Error #15: Initializing libiomp5.dylib, but found libiomp5.dylib already initialized

Python Problem Overview

Getting the error message when using matplotlib:

> Error #15: Initializing libiomp5.dylib, but found libiomp5.dylib > already initialized > OMP: Hint: This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can > degrade performance or cause incorrect results. The best thing to do > is to ensure that only a single OpenMP runtime is linked into the > process, e.g. by avoiding static linking of the OpenMP runtime in any > library. As an unsafe, unsupported, undocumented workaround you can > set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to allow the > program to continue to execute, but that may cause crashes or silently > produce incorrect results. For more information, please see > http://www.intel.com/software/products/support/.

Python Solutions

Solution 1 - Python

Do the following to solve the issue:

import os

os.environ['KMP_DUPLICATE_LIB_OK']='True'

Answer found at: https://github.com/dmlc/xgboost/issues/1715

Be aware of potential side-effects:

> but that may cause crashes or silently produce incorrect results.

Solution 2 - Python

This is a better solution, if applicable. Else, anyway gcamargo’s solution is likely to work. However, it comes with a warning "that it may cause crashes or silently produce incorrect results"

I had the same error on my Mac with a python program using numpy, keras, and matplotlib. I solved it with

conda install nomkl

Answer found at: https://github.com/dmlc/xgboost/issues/1715

Solution 3 - Python

I had the same issue on macOS and found the following reasons:

Problem:

I had a conda environment where Numpy, SciPy and TensorFlow were installed.

Conda is using Intel(R) MKL Optimizations, see docs:

> Anaconda has packaged MKL-powered binary versions of some of the most popular numerical/scientific Python libraries into MKL Optimizations for improved performance.

The Intel MKL functions (e.g. FFT, LAPACK, BLAS) are threaded with the OpenMP technology.

But on macOS you do not need MKL, because the Accelerate Framework comes with its own optimization algorithms and already uses OpenMP. That is the reason for the error message: OMP Error #15: ...

Workaround:

You should install all packages without MKL support:

conda install nomkl

and then use

conda install numpy scipy pandas tensorflow

followed by

conda remove mkl mkl-service

For more information see conda MKL Optimizations.

Solution 4 - Python

I had the same issue in a conda environment where TensorFlow was installed. After doing

pip uninstall tensorflow
pip install tensorflow

the problem was gone.

Solution 5 - Python

Had same issue in OSX when updating tensoflow to 1.13 using conda.

Solution 1: /gcamargo worked but 3x slower per training epoch.
Solution 2: /sjcoding worked and removed serious warining but also 3x slower in training.
Solution 3: that restored performance was: Install pip in new conda env and use pip to install tensorflow. Using conda-forge also worked but version of tf is old.

Apparently the new Intel-MKL optimizations in Anaconda are broken for OSX tensorflow.

Solution 6 - Python

Check if there's an update for the mkl package in your env (anaconda).

I was able to solve my case simply by updating mkl.

conda install -c intel mkl

(macOS Catalina 10.15.5)

Solution 7 - Python

So, for those of you getting this same issue with lightgbm, I found in the documentation that you can

pip uninstall lightgbm
pip install lightgbm
Run the following in anaconda environmnet (if you're running Conda)

ln -sf `ls -d "$(brew --cellar libomp)"/*/lib`/* $CONDA_PREFIX/lib

These three things worked for me.

Solution 8 - Python

Try to change the backend of matplotlib.

For example, Tkagg backend causes this problem in my case. I changed it to Qt5Agg

matplotlib.use('Qt5Agg')

and it helps.

Solution 9 - Python

Confronted with the same error #15, none of the solutions to-date (5 Feb 2021) fully worked despite being helpful. However, I did manage to solve it while avoiding: dithering with dylib libraries, installing from source, or setting the environment variable KMP_DUPLICATE_LIB_OK=TRUE and its downsides of being an “unsafe, unsupported, undocumented workaround” and its potential “crashes or silently produce incorrect results”.

The trouble was that conda wasn’t picking up the non-mkl builds of tensorflow (v2.0.0) despite loading the nomkl package. What finally made this solution work was to:

ensure I was loading packages from the defaults channel (ie. from a channel with a non-mkl version of tensorflow. As of 5 Feb 2021, conda-forge does not have a tensorflow version of 2.0 or greater).
specify the precise build of the tensorflow version I wanted: tensorflow>=2.*=eigen_py37h153756e_0. Without this, conda kept loading the mkl_... version of the package despite the nomkl package also being loaded.

I created a conda environment using the following environment.yml file (as per the conda documentation for managing environments) :

name: tf_nomkl
channels:
  - conda-forge
  - defaults
dependencies:
  - nomkl
  - python>=3.7
  - numpy
  - scipy
  - pandas
  - jupyter
  - jupyterlab
  - nb_conda
  - nb_conda_kernels
  - ipykernel
  - pathlib
  - matplotlib
  - seaborn
  - tensorflow>=2.*=eigen_py37h153756e_0

You could try to do the same without an environment.yml file, but it’s better to load all the packages you want in an environment in one go if you can. This solution works on MacOS Big Sur v11.1.

Solution 10 - Python

conda install --revision 0 doesn't solve UnsatisfiableError: The following specifications... for me. So I manually install nomkl and remove mkl and mil-service in Anaconda-Navigator environment, and it works great for me!

Solution 11 - Python

I was getting the same error as mentioned in the original question when I ran a code with Tensorflow on my macOS Monterey. I tried installing nomkl and removing mkl as suggested in many of the previous answers. However this gave me trouble on running readcsv module of pandas and many other modules from different packages. A friend told me that newer versions of macOS have trouble with the usual Tensorflow and therefore pypi has launched a special version of TF called tf-nightly.

https://pypi.org/project/tf-nightly/#description

This installation solved the problem for me.

Solution 12 - Python

I had the same problem. Nothing you suggested solved the issue. I found that a possible cause is that you have multiple OpenMP libraries installed on your machine and they conflict with each other. Plus, I found that the problem was numpy and I did the upgrade (conda update numpy) and FINALLY IT WORKED!!!

Content Type	Original Author	Original Content on Stackoverflow
Question	gcamargo	View Question on Stackoverflow
Solution 1 - Python	gcamargo	View Answer on Stackoverflow
Solution 2 - Python	sjcoding	View Answer on Stackoverflow
Solution 3 - Python	J.E.K	View Answer on Stackoverflow
Solution 4 - Python	user11874275	View Answer on Stackoverflow
Solution 5 - Python	Jayhou	View Answer on Stackoverflow
Solution 6 - Python	el3ati2	View Answer on Stackoverflow
Solution 7 - Python	ltjds	View Answer on Stackoverflow
Solution 8 - Python	thanhtang	View Answer on Stackoverflow
Solution 9 - Python	TransferOrbit	View Answer on Stackoverflow
Solution 10 - Python	gingerL	View Answer on Stackoverflow
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Solution 12 - Python	LauraD.	View Answer on Stackoverflow