HMMER MSAs aren't saved and repeated when running with --use_precomputed_msas=True about alphafold HOT 10 OPEN

I think mine are being saved but aren't being used. I'm looking into this and will let you know if I find anything but yea, I'm running the same issue.

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@apelin20 must be something wrong with the cli. If you modify the flag's' default to be True in run_docker.py then it works fyi.

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Hey @dltacube thanks for looking into this. Can you elaborate a little bit, what is cli? and which flag should I change to True in run_docker.py? My singularity container invokes alphafold/run_alphafold.py

Sorry, I am a bit newb at this

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That's totally fine. If you go to this file here on that line and change False to True I think it might work. modifying the docker_run.py file wouldn't help in your case.

CLI is just command line interface and the part of the code that accepts options like use_precomputed_msas I think might be where the issue lies. Hence why modifying the default value could solve your problem.

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hmmsearch and hhsearch are used for template search. Therefore it will run even if --use_precomputed_msas flag is present.
#469 (comment)
The DBs for templates are relatively small so it would not take much time... but it depends on users and their targets.

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I think there is no way to redirect without changing some lines of the python script.
I think the easiest way to speed up is increase the number of cpus to be used.

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There is. I mentioned how in my comment. Just change the default flag to true and it'll do it.

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Template search (hmmsearch, hhsearch) is not controlled by --use_precomputed_msas flag, I think.

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The result from the template search is actually saved. The issue is, like @yamule is pointing at, that the code doesn't check whether the output file already exists. In our modifications we have changed this but since the template search is dependent upon the previous parts you would ideally need to compare the timestamps as well before using it. We don't do this, hence why I'm reluctant to share it at this point.

If you want to minimize time spent on redoing work already done on the CPU you would even check if features.pkl already exists and load it into feature_dict .

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