[Dependency Update] Dependency update doc (apache#15045)

* first draft * 2nd draft * complete the draft * capitalize the comment * address the comment * address comment
larroy · Aug 6, 2019 · 51c07e5 · 51c07e5
1 parent 8c2bef5
commit 51c07e5
Showing 1 changed file with 298 additions and 0 deletions.
diff --git a/tools/dependencies/README.md b/tools/dependencies/README.md
@@ -40,3 +40,301 @@ This issue appeared in the OSX build with XCode version 8.0 above (reproduced on
 ```
 --without-libidn2
 ``` 
+
+***
+
+# Dependency Update Runbook
+
+MXNet is built on top of many dependencies. Managing these dependencies could be a big headache. This goal of this document is to give a overview of those dependencies and how to upgrade when new version of those are rolled out.
+
+## Overview
+
+The dependencies could be categorized by several groups: BLAS libraries, CPU-based performance boost library, i.e. MKLDNN and GPU-based performance boosting library including CUDA, cuDNN, NCCL. and others including OpenCV, Numpy, S3-related, PS-lite dependencies. The list below shows all the dependencies and their version. Except for CUDA, cuDNN, NCCL which the user is required to install on their environments, we statically link those dependencies into libmxnet.so when we build PyPi package. By doing this, the user can take advantage of these dependencies without being worry about it.
+
+| Dependencies  | MXNet Version |
+| :------------: |:-------------:| 
+|OpenBLAS| 0.3.3 |
+|MKLDNN| 0.19 | 
+|CUDA| 10.1 |
+|cuDNN| 7.5.1 |
+|NCCL| 2.4.2 |
+|numpy| >1.16.0,<2.0.0 |
+|request| >=2.20.0,< 3.0.0 |
+|graphviz| <0.9.0,>=0.8.1 |
+|OpenCV| 3.4.2 |
+|zlib| 1.2.6 |
+|libjpeg-turbo| 2.0.2 |
+|libpng| 1.6.35 |
+|libtiff| 4-0-10 |
+|eigen| 3.3.4 |
+|libcurl| 7.61.0 |
+|libssl-dev| 1.1.1b |
+|zmq| 4.2.2 |
+|protobuf| 3.5.1 |
+|lz4| r130 |
+|cityhash| 1.1.1 |
+
+## How to update them?
+
+### MKL, MKLDNN
+
+@pengzhao-intel (https://github.com/apache/incubator-mxnet/commits?author=pengzhao-intel) and his team are tracking and updating these versions. Kudos to them!
+
+### CUDA, cuDNN, NCCL
+#### 1. Environment Setup
+We will install all the prerequsite software.
+We demonstrate with CUDA10/cuDNN7.5/NCCL 2.4.2.
+You might want to change these versions to suit your needs.
+
+```
+# Take Ubuntu 16.04 for example.
+sudo apt update
+sudo apt-get install -y git \
+    cmake \
+    libcurl4-openssl-dev \
+    unzip \
+    gcc-4.8 \
+    g++-4.8 \
+    gfortran \
+    gfortran-4.8 \
+    binutils \
+    nasm \
+    libtool \
+    curl \
+    wget \
+    sudo \
+    gnupg \
+    gnupg2 \
+    gnupg-agent \
+    pandoc \
+    python3-pip \
+    automake \
+    pkg-config \
+    openjdk-8-jdk
+    
+# CUDA installation 
+# Take CUDA 10 for example, please follow the instructions on https://developer.nvidia.com/cuda-downloads
+# Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 410.48?
+# (y)es/(n)o/(q)uit: y
+# 
+# Do you want to install the OpenGL libraries?
+# (y)es/(n)o/(q)uit [ default is yes ]:
+#
+# Do you want to run nvidia-xconfig?
+# This will update the system X configuration file so that the NVIDIA X driver
+# is used. The pre-existing X configuration file will be backed up.
+# This option should not be used on systems that require a custom
+# X configuration, such as systems with multiple GPU vendors.
+# (y)es/(n)o/(q)uit [ default is no ]:
+# 
+# Install the CUDA 10.0 Toolkit?
+# (y)es/(n)o/(q)uit: y
+#
+# Enter Toolkit Location
+# [ default is /usr/local/cuda-10.0 ]:
+#
+# Do you want to install a symbolic link at /usr/local/cuda?
+# (y)es/(n)o/(q)uit: y
+#
+# Install the CUDA 10.0 Samples?
+# (y)es/(n)o/(q)uit: n
+
+# Set LD_LIBRARY_PATH
+export LD_LIBRARY_PATH=/usr/local/cuda/lib64:${LD_LIBRARY_PATH}
+
+# Check installation
+nvidia-smi
+
+# cuDNN Setup 
+# Take cuDNN 7.5.0 with CUDA 10 for example
+# https://developer.nvidia.com/rdp/cudnn-download
+# Register with NVIDIA and download cudnn-10.0-linux-x64-v7.5.0.56.tgz
+# scp it to your instance
+# https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html
+tar -xvzf cudnn-10.0-linux-x64-v7.5.0.56.tgz
+sudo cp cuda/include/cudnn.h /usr/local/cuda/include
+sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
+sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
+# Check cuDNN version
+cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2 
+# #define CUDNN_MAJOR 7
+# #define CUDNN_MINOR 5
+# #define CUDNN_PATCHLEVEL 0
+# --
+# #define CUDNN_VERSION (CUDNN_MAJOR * 1000 + CUDNN_MINOR * 100 + CUDNN_PATCHLEVEL)
+#
+# #include "driver_types.h"
+
+# install NCCL
+# take NCCL 2.4.2 for example
+# https://developer.nvidia.com/nccl/nccl2-download-survey
+# Register with NVIDIA and download nccl-repo-ubuntu1604-2.4.2-ga-cuda10.0_1-1_amd64.deb
+sudo dpkg -i nccl-repo-ubuntu1604-2.4.2-ga-cuda10.0_1-1_amd64.deb
+sudo apt-key add /var/nccl-repo-2.4.2-ga-cuda10.0/7fa2af80.pub
+sudo apt update
+sudo apt install libnccl2 libnccl-dev
+# we will check the NCCL version later
+```
+#### 2. Build
+We will build MXNet with statically linked dependencies.
+```
+# Clone MXNet repo
+git clone --recursive https://github.com/apache/incubator-mxnet.git
+cd incubator-mxnet
+# Make sure you pin to specific commit for all the performance sanity check to make fair comparison
+# Make corresponding change on tools/setup_gpu_build_tools.sh
+# to upgrade CUDA version, please refer to PR #14887.
+# Make sure you add new makefile and right debs CUDA uses on the website
+# http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/
+
+# Build PyPi package
+tools/staticbuild/build.sh cu100mkl pip
+
+# Wait for 10 - 30 mins, you will find libmxnet.so under the incubator-mxnet/lib
+
+# Install python frontend
+pip install -e python
+# Test MXNet
+>>> import mxnet as mx
+>>> mx.runtime.feature_list()
+
+# Test NCCL version
+export NCCL_DEBUG=VERSION
+vim tests/python/gpu/test_nccl.py
+# Remove @unittest.skip("Test requires NCCL library installed and enabled during build") then run
+nosetests --verbose tests/python/gpu/test_nccl.py
+# test_nccl.test_nccl_pushpull ... NCCL version 2.4.2+cuda10.0
+# ok
+# ----------------------------------------------------------------------
+# Ran 1 test in 67.666s
+
+OK
+```
+#### 3. Performance Sanity Check
+Please run performance test aginast the MXNet you build before raising the PR.
+
+#### 4. Raise a PR
+1. Update the tools/setup_gpu_build_tools.sh please refer to PR [#14988](https://github.com/apache/incubator-mxnet/pull/14988), [#14887](https://github.com/apache/incubator-mxnet/pull/14887/files)
+2. (optional) Update the CI-related configuration/shell script/Dockerfile. Please refer to PR [#14986](https://github.com/apache/incubator-mxnet/pull/14986/files), [#14950](https://github.com/apache/incubator-mxnet/pull/14950/files)
+
+#### 5. CI Test
+1. Our CI would test PyPi and Scala publish of latest CUDA version i.e. mxnet-cu101mkl
+
+### numpy, requests, graphviz (python dependencies)
+1. Please refer to [#14588](https://github.com/apache/incubator-mxnet/pull/14588/files) and make sure the version have both of upper bound and lower bound
+#### Checklist
+- [ ] Python/setup.py
+- [ ] tools/pip/setup.py
+- [ ] ci/docker/install/docs_requirements
+- [ ] ci/docker/install/ubuntu_publish.sh
+- [ ] ci/docker/install/ubuntu_python.sh
+- [ ] ci/qemu/mxnet_requirements.txt
+- [ ] docs/install/requirements.txt 
+
+2. Build from source to do sanity check
+```
+# Compile mxnet to get libmxnet.so
+pip install -e python
+python
+>>> import mxnet as mx
+>>> mx.nd.ones((1, 2))
+[[1. 1.]]
+<NDArray 1x2 @cpu(0)>
+```
+
+### OpenCV and its dependencies: zlib, libjpeg-turbo, libpng, libtiff, eigen
+
+#### Update the build script
+1. Find the library under `tools/dependencies` and update the version.
+
+#### Sanity Check
+1. Environment Setup
+```python
+# Take Ubuntu 16.04 for example
+sudo apt update
+sudo apt-get install -y git \
+    cmake \
+    libcurl4-openssl-dev \
+    unzip \
+    gcc-4.8 \
+    g++-4.8 \
+    gfortran \
+    gfortran-4.8 \
+    binutils \
+    nasm \
+    libtool \
+    curl \
+    wget \
+    sudo \
+    gnupg \
+    gnupg2 \
+    gnupg-agent \
+    pandoc \
+    python3-pip \
+    automake \
+    pkg-config \
+    openjdk-8-jdk
+```
+2. Build PyPi package
+```
+# Update the dependency under tools/dependencies, then
+tools/staticbuild/build.sh mkl pip
+
+# Wait for 10 - 30 mins, you will find libmxnet.so under the incubator-mxnet/lib
+
+# Install python frontend
+pip install -e python
+# Test MXNet
+>>> import mxnet as mx
+>>> mx.runtime.feature_list()
+```
+
+3. Run performance tests against image related tasks
+
+
+### Other dependencies under tools/dependencies
+
+#### Update the build script
+1. Find the library under `tools/dependencies` and update the version.
+
+#### Sanity Check
+1. Environment Setup
+```python
+# Take Ubuntu 16.04 for example
+sudo apt update
+sudo apt-get install -y git \
+    cmake \
+    libcurl4-openssl-dev \
+    unzip \
+    gcc-4.8 \
+    g++-4.8 \
+    gfortran \
+    gfortran-4.8 \
+    binutils \
+    nasm \
+    libtool \
+    curl \
+    wget \
+    sudo \
+    gnupg \
+    gnupg2 \
+    gnupg-agent \
+    pandoc \
+    python3-pip \
+    automake \
+    pkg-config \
+    openjdk-8-jdk
+```
+2. Build PyPi package
+```
+# Update the dependency under tools/dependencies, then
+tools/staticbuild/build.sh mkl pip
+
+# Wait for 10 - 30 mins, you will find libmxnet.so under the incubator-mxnet/lib
+
+# Install python frontend
+pip install -e python
+# Test MXNet
+>>> import mxnet as mx
+>>> mx.runtime.feature_list()
+```