This page aims to contain the setup in order to be able to run the deep learning tools that are currently being used at the BIOP = Setup for Windows = NOTE: This setup is intended for a workstation with a good GPU and Windows 10 Installed. It has been tested with the following cards: **GTX Titan, GTX 1080, GTX 1080 Ti, RTX 2080 Ti** == Software to Install == - [[ https://www.nvidia.com/Download/index.aspx | Download Latest NVIDIA Drivers ]] - We use the `Studio Drivers` - [[ https://visualstudio.microsoft.com/downloads/#build-tools-for-visual-studio-2019 |Download Build Tools for Visual Studio 2019 ]] - Install the `Build tools for C++`, namely - `MSVC v142 - VS 2019 C++ x64/x86 build tools` - `Windows 10 SDK (10.0.18362.0)` - [[ https://developer.nvidia.com/cuda-10.1-download-archive-update2?target_os=Windows&target_arch=x86_64&target_version=10&target_type=exenetwork | Download CUDA Toolkit 10.1 Update 2]] - Install just the `Developer` and `Runtime` parts - [[ https://developer.nvidia.com/compute/machine-learning/cudnn/secure/7.6.5.32/Production/10.1_20191031/cudnn-10.1-windows10-x64-v7.6.5.32.zip | Download CuDNN 7.6.5 for CUDA 10.1]] - Unzip into `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\` - [[https://www.python.org/downloads/release/python-379/| Install Python 3.7]] - We install it for all users and add Python to the `PATH` == Environment Variables == Create and add the two following environment variables. These are needed for compiling PyOpenCL, which is a dependency of `gpuools` for StarDist, but is beneficial for any other tools that use the GPU and may need compiling. |VARIABLE NAME | Path (Default) | |-------------------|-----------------| |INCLUDE| C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\include| |LIB| C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\lib\x64| == Creating Virtual Environments == NOTE: Consider upgrading `pip` to the latest vesion before starting: `python -m pip install --upgrade pip` To get started, we need to install `virtualenv` to manage our environments ``` pip install virtualenv ``` === StarDist === We are going to create a `stardist-tf2` environment in the `D:\environments` drive Start a command prompt and type ``` d: mkdir environments python -m venv environments\stardist-tf2 environments\stardist-tf2\Scripts\activate ``` (WARNING) **Checkpoint**: Ensure the right python version is installed using `where python` and you should have a result like below ``` D:\environments\stardist-tf2\Scripts\python.exe C:\Users\oburri\AppData\Local\Programs\Python\Python37\python.exe C:\Users\oburri\AppData\Local\Microsoft\WindowsApps\python.exe ``` Finally, we can install all of StarDist using the `stardist.txt` file below `pip install -r stardist.txt` {F15067175} === Noise2Void === Noise2Void currently needs a lower version of CUDA to function. Installations - [[ https://developer.nvidia.com/cuda-10.0-download-archive?target_os=Windows&target_arch=x86_64&target_version=10&target_type=exenetwork | Download CUDA Toolkit 10.0 ]] - [[ https://developer.nvidia.com/compute/machine-learning/cudnn/secure/7.6.5.32/Production/10.0_20191031/cudnn-10.0-windows10-x64-v7.6.5.32.zip | Download CuDNN 7.6.5 for CUDA 10.0]] - Unzip into `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\` We are going to create a `n2v` environment in the `D:\environments` drive Start a command prompt and type ``` d: mkdir environments python -m venv environments\n2v environments\n2v\Scripts\activate ``` Finally, we can install all of Noise2Void using the `n2v.txt` file below `pip install -r n2v.txt` {F15067171} = Setup for SCITAS GPU Clusters =