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- sections:
- local: index
title: 🤗 Accelerate
- local: basic_tutorials/install
title: Installation
- local: quicktour
title: Quicktour
title: Getting started
- sections:
- local: basic_tutorials/overview
title: Overview
- local: basic_tutorials/migration
title: Migrating to 🤗 Accelerate
- local: basic_tutorials/launch
title: Launching distributed code
- local: basic_tutorials/notebook
title: Launching distributed training from Jupyter Notebooks
- local: basic_tutorials/troubleshooting
title: Troubleshooting guide
title: Tutorials
- sections:
- local: usage_guides/explore
title: Start Here!
- local: usage_guides/training_zoo
title: Example Zoo
- local: usage_guides/big_modeling
title: How to perform inference on large models with small resources
- local: usage_guides/model_size_estimator
title: Knowing how big of a model you can fit into memory
- local: usage_guides/quantization
title: How to quantize model
- local: usage_guides/distributed_inference
title: How to perform distributed inference with normal resources
- local: usage_guides/gradient_accumulation
title: Performing gradient accumulation
- local: usage_guides/local_sgd
title: Accelerating training with local SGD
- local: usage_guides/checkpoint
title: Saving and loading training states
- local: usage_guides/tracking
title: Using experiment trackers
- local: usage_guides/mps
title: How to use Apple Silicon M1 GPUs
- local: usage_guides/low_precision_training
title: How to train in low precision (FP8)
- local: usage_guides/deepspeed
title: How to use DeepSpeed
- local: usage_guides/fsdp
title: How to use Fully Sharded Data Parallelism
- local: usage_guides/megatron_lm
title: How to use Megatron-LM
- local: usage_guides/sagemaker
title: How to use 🤗 Accelerate with SageMaker
- local: usage_guides/ipex
title: How to use 🤗 Accelerate with Intel® Extension for PyTorch for cpu
title: How-To Guides
- sections:
- local: concept_guides/internal_mechanism
title: 🤗 Accelerate's internal mechanism
- local: concept_guides/big_model_inference
title: Loading big models into memory
- local: concept_guides/performance
title: Comparing performance across distributed setups
- local: concept_guides/deferring_execution
title: Executing and deferring jobs
- local: concept_guides/gradient_synchronization
title: Gradient synchronization
- local: concept_guides/low_precision_training
title: How training in low-precision environments is possible (FP8)
- local: concept_guides/training_tpu
title: TPU best practices
title: Concepts and fundamentals
- sections:
- local: package_reference/accelerator
title: Main Accelerator class
- local: package_reference/state
title: Stateful configuration classes
- local: package_reference/cli
title: The Command Line
- local: package_reference/torch_wrappers
title: Torch wrapper classes
- local: package_reference/tracking
title: Experiment trackers
- local: package_reference/launchers
title: Distributed launchers
- local: package_reference/deepspeed
title: DeepSpeed utilities
- local: package_reference/logging
title: Logging
- local: package_reference/big_modeling
title: Working with large models
- local: package_reference/kwargs
title: Kwargs handlers
- local: package_reference/utilities
title: Utility functions and classes
- local: package_reference/megatron_lm
title: Megatron-LM Utilities
- local: package_reference/fsdp
title: Fully Sharded Data Parallelism Utilities
title: "Reference"
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