From a2c887915883b5a895dd4822f53dcea3d2a51640 Mon Sep 17 00:00:00 2001 From: "yi.wu" Date: Mon, 25 Jun 2018 17:14:01 +0800 Subject: [PATCH] add pics and env desc --- .../howto/training/dist_train_howto.md | 49 ++++++++++++++++-- .../training/src/dist_train_nccl2.graffle | Bin 0 -> 2188 bytes .../howto/training/src/dist_train_nccl2.png | Bin 0 -> 30859 bytes .../training/src/dist_train_pserver.graffle | Bin 0 -> 1985 bytes .../howto/training/src/dist_train_pserver.png | Bin 0 -> 50123 bytes .../howto/training/src/parallelism.png | Bin 0 -> 61287 bytes 6 files changed, 44 insertions(+), 5 deletions(-) create mode 100644 source/user_guides/howto/training/src/dist_train_nccl2.graffle create mode 100644 source/user_guides/howto/training/src/dist_train_nccl2.png create mode 100644 source/user_guides/howto/training/src/dist_train_pserver.graffle create mode 100644 source/user_guides/howto/training/src/dist_train_pserver.png create mode 100644 source/user_guides/howto/training/src/parallelism.png diff --git a/source/user_guides/howto/training/dist_train_howto.md b/source/user_guides/howto/training/dist_train_howto.md index 1a0df2b8f..799d315de 100644 --- a/source/user_guides/howto/training/dist_train_howto.md +++ b/source/user_guides/howto/training/dist_train_howto.md @@ -4,7 +4,7 @@ 分布式深度学习训练通常分为两种并行化方法:数据并行,模型并行,参考下图: - + 在模型并行方式下,模型的层和参数将被分布在多个节点上,模型在一个mini-batch的前向和反向训练中,将经过多次跨 节点之间的通信。每个节点只保存整个模型的一部分;在数据并行方式下,每个节点保存有完整的模型的层和参数,每个节点 @@ -15,8 +15,8 @@ 通信。其中RPC通信方式使用[gRPC](https://github.com/grpc/grpc/),Collective通信方式使用 [NCCL2](https://developer.nvidia.com/nccl)。下面是一个RPC通信和Collective通信的横向对比: -| Feature | Collective | RPC | -| ------------- |:-------------:| -----:| +| Feature | Collective | RPC | +| ------------- |:-------------:| :-----:| | Ring-Based Comm | Yes | No | | Async Training | Reduce ranks | Fast, Direct async updates | | Dist-Sparse-Table | No | Yes | @@ -24,14 +24,53 @@ | Performance | Faster | Fast | * RPC通信方式的结构: - + * NCCL2通信方式的结构: - + ## 使用parameter server方式的训练 +使用"trainer" API,程序可以自动的通过识别环境变量决定是否已分布式方式执行,需要在您的分布式环境中配置的环境变量包括: + +| Env Variable | Comment | +| ------------ | ------- | +| PADDLE_TRAINING_ROLE | role of current node, must be PSERVER or TRAINER | +| PADDLE_PSERVER_PORT | the port that the parameter servers will bind to | +| PADDLE_PSERVER_IPS | a comma separated list of parameter server ips or hostname | +| PADDLE_TRAINERS | number of trainers that in this distributed job | +| PADDLE_CURRENT_IP | current node ip address | +| PADDLE_TRAINER_ID | zero based ID for each trainer | + +使用更加底层的"transpiler" API可以提供自定义的分布式训练的方法,比如可以在同一台机器上,启动多个pserver和trainer +进行训练,使用底层API的方法可以参考下面的样例代码: + +```python +role = "PSERVER" +trainer_id = 0 +pserver_endpoints = "127.0.0.1:6170,127.0.0.1:6171" +current_endpoint = "127.0.0.1:6170" +trainers = 4 +t = fluid.DistributeTranspiler() +t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers) +if role == "PSERVER": + pserver_prog = t.get_pserver_program(current_endpoint) + pserver_startup = t.get_startup_program(current_endpoint, + pserver_prog) + exe.run(pserver_startup) + exe.run(pserver_prog) +elif role == "TRAINER": + train_loop(t.get_trainer_program()) +``` ## 使用NCCL2通信方式的训练 +注NCCL2模式目前仅支持"trainer" API,NCCL2方式并没有很多可选项,也没有"transpiler",所以并没有底层API。 +使用NCCL2方式同样需要配置每个节点的环境变量,此处与parameter server模式有所不同: +| Env Variable | Comment | +| ------------ | ------- | +| PADDLE_TRAINER_IPS | comma separated IP list of all trainer nodes | +| PADDLE_TRAINER_ID | zero based ID for each trainer, aka. 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