diff --git a/develop/doc/_sources/howto/usage/cluster/cluster_train_en.md.txt b/develop/doc/_sources/howto/usage/cluster/cluster_train_en.md.txt index f9819470c0c622b4bc0ea064303d742385603230..915405ca5b446981515e301ca4b7ee065a82a9ff 100644 --- a/develop/doc/_sources/howto/usage/cluster/cluster_train_en.md.txt +++ b/develop/doc/_sources/howto/usage/cluster/cluster_train_en.md.txt @@ -1,4 +1,4 @@ -# PaddlePaddle Distributed Training +# Distributed Training ## Introduction @@ -188,5 +188,4 @@ These cluster platforms provide API or environment variables for training proces - [fabric](fabric_en.md) - [openmpi](openmpi_en.md) - [kubernetes](k8s_en.md) - - kubernetes distributed - [kubernetes on AWS](k8s_aws_en.md) diff --git a/develop/doc/_sources/howto/usage/cluster/k8s_en.md.txt b/develop/doc/_sources/howto/usage/cluster/k8s_en.md.txt index 0c3ab05b708e7a924577c26496b8c55126e76c62..5a3ebfd8dcd144785769d43f9bc75135b5f9b0bd 100644 --- a/develop/doc/_sources/howto/usage/cluster/k8s_en.md.txt +++ b/develop/doc/_sources/howto/usage/cluster/k8s_en.md.txt @@ -1,13 +1,13 @@ -# Paddle On Kubernetes +# PaddlePaddle On Kubernetes ->In this article, we will introduce how to run Paddle training job on single CPU machine using Kubernetes. In next article, we will introduce how to run Paddle training job on distributed cluster. +In this article, we will introduce how to run PaddlePaddle training job on single CPU machine using Kubernetes. In next article, we will introduce how to run PaddlePaddle training job on distributed cluster. ## Build Docker Image -In distributed Kubernetes cluster, we will use Ceph or other shared storage system for storing training related data so that all processes in Paddle training can retrieve data from Ceph. In this example, we will only demo training job on single machine. In order to simplify the requirement of the environment, we will directly put training data into Paddle's Docker Image, so we need to create a Paddle Docker image that already includes the training data. +In distributed Kubernetes cluster, we will use Ceph or other shared storage system for storing training data so that all processes in the training job can retrieve data from Ceph. In this example, we will only demo training job on single machine. In order to simplify the requirement of the environment, we will directly put training data into PaddlePaddle's Docker Image, so we need to create a PaddlePaddle Docker image that already includes the training data. -Paddle's [Quick Start Tutorial](http://www.paddlepaddle.org/doc/demo/quick_start/index_en.html) introduces how to download and train data by using script from Paddle's source code. -And `paddledev/paddle:cpu-demo-latest` image has the Paddle source code and demo. (Caution: Default Paddle image `paddledev/paddle:cpu-latest` doesn't include the source code, Paddle's different versions of image can be referred here: [Docker installation guide](http://www.paddlepaddle.org/doc/build/docker_install.html)), so we run this container and download the training data, and then commit the whole container to be a new Docker image. +PaddlePaddle's [Quick Start Tutorial](http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/index_en.html) introduces how to download and train data by using script from PaddlePaddle's source code. +And `paddledev/paddle:cpu-demo-latest` image has the PaddlePaddle source code and demo. (Caution: Default PaddlePaddle image `paddledev/paddle:cpu-latest` doesn't include the source code, PaddlePaddle's different versions of image can be referred here: [Docker installation guide](http://www.paddlepaddle.org/doc/build/docker_install.html)), so we run this container and download the training data, and then commit the whole container to be a new Docker image. ### Run Docker Container @@ -67,7 +67,7 @@ $ docker commit quick_start_data mypaddle/paddle:quickstart ## Use Kubernetes For Training ->We will use Kubernetes job for training process, following steps shows how to do the training with Kubernetes. +We will use Kubernetes job for training process, following steps shows how to do the training with Kubernetes. ### Create Yaml Files @@ -99,7 +99,7 @@ spec: restartPolicy: Never ``` -### Start Paddle Job +### Start PaddlePaddle Job Using the above yaml file to start the Kubernetes job. diff --git a/develop/doc/api/index_en.html b/develop/doc/api/index_en.html index a02a372ca4cd99e66f317ca10d17bba534cdd2fe..e1a03198e13600894490f473c29e4ebbab537aaf 100644 --- a/develop/doc/api/index_en.html +++ b/develop/doc/api/index_en.html @@ -125,7 +125,13 @@
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    • @@ -590,7 +596,7 @@ add_test(NAME test_FCGrad Next - Previous + Previous diff --git a/develop/doc/howto/dev/new_op_en.html b/develop/doc/howto/dev/new_op_en.html index 9749a02469b10a9e89efd1c8e90453127a96b278..f1ad5373880b9f43234a74929a4777bed04049d2 100644 --- a/develop/doc/howto/dev/new_op_en.html +++ b/develop/doc/howto/dev/new_op_en.html @@ -123,7 +123,13 @@
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  • HOW TO >
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    -
    -

    PaddlePaddle Distributed Training

    +
    +

    Distributed Training

    Introduction

    In this article, we’ll explain how to run distributed training jobs with PaddlePaddle on different types of clusters. The diagram below shows the main architecture of a distributed trainning job:

    @@ -382,14 +388,15 @@ python train.py

    Use different clusters

    -
    @@ -399,7 +406,7 @@ python train.py
    -
    + +
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    -
    + +
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    - -
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  • Detail Description
    • -
  • PaddlePaddle Distributed Training
    • +
  • Distributed Training +
  • Write New Layers
  • Contribute Code
  • Contribute Documentation
    • diff --git a/develop/doc/v1_api_tutorials/README.html b/develop/doc/v1_api_tutorials/README.html index 69a97b0bf1529959c00ed77c6c24bd58b9526a59..ecfbcbfbb0b2ad0f4521e0c2f33051599b9dc326 100644 --- a/develop/doc/v1_api_tutorials/README.html +++ b/develop/doc/v1_api_tutorials/README.html @@ -123,7 +123,13 @@
  • Detail Description
    • -
  • PaddlePaddle Distributed Training
    • +
  • Distributed Training +
  • Write New Layers
  • Contribute Code
  • Contribute Documentation
    • diff --git a/develop/doc/v1_api_tutorials/embedding_model/index_en.html b/develop/doc/v1_api_tutorials/embedding_model/index_en.html index 614fce10129d7f63c205eda0d4dacc2de38f4f1c..35864a1aff2a74c0029812acef3a3bf8c60fdacc 100644 --- a/develop/doc/v1_api_tutorials/embedding_model/index_en.html +++ b/develop/doc/v1_api_tutorials/embedding_model/index_en.html @@ -123,7 +123,13 @@
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    • diff --git a/develop/doc_cn/_sources/howto/usage/cluster/cluster_train_cn.md.txt b/develop/doc_cn/_sources/howto/usage/cluster/cluster_train_cn.md.txt index c9f90538a669d4705d18c3cd9b6dbf4a535c35b8..659bae9c0ceaf2fb2df8446b9d406a822a9df0ea 100644 --- a/develop/doc_cn/_sources/howto/usage/cluster/cluster_train_cn.md.txt +++ b/develop/doc_cn/_sources/howto/usage/cluster/cluster_train_cn.md.txt @@ -1,4 +1,4 @@ -# PaddlePaddle分布式训练 +# 分布式训练 ## 概述 @@ -181,8 +181,8 @@ PaddlePaddle可以使用多种分布式计算平台构建分布式计算任务 ## 在不同集群中运行 - - [fabric](fabric_cn.md) - - [openmpi](openmpi_cn.md) - - [kubernetes](k8s_cn.md) - - [kubernetes distributed](k8s_distributed_cn.md) - - [kubernetes on AWS](k8s_aws_cn.md) + - [fabric集群](fabric_cn.md) + - [openmpi集群](openmpi_cn.md) + - [kubernetes单机](k8s_cn.md) + - [kubernetes distributed分布式](k8s_distributed_cn.md) + - [AWS上运行kubernetes集群训练](k8s_aws_cn.md) diff --git a/develop/doc_cn/_sources/howto/usage/cluster/k8s_cn.md.txt b/develop/doc_cn/_sources/howto/usage/cluster/k8s_cn.md.txt index ab07cb9cd5b135ddea82b3360720537f1dc5a801..9d49d0fa8cf01bbaef15c6e13a1e906fa5a9e367 100644 --- a/develop/doc_cn/_sources/howto/usage/cluster/k8s_cn.md.txt +++ b/develop/doc_cn/_sources/howto/usage/cluster/k8s_cn.md.txt @@ -1,16 +1,16 @@ # Kubernetes单机训练 -在这篇文档里，我们介绍如何在 Kubernetes 集群上启动一个单机使用CPU的Paddle训练作业。在下一篇中，我们将介绍如何启动分布式训练作业。 +在这篇文档里，我们介绍如何在 Kubernetes 集群上启动一个单机使用CPU的PaddlePaddle训练作业。在下一篇中，我们将介绍如何启动分布式训练作业。 ## 制作Docker镜像 -在一个功能齐全的Kubernetes机群里，通常我们会安装Ceph等分布式文件系统来存储训练数据。这样的话，一个分布式Paddle训练任务中的每个进程都可以从Ceph读取数据。在这个例子里，我们只演示一个单机作业，所以可以简化对环境的要求，把训练数据直接放在 -Paddle的Docker image里。为此，我们需要制作一个包含训练数据的Paddle镜像。 +在一个功能齐全的Kubernetes机群里，通常我们会安装Ceph等分布式文件系统来存储训练数据。这样的话，一个分布式PaddlePaddle训练任务中的每个进程都可以从Ceph读取数据。在这个例子里，我们只演示一个单机作业，所以可以简化对环境的要求，把训练数据直接放在 +PaddlePaddle的Docker image里。为此，我们需要制作一个包含训练数据的PaddlePaddle镜像。 -Paddle 的 [Quick Start Tutorial](http://www.paddlepaddle.org/doc/demo/quick_start/index_en.html) +Paddle 的 [Quick Start Tutorial](http://www.paddlepaddle.org/docs/develop/documentation/zh/getstarted/index_cn.html) 里介绍了用Paddle源码中的脚本下载训练数据的过程。 -而 `paddledev/paddle:cpu-demo-latest` 镜像里有 Paddle 源码与demo，（ 请注意，默认的 -Paddle镜像 `paddledev/paddle:cpu-latest` 是不包括源码的, Paddle的各版本镜像可以参考 [Docker installation guide](http://www.paddlepaddle.org/doc/build/docker_install.html) ），所以我们使用这个镜像来下载训练数据到Docker container中，然后把这个包含了训练数据的container保存为一个新的镜像。 +而 `paddledev/paddle:cpu-demo-latest` 镜像里有 PaddlePaddle 源码与demo，（ 请注意，默认的 +PaddlePaddle镜像 `paddledev/paddle:cpu-latest` 是不包括源码的, PaddlePaddle的各版本镜像可以参考 [Docker installation guide](http://www.paddlepaddle.org/doc/build/docker_install.html) ），所以我们使用这个镜像来下载训练数据到Docker container中，然后把这个包含了训练数据的container保存为一个新的镜像。 ### 运行容器 @@ -103,7 +103,7 @@ spec: restartPolicy: Never ``` -### 创建Paddle Job +### 创建PaddlePaddle Job 使用上文创建的yaml文件创建Kubernetes Job，命令为： diff --git a/develop/doc_cn/api/index_cn.html b/develop/doc_cn/api/index_cn.html index 64e01d8ab9a264dbefee132fb658350adc9c472f..eac7dfeb2703062fe4a9907b9eaeaae9c81ae475 100644 --- a/develop/doc_cn/api/index_cn.html +++ b/develop/doc_cn/api/index_cn.html @@ -127,12 +127,12 @@
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    -
    -

    PaddlePaddle分布式训练

    +
    +

    分布式训练

    -

    概述

    +

    概述

    本文将介绍如何使用PaddlePaddle在不同的集群框架下完成分布式训练。分布式训练架构如下图所示：

      @@ -241,7 +241,7 @@

      在使用同步SGD训练神经网络时，PaddlePaddle使用同步屏障（barrier），使梯度的提交和参数的更新按照顺序方式执行。在异步SGD中，则并不会等待所有trainer提交梯度才更新参数，这样极大地提高了计算的并行性：参数服务器之间不相互依赖，并行地接收梯度和更新参数，参数服务器也不会等待计算节点全部都提交梯度之后才开始下一步，计算节点之间也不会相互依赖，并行地执行模型的训练。可以看出，虽然异步SGD方式会提高参数更新并行度, 但是并不能保证参数同步更新，在任意时间某一台参数服务器上保存的参数可能比另一台要更新，与同步SGD相比，梯度会有噪声。

    -

    环境准备

    +

    环境准备

    1. 准备您的计算集群。计算集群通常由一组（几台到几千台规模）的Linux服务器组成。服务器之间可以通过局域网（LAN）联通，每台服务器具有集群中唯一的IP地址（或者可被DNS解析的主机名）。集群中的每台计算机通常被成为一个“节点”。
    2. 我们需要在集群的所有节点上安装 PaddlePaddle。 如果要启用GPU，还需要在节点上安装对应的GPU驱动以及CUDA。PaddlePaddle的安装可以参考build_and_install的多种安装方式。我们推荐使用Docker安装方式来快速安装PaddlePaddle。
      3. @@ -260,9 +260,9 @@ PaddlePaddle 0.10.0, compiled with

      下面以doc/howto/usage/cluster/src/word2vec中的代码作为实例，介绍使用PaddlePaddle v2 API完成分布式训练。

    -

    启动参数说明

    +

    启动参数说明

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    启动参数服务器

    +

    启动参数服务器

    执行以下的命令启动一个参数服务器并等待和计算节点的数据交互

    $ paddle pserver --port=7164 --ports_num=1 --ports_num_for_sparse=1 --num_gradient_servers=1
    @@ -280,7 +280,7 @@ PaddlePaddle 0.10.0, compiled with
    -

    启动计算节点

    +

    启动计算节点

    执行以下命令启动使用python编写的trainer程序（文件名为任意文件名，如train.py）

    $ python train.py
    @@ -322,7 +322,7 @@ PaddlePaddle 0.10.0, compiled with
    -

    准备数据集

    +

    准备数据集

    参考样例数据准备脚本prepare.py，准备训练数据和验证数据集，我们使用paddle.dataset.imikolov数据集，并根据分布式训练并发数（trainer节点个数），在prepare.py开头部分指定SPLIT_COUNT将数据切分成多份。

    在线上系统中，通常会使用MapReduce任务的输出结果作为训练结果，这样训练文件的个数会比较多，而且个数并不确定。在trainer中可以使用下面取模的方法为每个trainer分配训练数据文件：

    import os
@@ -349,7 +349,7 @@ PaddlePaddle 0.10.0, compiled with
 
    对于不同的训练任务，训练数据格式和训练程序的reader()会大不相同，所以开发者需要根据自己训练任务的实际场景完成训练数据的分割和reader()的编写。
    -

    准备训练程序

    +

    准备训练程序

    我们会对每个训练任务都会在每个节点上创建一个工作空间（workspace），其中包含了用户的训练程序、程序依赖、挂载或下载的训练数据分片。

    最后，工作空间应如下所示：

    .
@@ -388,7 +388,7 @@ PaddlePaddle 0.10.0, compiled with
    -

    使用分布式计算平台或工具

    +

    使用分布式计算平台或工具

    PaddlePaddle可以使用多种分布式计算平台构建分布式计算任务，包括：

    • Kubernetes Google开源的容器集群的调度框架，支持大规模集群生产环境的完整集群方案。
      • @@ -399,14 +399,14 @@ PaddlePaddle 0.10.0, compiled with

      在使用分布式计算平台进行训练时，任务被调度在集群中时，分布式计算平台通常会通过API或者环境变量提供任务运行需要的参数，比如节点的ID、IP和任务节点个数等。

    -

    在不同集群中运行

    +

    在不同集群中运行

    diff --git a/develop/doc_cn/howto/usage/cluster/fabric_cn.html b/develop/doc_cn/howto/usage/cluster/fabric_cn.html index 08e70554dc0941c474b532732a93e9d48f3b1acc..96af72c51f6fa2510ba070d5fd3b8f2b0255c795 100644 --- a/develop/doc_cn/howto/usage/cluster/fabric_cn.html +++ b/develop/doc_cn/howto/usage/cluster/fabric_cn.html @@ -33,9 +33,9 @@ href="../../../genindex.html"/> - + - + @@ -128,12 +128,12 @@
  • 细节描述
    • -
  • PaddlePaddle分布式训练 @@ -281,7 +281,7 @@ Next - Previous + Previous
    • diff --git a/develop/doc_cn/howto/usage/cluster/k8s_aws_cn.html b/develop/doc_cn/howto/usage/cluster/k8s_aws_cn.html index ae3098c23e8550a9f7dfd9581f0baef96be27cbc..b80e0b1365345f27d0f845cef42da422ab7b60ce 100644 --- a/develop/doc_cn/howto/usage/cluster/k8s_aws_cn.html +++ b/develop/doc_cn/howto/usage/cluster/k8s_aws_cn.html @@ -33,7 +33,7 @@ href="../../../genindex.html"/> - + @@ -128,12 +128,12 @@
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  • PaddlePaddle分布式训练 diff --git a/develop/doc_cn/howto/usage/cluster/k8s_cn.html b/develop/doc_cn/howto/usage/cluster/k8s_cn.html index 17f6e5a51ef24166919be4cb9e40f98c854824c3..8b86525e34de8609d4f3e69689b725edf7552892 100644 --- a/develop/doc_cn/howto/usage/cluster/k8s_cn.html +++ b/develop/doc_cn/howto/usage/cluster/k8s_cn.html @@ -33,7 +33,7 @@ href="../../../genindex.html"/> - + @@ -128,12 +128,12 @@
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  • PaddlePaddle分布式训练 @@ -230,15 +230,15 @@

    Kubernetes单机训练

    -

    在这篇文档里，我们介绍如何在 Kubernetes 集群上启动一个单机使用CPU的Paddle训练作业。在下一篇中，我们将介绍如何启动分布式训练作业。

    +

    在这篇文档里，我们介绍如何在 Kubernetes 集群上启动一个单机使用CPU的PaddlePaddle训练作业。在下一篇中，我们将介绍如何启动分布式训练作业。

    制作Docker镜像

    -

    在一个功能齐全的Kubernetes机群里，通常我们会安装Ceph等分布式文件系统来存储训练数据。这样的话，一个分布式Paddle训练任务中的每个进程都可以从Ceph读取数据。在这个例子里，我们只演示一个单机作业，所以可以简化对环境的要求，把训练数据直接放在 -Paddle的Docker image里。为此，我们需要制作一个包含训练数据的Paddle镜像。

    -

    Paddle 的 Quick Start Tutorial +

    在一个功能齐全的Kubernetes机群里，通常我们会安装Ceph等分布式文件系统来存储训练数据。这样的话，一个分布式PaddlePaddle训练任务中的每个进程都可以从Ceph读取数据。在这个例子里，我们只演示一个单机作业，所以可以简化对环境的要求，把训练数据直接放在 +PaddlePaddle的Docker image里。为此，我们需要制作一个包含训练数据的PaddlePaddle镜像。

    +

    Paddle 的 Quick Start Tutorial 里介绍了用Paddle源码中的脚本下载训练数据的过程。 -而 paddledev/paddle:cpu-demo-latest 镜像里有 Paddle 源码与demo，（ 请注意，默认的 -Paddle镜像 paddledev/paddle:cpu-latest 是不包括源码的, Paddle的各版本镜像可以参考 Docker installation guide ），所以我们使用这个镜像来下载训练数据到Docker container中，然后把这个包含了训练数据的container保存为一个新的镜像。

    +而 paddledev/paddle:cpu-demo-latest 镜像里有 PaddlePaddle 源码与demo，（ 请注意，默认的 +PaddlePaddle镜像 paddledev/paddle:cpu-latest 是不包括源码的, PaddlePaddle的各版本镜像可以参考 Docker installation guide ），所以我们使用这个镜像来下载训练数据到Docker container中，然后把这个包含了训练数据的container保存为一个新的镜像。

    运行容器

    $ docker run --name quick_start_data -it paddledev/paddle:cpu-demo-latest
@@ -327,8 +327,8 @@ paddle train \
    -
    -

    创建Paddle Job

    +
    +

    创建PaddlePaddle Job

    使用上文创建的yaml文件创建Kubernetes Job，命令为：

    $ kubectl  create -f paddle.yaml
    diff --git a/develop/doc_cn/howto/usage/cluster/k8s_distributed_cn.html b/develop/doc_cn/howto/usage/cluster/k8s_distributed_cn.html index a1c1fae0895f8accbf561d8d97eab7d1f73b1f10..f9799efde6a7a7ad8992155f264b5eca13f0eeb3 100644 --- a/develop/doc_cn/howto/usage/cluster/k8s_distributed_cn.html +++ b/develop/doc_cn/howto/usage/cluster/k8s_distributed_cn.html @@ -33,7 +33,7 @@ href="../../../genindex.html"/> - + @@ -128,12 +128,12 @@
  • 细节描述
    • -
  • PaddlePaddle分布式训练 diff --git a/develop/doc_cn/howto/usage/cluster/openmpi_cn.html b/develop/doc_cn/howto/usage/cluster/openmpi_cn.html index fdb9f67fc6b5b0396fdc1a0b77a33cf2bd3acbd4..c1eae9ea0add516450824d9ac38f6354df89d087 100644 --- a/develop/doc_cn/howto/usage/cluster/openmpi_cn.html +++ b/develop/doc_cn/howto/usage/cluster/openmpi_cn.html @@ -33,7 +33,7 @@ href="../../../genindex.html"/> - + @@ -128,12 +128,12 @@
  • 细节描述
    • -
  • PaddlePaddle分布式训练 diff --git a/develop/doc_cn/howto/usage/cluster/src/k8s_data/README.html b/develop/doc_cn/howto/usage/cluster/src/k8s_data/README.html index 1aeca744234c80083c766c4968bd9a8362519fcc..6f74e08018cdbd2ae8973a2c6244be4965b9f6f6 100644 --- a/develop/doc_cn/howto/usage/cluster/src/k8s_data/README.html +++ b/develop/doc_cn/howto/usage/cluster/src/k8s_data/README.html @@ -125,12 +125,12 @@
  • 细节描述
    • -
  • PaddlePaddle分布式训练
    • -
  • PaddlePaddle分布式训练
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  • PaddlePaddle分布式训练
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  • PaddlePaddle分布式训练
    • -
  • PaddlePaddle分布式训练
    • -
  • PaddlePaddle分布式训练
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  • PaddlePaddle分布式训练
    • -
  • PaddlePaddle分布式训练
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  • PaddlePaddle分布式训练
    • -
  • PaddlePaddle分布式训练
      -
    • fabric
      • -
    • openmpi
      • -
    • kubernetes
      • -
    • kubernetes distributed
      • -
    • kubernetes on AWS
      • +
    • 分布式训练
    • 如何贡献代码
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    • 细节描述
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  • PaddlePaddle分布式训练
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  • PaddlePaddle分布式训练
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  • PaddlePaddle分布式训练
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  • PaddlePaddle分布式训练
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  • PaddlePaddle分布式训练