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:
- Download Microsoft Visual Studio with SDK (Visual Studio 2017 Recommended)
- Once the IDE is installed successfully, the components for C++ development and Windows 10 SDK (Version 10.0.15063.0) must be installed.
- Download NVIDIA GeForce Experience
- Download CUDA Toolkit 10.0
- Add to environment variables>path:
- C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\bin
- C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\lib\x64
- C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\include
- Download NVIDIA cuDNN
- Copy files from NVIDIA cuDNN
- Copy <installpath>\cuda\bin\cudnn64_7.dll to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\bin.
- Copy <installpath>\cuda\ include\cudnn.h to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\include
- Copy <installpath>\cuda\lib\x64\cudnn.lib to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\lib\x64.
- Download and update Anaconda:
- conda update conda
- Download Pytorch or Tensorflow.
- pip install tensorflow-gpu
- Pytorch CUDA control:
- torch.cuda.is_available() returns True if GPU CUDA is configured correctly.
- Tensorflow CUDA control:
- tf.test.gpu_device_name() returns /device:GPU:0
Reference:
https://towardsdatascience.com/python-environment-setup-for-deep-learning-on-windows-10-c373786e36d1