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Adding a convenient Docker #322
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Hey there! I've made some changes to the script and Dockerfile that should make them a lot more readable and easy to work with. In the script, I've defined all the variables in one place and used string concatenation to make the command more compact. I've also broken the command into multiple lines for better readability. For the Dockerfile, I've made some changes to reduce the final size, by chaining multiple commands in one line. I've also added an environment variable and copied the current directory and a requirements file to the container, and set the working directory. I will run the Dockerfile in my infra to see if I got the same results as report in wiki. |
Hi @Div12345, Can you build/run/reproduce the docker script and see if everything is working? |
Minor update, I implemented all the points. I managed to run on the server to that I have access. In the test with one dataset (Cho), something is running. I left it running to download all the datasets, and then I will do a small stress test to run with as much CPU as possible. Can you check on your side, @Div12345 or @sylvchev? I added the instruction sequence in the README. |
Small update: Now, we have the based image in the docker hub =) I put it in that repository for now. Eventually, we will have to migrate to an organization account, but this involves costs, $$ https://hub.docker.com/r/baristimunha/moabb disclaimer: ignore the different user, someone configured the global user on the server. |
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Everything is working fine on the cluster and on my machine.
Thank you so much @bruAristimunha for your work! |
This pull request adds a Dockerfile to the MOABB repository, allowing users to easily build and run the MOABB code in a containerized environment.
The Dockerfile is based on one of the latest Pytorch images from Nvidia and installs all necessary dependencies for running MOABB. This pull request also creates a convenient script to include instructions for building and running MOABB using Docker.
I understand that the Nvidia image is the best choice for performance, and for the plan to add deep learning models in moabb, but this choice may add the step of logging into the Nvidia/torch hub with Docker.
[WIP] Script to run the moabb/run.py
[WIP] Increase description