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faq.md

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Frequently Asked Questions

Common Build Issues

Issue 1:

Lack of toolchain in a bare metal linux ENV.
Solution:

sudo apt-get update && sudo apt-get install -y python3 python3-pip python3-dev python3-distutils build-essential git libgl1-mesa-glx libglib2.0-0 numactl wget
ln -sf $(which python3) /usr/bin/python

Issue 2:

ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 88 from C header, got 80 from PyObject
Solution: reinstall pycocotools by "pip install pycocotools --no-cache-dir"

Issue 3:

ImportError: libGL.so.1: cannot open shared object file: No such file or directory
Solution: apt install or yum install python3-opencv

Issue 4:

Conda package neural-compressor-full (this binary is only available from v1.13 to v2.1.1) dependency conflict may pending on conda installation for a long time.
Solution: run conda install sqlalchemy=1.4.27 alembic=1.7.7 -c conda-forge before install neural-compressor-full.

Issue 5:

If you run 3X torch extension API inside a docker container, then you may encounter the following error:

ValueError: No threading layer could be loaded.
HINT:
Intel TBB is required, try:
$ conda/pip install tbb

Solution: It's actually already installed by requirements_pt.txt, so just need to set up with export LD_LIBRARY_PATH=/usr/local/lib/:$LD_LIBRARY_PATH.

Issue 6:

torch._C._LinAlgError: linalg.cholesky: The factorization could not be completed because the input is not positive-definite.
Solution: This is a known issue. For more details, refer to AutoGPTQ/AutoGPTQ#196. Try increasing percdamp (percent of the average Hessian diagonal to use for dampening), or increasing nsamples (the number of calibration samples).