Deep Picker - "Use class weights" missing?

Hi,
I’m trying to train Deep Picker on a set of manually picked micrographs, but particles are quite sparse so the model is not returning any particles when used for inference.
I would like to try the “Use class weights” parameter (https://guide.cryosparc.com/processing-data/all-job-types-in-cryosparc/deep-picking/guideline-for-supervised-particle-picking-using-deep-learning-models) to try and improve this, but I can’t see this functionality in my version (v3.1.0), even in advanced mode. Was this option removed recently?

I have the same question!

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Hey @ben_butt, @emil,

This is a bug- it was accidentally removed. We’ll release the fix that re-enables this parameter in the next patch to v3.2.0, which should be released tomorrow. Sorry for any inconvenience.

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@stephan Thanks for your help!

Hey @ben_butt, @emil,

@stephan

Thanks!
However, I just applied the patch and now the job can’t seem to find my GPUs (which worked previously):


Do you know why that is happening?

Hi @emil,

That’s odd. Do you think you can restart cryoSPARC, and try again? cryosparcm restart

I accidentally forgot to mention a previous problem, which might be good for you to know. :slight_smile: When I first started the job after patching, I got this message:

We have two GPUs and both were free at that point.

To troubleshoot, I restarted the job with “number of GPUs = 0” and got the error mentioned before, which maybe then makes sense? But why is it even possible to start the job with no GPUs? And why did I get the first error message?

Thanks for helping out!

Hi @emil,

When you set the job to use 0 GPUs, the job will use your CPUs for training (which is quite slow). Though as you pointed out, there seems to be a bug where the job still looks for active GPUs on the system even if it only wants to use CPUs to do the work.
This also means that there seems to be an issue detecting the available GPUs on your system.

Can you post the job log of this job?
cryosparcm joblog <project_uid> <job_uid>
e.g. cryosparcm joblog P23 J233

Can you also make sure you can run nvidia-smi on the workstation without error?

This is the output from nvidia-smi:
image

This is the job log (by the way, is it better if I post raw text instead of a screenshot in the future?):

So there seem to be some issue with a cuda dependency, right?

Another thing. In the preprocessing settings for Deep Picker Train, shouldn’t it be “Desired Angstroms per pixel” instead of “Desired pixels per Angstrom”? I mean, doesn’t downsampling lead to a higher Angstrom/pixel ratio? I am a bit new to this… :slight_smile: