How to install Tensorflow GPU with CUDA 9.2 for python on Ubuntu

Step 9: Install cuDNN 7.1.4:

NVIDIA cuDNN is a GPU-accelerated library of primitives for deep neural networks.

Goto https://developer.nvidia.com/cudnn and download Login and agreement required

After login and accepting agreement.

Download the following:

cuDNN v7.1.4 Library for Linux

Goto downloaded folder and in terminal perform following:

tar -xf cudnn-9.2-linux-x64-v7.1.tgz
sudo cp -R cuda/include/* /usr/local/cuda-9.2/include
sudo cp -R cuda/lib64/* /usr/local/cuda-9.2/lib64

Step 10: Install NCCL 2.2.13:

NVIDIA Collective Communications Library (NCCL) implements multi-GPU and multi-node collective communication primitives that are performance optimized for NVIDIA GPUs

Go to https://developer.nvidia.com/nccl and attend survey to download Nvidia NCCL.

Download following after completing survey.

Download NCCL v2.2.13, for CUDA 9.2 -> NCCL 2.2.13 O/S agnostic and CUDA 9.2

Goto downloaded folder and in terminal perform following:

tar -xf nccl_2.2.13-1+cuda9.2_x86_64.txz
cd nccl_2.2.13-1+cuda9.2_x86_64
sudo cp -R * /usr/local/cuda-9.2/targets/x86_64-linux/
sudo ldconfig

Step 11: Install Dependencies

libcupti

sudo apt-get install libcupti-dev
echo 'export LD_LIBRARY_PATH=/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc

Python related:

To install these packages for Python 2.7, issue the following command:

sudo apt-get install python-numpy python-dev python-pip python-wheel

To install these packages for Python 3.n, issue the following command:

sudo apt-get install python3-numpy python3-dev python3-pip python3-wheel

Step 12: Configure Tensorflow from source:

Download bazel:

cd ~/
wget https://github.com/bazelbuild/bazel/releases/download/0.14.0/bazel-0.14.0-installer-linux-x86_64.sh
chmod +x bazel-0.14.0-installer-linux-x86_64.sh
./bazel-0.14.0-installer-linux-x86_64.sh --user
echo 'export PATH="$PATH:$HOME/bin"' >> ~/.bashrc

Reload environment variables

source ~/.bashrc
sudo ldconfig

Start the process of building TensorFlow by downloading latest tensorflow 1.8.0 .

cd ~/
git clone https://github.com/tensorflow/tensorflow.git
cd tensorflow
git pull
git checkout r1.8
./configure

Give python path in

Please specify the location of python. [Default is /usr/bin/python]: /usr/bin/python3

Press enter two times

Do you wish to build TensorFlow with jemalloc as malloc support? [Y/n]: Y
Do you wish to build TensorFlow with Google Cloud Platform support? [Y/n]: Y
Do you wish to build TensorFlow with Hadoop File System support? [Y/n]: Y
Do you wish to build TensorFlow with Amazon S3 File System support? [Y/n]: Y
Do you wish to build TensorFlow with Apache Kafka Platform support? [Y/n]: Y
Do you wish to build TensorFlow with XLA JIT support? [y/N]: N
Do you wish to build TensorFlow with GDR support? [y/N]: N
Do you wish to build TensorFlow with VERBS support? [y/N]: N
Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: N
Do you wish to build TensorFlow with CUDA support? [y/N]: Y
Please specify the CUDA SDK version you want to use, e.g. 7.0. [Leave empty to default to CUDA 9.0]: 9.2
Please specify the location where CUDA 9.2 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: /usr/local/cuda-9.2
Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7.0]: 7.1.4
Please specify the location where cuDNN 7 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda-9.2]: /usr/local/cuda-9.2
Do you wish to build TensorFlow with TensorRT support? [y/N]: N
Please specify the NCCL version you want to use. [Leave empty to default to NCCL 1.3]: 2.2
Please specify the location where NCCL 2 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda-9.2]: /usr/local/cuda-9.2/targets/x86_64-linux

Now we need compute capability which we have noted at step 1 eg. 5.0

Please note that each additional compute capability significantly increases your build time and binary size. [Default is: 5.0] 5.0
Do you want to use clang as CUDA compiler? [y/N]: N
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
Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]: -march=native
Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]:N

Configuration finished

Step 13: Build Tensorflow using bazel

The next step in the process to install tensorflow GPU version will be to build tensorflow using bazel. This process takes a fairly long time.

To build a pip package for TensorFlow you would typically invoke the following command:

bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package

Note:-

 add "--config=mkl" if you want Intel MKL support for newer intel cpu for faster training on cpu
 add "--config=monolithic" if you want static monolithic build (try this if build failed)
 add "--local_resources 2048,.5,1.0" if your PC has low ram causing Segmentation fault or other related errors

This process will take a lot of time. It may take 1 – 2 hours or maybe even more.

Also if you got error like Segmentation Fault then try again it usually worked. 

The bazel build command builds a script named build_pip_package. Running this script as follows will build a .whl file within the tensorflow_pkg directory:

To build whl file issue following command:

bazel-bin/tensorflow/tools/pip_package/build_pip_package tensorflow_pkg

To install tensorflow with pip:

cd tensorflow_pkg

for existing virtual environment:

pip install tensorflow*.whl

With a new virtual environment using virtualenv:

sudo apt-get install virtualenv
virtualenv tf_1.8.0_cuda9.2 -p /usr/bin/python3
source tf_1.8.0_cuda9.2/bin/activate
pip install tensorflow*.whl

for python 2: (use sudo if required)

pip2 install tensorflow*.whl

for python 3: (use sudo if required)

pip3 install tensorflow*.whl

Note : if you got error like unsupported platform then make sure you are running correct pip command associated with the python you used while configuring tensorflow build.

You can check pip version and associated python by following command

pip -V

Step 14: Verify Tensorflow installation

Run in terminal

python

import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))

If the system outputs the following, then you are ready to begin writing TensorFlow programs:

Hello, TensorFlow!

Success! You have now successfully installed tensorflow 1.8.0 on your machine. If you are on Windows OS, you might want to check out our other post here, How to install Tensorflow 1.7.0 GPU with CUDA 9.1 and cuDNN 7.1.2 for Python 3 on Windows OS. Cheers!!

For prebuilt wheel with optimization for AVX2 and cuda 9.2, cudnn 7.1.4, compute capability 5.0 go to this link .

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