Create a Docker context for remote Machine (access via SSH).
# specifiy remote user, remote IP address, context name, and the description
remote_user=<user name>
remote_ip_address=<ip address of remote system>
context_name=<name for the context>
I tried different installation methods via
curl
which failed because of a non existing URLapt-get
which worked but the installed version does not GPU accesspip
which worked and the installed version does GPU accessBelow the tried methods in detail.
Following the instructions from https://docs.docker.com/compose/install/ resulted in the following error when trying to run docker-compose
afterwards.
Newer NVIDA GPUs like the GeForce RTX 3090
require CUDA 11
.
TensorFlow provides pre-built Docker images on dockerhub - tensorflow/tensorflow. The underlying Docker files are availble in the TensorFlow GitHub repository.
Currently latest TF version is 2.7
. In table gives an overview of CUDA version used within the pre-built Docker images for the specified TF versions. Versions were retrieved from the TensorFlow GitHub repository (date 2022-01-10).
NOTICE: This guide will help you set ssh keys for GitHub and GitLab. However, this is not going to change your commit
user.name
oruser.email
. If you need to change those for specific repositories, just run the following commands while in your repository:
git config user.name "Your Name Here"
git config user.email your@email.com
For more info, see this answer. Also, keep in mind this only changes the
.git
folder inside your repository which never gets added/committed/pushed/uploaded.
I recently had to manage two ssh keys (one for Github and one for Gitlab). I did some research to find the best solution. I am justing putting the pieces together here.