Distributed and Parallel Training Tutorials =========================================== Distributed training is a model training paradigm that involves spreading training workload across multiple worker nodes, therefore significantly improving the speed of training and model accuracy. While distributed training can be used for any type of ML model training, it is most beneficial to use it for large models and compute demanding tasks as deep learning. There are a few ways you can perform distributed training in PyTorch with each method having their advantages in certain use cases: * `DistributedDataParallel (DDP) <#learn-ddp>`__ * `Fully Sharded Data Parallel (FSDP) <#learn-fsdp>`__ * `Device Mesh <#device-mesh>`__ * `Remote Procedure Call (RPC) distributed training <#learn-rpc>`__ * `Custom Extensions <#custom-extensions>`__ Read more about these options in `Distributed Overview <../beginner/dist_overview.html>`__. .. _learn-ddp: Learn DDP --------- .. grid:: 3 .. grid-item-card:: :octicon:`file-code;1em` DDP Intro Video Tutorials :link: https://pytorch.org/tutorials/beginner/ddp_series_intro.html?utm_source=distr_landing&utm_medium=ddp_series_intro :link-type: url A step-by-step video series on how to get started with `DistributedDataParallel` and advance to more complex topics +++ :octicon:`code;1em` Code :octicon:`square-fill;1em` :octicon:`video;1em` Video .. grid-item-card:: :octicon:`file-code;1em` Getting Started with Distributed Data Parallel :link: https://pytorch.org/tutorials/intermediate/ddp_tutorial.html?utm_source=distr_landing&utm_medium=intermediate_ddp_tutorial :link-type: url This tutorial provides a short and gentle intro to the PyTorch DistributedData Parallel. +++ :octicon:`code;1em` Code .. grid-item-card:: :octicon:`file-code;1em` Distributed Training with Uneven Inputs Using the Join Context Manager :link: https://pytorch.org/tutorials/advanced/generic_join.html?utm_source=distr_landing&utm_medium=generic_join :link-type: url This tutorial describes the Join context manager and demonstrates it's use with DistributedData Parallel. +++ :octicon:`code;1em` Code .. _learn-fsdp: Learn FSDP ---------- .. grid:: 3 .. grid-item-card:: :octicon:`file-code;1em` Getting Started with FSDP :link: https://pytorch.org/tutorials/intermediate/FSDP_tutorial.html?utm_source=distr_landing&utm_medium=FSDP_getting_started :link-type: url This tutorial demonstrates how you can perform distributed training with FSDP on a MNIST dataset. +++ :octicon:`code;1em` Code .. grid-item-card:: :octicon:`file-code;1em` FSDP Advanced :link: https://pytorch.org/tutorials/intermediate/FSDP_adavnced_tutorial.html?utm_source=distr_landing&utm_medium=FSDP_advanced :link-type: url In this tutorial, you will learn how to fine-tune a HuggingFace (HF) T5 model with FSDP for text summarization. +++ :octicon:`code;1em` Code .. _device-mesh: Learn DeviceMesh ---------------- .. grid:: 3 .. grid-item-card:: :octicon:`file-code;1em` Getting Started with DeviceMesh :link: https://pytorch.org/tutorials/recipes/distributed_device_mesh.html?highlight=devicemesh :link-type: url In this tutorial you will learn about `DeviceMesh` and how it can help with distributed training. +++ :octicon:`code;1em` Code .. _learn-rpc: Learn RPC --------- .. grid:: 3 .. grid-item-card:: :octicon:`file-code;1em` Getting Started with Distributed RPC Framework :link: https://pytorch.org/tutorials/intermediate/rpc_tutorial.html?utm_source=distr_landing&utm_medium=rpc_getting_started :link-type: url This tutorial demonstrates how to get started with RPC-based distributed training. +++ :octicon:`code;1em` Code .. grid-item-card:: :octicon:`file-code;1em` Implementing a Parameter Server Using Distributed RPC Framework :link: https://pytorch.org/tutorials/intermediate/rpc_param_server_tutorial.html?utm_source=distr_landing&utm_medium=rpc_param_server_tutorial :link-type: url This tutorial walks you through a simple example of implementing a parameter server using PyTorch’s Distributed RPC framework. +++ :octicon:`code;1em` Code .. grid-item-card:: :octicon:`file-code;1em` Implementing Batch RPC Processing Using Asynchronous Executions :link: https://pytorch.org/tutorials/intermediate/rpc_async_execution.html?utm_source=distr_landing&utm_medium=rpc_async_execution :link-type: url In this tutorial you will build batch-processing RPC applications with the @rpc.functions.async_execution decorator. +++ :octicon:`code;1em` Code .. grid:: 3 .. grid-item-card:: :octicon:`file-code;1em` Combining Distributed DataParallel with Distributed RPC Framework :link: https://pytorch.org/tutorials/advanced/rpc_ddp_tutorial.html?utm_source=distr_landing&utm_medium=rpc_plus_ddp :link-type: url In this tutorial you will learn how to combine distributed data parallelism with distributed model parallelism. +++ :octicon:`code;1em` Code .. _custom-extensions: Custom Extensions ----------------- .. grid:: 3 .. grid-item-card:: :octicon:`file-code;1em` Customize Process Group Backends Using Cpp Extensions :link: https://pytorch.org/tutorials/intermediate/process_group_cpp_extension_tutorial.html?utm_source=distr_landing&utm_medium=custom_extensions_cpp :link-type: url In this tutorial you will learn to implement a custom `ProcessGroup` backend and plug that into PyTorch distributed package using cpp extensions. +++ :octicon:`code;1em` Code