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=220.127.116.11 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.