We have 2.5 TB particles extracted in the ‘extract’ file in J106, but also have the same data in the
/mnt/ssd/instance_xingcryoem2.oncology.wisc.edu:39001/projects/P5/J106/extract
directory. Why the duplication? And can I just delete the extract file under J106 in the ‘instance’ directory?
thanks
This is where cryoSPARC stores its cache files. I’m assuming this folder is in your SSD, and if you had the “SSD caching” parameter turned on, then cryoSPARC will automatically copy extracted particles to this location in order to speed up processing since SSDs are better at random read/writes than HDDs. Feel free to delete anything in this folder if you’re not actively using them.
When you build a job and are modifying the parameters, the option “Cache particles on SSD” will appear at the bottom of the job builder (right hand panel). You can turn this option off.
OK, but when the student did this she says she got this error, which went away when she turned cache on.
[CPU: 4.77 GB] Traceback (most recent call last): File "cryosparc2_compute/jobs/runcommon.py", line 1685, in run_with_except_hook run_old(*args, **kw)
File "cryosparc2_worker/cryosparc2_compute/engine/cuda_core.py", line 110, in cryosparc2_compute.engine.cuda_core.GPUThread.run
File "cryosparc2_worker/cryosparc2_compute/engine/cuda_core.py", line 111, in cryosparc2_compute.engine.cuda_core.GPUThread.run
File "cryosparc2_worker/cryosparc2_compute/engine/engine.py", line 991, in cryosparc2_compute.engine.engine.process.work
File "cryosparc2_worker/cryosparc2_compute/engine/engine.py", line 109, in cryosparc2_compute.engine.engine.EngineThread.load_image_data_gpu
File "cryosparc2_worker/cryosparc2_compute/engine/gfourier.py", line 33, in cryosparc2_compute.engine.gfourier.fft2_on_gpu_inplace
File "/mnt/ssd/cryosparc_user/cryosparc2_worker/deps/anaconda/lib/python2.7/site-packages/skcuda/fft.py", line 127, in __init__ onembed, ostride, odist, self.fft_type, self.batch)
File "/mnt/ssd/cryosparc_user/cryosparc2_worker/deps/anaconda/lib/python2.7/site-packages/skcuda/cufft.py", line 742, in cufftMakePlanMany cufftCheckStatus(status)
File "/mnt/ssd/cryosparc_user/cryosparc2_worker/deps/anaconda/lib/python2.7/site-packages/skcuda/cufft.py", line 117, in cufftCheckStatus
raise e cufftAllocFailed
This is a very interesting error- turning off caching shouldn’t raise a GPU memory allocation error!
Is it possible if you can provide me with a bit more information?
Job type, cryoSPARC version, if the user set any special job parameters, OS, GPU models, type of data (particle box size, # of particles)