Accelerate
  • ๐ŸŒGETTING STARTED
    • BOINC AI Accelerate
    • Installation
    • Quicktour
  • ๐ŸŒTUTORIALS
    • Overview
    • Migrating to BOINC AI Accelerate
    • Launching distributed code
    • Launching distributed training from Jupyter Notebooks
  • ๐ŸŒHOW-TO GUIDES
    • Start Here!
    • Example Zoo
    • How to perform inference on large models with small resources
    • Knowing how big of a model you can fit into memory
    • How to quantize model
    • How to perform distributed inference with normal resources
    • Performing gradient accumulation
    • Accelerating training with local SGD
    • Saving and loading training states
    • Using experiment trackers
    • Debugging timeout errors
    • How to avoid CUDA Out-of-Memory
    • How to use Apple Silicon M1 GPUs
    • How to use DeepSpeed
    • How to use Fully Sharded Data Parallelism
    • How to use Megatron-LM
    • How to use BOINC AI Accelerate with SageMaker
    • How to use BOINC AI Accelerate with Intelยฎ Extension for PyTorch for cpu
  • ๐ŸŒCONCEPTS AND FUNDAMENTALS
    • BOINC AI Accelerate's internal mechanism
    • Loading big models into memory
    • Comparing performance across distributed setups
    • Executing and deferring jobs
    • Gradient synchronization
    • TPU best practices
  • ๐ŸŒREFERENCE
    • Main Accelerator class
    • Stateful configuration classes
    • The Command Line
    • Torch wrapper classes
    • Experiment trackers
    • Distributed launchers
    • DeepSpeed utilities
    • Logging
    • Working with large models
    • Kwargs handlers
    • Utility functions and classes
    • Megatron-LM Utilities
    • Fully Sharded Data Parallelism Utilities
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  1. HOW-TO GUIDES

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Last updated 1 year ago

Learning how to incorporate ๐ŸŒ Accelerate features quickly!

Please use the interactive tool below to help you get started with learning about a particular feature of ๐ŸŒ Accelerate and how to utilize it! It will provide you with a code diff, an explaination towards what is going on, as well as provide you with some useful links to explore more within the documentation!

Most code examples start from the following python code before integrating ๐ŸŒ Accelerate in some way:

Copied

for batch in dataloader:
    optimizer.zero_grad()
    inputs, targets = batch
    inputs = inputs.to(device)
    targets = targets.to(device)
    outputs = model(inputs)
    loss = loss_function(outputs, targets)
    loss.backward()
    optimizer.step()
    scheduler.step()

๐ŸŒ
https://huggingface.co/docs/accelerate/usage_guides/explore#learning-how-to-incorporate--accelerate-features-quickly