- How does one use accelerate with the hugging face (HF) trainer?
For me, after several iterations and rewriting complete training loop to use Accelerate, I realized that I do not need to do any change to my code with Trainer I just need to wrap Trainer inside accelerator: For sure, I need to import accelerate first: from accelerate import Accelerator For example Trainer part:
- Data Loading and Processing | huggingface accelerate | DeepWiki
This page documents how Accelerate handles data loading and processing in distributed training environments It covers the core components responsible for efficiently distributing data across multiple
- Distributed Inference with Accelerate - Hugging Face
Loading parts of a model onto each GPU and processing a single input at one time; Loading parts of a model onto each GPU and using what is called scheduled Pipeline Parallelism to combine the two prior techniques We’re going to go through the first and the last bracket, showcasing how to do each as they are more realistic scenarios
- Working with large models - modeldatabase. com
Once loaded across devices, you still need to call dispatch_model() on your model to make it able to run To group the checkpoint loading and dispatch in one single call, use load_checkpoint_and_dispatch()
- Using DeepSpeed and FSDP with Accelerate — Part 1
Types of Parallelism: 1 Data Parallelism: Distribute the data across multiple GPUs or machines Each process (GPU) trains over a subset of the data while hosting a complete copy of the model
- Squeeze more out of your GPU for LLM inference—a . . . - Medium
A device map is a dictionary that tells the library which layer of the model to load to what device In its “auto” mode, Accelerate will try to load as many layers (sequentially) into GPUs, and then to CPUs or even the hard drive An example device map looks like this: {0: “10GiB”, 1: “10GiB”, “cpu”: “30GiB”} Loading load
- Quick tour — accelerate documentation - Hugging Face
Like for your training dataloader, it will mean that (should you run your script on multiple devices) each device will only see part of the evaluation data This means you will need to group your predictions together This is very easy to do with the gather() method
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