Month: March 2019

Speed your algorithms with GPU. Using GPU increases your computation speed x45-x50 times more than using CPU.

Look at the speed benchmark: Deep Learning Tensorflow Benchmark: Intel i5 4210U Vs GeForce Nvidia 1060 6GB. Sometimes, enabling GPU Cuda platform can be difficult.

Configuration List:

1. Download Microsoft Visual Studio with SDK (Visual Studio 2017 Recommended)
   1. Once the IDE is installed successfully, the components for C++ development and Windows 10 SDK (Version 10.0.15063.0) must be installed.
2. Download NVIDIA GeForce Experience
3. Download CUDA Toolkit 10.0
4. Add to environment variables>path:
   1. C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\bin
   2. C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\lib\x64
   3. C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\include
5. Download NVIDIA cuDNN
6. Copy files from NVIDIA cuDNN
   1. Copy <installpath>\cuda\bin\cudnn64_7.dll to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\bin.
   2. Copy <installpath>\cuda\ include\cudnn.h to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\include
   3. Copy <installpath>\cuda\lib\x64\cudnn.lib to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\lib\x64.
7.  Download and update Anaconda:
   1. conda update conda
8.  Download Pytorch or Tensorflow.
   1. pip install tensorflow-gpu
9. Pytorch CUDA control:
   1. torch.cuda.is_available() returns True if GPU CUDA is configured correctly.
10. Tensorflow CUDA control:
    1. tf.test.gpu_device_name() returns /device:GPU:0

Reference:

https://towardsdatascience.com/python-environment-setup-for-deep-learning-on-windows-10-c373786e36d1