less than 1 minute read

How to install CUDA 9.2, CuDNN 7.2.1, PyTorch nightly on Google Compute Engine. I expect this to be outdated when PyTorch 1.0 is released (built with CUDA 10.0).

This uses Conda, but pip should ideally be as easy.

Step 0: GCP setup (~1 minute)

Create a GCP instance with 8 CPUs, 1 P100, 30 GB of HDD space with Ubuntu 16.04. Turn off host migration (GPU jobs can’t be resumed).

Step 1: Installing CUDA (~5.5 minutes)

You can also install CUDA directly from the offline installer, but this is a little easier.

mkdir install ; cd install
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_9.2.148-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1604_9.2.148-1_amd64.deb
sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
sudo apt update
time sudo apt install cuda-9.2

Step 2: Installing CuDNN (~2 minutes)

  1. Download CuDNN here (BOTH the runtime and dev, deb). Use version 7.2.1.
  2. scp the deb files to GCP 3.
    sudo dpkg -i libcudnn7_7.2.1.38-1+cuda9.2_amd64.deb
    sudo dpkg -i libcudnn7-dev_7.2.1.38-1+cuda9.2_amd64.deb

Step 3: Installing Conda / PyTorch nightly (~9 minutes)

Installing Conda (~3.5 minutes):

wget https://repo.anaconda.com/archive/Anaconda3-5.3.0-Linux-x86_64.sh
time bash Anaconda3-5.3.0-Linux-x86_64.sh

Making a new virtual environment, with Python 3.5 (~3.5 minutes):

conda create -n pytorch-default python=3.5 anaconda

Installing PyTorch nightly (~2 minutes):

conda install pytorch-nightly cuda92 -c pytorch