What it does

This repo is a step-by-step recipe for installing five deep learning frameworks—Keras, TensorFlow, CNTK, MXNet, and PyTorch—with GPU acceleration on bare-metal Windows 10. It specifies exact versions of everything: Visual Studio 2015 Update 3, CUDA 9.0.176, cuDNN 7.0.4, Anaconda 5.2.0, and Python library versions down to the patch level. The author walks through environment variables, PATH tweaks, and conda environment setup so you can validate that your GPU is actually being used.

The interesting bit

The README is essentially a time capsule of dependency hell. The author explicitly warns that most guides force you into Ubuntu VMs or Docker, which they argue are “unnecessary—and ultimately sub-optimal” steps. There’s something almost archaeological about seeing which compiler flags broke Theano and why MKL still matters for CPU-side data augmentation while the GPU trains.

Key highlights

• Supports three Keras backends with GPU: TensorFlow 1.8.0, CNTK 2.5.1, and MXNet 1.2.0 (PyTorch stands alone, no Keras backend)
• Includes specific workarounds for Visual Studio header/lib paths that break compilation
• Recommends CPU-optimized MKL for data augmentation pipelines running parallel to GPU training
• Provides validation scripts to confirm each framework sees your NVIDIA card
• Maintains archived instructions for older installs going back to January 2017

Caveats

• Last updated June 2018; versions are extremely stale (TensorFlow 1.x, CUDA 9, Python 3.6)
• Theano is explicitly noted as “legacy backend no longer in active development”
• Hardware validation was done on a Dell Precision with Titan X / GTX 1080 Ti; your mileage with modern cards is uncertain

Verdict

Worth a look if you’re maintaining legacy Windows DL infrastructure or studying how fragile ML tooling ecosystems used to be. If you’re starting fresh in 2024, use WSL2 or native Windows packages and skip this entirely.