Intel MKL FATAL ERROR: Cannot load libmkl_avx2.so or libmkl_def.so
Solution 1:
If you use conda, try with these two commands:
conda install nomkl numpy scipy scikit-learn numexpr
conda remove mkl mkl-service
It should fix your problem.
Solution 2:
I ran into this problem after installing anaconda3 (vesion 4.2.0). The fix for me was simple, and I was able to keep using mkl. Just update to the latest numpy version.
conda update numpy
Solution 3:
Just wanted to note that Anaconda 4.0.0, shipped with mkl enabled by default, has this issue. The problem is indeed with Anaconda, as it can be reproduced with the simple python test below.
The actual issue is that Anaconda linked with mkl, but not with libmkl_core.so, thus it has a missing symbol, and can be seen by running:
$ LD_DEBUG=symbols python -c 'import sklearn.linear_model.tests.test_randomized_l1' 2>&1 | grep -i error
2200: /opt/anaconda/lib/python2.7/site-packages/scipy/special/../../../../libmkl_avx.so: error: symbol lookup error: undefined symbol: mkl_dft_fft_fix_twiddle_table_32f (fatal)
I didn't want to uninstall mkl, as I'd like to have the performance boost, so I found a workaround which worked for me - preload libmkl_core.so before execution.
$ python -c 'import sklearn.linear_model.tests.test_randomized_l1'
Intel MKL FATAL ERROR: Cannot load libmkl_avx.so or libmkl_def.so.
$
$ LD_PRELOAD=/opt/anaconda/lib/libmkl_core.so python -c 'import sklearn.linear_model.tests.test_randomized_l1'
$
Solution 4:
I had this same issue using scikit-learn 0.19 and numpy 1.13.3 when running MLPRegressor (and also with a package called pyearth running an algorithm called MARS). I believe the root of the problem was that our python is part of an Anaconda install, but scikit-learn and numpy were installed via pip, and their expectations for mkl must not agree.
Unfortunately my framework is managed by some dedicated company admins, not by me, so I haven't gotten my guy to try recompiling numpy yet. But I was able to find a workaround based on this thread: Adding export LD_PRELOAD=/path/to/anaconda/lib/libmkl_def.so:/path/to/anaconda/lib/libmkl_avx.so:/path/to/anaconda/lib/libmkl_core.so:/path/to/anaconda/lib/libmkl_intel_lp64.so:/path/to/anaconda/lib/libmkl_intel_thread.so:/path/to/anaconda/lib/libiomp5.so
to my ~/.bashrc
causes the problem to disappear. It's super hacky, and I'd be lying if I said I knew exactly what it's doing (but this is helpful), so I'm hoping a recompile of numpy is a cleaner fix. But at least it works.
Note it is better performance-wise to have the versions of these packages that use mkl. Installing the nomkl versions is a workaround but not a true solution.