different models and situations. A basic pipeline using the diffusers library might look something like so:. How to guides. All model parameters are references to tensors, so this loads your weights inside model. Launching your training from a notebook. There are many ways to launch and run your code depending on your training environment torchrun , DeepSpeed , etc. For an introduction to DDP, please refer to the following wonderful resources:. The local means per machine: if you are running your training on two servers with several GPUs, the instruction will be executed once on each of those servers. Never lose track of another ML project.

It covers the essential steps you need to take to enable distributed training, as well as the adjustments that you need to make in some common scenarios. Add this at the beginning of your training script as it will initialize everything necessary for distributed training.

Overview Quantization Efficient training techniques. Launching training using DeepSpeed. Add a sampler of type torch. The actual batch size for your training will be the number of devices used multiplied by the batch size you set in your script: for instance training on 4 GPUs with a batch size of 16 set when creating the training dataloader will train at an actual batch size of This is False by default as it incurs a communication call. To do this, wrap the statement in a test like this:. Will default to self. And more! When passing inputs, we highly recommend to pass them in as a tuple of arguments. Concepts and fundamentals.