Storage should not be done in /home as there is limited space. Rather use /media/storage, this is a 12 TB disk which has raid backup.
To use python you can install with pip install --user or make your own virtual environment, virtualenv and virtualenvwrapper is installed and you only have to add the following to your .bashrc
# virtualenvwrapper
export WORKON_HOME=~/.virtualenvs
export VIRTUALENVWRAPPER_PYTHON=/usr/bin/python3
export VIRTUALENVWRAPPER_VIRTUALENV=/home/username/.local/bin/virtualenv
source /home/username/.local/bin/virtualenvwrapper.sh
Install CUDA 18.04 docker image
add-apt-repository main
apt install software-properties-common
apt install dkms build-essential
apt install ubuntu-drivers-common
ubuntu-drivers devices
ubuntu-drivers autoinstall
docker run -it <image/tag> bash
docker commit docker_id new/name
docker attach docker_id
install docker install nvidia-docker Use cuda docker with tensorflow
Tensorflow docker images devel-gpu uses python 3.6
docker run -u $(id -u):$(id -g) --gpus all -it tensorflow/tensorflow:latest-gpu-py3 bash
run tensorflow docker jupyter remotely
Use tensorflow/tensorflow:latest-gpu-py3
docker run --gpus all -it --rm -v "$(realpath ~/projects):/projects" -p 8888:8888 lepmik/tensorflow:latest-gpu-py3
docker run --gpus all -it -v "$(realpath ~/projects):/projects" lepmik/tensorflow:latest-gpu-py3 bash
in the docker:
cd projects
jupyter lab --ip 0.0.0.0 --port 8888 --allow-root
https://ngc.nvidia.com/catalog/containers/nvidia:pytorch https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/running.html#running
docker pull nvcr.io/nvidia/pytorch:21.05-py3
docker run --gpus all -it --rm nvcr.io/nvidia/pytorch:21.05-py3 bash