A CUDA Environment Runbook for 0.11.8

So, I have a runbook for anyone else who wants to know a precise sequence of steps for installation etc. that result in a viable, GPU-using TensorFlow, Perceptilabs & Jupyterlab Python environment, starting with Anaconda - because only Anaconda provides an easy way to setup the CUDA toolkit - and cuDNN - in an environment.

  • Note that the channel for cuDNN is conda-forge, but for the cudatoolkit it is anaconda
  • Python 3.7.10 doesn’t exist at Anaconda, so the latest available 3.7 is used
  • Mixing conda and pip isn’t ideal so we do only the absolute minimum with conda, and then use pip exclusively
  • conda and pip use “==” and “=” respectively for version specificaitons


conda create --name envname python=3.7.9
conda activate envname
pip install --upgrade pip setuptools
conda install -c anaconda cudatoolkit=10.0
conda install -c conda-forge cudnn= 
pip install tensorflow-gpu==1.15.0
pip install perceptilabs-gpu
pip install jupyterlab
pip install -f https://github.com/Kojoley/atari-py/releases atari_py
pip install gym[atari]

If you mess it up, start again after removing the environment

conda remove --name envname --all

I have attached the live notes of the build, complete with the pip & conda lists so you can compare your results with mine.

Just for the record I note that the result of all this is…

  • No cuDNN initialisation failures, no missing dtypes errors, no CUDA Out Of Memory errors
  • Therefore no need for tensorflow option tweaks to workaround such errors
  • Perceptilabs 0.11.8 humming along with the Textile model, and GPU is maxed out
  • It only took a whole day, but now you should be able to do this in ~10 minutes

My build is recorded in various posts in the forum; I’m not going to repeat it here.

Building a fresh perceptilabs & TF1.15 environment.txt (17.0 KB)


Thank you very much for this @JulianSMoore, this is amazingly helpful! :pray:

1 Like