The version of python included with base environment is 3.9. As of this writing I am using Miniconda 3 64 bit version. Miniconda: You can get the latest version on Miniconda from here.I will explain the installation process below. Please note that Visual Studio 2019 community edition is free to download and install. Visual Studio 2019 – for C++ runtime: You can down it from the official website here.You can download the current version of the drivers from this link. These drivers are special because they also include WSL 2 support which means that I would be able to use the same drivers inside my WSL2 distributions – if you don’t understand WSl 2 – that is fine – you don’t need to. NVIDIA Graphics Driver: I am using driver version 510.06 gameready drivers.You will need the following installed on your machine. I have added a new way of running tensorflow with GPU using docker containers, that approach is much simpler and more robust then configuring the entire drivers and CUDA libraries on your local machine. Graphics Card: NVIDIA GeForce RTX 2070 8GB GDRR6 (any RTX or Quadro GPU is fine).Motherboard: Asus Rog X399 (Any motherboard is fine).Processor: AMD Ryzen Threadripper 2970WX (CPU doesn’t matter).Operating System: Windows 11 Pro (everything in artical would work on Windows 11 editions).Some of this software is optional but I like to have a fully loaded environment so that I don’t have to wait for the software/tools installation later on when I have some awesome idea. Before we get started here are the set of software that you need to install on your machine. I would argue that recently it has become very simple and straightforward to get up and running on Windows (Windows 11 specifically). This blog post is yet another such post but the idea here is – I am writing this as I am setting up the things and this blog post should help you setup your environment just like I did. I know getting the CUDA support up and running on tensorflow is such a pain point and that is why we have all these posts and videos on different platforms how to do it properly. I have a decent machine with a decent GPU and I do some hobby machine learning projects. This is going to be a handson practical step by step guide to create this environment from scratch with a fresh Windows 11 installation. Today we are going to setup a new anaconda environment with tensorflow 2.5 with GPU support using NVIDIA CUDA 11.4 and CUDNN 8.2.4 along with Python 3.8. It’s been just 2 days since Windows 11 came out and I am already setting up my system for the ultimate machine learning environment.
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