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, huggingface accelerate. Accelerate offers a friendly way to interface with these distributed training frameworks without having to huggingface accelerate the specific details of each one.

Go to latest documentation instead. Import the Accelerator main class instantiate one in an accelerator object:. This should happen as early as possible in your training script as it will initialize everything necessary for distributed training. Remove the call to device or cuda for your model and input data. The accelerator object will handle this for you and place all those objects on the right device for you. If you place your objects manually on the proper device, be careful to create your optimizer after putting your model on accelerator. Pass all objects relevant to training optimizer, model, training dataloader to the prepare method.

Huggingface accelerate

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? Does it add unneeded extra code however: also yes. This function will automatically split whatever data you pass to it be it a prompt, a set of tensors, a dictionary of the prior data, etc. If you have generated a config file to be used using accelerate config :. Note: You will get some warnings about values being guessed based on your system. To remove these you can do accelerate config default or go through accelerate config to create a config file. But what if we have an odd distribution of prompts to GPUs?

Useful before saving the model. An example can be found in this notebook. Launching script.

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.

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.

Huggingface accelerate

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. The accelerator object will handle placing these objects on the right device for you. If you choose to leave those. However, if you place your objects manually on the proper device, be careful to create your optimizer after putting your model on accelerator. In other words, if there are 8 processes and a dataset of 64 items, each process will see 8 of these items per iteration. The actual batch size for your training will be the number of devices used multiplied by the batch size you set in your script. You can perform regular evaluation in your training script if you leave your validation dataloader out of the prepare method. In this case, you will need to put the input data on the accelerator. To perform distributed evaluation, send along your validation dataloader to the prepare method:.

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To perform distributed evaluation, send along your validation dataloader to the prepare method:. The Accelerator also knows which device to move your PyTorch objects to, so it is recommended to let Accelerate handle this for you. The NLP example shows an example in situation with dynamic padding. Accelerate offers a friendly way to interface with these distributed training frameworks without having to learn the specific details of each one. You can use the regular commands to launch your distributed training like torch. Now, moving on to the DataLoaders - this is where most of the work needs to be done. To do so, use the inference. If you're a PyTorch user like I am and have previously tried to implement DDP in PyTorch to train your models on multiple GPUs in the past, then you know how painful it can be, especially if you're doing it the first time. You can also override any of the arguments determined by your config file, see TODO: insert ref here. Launching script.

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?

I have already started using the package for my internal workflows, and it's been really easy to use! Multimodal models. Sending chunks of a batch automatically to each loaded model 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. This is only needed when trying to perform an action such as gathering the results, where the data on each device needs to be the same length. For printing statements you only want executed once per machine, you can just replace the print function by accelerator. Reload to refresh your session. Collaborate on models, datasets and Spaces. This will launch a short script that will test the distributed environment. Creates a new torch. Then, when calling prepare , the library: wraps your model s in the container adapted for the distributed setup, wraps your optimizer s in a AcceleratedOptimizer , creates a new version of your dataloader s in a DataLoaderShard. If it is not provided, the default Accelerator value wil be used. Another example is progress bars: to avoid having multiple progress bars in your output, you should only display one on the local main process:. Models passed to accumulate will skip gradient syncing during backward pass in distributed training. Accelerate documentation Quicktour.