awangga/delman

Python with Cuda Support

delman

Python with Cuda Support

Tensorflow 2.1.0 Windows

Compile tensorflow 2.1 in old CPU without AVX support Requirements:

1. tensorflow 2.1.0
2. python 3.7
3. MSVC 2017
4. Bazel 0.27.1
5. cuDNN 7.4.2.24
6. CUDA 10.0.130 and replace C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\bin\cudafe++.exe from CUDA 10.1 update 1(check only to install nvcc)
7. Edit .bazelrc file on line 131 and 132 change -std=c++14 to /std:c++14
8. Run: python configure.py; Make sure set XLA JIT support = no and Please set to unknown option like /arch:SSE42

git clone https://github.com/tensorflow/tensorflow.git
cd tensorflow
git checkout r2.1
cmd admin:
python configure.py

bazel build --config=opt --config=cuda --define=no_tensorflow_py_deps=true --copt=-nvcc_options=disable-warnings //tensorflow/tools/pip_package:build_pip_package

bazel-bin\tensorflow\tools\pip_package\build_pip_package C:/Users/LENOVO/Downloads/tmp/tensorflow_pkg

pip install package_name

Tensorflow 2.0.0 / 2.0.1 Windows

Compile tensorflow 2.0 in old CPU without AVX support Requirements:

1. tensorflow_gpu-2.0.0
2. python 3.7
3. MSVC 2017
4. Bazel 0.26.1
5. cuDNN 7.4.2.24 (on ubuntu not tested in windows use v7.6.5)
6. CUDA 10.0.130 and replace C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\bin\cudafe++.exe from CUDA 10.1 update 1(check only to install nvcc)
7. Make sure avoid default set extension /arch:AVX. Please set to unknown option like /arch:SSE4

git clone https://github.com/tensorflow/tensorflow.git
cd tensorflow
git checkout r2.0
cmd admin:
python configure.py

XLA JIT support : no
/arch:SSE4

bazel build --config=opt --config=cuda --define=no_tensorflow_py_deps=true --copt=-nvcc_options=disable-warnings //tensorflow/tools/pip_package:build_pip_package

bazel-bin\tensorflow\tools\pip_package\build_pip_package C:/Users/LENOVO/Downloads/tmp/tensorflow_pkg

pip install package_name

Tensorflow 1.x keras gpu

Installation of keras gpu :

conda install python=3.6
conda install keras-gpu

test if GPU was connected to the tensorflow

import tensorflow as tf
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))

Check GPU Support in Matlab

To show Existing GPU, in the Command Window of Matlab

gpuDeviceCount
gpuDevice(1)

To use GPUArray

g = gpuDevice(1);
M = gpuArray(magic(4));
M_exists = existsOnGPU(M)