Frequently asked questions¶
Does the tensor data consume extra memory when compiled into C++ code?¶
When compiled into C++ code, the data will be mmaped by the system loader. For CPU runtime, the tensor data are used without memory copy. For GPU and DSP runtime, the tensor data is used once during model initialization. The operating system is free to swap the pages out, however, it still consumes virtual memory space. So generally speaking, it takes no extra physical memory. If you are short of virtual memory space (this should be very rare), you can choose load the tensor data from a file, which can be unmapped after initialization.
Why is the generated static library file size so huge?¶
The static library is simply an archive of a set of object files which are intermediate and contains many extra information, please check whether the final binary file size is as expected.
OpenCL allocator failed with CL_OUT_OF_RESOURCES¶
OpenCL runtime usually requires continuous virtual memory for its image buffer, the error will occur when the OpenCL driver can't find the continuous space due to high memory usage or fragmentation. Several solutions can be tried:
- Change the model by reducing its memory usage
- Split the Op with the biggest single memory buffer
- Changed from armeabi-v7a to arm64-v8a to expand the virtual address space
- Reduce the memory consumption of other modules of the same process
Why the performance is worce than the official result for the same model?¶
The power options may not set properly, see mace/public/mace_runtime.h
for
details.
Why the UI is getting poor responsiveness when running model with GPU runtime?¶
Try to set limit_opencl_kernel_time
to 1
. If still not resolved, try to
modify the source code to use even smaller time intervals or changed to CPU
or DSP runtime.
How to include more than one deployment files in one application(process)?¶
This case may happen when an application is developed by multiple teams as submodules. If the all the submodules are linked into a single shared library, then use the same version of MiAI Compute Engine will resolve this issue. Ortherwise, different deployment models are contained in different shared libraries, it's not required to use the same MiAI version but you should controls the exported symbols from the shared library. This is actually a best practice for all shared library, please read about GNU loader version script for more details.