Comments (3)
Solved. I had to set explicitly export CUDA_CACHE_DISABLE=0. Thought it would have been enabled by default. First run now takes 2 minutes, second run 2 seconds.
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The slow startup speed is due to the JIT compilation of the CUDA code. Since the generated binaries are bigger than the default cache, the workaround is to increase the max cuda cache size (this is what nvidia also suggests). Maybe your cache size is set somewhere else? Try setting it in your .bashrc
file
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Checked setting with env. Gives a chache size of CUDA_CACHE_MAXSIZE=2147483648. Even tried with double size (4GB). The folder ~/.nv is there and readable/writable.
I read that JIT compilation takes place if the host architecture is different from the compile architecture. Maybe if all features are compiled for all architectures (i.e. sm_10,sm_13,sm_20,sm_30,sm_35), the JIT will not be necessary (but I might be wrong).
Anyone else having the slow start-up issue?
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Related Issues (20)
- Is it possible to see the source code of PyHandTracker? HOT 1
- Is it possible to integrate HandTracker into Unity Development? HOT 1
- GLSL 3.30 is not supported HOT 1
- unrecognise option 'libs' HOT 3
- Thumb in roll movement HOT 3
- Error running batch script HOT 3
- Get data of the hand pose HOT 3
- Recognition Problem with the HandObjectTracking demo HOT 1
- With and without PSO data HOT 3
- Documentations
- About OS
- Is this project compatible with DS325 camera? HOT 3
- RuntimeError at OpenGLHelpers.cpp(xxx) HOT 4
- execute runme.bat failed after config environment HOT 1
- import error about 'libcore.so' HOT 5
- Incomatible with today's software HOT 2
- can I use this lib to track two hands ? HOT 1
- Consider licensing under a free license.
- loop.oni file not found
- Download links are not working
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