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 I expect this to be outdated when PyTorch 1.0 is released (built with CUDA 10.0).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.

sudo apt update
sudo apt upgrade -y

mkdir install ; cd install
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin
sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/ /"
sudo apt-get update
sudo apt-get -y install cuda-10-1

Step 2: Installing CuDNN (~2 minutes)

  1. Download CuDNN here (BOTH the runtime and dev, deb). Use version 7.6.3.
  2. scp the deb files to GCP 3. 
    sudo dpkg -i libcudnn7_7.6.3.30-1+cuda10.1_amd64.deb
sudo dpkg -i libcudnn7-dev_7.6.3.30-1+cuda10.1_amd64.deb

Step 3: Installing TensorRT (~2 minutes)

  1. Download TensorRT here. Use version 6.0.1.
  2. scp the deb files to AWS. 3. 
    sudo dpkg -i nv-tensorrt-repo-ubuntu1804-cuda10.1-trt6.0.1.5-ga-20190913_1-1_amd64.deb
sudo apt update
sudo apt install tensorrt libnvinfer6

Step 3.5: Add to .bashrc

I don’t actually know if this step is required, but it might be helpful for other things.

export CUDA_HOME=/usr/local/cuda
export DYLD_LIBRARY_PATH=$CUDA_HOME/lib64:$DYLD_LIBRARY_PATH
export PATH=$CUDA_HOME/bin:$PATH
export C_INCLUDE_PATH=$CUDA_HOME/include:$C_INCLUDE_PATH
export CPLUS_INCLUDE_PATH=$CUDA_HOME/include:$CPLUS_INCLUDE_PATH
export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
export LD_RUN_PATH=$CUDA_HOME/lib64:$LD_RUN_PATH