Comments (2)
The same issue was with the run_multicut job. Here I adjusted the memory limit to 32000 in the snakefile.
But the workflow finished and succesfully produced a segmentation (I will improve it with further iterations). I am very happy.
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Hi Marco,
Regarding the memory issue with the normalization job, I assume you are supplying a mask in relatively high resolution. Within this step, the mask is fully loaded into memory such that I recommend using a higher binning. You can set this during project initialization within the mask parameters. (downsample level >1). Anyways, your results will be fine.
For the multicut, sometimes the jobs require quite a lot of memory, hence your solution is fine.
A quick note on the side: consider supplying multiple values for the parameter beta (mc_args) in the segmentation parameters. Then multiple segmentations will be computed with the respective settings and you can check for the best one. You can change this even when you already ran a segmentation within the respective config_seg.json. run.py -t seg will then trigger the additional segmentation maps.
Cheers,
Julian
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