distributed-training-pytorch-fsdp

原文涵盖的主题

• Pytorch-Fsdp Skill
• When to Use This Skill
• Quick Reference
• Common Patterns

Comprehensive assistance with pytorch-fsdp development, generated from official documentation. This skill should be triggered when: Working with pytorch-fsdp Asking about pytorch-fsdp features or APIs Implementing pytorch-fsdp solutions Debugging pytorch-fsdp code Learning pytorch-fsdp best practices Pattern 1: Generic Join Context Manager# Created On: Jun 06, 2025 | Last Updated On: Jun 06, 2025 The generic join context manager facilitates distributed training on uneven inputs. This page outlines the API of the relevant classes: Join, Joinable, and JoinHook. For a tutorial, see Distributed Training with Uneven Inputs Using the Join Context Manager. class torch.distributed.algorithms.Join(joinables, enable=True, throwonearlytermination=False, kwargs)[source]# This class defines the generic join context manager, which allows custom hooks to be called after a process joins. These hooks should shadow the collective communications of non-joined processes to prevent hanging and erroring and to ensure algorithmic correctness. Refer to JoinHook for details about the hook definition. Warning The context manager requires each participating Joinable to call the method notifyjoincontext() bef

这里只展示节选，请以完整原文和当前要求为准。