TensorFlow 安装笔记

sudo apt-get update
sudo apt-get upgrade

sudo apt install -y python-dev python-pip python3-dev python3-pip

lspci | grep -i nvidia

01:00.0 VGA compatible controller: NVIDIA Corporation Device 1cbb (rev a1)
01:00.1 Audio device: NVIDIA Corporation GP107GL High Definition Audio Controller (rev a1)

uname -m && cat /etc/*release
gcc --version

sudo apt-get install -y build-essential \
     cmake git unzip zip \
     python-dev python3-dev python-pip python3-pip

# 查看内核版本
uname -r
# 安装内核的头文件
sudo apt-get install linux-headers-$(uname -r)

sudo touch /etc/modprobe.d/blacklist-nouveau.conf
sudo echo 'blacklist nouveau' > /etc/modprobe.d/blacklist-nouveau.conf
sudo echo 'options nouveau modeset=0' >> /etc/modprobe.d/blacklist-nouveau.conf

sudo update-initramfs -u

sudo apt-get purge nvidia*
sudo apt-get autoremove
sudo apt-get autoclean
sudo rm -rf /usr/local/cuda*

sudo sh cuda_10.0.130_410.48_linux.run

echo 'export PATH=/usr/local/cuda-10.0/bin${PATH:+:${PATH}}' >> ~/.bashrc
echo 'export LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}' >> ~/.bashrc

source ~/.bashrc
sudo ldconfig

# 查看 CUDA 是否安装成功
nvidia-smi

tar xvf cudnn-10.0-linux-x64-v7.4.2.24.tgz

cuda
├── include
│   └── cudnn.h
├── lib64
│   ├── libcudnn.so -> libcudnn.so.7
│   ├── libcudnn.so.7 -> libcudnn.so.7.4.2
│   ├── libcudnn.so.7.4.2
│   └── libcudnn_static.a
└── NVIDIA_SLA_cuDNN_Support.txt

sudo cp -R cuda/include/* /usr/local/cuda-10.0/include
sudo cp -R cuda/lib64/* /usr/local/cuda-10.0/lib64

tar xvf nccl_2.3.7-1+cuda10.0_x86_64.txz

nccl_2.3.7-1+cuda10.0_x86_64
├── include
│   └── nccl.h
├── lib
│   ├── libnccl.so -> libnccl.so.2
│   ├── libnccl.so.2 -> libnccl.so.2.3.7
│   ├── libnccl.so.2.3.7
│   └── libnccl_static.a
└── LICENSE.txt

cd nccl_2.3.7-1+cuda10.0_x86_64
sudo cp -R include/* /usr/local/cuda-10.0/include
sudo cp -R lib/* /usr/local/cuda-10.0/lib64

mkdir ~/.venv && cd ~/.venv

# 安装 virtualenv 依赖
pip install virtualenv

# 新建虚拟环境
virtualenv tfenv --python=python3
# 另外一种建虚拟环境的方式
python3 -m virtualenv tfenv

# 启用虚拟环境
source ~/.venv/tfenv/bin/activate

pip install pip six numpy==1.15.4 wheel setuptools mock future>=0.17.1
pip install keras_applications==1.0.6 --no-deps
pip install keras_preprocessing==1.0.5 --no-deps

#  bazel 依赖
sudo apt-get install -y build-essential pkg-config zip g++ zlib1g-dev unzip

./bazel-0.18.0-installer-linux-x86_64.sh --user
echo 'export PATH=${HOME}/bin:${PATH:+:${PATH}}' >> ~/.bashrc
source ~/.bashrc
sudo ldconfig

git clone https://github.com/tensorflow/tensorflow.git
git checkout r1.12

./tensorflow-gpu$ ./configure
WARNING: Processed legacy workspace file /home/ub64/Code/github/tensorflow-gpu/tools/bazel.rc. This file will not be processed in the next release of Bazel. Please read https://github.com/bazelbuild/bazel/issues/6319 for further information, including how to upgrade.
WARNING: An illegal reflective access operation has occurred
WARNING: Illegal reflective access by com.google.protobuf.UnsafeUtil (file:/home/ub64/.cache/bazel/_bazel_ub64/install/f1e11885a5cc7ba9947679cffb18bf94/_embedded_binaries/A-server.jar) to field java.lang.String.value
WARNING: Please consider reporting this to the maintainers of com.google.protobuf.UnsafeUtil
WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations
WARNING: All illegal access operations will be denied in a future release
WARNING: --batch mode is deprecated. Please instead explicitly shut down your Bazel server using the command "bazel shutdown".
You have bazel 0.18.0 installed.
Please specify the location of python. [Default is /home/ub64/.venv/gpu/bin/python]:

Found possible Python library paths:
  /home/ub64/.venv/gpu/lib/python3.6/site-packages
  .
Please input the desired Python library path to use.  Default is [/home/ub64/.venv/gpu/lib/python3.6/site-packages]

Do you wish to build TensorFlow with Apache Ignite support? [Y/n]: Y
Apache Ignite support will be enabled for TensorFlow.

Do you wish to build TensorFlow with XLA JIT support? [Y/n]: Y
XLA JIT support will be enabled for TensorFlow.

Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: N
No OpenCL SYCL support will be enabled for TensorFlow.

Do you wish to build TensorFlow with ROCm support? [y/N]: N
No ROCm support will be enabled for TensorFlow.

Do you wish to build TensorFlow with CUDA support? [y/N]: y
CUDA support will be enabled for TensorFlow.

Please specify the CUDA SDK version you want to use. [Leave empty to default to CUDA 9.0]: 10.0


Please specify the location where CUDA 10.0 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: /usr/local/cuda-10.0


Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7]: 7.4.2


Please specify the location where cuDNN 7 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda-10.0]: /usr/local/cuda-10.0


Do you wish to build TensorFlow with TensorRT support? [y/N]: N
No TensorRT support will be enabled for TensorFlow.

Please specify the NCCL version you want to use. If NCCL 2.2 is not installed, then you can use version 1.3 that can be fetched automatically but it may have worse performance with multiple GPUs. [Default is 2.2]: 2.3.7


Please specify the location where NCCL 2 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda-10.0]:/usr/local/cuda-10.0


NCCL found at /usr/local/cuda-10.0/lib64/libnccl.so.2
Assuming NCCL header path is /usr/local/cuda-10.0/lib64/../include/nccl.h
Please specify a list of comma-separated Cuda compute capabilities you want to build with.
You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.
Please note that each additional compute capability significantly increases your build time and binary size. [Default is: 6.1]: 6.1


Do you want to use clang as CUDA compiler? [y/N]: N
nvcc will be used as CUDA compiler.

Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]: /usr/bin/gcc


Do you wish to build TensorFlow with MPI support? [y/N]: N
No MPI support will be enabled for TensorFlow.

Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]:


Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: N
Not configuring the WORKSPACE for Android builds.

Preconfigured Bazel build configs. You can use any of the below by adding "--config=<>" to your build command. See tools/bazel.rc for more details.
        --config=mkl            # Build with MKL support.
        --config=monolithic     # Config for mostly static monolithic build.
        --config=gdr            # Build with GDR support.
        --config=verbs          # Build with libverbs support.
        --config=ngraph         # Build with Intel nGraph support.
Configuration finished
(gpu) ub64@hpz2:~/Code/github/tensorflow-gpu$

# CPU only
bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package
# GPU version with CUDA
bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package

./bazel-bin/tensorflow/tools/pip_package/build_pip_package tensorflow_pkg
pip install tensorflow_pkg/tensorflow-1.12.0-cp36-cp36m-linux_x86_64.whl

pip install tensorflow

pip install --upgrade tensorflow