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Containers are ubiquitous in the software industry, so I would recommend taking the time to read the official Docker documentation (https://docs.docker.com/get-started/). However, if you're in a hurry and just want to get a GPU-powered python environment up and running asap, the guide below should help you get started.
Step-by-step guide
1 - Making sure you have access to docker
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cd ~/docker/myproject docker build -t <image_name> . |
Note the dot ‘.’ at the end of the command; don't forget it as it tells docker where to look for a Dockerfile. <image_name> is a user-specified name used to identify the created image. By convention, since all images created on the machine is stored in one place, it is common to include your username in the image name; e.g.: olanorm/testproject.
If the build command executed smoothly, your image should now be ready. You can verify this by running:
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docker image list |
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