Set up deep learning system for Windows with Nvidia GPU
To prepare deep learning system in Windows with Nvidia GPU, we need to install the following prerequisite with Administrator access.
- Nvidia driver
- Microsoft Visual C++ Redistributable
- Cuda toolkit
- Install conda: Anaconda/miniconda
Above prerequisite is for all users and just need to do once for each PC.
Then, we set up conda for each user without administrator access:
- Create a conda environment
- Install deep learning platforms
- Trouble Shooting
This section is for all account and require administrator access.
Install Nvidia driver
To download GPU driver, we can
- Go to this link
- Choose our GPU/operation, and click serach.
- Download the correspond driver. For a Windows 10 with GTX Titan X, the newest driver name should be 388.00-desktop-win10-64bit-international-whql.exe as date of 2017-10-24.
- Move to the folder that the driver downloaded and double click it to install.
Install Visual C Redistributable
Download this link and double click it to install. Restart the Operation system after installation complete.
Install Cuda toolkit
Cuda 9.0 is recently released, unfortunately however, most dl platform currently only support cuda 8.0. Therefore, we need to install older verion.
- Download the archived release from here.
- Pick up the operation system. As date of 2017-10-24, we can choose cuda 8.0 GA2 and download both files. Their filename should be cuda_8.0.61_win10.exe and cuda_18.104.22.168_windows.exe.
- Double click them to install. Install cuda_8.0.61_win10.exe first and then cuda_22.214.171.124_windows.exe.
- We might encouter a warning saying the cuda version is not compatible with our driver, just ignore it.
More details about installing cuda is avaliable here
After downloaded Nvidia driver and Cuda toolkit, we can install cnDNN.
- Newest version of cuDNN can be downloaded from here, people need to register before downloading them. We can download the cuDNN v7.0 Library for CUDA 8.0 for Windows 10, this link will give us a file named “cudnn-8.0-windows10-x64-v7.zip”.
- Unzip and you will get three folders “bin”, “include”, and “lib”.
- Copy the three folders to “C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0”.
- Go the the “bin” folder and copy “cudnn64_7.dll” file. Rename the copied file to “cudnn64_6.dll”.
More details about installing cuDNN is avaliable here.
Usually, to avoid any package conflictions between different platforms or users, we expect to establish independent environment for different users, platforms, or projects. Conda is a package, dependency, and environment management platform that can easily achieve this goal. We also can use it to easily manage and use different version of softwares.
To install conda, we have two options: anaconda and miniconda. Compare to miniconda, anaconda has more preinstalled packages. Here, take miniconda as an example:
- Download miniconda from this link,
- Double click to install it.
- During installation, choose install conda for all users.
- During installation, choose include conda in PATH to use it in cmd (ignore the warning).
Set up deep learning environment
This section is user specific.
Create a conda environment
Then, we can create a new conda environment. As an example, we create a environment named “tf“(any other name is OK) using the following script.
conda create -name tf python=3.6
After creating a env, we can enter this env by:
After entering the env, we can install packages (take installing numpy as an example) for this env by:
conda install numpy
We can install most other python packages like scipy, scikit-learn in this way without warrying about dependencies. Install through conda is usually better than from pip because of less conflicts and more comprehensize dependencies.
Install deep learning platforms
We can install any of the following platforms we like. I recommend to create an independent conda env for each platform (except Keras).
After creating a env named tf in above section, we can enter that environment by the following command:
At the same conda env, we can use the following command to install a stable version of Keras. Make sure to install keras first before installing tensorflow.
conda install keras
To install a newest version of keras, use this command.
pip install keras
After installing keras, we need to pick a backend. Keras use tensorflow by default. We can change it by editing the “~/.keras/keras.json” file. ~ is the path of user folder.
conda install tensorflow-gpu
Pytorch is community-supported in Windows. After entering a conda env, simply type the following command:
conda install -c peterjc123 pytorch
CNTK, though not very popular, is a great option for Keras backend because it runs faster than current version of tensorflow. It can be downloaded and installed by the following commands (after entering a conda env).
pip install https://cntk.ai/PythonWheel/GPU/cntk-2.2-cp36-cp36m-win_amd64.whl
Unexpected keyword argument error in pip
This occurs after installing tensorflow from pip because tensorflow downgrade html5lib to an older version. Need to update html5lib to fix this issue. Run the following command:
conda install html5lib
If tensorflow is installed from conda, this won’t happen.
For python 3.6, opencv cannot be directly installed by using
conda install opencv
Instead, we need to the following script.
conda install -c menpo opencv3
However, this is still not guranteed to work, we might want to do some Google to search for new options, for example, install a pre-complied whl file by pip.