What it does

This is a step-by-step shell recipe for turning a bare Ubuntu 14.04 box with an Nvidia Titan X into a working deep learning workstation. It walks through Nvidia drivers, CUDA 7.5, cuDNN v4, and the full framework buffet of the mid-2010s: TensorFlow 0.8, Caffe, Theano, Keras, and Torch. There’s even a bonus chapter on X2Go remote access with XFCE, because Unity and remote desktops don’t mix.

The interesting bit

The author used this repo as a springboard to build FloydHub, a “run TensorFlow on AWS in <30 seconds” service. The README itself is essentially a fossilized pre-cloud workflow — the kind of weekend-killing setup that makes managed services look like a bargain at any price. It’s a time capsule of dependency hell before Docker solved this for most people.

Key highlights

• Covers the full stack: drivers, CUDA, cuDNN, Python scientific packages, and five deep learning frameworks
• Includes verification steps after each major component (nvidia-smi, deviceQuery, import tests)
• Uses apt-get for Nvidia drivers to avoid the “quit X server and pray” manual installation path
• Parallel build flags (-j $(($(nproc) + 1))) throughout for faster compilation
• X2Go remote desktop setup with XFCE workaround for Unity incompatibility

Caveats

• Frozen in 2016: Ubuntu 14.04, CUDA 7.5, TensorFlow 0.8, cuDNN v4 — all significantly outdated
• The FloydHub service promoted at the top appears to be defunct (floydhub.com redirects elsewhere)
• Some steps assume specific hardware (Nvidia Titan X) and may need adaptation for modern GPUs

Verdict

Worth a skim for historical context or if you’re maintaining legacy systems. For actual modern setup, use a current CUDA/cuDNN container or a cloud instance with pre-installed drivers. The author clearly agreed — that’s literally the business they built.