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Very large install size in Docker #72
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After having a look at Issue 68 I tried to use a CPU only version of Torch. This moved the huge layer to the "pip install" layer. Which implies that Torch was being installed in the So it looks like the problem was Torch all along. I'm not planning on using GPU support, for now, but ~1.5 Gigs is much better :
Requirements.txt
|
I did a quick search in the git repo to see where Torch was being used:
This might be a silly question, but is Torch integral to Repast4Py? |
Thanks for sharing your experience. I created #68 when encountering the same issue with creating Docker images. I wasn't able to get the docker image smaller than about what you are showing. I'll look into further ways to reduce the size of the repast4py installs based on individual use case requirements. |
Thanks for looking into this! Let me know if I can help in any way. |
I'm building up an environmnet to use in a HPC setting. The goal is to build a docker container with several Python packages and to then convert this to a Singularity file to be used in the univesity's Slurm HPC cluster.
The one thing I've noticed, which isn't great, is that the install size of Repast4py is huge. The docker image is ~8.2 GB in size. After taking a look at the docker image layers:
And the dockerfile (similar to the file in the repast git repo):
It's clear that the 8 Gig layer is coming from the
RUN env CC=mpicxx CXX=mpicxx pip install repast4py
command.Using a file this large isn't impossible, but it introduces some issues with storing this in a limited free repo, building it with CI/CD, moving it to nodes in the cluster etc.
Is there a simple way to reduce the build size? I can't really believe that it's using 8 gigs of compiled C code to run repast4py.
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