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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GETTING STARTED

NextBOINC AI Accelerate

Last updated 1 year ago

🌍
BOINC AI Accelerate
Installation
Quicktour