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So many dependencies!
See original GitHub issueIt takes a very long time to install all dependencies. Are really all of the dependencies required? Maybe some are only for dev and could be moved e.g. to requirements-dev.txt?
Also some of the dependencies have no version pinning. This is not a good practice.
What do you think? Less dependencies could increase deploy time…
Issue Analytics
- State:
- Created 3 years ago
- Comments:9 (3 by maintainers)
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Quite challenging to get the large list of libraries to function. Using pip on the requirements.txt file did not cut it for me. I use Miniconda on Windows 10 and applied the command ‘conda install -c conda-forge <library name>’ successfully where my first attempt with pip failed. Also, I downloaded the PyTorch library directly from its home page. Tensorflow also includes the Cuda Toolkit since I enjoy an nVidia GPU in my HW setup.
If you have installed a library with pip which is not functioning, then you need to delete it with pip before re-installing it with other means.
I was able to install GamestonkTerminal with Python 3.7.9 and TensorFlow 2.4.1 and my complete list of dependencies can be found in the attached .yml file (dubbed as .txt since Github does not recognize the .yml format…).
I do think there is much to gain from streamlining the use of libraries and simplify installation for the end user of this impressive effort by Didier R. Lopes.
gs_environment_latest.txt
Hey @exislow ,
You’re absolutely right. And this is what I’m working on today!
I’ll leave requirements.txt to what is absolutely necessary. Add poetry for package management, and make it possible to build a docker image from it.
This should make the installation much more simple, and also decrease deploy time since I think the current requirements.txt has libraries that aren’t being used.