提交 aa55112f 编写于 作者: T tensor-tang

init branch

上级

要显示的变更太多。

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| Github account | name |
|---|---|
| abhinavarora | Abhinav Arora |
| backyes | Yan-Fei Wang |
| baiyfbupt | Yi-Fan Bai |
| beckett1124 | Bin Qi |
| ChengduoZH | Cheng-Duo Zhao|
| chengxiaohua1105 | Xiao-Hua Cheng |
| cxwangyi, yiwangbaidu, wangkuiyi | Yi Wang |
| cxysteven | Xing-Yi Cheng |
| dzhwinter | Zhi-Hong Dong |
| dragonwarrior | Long Wang |
| dyning | Yuning Du |
| emailweixu | Wei Xu |
| gangliao | Gang Liao |
| gongweibao | Wei-Bao Gong |
| Guo Sheng | Sheng Guo |
| Haichao-Zhang | Hai-Chao Zhang |
| hedaoyuan | Dao-Yuan He |
| helinwang | He-Lin Wang |
| jacquesqiao | Long-Fei Qiao |
| jczaja | Jacek Czaja |
| JiayiFeng | Jia-Yi Feng |
| kbinias | Krzysztof Binias |
| kexinzhao | Ke-Xin Zhao |
| kuke | Yi-Bing Liu |
| lcy-seso | Ying Cao |
| cjld | Dun Liang |
| lipeng-unisound | Peng Li |
| liuyuan | Yuan Liu |
| livc | Zhao Li |
| llxxxll | Yong-Feng Liu |
| luotao01 | Tao Luo |
| lzhao4ever | Liang Zhao |
| mozga-intel | Mateusz Ozga |
| NHZlX | Zhao-Long Xing |
| Noplz | Yuan Gao |
| pakchoi | Chuan-Jiang Song |
| panyx0718 | Xin Pan |
| pengli09 | Peng Li |
| pkuyym | Ya-Ming Yang |
| pzelazko-intel | Pawel Zelazko |
| QiJune | Jun Qi |
| qingqing01 | Qing-Qing Dang |
| reyoung | Yang Yu |
| Sand3r- | Michal Gallus |
| sfraczek | Sylwester Fraczek |
| Superjom | Chun-Wei Yan |
| tensor-tang | Jian Tang |
| tianbingsz | Tian-Bing Xu |
| tpatejko | Tomasz Patejko |
| typhoonzero | Yi Wu |
| velconia | Qi-Yang Min |
| wanghaoshuang | Hao-Shuang Wang |
| wangyang59 | Yang Wang |
| wangzhen-nlp | Zhen Wang |
| wen-bo-yang | Wen-Bo Yang |
| wojtuss | Wojciech Uss |
| wwhu | Wei-Wei Hu |
| xinghai-sun | Xing-Hai Sun |
| Xreki | Yi-Qun Liu |
| xujun05 | Jun Xu |
| xushaoyong | Shao-Yong Xu |
| Yancey1989 | Xu Yan |
| zhaopu7 | Pu Zhao |
| zhouxiao-coder | Xiao Zhou |
| Zrachel | Rui-Qing Zhang |
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License
cmake_minimum_required(VERSION 3.0)
set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/cmake")
set(PADDLE_SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR})
set(PADDLE_BINARY_DIR ${CMAKE_CURRENT_BINARY_DIR})
include(system)
if(LITE_WITH_LIGHT_WEIGHT_FRAMEWORK)
cmake_minimum_required(VERSION 3.10)
# TODO(TJ): make as function check_default
if(NOT DEFINED ARM_TARGET_OS)
set(ARM_TARGET_OS "android" CACHE STRING "Choose ARM Target OS")
endif()
set(ARM_TARGET_OS_LIST "android" "armlinux") # TODO: "ios"
set_property(CACHE ARM_TARGET_OS PROPERTY STRINGS ${ARM_TARGET_OS_LIST})
if (NOT ARM_TARGET_OS IN_LIST ARM_TARGET_OS_LIST)
message(FATAL_ERROR "ARM_TARGET_OS must be in one of ${ARM_TARGET_OS_LIST}")
endif()
if(NOT DEFINED ARM_TARGET_ARCH_ABI)
set(ARM_TARGET_ARCH_ABI "arm64-v8a" CACHE STRING "Choose ARM Target ARCH ABI")
endif()
set(ARM_TARGET_ARCH_ABI_LIST "arm64-v8a" "armeabi-v7a" "armeabi-v7a-softfp" "armeabi-v7a-hf")
set_property(CACHE ARM_TARGET_ARCH_ABI PROPERTY STRINGS ${ARM_TARGET_ARCH_ABI_LIST})
if (NOT ARM_TARGET_ARCH_ABI IN_LIST ARM_TARGET_ARCH_ABI_LIST)
message(FATAL_ERROR "ARM_TARGET_ARCH_ABI must be in one of ${ARM_TARGET_ARCH_ABI_LIST}")
endif()
if(NOT DEFINED TARGET_ARCH_ABI)
set(ARCH_ABI "arm64-v8a" CACHE STRING "Choose android platform")
endif()
include(cross_compiling/host)
include(cross_compiling/armlinux)
include(cross_compiling/android)
endif()
project(paddle CXX C)
message(STATUS "CXX compiler: ${CMAKE_CXX_COMPILER}, version: "
"${CMAKE_CXX_COMPILER_ID} ${CMAKE_CXX_COMPILER_VERSION}")
message(STATUS "C compiler: ${CMAKE_C_COMPILER}, version: "
"${CMAKE_C_COMPILER_ID} ${CMAKE_C_COMPILER_VERSION}")
message(STATUS "AR tools: ${CMAKE_AR}")
if(WIN32)
set(CMAKE_SUPPRESS_REGENERATION ON)
set(CMAKE_STATIC_LIBRARY_PREFIX lib)
add_definitions("/DGOOGLE_GLOG_DLL_DECL=")
set(CMAKE_C_FLAGS_DEBUG "${CMAKE_C_FLAGS_DEBUG} /bigobj /MTd")
set(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} /bigobj /MT")
set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} /bigobj /MTd")
set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /bigobj /MT")
add_compile_options(/wd4068 /wd4129 /wd4244 /wd4267 /wd4297 /wd4530 /wd4577 /wd4819 /wd4838)
set(PADDLE_LINK_FLAGS "/IGNORE:4006 /IGNORE:4098 /IGNORE:4217 /IGNORE:4221")
set(CMAKE_STATIC_LINKER_FLAGS "${CMAKE_STATIC_LINKER_FLAGS} ${PADDLE_LINK_FLAGS}")
set(CMAKE_SHARED_LINKER_FLAGS "${CMAKE_SHARED_LINKER_FLAGS} ${PADDLE_LINK_FLAGS}")
set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${PADDLE_LINK_FLAGS}")
endif(WIN32)
if(NOT LITE_WITH_LIGHT_WEIGHT_FRAMEWORK)
find_package(CUDA QUIET)
endif()
find_package(Git REQUIRED)
find_package(Threads REQUIRED)
include(simd)
################################ Exposed Configurations #######################################
option(WITH_GPU "Compile PaddlePaddle with NVIDIA GPU" ${CUDA_FOUND})
option(WITH_DSO "Compile PaddlePaddle with dynamic linked CUDA" ON)
option(WITH_AVX "Compile PaddlePaddle with AVX intrinsics" ${AVX_FOUND})
option(WITH_PYTHON "Compile PaddlePaddle with python interpreter" ON)
option(WITH_TESTING "Compile PaddlePaddle with unit testing" OFF)
option(WITH_MKL "Compile PaddlePaddle with MKL support." ${AVX_FOUND})
option(WITH_SYSTEM_BLAS "Use system blas library" OFF)
option(WITH_DISTRIBUTE "Compile with distributed support" OFF)
option(WITH_BRPC_RDMA "Use brpc rdma as the rpc protocal" OFF)
option(ON_INFER "Turn on inference optimization." OFF)
################################ Internal Configurations #######################################
option(WITH_ANAKIN "Compile with Anakin library" OFF)
option(WITH_AMD_GPU "Compile PaddlePaddle with AMD GPU" OFF)
option(WITH_NGRAPH "Compile PaddlePaddle with nGraph support." OFF)
option(WITH_PROFILER "Compile PaddlePaddle with GPU profiler and gperftools" OFF)
option(WITH_JEMALLOC "Compile PaddlePaddle with jemalloc" OFF)
option(WITH_COVERAGE "Compile PaddlePaddle with code coverage" OFF)
option(COVERALLS_UPLOAD "Package code coverage data to coveralls" OFF)
option(WITH_PSLIB "Compile with pslib support" OFF)
option(WITH_CONTRIB "Compile the third-party contributation" OFF)
option(REPLACE_ENFORCE_GLOG "Replace PADDLE_ENFORCE with glog/CHECK for better debug." OFF)
# TODO(Superjomn) Remove WITH_ANAKIN option if not needed latter.
option(ANAKIN_BUILD_FAT_BIN "Build anakin cuda fat-bin lib for all device plantform, ignored when WITH_ANAKIN=OFF" OFF)
option(ANAKIN_BUILD_CROSS_PLANTFORM "Build anakin lib for any nvidia device plantform. ignored when WITH_ANAKIN=OFF" ON)
option(WITH_GRPC "Use grpc as the default rpc framework" ${WITH_DISTRIBUTE})
option(WITH_INFERENCE_API_TEST "Test fluid inference C++ high-level api interface" OFF)
option(WITH_HIGH_LEVEL_API_TEST "Test fluid python high-level api interface" OFF)
option(PY_VERSION "Compile PaddlePaddle with python3 support" ${PY_VERSION})
option(WITH_FAST_MATH "Make use of fast math library, might affect the precision to some extent" ON)
option(WITH_DGC "Use DGC(Deep Gradient Compression) or not" ON)
if(ANDROID OR IOS OR ARMLINUX)
set(WITH_GPU OFF CACHE STRING
"Disable GPU when cross-compiling for Android and iOS" FORCE)
set(WITH_DSO OFF CACHE STRING
"Disable DSO when cross-compiling for Android and iOS" FORCE)
set(WITH_AVX OFF CACHE STRING
"Disable AVX when cross-compiling for Android and iOS" FORCE)
set(WITH_PYTHON OFF CACHE STRING
"Disable PYTHON when cross-compiling for Android and iOS" FORCE)
set(WITH_RDMA OFF CACHE STRING
"Disable RDMA when cross-compiling for Android and iOS" FORCE)
set(WITH_MKL OFF CACHE STRING
"Disable MKL when cross-compiling for Android and iOS" FORCE)
if(NOT CMAKE_BUILD_TYPE)
set(CMAKE_BUILD_TYPE "Release" CACHE STRING
"Default use Release in android" FORCE)
endif()
if(NOT THIRD_PARTY_BUILD_TYPE)
set(THIRD_PARTY_BUILD_TYPE "MinSizeRel" CACHE STRING
"Default use MinSizeRel in android" FORCE)
endif()
endif()
# for lite, both server and mobile framework.
option(WITH_LITE "Enable lite framework" OFF)
option(LITE_WITH_CUDA "Enable CUDA in lite mode" OFF)
option(LITE_WITH_X86 "Enable X86 in lite mode" ON)
option(LITE_WITH_ARM "Enable ARM in lite mode" OFF)
option(LITE_WITH_LIGHT_WEIGHT_FRAMEWORK "Enable light-weight framework" OFF)
option(LITE_WITH_PROFILE "Enable profile mode in lite framework" OFF)
set(THIRD_PARTY_PATH "${CMAKE_BINARY_DIR}/third_party" CACHE STRING
"A path setting third party libraries download & build directories.")
# CMAKE_BUILD_TYPE
if(NOT CMAKE_BUILD_TYPE)
set(CMAKE_BUILD_TYPE "RelWithDebInfo" CACHE STRING
"Choose the type of build, options are: Debug Release RelWithDebInfo MinSizeRel"
FORCE)
endif()
include_directories("${PADDLE_SOURCE_DIR}")
# for mobile
if (WITH_LITE AND LITE_WITH_LIGHT_WEIGHT_FRAMEWORK)
message(STATUS "Building the mobile framework")
# include the necessary thirdparty dependencies
include(external/gflags) # download, build, install gflags
include(external/glog) # download, build, install glog
include(external/gtest) # download, build, install gtest
#include(external/zlib) # download, build, install gtest
include(external/protobuf) # download, build, install protobuf
include(external/eigen) # download eigen3
include(generic) # simplify cmake module
include(configure) # add paddle env configuration
add_definitions(-std=c++11)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
add_subdirectory(paddle)
return()
endif()
# PY_VERSION
if(NOT PY_VERSION)
set(PY_VERSION 2.7)
endif()
set(PYBIND11_PYTHON_VERSION ${PY_VERSION})
if (APPLE)
set(WITH_MKL OFF CACHE STRING
"Disable MKL for building on mac" FORCE)
endif()
if (WIN32)
set(WITH_DISTRIBUTE OFF CACHE STRING
"Disable DISTRIBUTE when compiling for Windows" FORCE)
endif()
set(FLUID_INSTALL_DIR "${CMAKE_BINARY_DIR}/fluid_install_dir" CACHE STRING
"A path setting fluid shared and static libraries")
set(FLUID_INFERENCE_INSTALL_DIR "${CMAKE_BINARY_DIR}/fluid_inference_install_dir" CACHE STRING
"A path setting fluid inference shared and static libraries")
set(WITH_MKLML ${WITH_MKL})
if (NOT DEFINED WITH_MKLDNN)
if (WITH_MKL AND AVX2_FOUND)
set(WITH_MKLDNN ON)
else()
message(STATUS "Do not have AVX2 intrinsics and disabled MKL-DNN")
set(WITH_MKLDNN OFF)
endif()
endif()
if (REPLACE_ENFORCE_GLOG)
add_definitions("-DREPLACE_ENFORCE_GLOG")
endif()
########################################################################################
include(external/mklml) # download mklml package
include(external/xbyak) # download xbyak package
include(external/libxsmm) # download, build, install libxsmm
include(external/zlib) # download, build, install zlib
include(external/gflags) # download, build, install gflags
include(external/glog) # download, build, install glog
include(external/gtest) # download, build, install gtest
include(external/protobuf) # download, build, install protobuf
include(external/python) # download, build, install python
include(external/openblas) # download, build, install openblas
include(external/mkldnn) # download, build, install mkldnn
include(external/ngraph) # download, build, install nGraph
include(external/boost) # download boost
include(external/eigen) # download eigen3
include(external/pybind11) # download pybind11
include(external/cares)
include(external/cub)
include(external/rocprim)
include(external/xxhash) # download xxhash
include(external/dlpack)
include(external/snappy) # download snappy
include(external/snappystream) # download snappystream
include(external/warpctc) # download, build, install warpctc
if (NOT WIN32)
# there is no official support of nccl, cupti in windows
include(cupti)
include(external/gzstream)
endif (NOT WIN32)
if(WITH_PSLIB)
include(external/libmct)
include(external/pslib_brpc)
include(external/pslib)
endif(WITH_PSLIB)
if(WITH_DISTRIBUTE)
if(WITH_GRPC)
include(external/grpc)
message(STATUS "Use grpc framework.")
else()
message(STATUS "Use brpc framework.")
include(external/leveldb)
include(external/brpc)
endif()
endif()
if(WITH_BRPC_RDMA)
message(STATUS "Use brpc with rdma.")
if(WITH_GRPC)
message(FATAL_ERROR "Can't use grpc with brpc rdma.")
endif()
if(NOT WITH_DISTRIBUTE)
message(FATAL_ERROR "Can't use brpc rdma in no distribute env.")
endif()
endif()
include(external/threadpool)
include(flags) # set paddle compile flags
include(cudnn) # set cudnn libraries, must before configure
include(configure) # add paddle env configuration
if(WITH_GPU)
include(cuda)
include(tensorrt)
include(anakin_subgraph)
endif()
if(WIN32 OR APPLE OR NOT WITH_GPU OR ON_INFER)
set(WITH_DGC OFF)
endif()
if(WITH_DGC)
message(STATUS "add dgc lib.")
include(external/dgc)
add_definitions(-DPADDLE_WITH_DGC)
endif()
if(WITH_MKL OR WITH_MKLML)
include(external/anakin)
elseif()
set(WITH_ANAKIN OFF CACHE STRING "Anakin is used in MKL only now." FORCE)
endif()
if (WITH_PROFILER)
find_package(Gperftools REQUIRED)
include_directories(${GPERFTOOLS_INCLUDE_DIR})
add_definitions(-DWITH_GPERFTOOLS)
endif()
if (WITH_JEMALLOC)
find_package(JeMalloc REQUIRED)
include_directories(${JEMALLOC_INCLUDE_DIR})
add_definitions(-DPADDLE_WITH_JEMALLOC)
endif()
include(generic) # simplify cmake module
include(package) # set paddle packages
include(ccache) # set ccache for compilation
include(util) # set unittest and link libs
include(version) # set PADDLE_VERSION
include(coveralls) # set code coverage
include(inference_lib) # add paddle fluid inference libraries
if(WITH_AMD_GPU)
find_package(HIP)
include(hip)
endif(WITH_AMD_GPU)
set(PADDLE_PYTHON_BUILD_DIR "${CMAKE_CURRENT_BINARY_DIR}/python/build")
set(CMAKE_CXX_FLAGS_RELWITHDEBINFO "-O3 -g -DNDEBUG")
set(CMAKE_C_FLAGS_RELWITHDEBINFO "-O3 -g -DNDEBUG")
if (ON_INFER)
message(STATUS "On inference mode, will take place some specific optimization.")
add_definitions(-DPADDLE_ON_INFERENCE)
else()
#TODO(luotao), combine this warning with `make inference_lib_dist` command.
message(WARNING "On inference mode, will take place some specific optimization. Turn on the ON_INFER flag when building inference_lib only.")
endif()
add_subdirectory(paddle)
if(WITH_PYTHON)
add_subdirectory(python)
endif()
# Contributor Covenant Code of Conduct
## Our Pledge
In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation.
## Our Standards
Examples of behavior that contributes to creating a positive environment include:
* Using welcoming and inclusive language
* Being respectful of differing viewpoints and experiences
* Gracefully accepting constructive criticism
* Focusing on what is best for the community
* Showing empathy towards other community members
Examples of unacceptable behavior by participants include:
* The use of sexualized language or imagery and unwelcome sexual attention or advances
* Trolling, insulting/derogatory comments, and personal or political attacks
* Public or private harassment
* Publishing others' private information, such as a physical or electronic address, without explicit permission
* Other conduct which could reasonably be considered inappropriate in a professional setting
## Our Responsibilities
Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior.
Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful.
## Scope
This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers.
## Enforcement
Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at paddle-dev@baidu.com. The project team will review and investigate all complaints, and will respond in a way that it deems appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately.
Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership.
## Attribution
This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, available at [http://contributor-covenant.org/version/1/4][version]
[homepage]: http://contributor-covenant.org
[version]: http://contributor-covenant.org/version/1/4/
# 参与者公约
## 我们的保证
为了促进一个开放透明且友好的环境,我们作为贡献者和维护者保证:无论年龄、种族、民族、性别认同和表达(方式)、体型、身体健全与否、经验水平、国籍、个人表现、宗教或性别取向,参与者在我们项目和社区中都免于骚扰。
## 我们的标准
有助于创造正面环境的行为包括但不限于:
* 使用友好和包容性语言
* 尊重不同的观点和经历
* 耐心地接受建设性批评
* 关注对社区最有利的事情
* 友善对待其他社区成员
身为参与者不能接受的行为包括但不限于:
* 使用与性有关的言语或是图像,以及不受欢迎的性骚扰
* 捣乱/煽动/造谣的行为或进行侮辱/贬损的评论,人身攻击及政治攻击
* 公开或私下的骚扰
* 未经许可地发布他人的个人资料,例如住址或是电子地址
* 其他可以被合理地认定为不恰当或者违反职业操守的行为
## 我们的责任
项目维护者有责任为「可接受的行为」标准做出诠释,以及对已发生的不被接受的行为采取恰当且公平的纠正措施。
项目维护者有权利及责任去删除、编辑、拒绝与本行为标准有所违背的评论(comments)、提交(commits)、代码、wiki 编辑、问题(issues)和其他贡献,以及项目维护者可暂时或永久性的禁止任何他们认为有不适当、威胁、冒犯、有害行为的贡献者。
## 使用范围
当一个人代表该项目或是其社区时,本行为标准适用于其项目平台和公共平台。
代表项目或是社区的情况,举例来说包括使用官方项目的电子邮件地址、通过官方的社区媒体账号发布或线上或线下事件中担任指定代表。
该项目的呈现方式可由其项目维护者进行进一步的定义及解释。
## 强制执行
可以通过paddle-dev@baidu.com,来联系项目团队来举报滥用、骚扰或其他不被接受的行为。
任何维护团队认为有必要且适合的所有投诉都将进行审查及调查,并做出相对应的回应。项目小组有对事件回报者有保密的义务。具体执行的方针近一步细节可能会单独公布。
没有切实地遵守或是执行本行为标准的项目维护人员,可能会因项目领导人或是其他成员的决定,暂时或是永久地取消其参与资格。
## 来源
本行为标准改编自[贡献者公约][主页],版本 1.4
可在此观看https://www.contributor-covenant.org/zh-cn/version/1/4/code-of-conduct.html
[主页]: https://www.contributor-covenant.org
# Contribute Code
You are welcome to contribute to project PaddlePaddle. To contribute to PaddlePaddle, you have to agree with the
[PaddlePaddle Contributor License Agreement](https://gist.github.com/wangkuiyi/0c22c7b1bd3bb7eb27d76f85c3a3e329).
We sincerely appreciate your contribution. This document explains our workflow and work style.
## Workflow
PaddlePaddle uses this [Git branching model](http://nvie.com/posts/a-successful-git-branching-model/). The following steps guide usual contributions.
1. Fork
Our development community has been growing fastly; it doesn't make sense for everyone to write into the official repo. So, please file Pull Requests from your fork. To make a fork, just head over to the GitHub page and click the ["Fork" button](https://help.github.com/articles/fork-a-repo/).
1. Clone
To make a copy of your fork to your local computers, please run
```bash
git clone https://github.com/your-github-account/paddle
cd paddle
```
1. Create the local feature branch
For daily works like adding a new feature or fixing a bug, please open your feature branch before coding:
```bash
git checkout -b my-cool-stuff
```
1. Commit
Before issuing your first `git commit` command, please install [`pre-commit`](http://pre-commit.com/) by running the following commands:
```bash
pip install pre-commit
pre-commit install
```
Our pre-commit configuration requires clang-format 3.8 for auto-formating C/C++ code and yapf for Python.
Once installed, `pre-commit` checks the style of code and documentation in every commit. We will see something like the following when you run `git commit`:
```
➜ git commit
CRLF end-lines remover...............................(no files to check)Skipped
yapf.................................................(no files to check)Skipped
Check for added large files..............................................Passed
Check for merge conflicts................................................Passed
Check for broken symlinks................................................Passed
Detect Private Key...................................(no files to check)Skipped
Fix End of Files.....................................(no files to check)Skipped
clang-formater.......................................(no files to check)Skipped
[my-cool-stuff c703c041] add test file
1 file changed, 0 insertions(+), 0 deletions(-)
create mode 100644 233
```
NOTE: The `yapf` installed by `pip install pre-commit` and `conda install -c conda-forge pre-commit` is slightly different. Paddle developers use `pip install pre-commit`.
1. Build and test
Users can build PaddlePaddle natively on Linux and Mac OS X. But to unify the building environment and to make it easy for debugging, the recommended way is [using Docker](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/dev/build_en.md).
1. Keep pulling
An experienced Git user pulls from the official repo often -- daily or even hourly, so they notice conflicts with others work early, and it's easier to resolve smaller conflicts.
```bash
git remote add upstream https://github.com/PaddlePaddle/Paddle
git pull upstream develop
```
1. Push and file a pull request
You can "push" your local work into your forked repo:
```bash
git push origin my-cool-stuff
```
The push allows you to create a pull request, requesting owners of this [official repo](https://github.com/PaddlePaddle/Paddle) to pull your change into the official one.
To create a pull request, please follow [these steps](https://help.github.com/articles/creating-a-pull-request/).
If your change is for fixing an issue, please write ["Fixes <issue-URL>"](https://help.github.com/articles/closing-issues-using-keywords/) in the description section of your pull request. Github would close the issue when the owners merge your pull request.
Please remember to specify some reviewers for your pull request. If you don't know who are the right ones, please follow Github's recommendation.
1. Delete local and remote branches
To keep your local workspace and your fork clean, you might want to remove merged branches:
```bash
git push origin :my-cool-stuff
git checkout develop
git pull upstream develop
git branch -d my-cool-stuff
```
### Code Review
- Please feel free to ping your reviewers by sending them the URL of your pull request via IM or email. Please do this after your pull request passes the CI.
- Please answer reviewers' every comment. If you are to follow the comment, please write "Done"; please give a reason otherwise.
- If you don't want your reviewers to get overwhelmed by email notifications, you might reply their comments by [in a batch](https://help.github.com/articles/reviewing-proposed-changes-in-a-pull-request/).
- Reduce the unnecessary commits. Some developers commit often. It is recommended to append a sequence of small changes into one commit by running `git commit --amend` instead of `git commit`.
## Coding Standard
### Code Style
Our C/C++ code follows the [Google style guide](http://google.github.io/styleguide/cppguide.html).
Our Python code follows the [PEP8 style guide](https://www.python.org/dev/peps/pep-0008/).
Our build process helps to check the code style. In [`build.sh`](https://github.com/PaddlePaddle/Paddle/blob/b84e8226514b8bb4405c3c28e54aa5077193d179/paddle/scripts/docker/build.sh#L42), the entry point of our [builder Docker image](https://github.com/PaddlePaddle/Paddle/blob/b84e8226514b8bb4405c3c28e54aa5077193d179/Dockerfile#L88), the CMake argument `WITH_STYLE_CHECK` is set to `ON` by default. This flag is on
Please install pre-commit, which automatically reformat the changes to C/C++ and Python code whenever we run `git commit`. To check the whole codebase, we can run the command `pre-commit run -a`, as in the [`check_style.sh` file](https://github.com/PaddlePaddle/Paddle/blob/b84e8226514b8bb4405c3c28e54aa5077193d179/paddle/scripts/travis/check_style.sh#L30), which is invoked by [our Travis CI configuration](https://github.com/PaddlePaddle/Paddle/blob/b84e8226514b8bb4405c3c28e54aa5077193d179/.travis.yml#L43).
### Unit Tests
Please remember to add related unit tests.
- For C/C++ code, please follow [`google-test` Primer](https://github.com/google/googletest/blob/master/googletest/docs/primer.md) .
- For Python code, please use [Python's standard `unittest` package](http://pythontesting.net/framework/unittest/unittest-introduction/).
### Writing Logs
We use [glog](https://github.com/google/glog) for logging in our C/C++ code.
For general information, please use `LOG`. For debug information, please use [`VLOG`](http://htmlpreview.github.io/?https://github.com/google/glog/blob/master/doc/glog.html#verbose). The reason is at [here](https://groups.google.com/a/chromium.org/d/msg/chromium-dev/3NDNd1KzXeY/AZKMMx37fdQJ).
`VLOG` requires a *verbose level* parameter. For example:
```c++
VLOG(3) << "Operator FC is taking " << num_inputs << "inputs."
```
When we run a PaddlePaddle application or test, we can specify a verbose threshold. For example:
```bash
GLOG_vmodule=buddy_allocator=2 \
GLOG_v=10 \
python \
../python/paddle/v2/framework/tests/test_recurrent_op.py
```
This will enable VLOG messages generated by `buddy_allocator.{h,cc}` and in the verbose range of 0 to 3, so you will see above example VLOG message, which is in level 3. This suggests that we output overall messages in lower verbose levels, so they display with higher probability. When coding C++, please follow the verbose level convention as follows:
- verbose level 1: [framework](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/fluid/framework)
- verbose level 3: [operators](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/fluid/operators)
- verbose level 5: [memory](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/fluid/memory), [platform](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/fluid/platform)
- verbose level 7: [math](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/fluid/operators/math/)
# A image for building paddle binaries
# Use cuda devel base image for both cpu and gpu environment
# When you modify it, please be aware of cudnn-runtime version
# and libcudnn.so.x in paddle/scripts/docker/build.sh
FROM nvidia/cuda:8.0-cudnn7-devel-ubuntu16.04
MAINTAINER PaddlePaddle Authors <paddle-dev@baidu.com>
ARG UBUNTU_MIRROR
RUN /bin/bash -c 'if [[ -n ${UBUNTU_MIRROR} ]]; then sed -i 's#http://archive.ubuntu.com/ubuntu#${UBUNTU_MIRROR}#g' /etc/apt/sources.list; fi'
# ENV variables
ARG WITH_GPU
ARG WITH_AVX
ENV WOBOQ OFF
ENV WITH_GPU=${WITH_GPU:-ON}
ENV WITH_AVX=${WITH_AVX:-ON}
ENV HOME /root
# Add bash enhancements
COPY ./paddle/scripts/docker/root/ /root/
# Prepare packages for Python
RUN apt-get update && \
apt-get install -y make build-essential libssl-dev zlib1g-dev libbz2-dev \
libreadline-dev libsqlite3-dev wget curl llvm libncurses5-dev libncursesw5-dev \
xz-utils tk-dev libffi-dev liblzma-dev
# Install Python3.6
RUN mkdir -p /root/python_build/ && wget -q https://www.sqlite.org/2018/sqlite-autoconf-3250300.tar.gz && \
tar -zxf sqlite-autoconf-3250300.tar.gz && cd sqlite-autoconf-3250300 && \
./configure -prefix=/usr/local && make -j8 && make install && cd ../ && rm sqlite-autoconf-3250300.tar.gz && \
wget -q https://www.python.org/ftp/python/3.6.0/Python-3.6.0.tgz && \
tar -xzf Python-3.6.0.tgz && cd Python-3.6.0 && \
CFLAGS="-Wformat" ./configure --prefix=/usr/local/ --enable-shared > /dev/null && \
make -j8 > /dev/null && make altinstall > /dev/null
# Install Python3.7
RUN wget -q https://www.python.org/ftp/python/3.7.0/Python-3.7.0.tgz && \
tar -xzf Python-3.7.0.tgz && cd Python-3.7.0 && \
CFLAGS="-Wformat" ./configure --prefix=/usr/local/ --enable-shared > /dev/null && \
make -j8 > /dev/null && make altinstall > /dev/null
RUN rm -r /root/python_build
RUN apt-get update && \
apt-get install -y --allow-downgrades patchelf \
python3 python3-dev python3-pip \
git python-pip python-dev python-opencv openssh-server bison \
libnccl2=2.1.2-1+cuda8.0 libnccl-dev=2.1.2-1+cuda8.0 \
wget unzip unrar tar xz-utils bzip2 gzip coreutils ntp \
curl sed grep graphviz libjpeg-dev zlib1g-dev \
python-matplotlib gcc-4.8 g++-4.8 \
automake locales clang-format swig cmake \
liblapack-dev liblapacke-dev \
clang-3.8 llvm-3.8 libclang-3.8-dev \
net-tools libtool ccache && \
apt-get clean -y
# Install Go and glide
RUN wget -qO- https://storage.googleapis.com/golang/go1.8.1.linux-amd64.tar.gz | \
tar -xz -C /usr/local && \
mkdir /root/gopath && \
mkdir /root/gopath/bin && \
mkdir /root/gopath/src
ENV GOROOT=/usr/local/go GOPATH=/root/gopath
# should not be in the same line with GOROOT definition, otherwise docker build could not find GOROOT.
ENV PATH=${PATH}:${GOROOT}/bin:${GOPATH}/bin
# install glide
RUN curl -s -q https://glide.sh/get | sh
# Install TensorRT
# following TensorRT.tar.gz is not the default official one, we do two miny changes:
# 1. Remove the unnecessary files to make the library small. TensorRT.tar.gz only contains include and lib now,
# and its size is only one-third of the official one.
# 2. Manually add ~IPluginFactory() in IPluginFactory class of NvInfer.h, otherwise, it couldn't work in paddle.
# See https://github.com/PaddlePaddle/Paddle/issues/10129 for details.
RUN wget -q https://paddlepaddledeps.cdn.bcebos.com/TensorRT-4.0.1.6-ubuntu14.04.x86_64-gnu.cuda.8.0.cudnn7.0.tar.gz --no-check-certificate && \
tar -zxf TensorRT-4.0.1.6-ubuntu14.04.x86_64-gnu.cuda.8.0.cudnn7.0.tar.gz -C /usr/local && \
cp -rf /usr/local/TensorRT/include /usr && \
cp -rf /usr/local/TensorRT/lib /usr
# git credential to skip password typing
RUN git config --global credential.helper store
# Fix locales to en_US.UTF-8
RUN localedef -i en_US -f UTF-8 en_US.UTF-8
# FIXME: due to temporary ipykernel dependency issue, specify ipykernel jupyter
# version util jupyter fixes this issue.
# specify sphinx version as 1.5.6 and remove -U option for [pip install -U
# sphinx-rtd-theme] since -U option will cause sphinx being updated to newest
# version(1.7.1 for now), which causes building documentation failed.
RUN pip3 --no-cache-dir install -U wheel && \
pip3 --no-cache-dir install -U docopt PyYAML sphinx==1.5.6 && \
pip3 --no-cache-dir install sphinx-rtd-theme==0.1.9 recommonmark && \
pip3.6 --no-cache-dir install -U wheel && \
pip3.6 --no-cache-dir install -U docopt PyYAML sphinx==1.5.6 && \
pip3.6 --no-cache-dir install sphinx-rtd-theme==0.1.9 recommonmark && \
pip3.7 --no-cache-dir install -U wheel && \
pip3.7 --no-cache-dir install -U docopt PyYAML sphinx==1.5.6 && \
pip3.7 --no-cache-dir install sphinx-rtd-theme==0.1.9 recommonmark && \
easy_install -U pip && \
pip --no-cache-dir install -U pip setuptools wheel && \
pip --no-cache-dir install -U docopt PyYAML sphinx==1.5.6 && \
pip --no-cache-dir install sphinx-rtd-theme==0.1.9 recommonmark
RUN pip3 --no-cache-dir install 'pre-commit==1.10.4' 'ipython==5.3.0' && \
pip3 --no-cache-dir install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
pip3 --no-cache-dir install opencv-python && \
pip3.6 --no-cache-dir install 'pre-commit==1.10.4' 'ipython==5.3.0' && \
pip3.6 --no-cache-dir install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
pip3.6 --no-cache-dir install opencv-python && \
pip3.7 --no-cache-dir install 'pre-commit==1.10.4' 'ipython==5.3.0' && \
pip3.7 --no-cache-dir install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
pip3.7 --no-cache-dir install opencv-python && \
pip --no-cache-dir install 'pre-commit==1.10.4' 'ipython==5.3.0' && \
pip --no-cache-dir install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
pip --no-cache-dir install opencv-python
#For docstring checker
RUN pip3 --no-cache-dir install pylint pytest astroid isort
RUN pip3.6 --no-cache-dir install pylint pytest astroid isort
RUN pip3.7 --no-cache-dir install pylint pytest astroid isort
RUN pip --no-cache-dir install pylint pytest astroid isort LinkChecker
COPY ./python/requirements.txt /root/
RUN pip3 --no-cache-dir install -r /root/requirements.txt
RUN pip3.6 --no-cache-dir install -r /root/requirements.txt
RUN pip3.7 --no-cache-dir install -r /root/requirements.txt
RUN pip --no-cache-dir install -r /root/requirements.txt
# To fix https://github.com/PaddlePaddle/Paddle/issues/1954, we use
# the solution in https://urllib3.readthedocs.io/en/latest/user-guide.html#ssl-py2
RUN apt-get install -y libssl-dev libffi-dev && apt-get clean -y
RUN pip3 --no-cache-dir install certifi urllib3[secure]
RUN pip3.6 --no-cache-dir install certifi urllib3[secure]
RUN pip3.7 --no-cache-dir install certifi urllib3[secure]
RUN pip --no-cache-dir install certifi urllib3[secure]
# Install woboq_codebrowser to /woboq
RUN git clone https://github.com/woboq/woboq_codebrowser /woboq && \
(cd /woboq \
cmake -DLLVM_CONFIG_EXECUTABLE=/usr/bin/llvm-config-3.8 \
-DCMAKE_BUILD_TYPE=Release . \
make)
# ar mishandles 4GB files
# https://sourceware.org/bugzilla/show_bug.cgi?id=14625
# remove them when apt-get support 2.27 and higher version
RUN wget -q https://launchpad.net/ubuntu/+archive/primary/+sourcefiles/binutils/2.27-9ubuntu1/binutils_2.27.orig.tar.gz && \
tar -xzf binutils_2.27.orig.tar.gz && \
cd binutils-2.27 && \
./configure && make -j && make install && cd .. && rm -rf binutils-2.27 binutils_2.27.orig.tar.gz
# Configure OpenSSH server. c.f. https://docs.docker.com/engine/examples/running_ssh_service
RUN mkdir /var/run/sshd
RUN echo 'root:root' | chpasswd
RUN sed -ri 's/^PermitRootLogin\s+.*/PermitRootLogin yes/' /etc/ssh/sshd_config
RUN sed -ri 's/UsePAM yes/#UsePAM yes/g' /etc/ssh/sshd_config
EXPOSE 22
Thank you for contributing to PaddlePaddle. Submitting an issue is a great help for us.
Both Chinese and English issues are welcome.
It's hard to solve a problem when important details are missing.
Before submitting the issue, look over the following criteria before handing your request in.
- [ ] Was there a similar issue submitted or resolved before ? You could search issue in the github.
- [ ] Did you retrieve your issue from widespread search engines ?
- [ ] Is my description of the issue clear enough to reproduce this problem?
* If some errors occurred, we need details about `how do you run your code?`, `what system do you use?`, `Are you using GPU or not?`, etc.
* If you use an recording [asciinema](https://asciinema.org/) to show what you are doing to make it happen, that's awesome! We could help you solve the problem more quickly.
- [ ] Is my description of the issue use the github markdown correctly?
* Please use the proper markdown syntaxes for styling all forms of writing, e.g, source code, error information, etc.
* Check out [this page](https://guides.github.com/features/mastering-markdown/) to find out much more about markdown.
Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
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# PaddlePaddle
English | [简体中文](./README_cn.md)
[![Build Status](https://travis-ci.org/PaddlePaddle/Paddle.svg?branch=develop)](https://travis-ci.org/PaddlePaddle/Paddle)
[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](http://www.paddlepaddle.org/documentation/docs/en/1.4/beginners_guide/index_en.html)
[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](http://www.paddlepaddle.org/documentation/docs/zh/1.4/beginners_guide/index_cn.html)
[![Release](https://img.shields.io/github/release/PaddlePaddle/Paddle.svg)](https://github.com/PaddlePaddle/Paddle/releases)
[![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE)
Welcome to the PaddlePaddle GitHub.
PaddlePaddle (PArallel Distributed Deep LEarning) is an easy-to-use,
efficient, flexible and scalable deep learning platform, which is originally
developed by Baidu scientists and engineers for the purpose of applying deep
learning to many products at Baidu.
Our vision is to enable deep learning for everyone via PaddlePaddle.
Please refer to our [release announcement](https://github.com/PaddlePaddle/Paddle/releases) to track the latest feature of PaddlePaddle.
### Latest PaddlePaddle Release: [Fluid 1.4.1](https://github.com/PaddlePaddle/Paddle/tree/release/1.4)
### Install Latest Stable Release:
```
# Linux CPU
pip install paddlepaddle
# Linux GPU cuda9cudnn7
pip install paddlepaddle-gpu
# Linux GPU cuda8cudnn7
pip install paddlepaddle-gpu==1.4.1.post87
# Linux GPU cuda8cudnn5
pip install paddlepaddle-gpu==1.4.1.post85
# For installation on other platform, refer to http://paddlepaddle.org/
```
## Features
- **Flexibility**
PaddlePaddle supports a wide range of neural network architectures and
optimization algorithms. It is easy to configure complex models such as
neural machine translation model with attention mechanism or complex memory
connection.
- **Efficiency**
In order to unleash the power of heterogeneous computing resource,
optimization occurs at different levels of PaddlePaddle, including
computing, memory, architecture and communication. The following are some
examples:
- Optimized math operations through SSE/AVX intrinsics, BLAS libraries
(e.g. MKL, OpenBLAS, cuBLAS) or customized CPU/GPU kernels.
- Optimized CNN networks through MKL-DNN library.
- Highly optimized recurrent networks which can handle **variable-length**
sequence without padding.
- Optimized local and distributed training for models with high dimensional
sparse data.
- **Scalability**
With PaddlePaddle, it is easy to use many CPUs/GPUs and machines to speed
up your training. PaddlePaddle can achieve high throughput and performance
via optimized communication.
- **Connected to Products**
In addition, PaddlePaddle is also designed to be easily deployable. At Baidu,
PaddlePaddle has been deployed into products and services with a vast number
of users, including ad click-through rate (CTR) prediction, large-scale image
classification, optical character recognition(OCR), search ranking, computer
virus detection, recommendation, etc. It is widely utilized in products at
Baidu and it has achieved a significant impact. We hope you can also explore
the capability of PaddlePaddle to make an impact on your product.
## Installation
It is recommended to read [this doc](http://www.paddlepaddle.org/documentation/docs/en/1.4/beginners_guide/index_en.html) on our website.
## Documentation
We provide [English](http://www.paddlepaddle.org/documentation/docs/en/1.4/beginners_guide/index_en.html) and
[Chinese](http://www.paddlepaddle.org/documentation/docs/zh/1.4/beginners_guide/install/index_cn.html) documentation.
- [Deep Learning 101](https://github.com/PaddlePaddle/book)
You might want to start from this online interactive book that can run in a Jupyter Notebook.
- [Distributed Training](http://paddlepaddle.org/documentation/docs/en/1.4/user_guides/howto/training/multi_node_en.html)
You can run distributed training jobs on MPI clusters.
- [Python API](http://paddlepaddle.org/documentation/docs/en/1.4/api/index_en.html)
Our new API enables much shorter programs.
- [How to Contribute](http://paddlepaddle.org/documentation/docs/en/1.4/advanced_usage/development/contribute_to_paddle/index_en.html)
We appreciate your contributions!
## Ask Questions
You are welcome to submit questions and bug reports as [Github Issues](https://github.com/PaddlePaddle/Paddle/issues).
## Copyright and License
PaddlePaddle is provided under the [Apache-2.0 license](LICENSE).
# PaddlePaddle
[English](./README.md) | 简体中文
[![Build Status](https://travis-ci.org/PaddlePaddle/Paddle.svg?branch=develop)](https://travis-ci.org/PaddlePaddle/Paddle)
[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](http://www.paddlepaddle.org/documentation/docs/en/1.4/beginners_guide/index_en.html)
[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](http://www.paddlepaddle.org/documentation/docs/zh/1.4/beginners_guide/index_cn.html)
[![Release](https://img.shields.io/github/release/PaddlePaddle/Paddle.svg)](https://github.com/PaddlePaddle/Paddle/releases)
[![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE)
欢迎来到 PaddlePaddle GitHub
PaddlePaddle (PArallel Distributed Deep LEarning) 是一个简单易用、高效灵活、可扩展的深度学习平台,最初由百度科学家和工程师共同开发,目的是将深度学习技术应用到百度的众多产品中。
我们的愿景是让每个人都能通过PaddlePaddle接触深度学习
跟进PaddlePaddle最新特性请参考我们的[版本说明](https://github.com/PaddlePaddle/Paddle/releases)
### PaddlePaddle最新版本: [Fluid 1.4.1](https://github.com/PaddlePaddle/Paddle/tree/release/1.4)
### 安装最新稳定版本:
```
# Linux CPU
pip install paddlepaddle
# Linux GPU cuda9cudnn7
pip install paddlepaddle-gpu
# Linux GPU cuda8cudnn7
pip install paddlepaddle-gpu==1.4.1.post87
# Linux GPU cuda8cudnn5
pip install paddlepaddle-gpu==1.4.1.post85
# 其他平台上的安装指引请参考 http://paddlepaddle.org/
```
## 特性
- **灵活性**
PaddlePaddle支持丰富的神经网络架构和优化算法。易于配置复杂模型,例如带有注意力机制或复杂记忆连接的神经网络机器翻译模型。
- **高效性**
为了高效使用异步计算资源,PaddlePaddle对框架的不同层进行优化,包括计算、存储、架构和通信。下面是一些样例:
- 通过SSE/AVX 内置函数、BLAS库(例如MKL、OpenBLAS、cuBLAS)或定制的CPU/GPU内核优化数学操作。
- 通过MKL-DNN库优化CNN网络
- 高度优化循环网络,无需执行 `padding` 操作即可处理 **变长** 序列
- 针对高维稀疏数据模型,优化了局部和分布式训练。
- **稳定性**
有了 PaddlePaddle,使得利用各种CPU/GPU和机器来加速训练变得简单。PaddlePaddle 通过优化通信可以实现巨大吞吐量和快速执行。
- **与产品相连**
另外,PaddlePaddle 的设计也易于部署。在百度,PaddlePaddle 已经部署到含有巨大用户量的产品和服务上,包括广告点击率(CTR)预测、大规模图像分类、光学字符识别(OCR)、搜索排序,计算机病毒检测、推荐系统等等。PaddlePaddle广泛应用于百度产品中,产生了非常重要的影响。我们希望您也能探索 PaddlePaddle 的能力,为您的产品创造新的影响力和效果。
## 安装
推荐阅读官网上的[安装说明](http://www.paddlepaddle.org/documentation/docs/zh/1.4/beginners_guide/install/index_cn.html)
## 文档
我们提供[英文](http://www.paddlepaddle.org/documentation/docs/en/1.4/beginners_guide/index_en.html)
[中文](http://www.paddlepaddle.org/documentation/docs/zh/1.4/beginners_guide/install/index_cn.html) 文档
- [深度学习101](https://github.com/PaddlePaddle/book)
或许您想从这个在线交互式书籍开始,可以在Jupyter Notebook中运行
- [分布式训练](http://paddlepaddle.org/documentation/docs/zh/1.4/user_guides/howto/training/multi_node.html)
可以在MPI集群上运行分布式训练任务
- [Python API](http://paddlepaddle.org/documentation/docs/zh/1.4/api_cn/index_cn.html)
新的API支持代码更少更简洁的程序
- [贡献方式](http://paddlepaddle.org/documentation/docs/zh/1.4/advanced_usage/development/contribute_to_paddle/index_cn.html)
欢迎您的贡献!
## 答疑
欢迎您将问题和bug报告以[Github Issues](https://github.com/PaddlePaddle/Paddle/issues)的形式提交
## 版权和许可证
PaddlePaddle由[Apache-2.0 license](LICENSE)提供
# Release Note
Please turn to [here](https://github.com/PaddlePaddle/Paddle/releases) for release note.
paddle/image/logs
paddle/image/*.pyc
paddle/image/train.list
paddle/rnn/logs
paddle/rnn/*.pyc
paddle/rnn/imdb.pkl
caffe/image/logs
tensorflow/image/logs
tensorflow/rnn/logs
fluid/models/*.pyc
fluid/logs
fluid/nohup.out
name: "alexnet"
input: "data"
input_dim: 64
input_dim: 3
input_dim: 227
input_dim: 227
input: "label"
input_dim: 64
input_dim: 1
input_dim: 1
input_dim: 1
force_backward: true
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 1
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 1
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 1
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 1000
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
此差异已折叠。
set -e
function test() {
cfg=$1
batch=$2
prefix=$3
sed -i "/input: \"data\"/{n;s/^input_dim.*/input_dim: $batch/g}" $cfg
sed -i "/input: \"label\"/{n;s/^input_dim.*/input_dim: $batch/g}" $cfg
caffe time --model=$cfg --iterations=50 --gpu 0 > logs/$prefix-1gpu-batch${batch}.log 2>&1
}
if [ ! -d "logs" ]; then
mkdir logs
fi
# alexnet
test alexnet.prototxt 64 alexnet
test alexnet.prototxt 128 alexnet
test alexnet.prototxt 256 alexnet
test alexnet.prototxt 512 alexnet
# googlenet
test googlenet.prototxt 64 googlenet
test googlenet.prototxt 128 googlenet
# small net
test smallnet_mnist_cifar.prototxt 64 smallnet
test smallnet_mnist_cifar.prototxt 128 smallnet
test smallnet_mnist_cifar.prototxt 256 smallnet
test smallnet_mnist_cifar.prototxt 512 smallnet
#!/bin/bash
set -e
function test() {
cfg=$1
batch=$2
prefix=$3
batch_per_gpu=`expr ${batch} / 4`
sed -i "/input: \"data\"/{n;s/^input_dim.*/input_dim: ${batch_per_gpu}/g}" $cfg
sed -i "/input: \"label\"/{n;s/^input_dim.*/input_dim: ${batch_per_gpu}/g}" $cfg
sed -i "1c\net : \"${cfg}\"" solver.prototxt
caffe train --solver=solver.prototxt -gpu 0,1,2,3 > logs/${prefix}-4gpu-batch${batch}.log 2>&1
}
if [ ! -d "logs" ]; then
mkdir logs
fi
# alexnet
test alexnet.prototxt 512 alexnet
test alexnet.prototxt 1024 alexnet
# googlnet
test googlenet.prototxt 512 googlenet
name: "mnist/cifar"
input: "data"
input_dim: 128
input_dim: 3
input_dim: 32
input_dim: 32
input: "label"
input_dim: 128
input_dim: 1
input_dim: 1
input_dim: 1
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 32
pad: 2
kernel_size: 5
stride: 1
weight_filler {
type: "gaussian"
std: 0.0001
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "pool1"
top: "pool1"
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 32
pad: 2
kernel_size: 5
stride: 1
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: AVE
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 64
pad: 2
kernel_size: 5
stride: 1
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "pool3"
type: "Pooling"
bottom: "conv3"
top: "pool3"
pooling_param {
pool: AVE
kernel_size: 3
stride: 2
}
}
layer {
name: "ip1"
type: "InnerProduct"
bottom: "pool3"
top: "ip1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 64
weight_filler {
type: "gaussian"
std: 0.1
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "ip2"
type: "InnerProduct"
bottom: "ip1"
top: "ip2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 10
weight_filler {
type: "gaussian"
std: 0.1
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "ip2"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "ip2"
bottom: "label"
top: "loss"
}
net: "alexnet.prototxt"
base_lr: 0.01
lr_policy: "fixed"
display: 20
max_iter: 200
momentum: 0.9
weight_decay: 0.0005
snapshot: 10000
snapshot_prefix: "models/caffe_alexnet_train"
solver_mode: GPU
FROM nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04
# Use UBUNTU_MIRROR can speed up apt-get speed.
# ARG UBUNTU_MIRROR
# RUN /bin/bash -c 'if [[ -n ${UBUNTU_MIRROR} ]]; then sed -i 's#http://archive.ubuntu.com/ubuntu#${UBUNTU_MIRROR}#g' /etc/apt/sources.list; fi'
RUN apt-get update && apt-get install -y python python-pip iputils-ping libgtk2.0-dev wget vim net-tools iftop python-opencv
RUN ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so.7 /usr/lib/libcudnn.so && ln -s /usr/lib/x86_64-linux-gnu/libnccl.so.2 /usr/lib/libnccl.so
# IMPORTANT:
# Add "ENV http_proxy=http://ip:port" if your download is slow, and don't forget to unset it at runtime.
# exmaple: unset http_proxy && unset https_proxy && python fluid_benchmark.py ...
RUN pip install -U pip
RUN pip install -U kubernetes paddlepaddle
RUN pip uninstall -y paddlepaddle && mkdir /workspace
ADD https://raw.githubusercontent.com/PaddlePaddle/cloud/develop/docker/paddle_k8s /usr/bin
ADD https://raw.githubusercontent.com/PaddlePaddle/cloud/develop/docker/k8s_tools.py /root
RUN chmod +x /usr/bin/paddle_k8s
ADD *.whl /
RUN pip install /*.whl && rm -f /*.whl
ENV LD_LIBRARY_PATH=/usr/local/lib
ADD fluid_benchmark.py recordio_converter.py args.py recordio_converter.py run.sh run_fluid_benchmark.sh imagenet_reader.py /workspace/
ADD models/ /workspace/models/
# Fluid Benchmark
This directory contains several models configurations and tools that used to run
Fluid benchmarks for local and distributed training.
## Run the Benchmark
To start, run the following command to get the full help message:
```bash
python fluid_benchmark.py --help
```
Currently supported `--model` argument include:
* mnist
* resnet
* you can chose to use different dataset using `--data_set cifar10` or
`--data_set flowers`.
* vgg
* stacked_dynamic_lstm
* machine_translation
* Run the following command to start a benchmark job locally:
```bash
python fluid_benchmark.py --model mnist --device GPU
```
You can choose to use GPU/CPU training. With GPU training, you can specify
`--gpus <gpu_num>` to run multi GPU training.
You can set async mode parameter server. With async mode, you can specify
`--async_mode` to train model asynchronous.
* Run distributed training with parameter servers:
* see [run_fluid_benchmark.sh](https://github.com/PaddlePaddle/Paddle/blob/develop/benchmark/fluid/run_fluid_benchmark.sh) as an example.
* start parameter servers:
```bash
PADDLE_TRAINING_ROLE=PSERVER PADDLE_PSERVER_PORT=7164 PADDLE_PSERVER_IPS=127.0.0.1 PADDLE_TRAINERS=1 PADDLE_CURRENT_IP=127.0.0.1 PADDLE_TRAINER_ID=0 python fluid_benchmark.py --model mnist --device GPU --update_method pserver
sleep 15
```
* start trainers:
```bash
PADDLE_TRAINING_ROLE=TRAINER PADDLE_PSERVER_PORT=7164 PADDLE_PSERVER_IPS=127.0.0.1 PADDLE_TRAINERS=1 PADDLE_CURRENT_IP=127.0.0.1 PADDLE_TRAINER_ID=0 python fluid_benchmark.py --model mnist --device GPU --update_method pserver
```
* Run distributed training using NCCL2
```bash
PADDLE_PSERVER_PORT=7164 PADDLE_TRAINER_IPS=192.168.0.2,192.168.0.3 PADDLE_CURRENT_IP=127.0.0.1 PADDLE_TRAINER_ID=0 python fluid_benchmark.py --model mnist --device GPU --update_method nccl2
```
## Prepare the RecordIO file to Achieve Better Performance
Run the following command will generate RecordIO files like "mnist.recordio" under the path
and batch_size you choose, you can use batch_size=1 so that later reader can change the batch_size
at any time using `fluid.batch`.
```bash
python -c 'from recordio_converter import *; prepare_mnist("data", 1)'
```
## Run Distributed Benchmark on Kubernetes Cluster
You may need to build a Docker image before submitting a cluster job onto Kubernetes, or you will
have to start all those processes mannually on each node, which is not recommended.
To build the Docker image, you need to choose a paddle "whl" package to run with, you may either
download it from
http://www.paddlepaddle.org/docs/develop/documentation/zh/build_and_install/pip_install_en.html or
build it by your own. Once you've got the "whl" package, put it under the current directory and run:
```bash
docker build -t [your docker image name]:[your docker image tag] .
```
Then push the image to a Docker registry that your Kubernetes cluster can reach.
We provide a script `kube_gen_job.py` to generate Kubernetes yaml files to submit
distributed benchmark jobs to your cluster. To generate a job yaml, just run:
```bash
python kube_gen_job.py --jobname myjob --pscpu 4 --cpu 8 --gpu 8 --psmemory 20 --memory 40 --pservers 4 --trainers 4 --entry "python fluid_benchmark.py --model mnist --gpus 8 --device GPU --update_method pserver " --disttype pserver
```
Then the yaml files are generated under directory `myjob`, you can run:
```bash
kubectl create -f myjob/
```
The job shall start.
## Notes for Run Fluid Distributed with NCCL2 and RDMA
Before running NCCL2 distributed jobs, please check that whether your node has multiple network
interfaces, try to add the environment variable `export NCCL_SOCKET_IFNAME=eth0` to use your actual
network device.
To run high-performance distributed training, you must prepare your hardware environment to be
able to run RDMA enabled network communication, please check out [this](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/fluid/howto/cluster/nccl2_rdma_training.md)
note for details.
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
__all__ = ['parse_args', ]
BENCHMARK_MODELS = [
"machine_translation", "resnet", "se_resnext", "vgg", "mnist",
"stacked_dynamic_lstm", "resnet_with_preprocess"
]
def parse_args():
parser = argparse.ArgumentParser('Fluid model benchmarks.')
parser.add_argument(
'--model',
type=str,
choices=BENCHMARK_MODELS,
default='resnet',
help='The model to run benchmark with.')
parser.add_argument(
'--batch_size', type=int, default=32, help='The minibatch size.')
# args related to learning rate
parser.add_argument(
'--learning_rate', type=float, default=0.001, help='The learning rate.')
# TODO(wuyi): add "--use_fake_data" option back.
parser.add_argument(
'--skip_batch_num',
type=int,
default=5,
help='The first num of minibatch num to skip, for better performance test'
)
parser.add_argument(
'--iterations', type=int, default=80, help='The number of minibatches.')
parser.add_argument(
'--pass_num', type=int, default=100, help='The number of passes.')
parser.add_argument(
'--data_format',
type=str,
default='NCHW',
choices=['NCHW', 'NHWC'],
help='The data data_format, now only support NCHW.')
parser.add_argument(
'--device',
type=str,
default='GPU',
choices=['CPU', 'GPU'],
help='The device type.')
parser.add_argument(
'--gpus',
type=int,
default=1,
help='If gpus > 1, will use ParallelExecutor to run, else use Executor.')
# this option is available only for vgg and resnet.
parser.add_argument(
'--cpus',
type=int,
default=1,
help='If cpus > 1, will set ParallelExecutor to use multiple threads.')
parser.add_argument(
'--data_set',
type=str,
default='flowers',
choices=['cifar10', 'flowers', 'imagenet'],
help='Optional dataset for benchmark.')
parser.add_argument(
'--infer_only', action='store_true', help='If set, run forward only.')
parser.add_argument(
'--use_cprof', action='store_true', help='If set, use cProfile.')
parser.add_argument(
'--use_nvprof',
action='store_true',
help='If set, use nvprof for CUDA.')
parser.add_argument(
'--no_test',
action='store_true',
help='If set, do not test the testset during training.')
parser.add_argument(
'--memory_optimize',
action='store_true',
help='If set, optimize runtime memory before start.')
parser.add_argument(
'--use_fake_data',
action='store_true',
help='If set ommit the actual read data operators.')
parser.add_argument(
'--profile', action='store_true', help='If set, profile a few steps.')
parser.add_argument(
'--update_method',
type=str,
default='local',
choices=['local', 'pserver', 'nccl2'],
help='Choose parameter update method, can be local, pserver, nccl2.')
parser.add_argument(
'--no_split_var',
action='store_true',
default=False,
help='Whether split variables into blocks when update_method is pserver')
parser.add_argument(
'--async_mode',
action='store_true',
default=False,
help='Whether start pserver in async mode to support ASGD')
parser.add_argument(
'--use_reader_op',
action='store_true',
help='Whether to use reader op, and must specify the data path if set this to true.'
)
parser.add_argument(
'--data_path',
type=str,
default="",
help='Directory that contains all the training recordio files.')
parser.add_argument(
'--test_data_path',
type=str,
default="",
help='Directory that contains all the test data (NOT recordio).')
parser.add_argument(
'--use_inference_transpiler',
action='store_true',
help='If set, use inference transpiler to optimize the program.')
parser.add_argument(
'--no_random',
action='store_true',
help='If set, keep the random seed and do not shuffle the data.')
parser.add_argument(
'--reduce_strategy',
type=str,
choices=['reduce', 'all_reduce'],
default='all_reduce',
help='Specify the reduce strategy, can be reduce, all_reduce')
parser.add_argument(
'--fuse_broadcast_op',
action='store_true',
help='If set, would fuse multiple broadcast operators into one fused_broadcast operator.'
)
args = parser.parse_args()
return args
#!/bin/bash
if [ "`uname -s`" != "Linux" ]; then
echo "Current scenario only support in Linux yet!"
exit 0
fi
echo "========================= Hardware Information ========================="
sockets=`grep 'physical id' /proc/cpuinfo | sort -u | wc -l`
cores_per_socket=`grep 'core id' /proc/cpuinfo | sort -u | wc -l`
ht=`lscpu |grep "per core" |awk -F':' '{print $2}'|xargs`
physical_cores=$((sockets * cores_per_socket))
virtual_cores=`grep 'processor' /proc/cpuinfo | sort -u | wc -l`
numa_nodes=`lscpu |grep "NUMA node(s)"|awk -F':' '{print $2}'|xargs`
echo "CPU Name : `cat /proc/cpuinfo |grep -i "model name" |uniq |awk -F ':' '{print $2}'|xargs`"
echo "CPU Family : `lscpu |grep \"CPU family\" |awk -F':' '{print $2}'|xargs`"
echo "Socket Number : $sockets"
echo "Cores Per Socket : $cores_per_socket"
echo "Total Physical Cores : $physical_cores"
echo "Total Virtual Cores : $virtual_cores"
if [ $ht -eq 1 ]; then
echo "Hyper Threading : OFF"
if [ $physical_cores -ne $virtual_cores ]; then
echo "Error: HT logical error"
fi
else
echo "Hyper Threading : ON"
if [ $physical_cores -ge $virtual_cores ]; then
echo "Error: HT logical error"
fi
fi
echo "NUMA Nodes : $numa_nodes"
if [ $numa_nodes -lt $sockets ]; then
echo "Warning: NUMA node is not enough for the best performance,\
at least $sockets"
fi
echo "-------------------------- Memory Information --------------------------"
# dmidecode support start from 2.11
dmi_ver=`dmidecode --version|awk -F '.' '{print $1}'|xargs`
if [ $dmi_ver -lt 2 ]; then
echo "Error: dmidecode unknown or version is too old"
exit 0
fi
if [ `dmidecode | grep -ic "Permission denied"` -ne 0 ]; then
echo "Error: need root to run dmidecode"
exit 0
fi
max_dimms=0
num_dimms_installed=0
for dimm_id in `dmidecode |grep Locator|sort -u | awk -F ':' '{print $2}'`; do
num_refered=`dmidecode |grep -wc "$dimm_id"`
# the actual dimm id should be refered only once
if [ $num_refered -eq 1 ]; then
num_unknown=`dmidecode | awk '/'$dimm_id'/ {s=1; f=0};
/Unknown/ {f=1};
/Manufacturer/ {if (s==1) {print f; exit 0;}};'`
if [ $num_unknown -eq 0 ]; then
dimms_installed="$dimms_installed \n $dimm_id"
((num_dimms_installed++))
else
dimms_uninstalled="$dimms_uninstalled \n $dimm_id"
fi
((max_dimms++))
fi
done
echo "Installed DIMM number : $num_dimms_installed"
num_dimms_mapped=`dmidecode | grep "Memory Device Mapped" | wc -l`
if [ $num_dimms_installed -ne $num_dimms_mapped ]; then
echo "Error: The installed DIMMs number does ont match the mapped memory device: $num_dimms_mapped"
fi
num_clock_configed=`dmidecode | grep -i "Configured Clock Speed" |grep -ic "Hz"`
if [ $num_dimms_installed -ne $num_clock_configed ]; then
echo "Error: The installed DIMMs number does ont match configured clocks: $num_clock_configed"
fi
echo -e "Installed DIMMs Locator: $dimms_installed"
echo -e "Not installed DIMMs : $dimms_uninstalled"
max_dimm_slots=`dmidecode | grep -c "Bank Locator"`
echo "DIMMs max slots : $max_dimm_slots"
if [ $max_dimms -ne $max_dimm_slots ]; then
echo "Error: The max dimm slots do not match the max dimms: $max_dimms"
fi
free_ver_main=`free -V|awk -F ' ' '{print $NF}'|awk -F '.' '{print $1}'`
free_ver_sub=`free -V|awk -F ' ' '{print $NF}'|awk -F '.' '{print $2}'`
if [ $free_ver_main -lt 3 ] || [ $free_ver_sub -lt 3 ]; then
mem_sz=`free |grep -i mem |awk -F' ' '{print $2}'|xargs`
swap_sz=`free |grep -i swap |awk -F' ' '{print $2}'|xargs`
total_sz=`free -t |grep -i total |tail -n 1| awk -F' ' '{print $2}'|xargs`
mem_sz="`awk 'BEGIN{printf "%.1f\n",('$mem_sz'/1024/1024)}'` GB"
swap_sz="`awk 'BEGIN{printf "%.1f\n",('$swap_sz'/1024/1024)}'` GB"
total_sz="`awk 'BEGIN{printf "%.1f\n",('$total_sz'/1024/1024)}'` GB"
else
mem_sz=`free -h |grep -i mem |awk -F' ' '{print $2}'|xargs`
swap_sz=`free -h |grep -i swap |awk -F' ' '{print $2}'|xargs`
total_sz=`free -th |grep -i total |tail -n 1| awk -F' ' '{print $2}'|xargs`
fi
echo "Memory Size : $mem_sz"
echo "Swap Memory Size : $swap_sz"
echo "Total Memory Size : $total_sz"
echo "Max Memory Capacity : `dmidecode |grep -i \"maximum capacity\"|sort -u|awk -F':' '{print $2}'|xargs`"
# DIMMs fequency
clock_speeds=`dmidecode | grep -i "Configured Clock Speed" | grep -i "Hz" |sort -u | awk -F':' '{print $2}'|xargs`
echo "Configed Clock Speed : $clock_speeds"
num_clock_type=`dmidecode | grep -i "Configured Clock Speed" | grep -i "Hz" |sort -u | wc -l`
if [ $num_clock_type -ne 1 ]; then
echo "Warning: Have more than 1 speed type, all DIMMs should have same fequency: $clock_speeds"
fi
echo "-------------------------- Turbo Information --------------------------"
scaling_drive=`cat /sys/devices/system/cpu/cpu0/cpufreq/scaling_driver`
echo "Scaling Driver : $scaling_drive"
if [ $scaling_drive == "intel_pstate" ] && [ -e /sys/devices/system/cpu/intel_pstate/no_turbo ]; then
turbo=`cat /sys/devices/system/cpu/intel_pstate/no_turbo`
if [ $turbo -eq 1 ]; then
echo "Turbo Status : OFF"
else
echo "Turbo Status : ON"
fi
else
echo "Warning: Scaling driver is not intel_pstarte, maybe should enable it in BIOS"
echo "Turbo Status : Unknown"
fi
# cpu frequency
num_max_freq=`cat /sys/devices/system/cpu/cpu*/cpufreq/scaling_max_freq| sort -u |wc -l`
num_min_freq=`cat /sys/devices/system/cpu/cpu*/cpufreq/scaling_min_freq| sort -u |wc -l`
if [ $num_max_freq -ne 1 ]; then
echo "Error: the max_frequency of all CPU should be equal"
fi
if [ $num_min_freq -ne 1 ]; then
echo "Error: the min_frequency of all CPU should be equal"
fi
max_freq=`cat /sys/devices/system/cpu/cpu*/cpufreq/scaling_max_freq| uniq|xargs` # kHz
max_freq=`awk 'BEGIN{printf "%.2f",('$max_freq' / 1000000)}'` # GHz
min_freq=`cat /sys/devices/system/cpu/cpu*/cpufreq/scaling_min_freq| uniq|xargs` # kHz
min_freq=`awk 'BEGIN{printf "%.2f",('$min_freq' / 1000000)}'` # GHz
echo "CPU Max Frequency : $max_freq GHz"
echo "CPU Min Frequency : $min_freq GHz"
# cpu governor
num_governor=`cat /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor| sort -u |wc -l`
if [ $num_governor -ne 1 ]; then
echo "Error: the governor of all CPU should be the same"
fi
governor=`cat /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor| sort -u |uniq`
echo "CPU Freq Governor : $governor"
echo "========================= Software Information ========================="
echo "BIOS Release Date : `dmidecode | grep "Release Date"|awk -F ':' '{print $2}'|xargs`"
echo "OS Version : `cat /etc/redhat-release`"
echo "Kernel Release Version : `uname -r`"
echo "Kernel Patch Version : `uname -v`"
echo "GCC Version :`gcc --version | head -n 1|awk -F '\\\(GCC\\\)' '{print $2}'`"
if command -v cmake >/dev/null 2>&1; then
cmake_ver=`cmake --version | head -n 1 | awk -F 'version' '{print $2}'`
else
cmake_ver=" Not installed"
fi
echo "CMake Version :$cmake_ver"
echo "------------------ Environment Variables Information -------------------"
kmp_affinity=`env | grep KMP_AFFINITY`
omp_dynamic=`env | grep OMP_DYNAMIC`
omp_nested=`env | grep OMP_NESTED`
omp_num_threads=`env | grep OMP_NUM_THREADS`
mkl_num_threads=`env | grep MKL_NUM_THREADS`
mkl_dynamic=`env | grep MKL_DYNAMIC`
if [ ! $kmp_affinity ]; then kmp_affinity="unset"; fi
if [ ! $omp_dynamic ]; then omp_dynamic="unset"; fi
if [ ! $omp_nested ]; then omp_nested="unset"; fi
if [ ! $omp_num_threads ]; then omp_num_threads="unset"; fi
if [ ! $mkl_num_threads ]; then mkl_num_threads="unset"; fi
if [ ! $mkl_dynamic ]; then mkl_dynamic="unset"; fi
echo "KMP_AFFINITY : $kmp_affinity"
echo "OMP_DYNAMIC : $omp_dynamic"
echo "OMP_NESTED : $omp_nested"
echo "OMP_NUM_THREADS : $omp_num_threads"
echo "MKL_NUM_THREADS : $mkl_num_threads"
echo "MKL_DYNAMIC : $mkl_dynamic"
# Check if any MKL related libraries have been installed in LD_LIBRARY_PATH
for path in `echo $LD_LIBRARY_PATH | awk -F ':' '{for(i=1;i<=NF;++i)print $i}'`; do
mkldnn_found=`find $path -name "libmkldnn.so"`
if [ "$mkldnn_found" ]; then
echo "Found MKL-DNN : $mkldnn_found"
fi
mklml_found=`find $path -name "libmklml_intel.so"`
if [ "$mklml_found" ]; then
echo "Found MKLML : $mklml_found"
fi
iomp_found=`find $path -name "libiomp5.so"`
if [ "$iomp_found" ]; then
echo "Found IOMP : $iomp_found"
fi
done
# dump all details for fully check
lscpu > lscpu.dump
dmidecode > dmidecode.dump
# The expected result would be like:
# ========================= Hardware Information =========================
# CPU Name : Intel(R) Xeon(R) Gold 6148M CPU @ 2.40GHz
# CPU Family : 6
# Socket Number : 2
# Cores Per Socket : 20
# Total Physical Cores : 40
# Total Virtual Cores : 40
# Hyper Threading : OFF
# NUMA Nodes : 2
# -------------------------- Memory Information --------------------------
# Installed DIMM number : 12
# Installed DIMMs Locator:
# CPU1_DIMM_A1
# CPU1_DIMM_B1
# CPU1_DIMM_C1
# CPU1_DIMM_D1
# CPU1_DIMM_E1
# CPU1_DIMM_F1
# CPU2_DIMM_A1
# CPU2_DIMM_B1
# CPU2_DIMM_C1
# CPU2_DIMM_D1
# CPU2_DIMM_E1
# CPU2_DIMM_F1
# Not installed DIMMs :
# CPU1_DIMM_A2
# CPU1_DIMM_B2
# CPU1_DIMM_C2
# CPU1_DIMM_D2
# CPU1_DIMM_E2
# CPU1_DIMM_F2
# CPU2_DIMM_A2
# CPU2_DIMM_B2
# CPU2_DIMM_C2
# CPU2_DIMM_D2
# CPU2_DIMM_E2
# CPU2_DIMM_F2
# DIMMs max slots : 24
# Memory Size : 376G
# Swap Memory Size : 4.0G
# Total Memory Size : 380G
# Max Memory Capacity : 2304 GB
# Configed Clock Speed : 2666 MHz
# -------------------------- Turbo Information --------------------------
# Scaling Driver : intel_pstate
# Turbo Status : ON
# CPU Max Frequency : 3.70 GHz
# CPU Min Frequency : 1.00 GHz
# CPU Freq Governor : performance
# ========================= Software Information =========================
# BIOS Release Date : 03/10/2017
# OS Version : CentOS Linux release 7.3.1611 (Core)
# Kernel Release Version : 3.10.0-514.el7.x86_64
# Kernel Patch Version : #1 SMP Tue Nov 22 16:42:41 UTC 2016
# GCC Version : 4.8.5 20150623 (Red Hat 4.8.5-11)
# CMake Version : 3.5.2
# ------------------ Environment Variables Information -------------------
# KMP_AFFINITY : unset
# OMP_DYNAMIC : unset
# OMP_NESTED : unset
# OMP_NUM_THREADS : unset
# MKL_NUM_THREADS : unset
# MKL_DYNAMIC : unset
此差异已折叠。
此差异已折叠。
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import yaml
import copy
import argparse
import random
import os
import copy
from kube_templates import pserver, trainer, envs
def parse_args():
parser = argparse.ArgumentParser(description='Generate dist job yamls.')
parser.add_argument(
'--jobname', default="paddlejob", help='unique job name')
parser.add_argument(
'--cpu', default=1, type=int, help='CPU cores per trainer node')
parser.add_argument(
'--pscpu', default=1, type=int, help='CPU cores per pserver node')
parser.add_argument(
'--gpu', default=0, type=int, help='num of GPUs per node')
parser.add_argument(
'--image',
default="bootstrapper:5000/fluid_benchmark:gpu",
help='num of GPUs per node')
parser.add_argument(
'--pservers', default=1, type=int, help='num of pservers')
parser.add_argument(
'--trainers', default=1, type=int, help='num of trainers')
parser.add_argument('--memory', default=1, type=int, help='trainer memory')
parser.add_argument(
'--psmemory', default=1, type=int, help='pserver memory')
parser.add_argument(
'--port', default=30236, type=int, help='num of trainers')
parser.add_argument(
'--entry', default="python train.py", help='command to run')
parser.add_argument(
'--fluid', default=1, type=int, help='whether is fluid job')
parser.add_argument(
'--rdma', action='store_true', help='whether mount rdma libs')
parser.add_argument(
'--disttype',
default="pserver",
type=str,
choices=['pserver', 'nccl2', 'local'],
help='pserver or nccl2 or local')
args = parser.parse_args()
return args
def gen_job():
ps = pserver
tn = trainer
args = parse_args()
ps_container = ps["spec"]["template"]["spec"]["containers"][0]
tn_container = tn["spec"]["template"]["spec"]["containers"][0]
if args.fluid == 1:
ps_container["command"] = \
["paddle_k8s", "start_fluid"]
tn_container["command"] = \
["paddle_k8s", "start_fluid"]
ps["metadata"]["name"] = args.jobname + "-pserver"
ps["spec"]["template"]["metadata"]["labels"][
"paddle-job-pserver"] = args.jobname
tn["metadata"]["name"] = args.jobname + "-trainer"
tn["spec"]["template"]["metadata"]["labels"]["paddle-job"] = args.jobname
ps_container["image"] = args.image
tn_container["image"] = args.image
ps_container["resources"]["requests"]["cpu"] = str(args.pscpu)
ps_container["resources"]["requests"]["memory"] = str(args.psmemory) + "Gi"
ps_container["resources"]["limits"]["cpu"] = str(args.pscpu)
ps_container["resources"]["limits"]["memory"] = str(args.psmemory) + "Gi"
tn_container["resources"]["requests"]["cpu"] = str(args.cpu)
tn_container["resources"]["requests"]["memory"] = str(args.memory) + "Gi"
tn_container["resources"]["limits"]["cpu"] = str(args.cpu)
tn_container["resources"]["limits"]["memory"] = str(args.memory) + "Gi"
if args.gpu > 0:
tn_container["resources"]["requests"][
"alpha.kubernetes.io/nvidia-gpu"] = str(args.gpu)
tn_container["resources"]["limits"][
"alpha.kubernetes.io/nvidia-gpu"] = str(args.gpu)
ps["spec"]["replicas"] = int(args.pservers)
tn["spec"]["parallelism"] = int(args.trainers)
tn["spec"]["completions"] = int(args.trainers)
ps_container["ports"][0]["name"] = "jobport-" + str(args.port)
ps_container["ports"][0]["containerPort"] = args.port
spreadport = random.randint(40000, 60000)
tn_container["ports"][0]["name"] = "spr-" + str(spreadport)
tn_container["ports"][0]["containerPort"] = spreadport
envs.append({"name": "PADDLE_JOB_NAME", "value": args.jobname})
envs.append({"name": "PADDLE_TRAINERS", "value": str(args.trainers)})
envs.append({"name": "PADDLE_PSERVERS", "value": str(args.pservers)})
envs.append({"name": "ENTRY", "value": args.entry})
envs.append({"name": "PADDLE_PSERVER_PORT", "value": str(args.port)})
# NOTE: these directories below are cluster specific, please modify
# this settings before you run on your own cluster.
envs.append({
"name": "LD_LIBRARY_PATH",
"value":
"/usr/local/lib:/usr/local/nvidia/lib64:/usr/local/rdma/lib64:/usr/lib64/mlnx_ofed/valgrind"
})
volumes = [{
"name": "nvidia-driver",
"hostPath": {
"path": "/usr/local/nvidia/lib64"
}
}]
volumeMounts = [{
"mountPath": "/usr/local/nvidia/lib64",
"name": "nvidia-driver"
}]
if args.rdma:
volumes.extend([{
"name": "ibetc",
"hostPath": {
"path": "/etc/libibverbs.d"
}
}, {
"name": "iblibs",
"hostPath": {
"path": "/usr/local/rdma"
}
}, {
"name": "valgrind",
"hostPath": {
"path": "/usr/lib64/mlnx_ofed/valgrind"
}
}])
volumeMounts.extend([{
"mountPath": "/etc/libibverbs.d",
"name": "ibetc"
}, {
"mountPath": "/usr/local/rdma",
"name": "iblibs"
}, {
"mountPath": "/usr/lib64/mlnx_ofed/valgrind",
"name": "valgrind"
}])
# append shm for NCCL2
volumes.append({"name": "dshm", "emptyDir": {"medium": "Memory"}})
volumeMounts.append({"mountPath": "/dev/shm", "name": "dshm"})
# add ceph volumes
volumes.append({
"name": "ceph-data",
"cephfs": {
"monitors": ["192.168.16.23:6789"],
"secretRef": {
"name": "ceph-secret"
},
"user": "admin",
}
})
volumeMounts.append({"mountPath": "/mnt/data", "name": "ceph-data"})
tn["spec"]["template"]["spec"]["volumes"] = volumes
tn_container["volumeMounts"] = volumeMounts
ps_container["env"] = copy.deepcopy(envs)
ps_container["env"].append({
"name": "PADDLE_TRAINING_ROLE",
"value": "PSERVER"
})
tn_container["env"] = envs
if args.disttype == "pserver":
tn_container["env"].append({
"name": "PADDLE_TRAINING_ROLE",
"value": "TRAINER"
})
elif args.disttype == "nccl2" or args.disttype == "local":
# NCCL2 have no training role, set to plain WORKER
tn_container["env"].append({
"name": "PADDLE_TRAINING_ROLE",
"value": "WORKER"
})
os.mkdir(args.jobname)
if args.disttype == "pserver":
with open("%s/pserver.yaml" % args.jobname, "w") as fn:
yaml.dump(ps, fn)
with open("%s/trainer.yaml" % args.jobname, "w") as fn:
yaml.dump(tn, fn)
if __name__ == "__main__":
gen_job()
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
__all__ = [
"machine_translation", "resnet", "vgg", "mnist", "stacked_dynamic_lstm",
"resnet_with_preprocess"
]
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#!/bin/bash
PADDLE_TRAINING_ROLE=PSERVER PADDLE_PSERVER_PORT=7164 PADDLE_PSERVER_IPS=127.0.0.1 PADDLE_TRAINERS=2 PADDLE_CURRENT_IP=127.0.0.1 PADDLE_TRAINER_ID=0 python fluid_benchmark.py --model resnet --device CPU --update_method pserver --iterations=10000 &
sleep 15
CUDA_VISIBLE_DEVICES=0,1 PADDLE_TRAINING_ROLE=TRAINER PADDLE_PSERVER_PORT=7164 PADDLE_PSERVER_IPS=127.0.0.1 PADDLE_TRAINERS=2 PADDLE_CURRENT_IP=127.0.0.1 PADDLE_TRAINER_ID=0 python fluid_benchmark.py --model resnet --device GPU --update_method pserver --iterations=10000 --gpus 2 &
CUDA_VISIBLE_DEVICES=2,3 PADDLE_TRAINING_ROLE=TRAINER PADDLE_PSERVER_PORT=7164 PADDLE_PSERVER_IPS=127.0.0.1 PADDLE_TRAINERS=2 PADDLE_CURRENT_IP=127.0.0.1 PADDLE_TRAINER_ID=1 python fluid_benchmark.py --model resnet --device GPU --update_method pserver --iterations=10000 --gpus 2 &
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You also should install tflearn:
```bash
pip install -r requirements.txt
```
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