Tuning the environment further

We have now fresh Ubuntu’s with all the machine learning libraries and GPU support handily configured by Docker. Tensorflow docker images come also with Jupyter notebook, so I played with it around a little bit. It seems like a great tool for collaboration and teaching, but could it work with Git? And in this project?

Combining Jupyter notebooks inside a container and Git turned out to be not so straightforward. There are bound to be more changes in the files due to how Jupyter works, and I probably would have to do some tweaking with the container itself.

PyCharm works like a charm, with Docker too!

PyCharm has been my personal favorite IDE for Python, and it plays nice with Git already. It turned out PyCharm can control Docker containers! I had the official tensorflow with cuda/gpu acceleration running, and PyCharm had no trouble finding it. All I had to do was to install the relevant plugin for PyCharm.



Hitting the paywall

All this was great and looked promising, but there was a catch: the ‘professional’ -tag on the guide. In order to configure a remote Python interpreter (in the Tensorflow container) I would need the pro version. 30-day trial for the rescue, and personal licenses are not _that_ expensive (8,90€/month).

Breaking the paywall

Wanting badly to test this, I installed the professional version anyway, and while shuffling through the JetBrains webpages, I saw that as a student I might be eligible for a free license!


I registered an account with all the email-hassle and started the pro-version, it asked for my credentials:

Screenshot from 2020-02-17 13-16-28

Sweet. Now the PyCharm-Docker-Git combination has a chance 🙂


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