pytorch 分布式 Node/Worker/Rank等基础概念

分布式训练相关基本参数的概念如下:

Definitions

1. Node - A physical instance or a container; maps to the unit that the job manager works with.

2. Worker - A worker in the context of distributed training.

3. WorkerGroup - The set of workers that execute the same function (e.g. trainers).

4. LocalWorkerGroup - A subset of the workers in the worker group running on the same node.

5. RANK - The rank of the worker within a worker group.

6. WORLD_SIZE - The total number of workers in a worker group.

7. LOCAL_RANK - The rank of the worker within a local worker group.

8. LOCAL_WORLD_SIZE - The size of the local worker group.

9. rdzv_id - A user-defined id that uniquely identifies the worker group for a job. This id is used by each node to join as a member of a particular worker group.

1. rdzv_backend - The backend of the rendezvous (e.g. c10d). This is typically a strongly consistent key-value store.

2. rdzv_endpoint - The rendezvous backend endpoint; usually in form :.

A Node runs LOCAL_WORLD_SIZE workers which comprise a LocalWorkerGroup. The union of all LocalWorkerGroups in the nodes in the job comprise the WorkerGroup.

翻译:

Node: 通常代表有几台机器

Worker: 指一个训练进程

WORD_SIZE: 总训练进程数,通常与所有机器加起来的GPU数相等(通常每个GPU跑一个训练进程)

RANK:  每个Worker的标号,用来标识每个每个训练进程(所有机器)

LOCAL_RANK :  在同一台机器上woker的标识,例如一台8卡机器上的woker标识就是0-7

总结:

一个节点(一台机器) 跑 LOCAL_WORLD_SIZE 个数的workers, 这些workers 构成了LocalWorkerGroup(组的概念), 

所有机器上的LocalWorkerGroup 就组成了WorkerGroup 

ps: Local 就是代表一台机器上的相关概念, 当只有一台机器时,Local的数据和不带local的数据时一致的

reference:

torchrun (Elastic Launch) — PyTorch 2.1 documentation