Accelerate
  • 🌍GETTING STARTED
    • BOINC AI Accelerate
    • Installation
    • Quicktour
  • 🌍TUTORIALS
    • Overview
    • Migrating to BOINC AI Accelerate
    • Launching distributed code
    • Launching distributed training from Jupyter Notebooks
  • 🌍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 Intel® Extension for PyTorch for cpu
  • 🌍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
  • 🌍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. TUTORIALS

Overview

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Last updated 1 year ago

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