PhamNguyen97/Build-Tensorflow_GPU-version1.12-no-AVX

Tensorflow_GPU version 1.12 UBUNTU 18.04 for old CPU with no AVX support.

Build-Tensorflow_GPU-version1.12-no-AVX

Tensorflow_GPU version 1.12 UBUNTU 18.04 for old CPU with no AVX support.

Build from source

Build a TensorFlow pip package from source and install it on Ubuntu Linux and macOS.

Install Software requirements : link _ https://www.tensorflow.org/install/gpu

The following NVIDIA® software must be installed on your system:

NVIDIA® GPU drivers —CUDA 9.0 requires 384.x or higher. ( SHOULD BE VERSION 9.0)
CUDA® Toolkit —TensorFlow supports CUDA 9.0.
CUPTI ships with the CUDA Toolkit.
cuDNN SDK (>= 7.2)
(Optional) NCCL 2.2 for multiple GPU support.
(Optional) TensorRT 4.0 to improve latency and throughput for inference on some models.

Install Python and the TensorFlow package dependencies UBUNTU,MAC OS

sudo apt install python-dev python-pip # or python3-dev python3-pip

Install the TensorFlow pip package dependencies (if using a virtual environment, omit the --user argument):

pip install -U --user pip six numpy wheel mock
pip install -U --user keras_applications==1.0.6 --no-deps
pip install -U --user keras_preprocessing==1.0.5 --no-deps

Install Bazel 0.19.0 _ For UBUNTU:

Step 1: Install required packages:

sudo apt-get install pkg-config zip g++ zlib1g-dev unzip python

Step 2: Download Bazel. (bazel-0.19.2-installer-linux-x86_64.sh):

https://github.com/bazelbuild/bazel/releases

Step 3: Run the installer:

chmod +x bazel-0.19.2-installer-linux-x86_64.sh
./bazel-0.19.2-installer-linux-x86_64.sh --user

Step 4: Set up your environment:

export PATH="$PATH:$HOME/bin"

Step 5: Install the JDK:

sudo apt-get install openjdk-8-jdk

Step 6: Add Bazel distribution URI as a package source:

echo "deb [arch=amd64] http://storage.googleapis.com/bazel-apt stable jdk1.8" | sudo tee /etc/apt/sources.list.d/bazel.list
curl https://bazel.build/bazel-release.pub.gpg | sudo apt-key add -

Step 7: Install and update Bazel:

sudo apt-get update && sudo apt-get install bazel

BUILD TENSORFLOW:

Download the TensorFlow source code

git clone https://github.com/tensorflow/tensorflow.git
cd tensorflow
git checkout branch_name # r1.9, r1.10, etc. (for example, git checkout r1.12 for tensorflow version 1.12)

Configure the build

./configure *** (I hoose No for all except for those say about CUDA)

BUILD _ GPU support:

Using bazel version of bazel 0.19.0, Added the content of file "/home//tensorflow/tools/bazel.rc" on top of (hidden) file "/home//tensorflow/.tf_configure.bazelrc".
bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
./bazel-bin/tensorflow/tools/pip_package/build_pip_package /mnt # create package

Copy .whl file built in the previous step into place you want:

nautilus /mnt

Install tensorflow: In the folder you put your .whl file:

pip3 install <name_of_file>.whl

for example: pip3 install tensorflow-1.12.0-cp36-cp36m-linux_x86_64.whl p/s: replace pip3 by pip for python2.

Test tensorflow-gpu:

in terminal:

python3
import tensorflow as tf
print(tf.contrib.eager.num_gpus())

output:

yyyy-mm-dd hh:mm:ss.nnnnn: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2212 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060 3GB, pci bus id: 0000:01:00.0, compute capability: 6.1)

Good luck Guys :)))

P/S: Bazel should not be updated to version 0.20.0 or higher due to bugs.