Intel® Distribution for Python* Known Issues

ID 673165
Updated 11/17/2023
Version Latest



Intel Distribution for Python

2024.0 Release

There are no known issues in the 2024.0 release at this time.

2023.2 Release

There are no known issues in the 2023.2 release at this time.

Intel Distribution for Python 2023.1 Release

  • Missing bzip2 may lead to installation failure on Ubuntu systems

    • Intel® Distribution for Python depends on bzip2 to extract the necessary packages for installation on Ubuntu. Without it, installation may fail.

    • Workaround:

      • Run: sudo apt update

      • Then run: sudo apt -y install bzip2

  • Return key may cause Python 3.10 to hang during SSH session

    • Users may experience having their Python 3.10 interpreter hang after pressing the Return key
    • Workaround:
      • Run conda uninstall pyeditline --offline to remove problematic pyeditline package, the source of the program hang
  • Tab completion may cause a segmentation fault

    • Users who press the tab key while running Python interpreter may experience a segmentation fault
    • Workaround:
      • Run conda uninstall pyeditline --offline to remove problematic pyeditline package, the source of the segmentation fault
  • Data parallel Python extension stack may not find GPU devices

    • When using Data Parallel extensions for Python* for GPU programming on Linux OSes that require standard C++ library supporting GLIBCXX_3.4.30 (such as Ubuntu 22.04) and compute runtime user-mode driver libraries installed for this OS, data parallel Python extension stack may fail to see GPU devices.
    • Workaround:
      • Run conda install conda-forge::libstdcxx-ng to install newer standard C++ library, currently available on conda-forge channel
  • [Fixed] Online and offline installer fails on Microsoft* Windows* 11 Enterprise and Windows Server 2022

Previous Releases

Intel Distribution for Python 2022.3 Release

Intel® oneAPI Toolkits 2022.1.3 and earlier and Intel® Parallel Studio XE (any version) do not support Microsoft Visual Studio 2022. On systems with Microsoft Visual Studio 2022 installed, Intel® oneAPI and Intel® Parallel Studio XE installers may fail during installation, upgrade, modification, or uninstallation. Refer to this article for more information.


Tests for dpnp.ptp are failing because mkl::stats::max is incorrectly ignoring NaN values. A workaround is being investigated.

Intel Distribution for Python 2022.2 Release

Online and offline installer fails on Microsoft* Windows* 11 Enterprise and Windows Server 2022

Intel® Distribution for Python* stand-alone version online and offline installer fails on Microsoft* Windows* 11 Enterprise and Windows Server 2022.


  • Install Intel Distribution for Python with Anaconda using directions here.

Installation may fail if bzip2 is not installed on Linux machine

Bzip2 is required to extract packages, and a system without this software will result in an installation failure

Intel Distribution for Python 2022.1 Release

Installation may fail because of missing pkgs/python error : The IntelPython installation requires bzip2 to be present in order to successfully run the extraction step. Installing bzip2 is as simple as running a command for your respective package manager (e.g. Ubuntu users can install bzip2 by executing 'apt-get install bzip2')

Intel Distribution for Python 2021.4 Release

No known issues found at this time.

Intel Distribution for Python 2021.3 Release

cpython socket does not have SO_REUSEPORT attribute

On some operating systems, cpython's socket module does not have SO_REUSEPORT attribute. A workaround is to instead use SO_BROADCAST to achieve the same effect.

Intel Distribution for Python 2021.2 Release

Conda installing may display clobber warnings

The files in the ca-certificates conda package used to be in the openssl conda package. Not having removed these files in the openssl package results in potential harmless clobber warnings.

Tampering with PS1 can corrupt command prompt

The conda backend relies on modifying the PS1 environment variable to properly display the activated conda environment in the command line. Any tampering with the variable can result in unexpected behavior when activating conda environments.

Intel Distribution for Python 2021.1 Release

DPNP may experience the occasional crash

The DPNP module can crash when an underlying library call is not implemented for a given device (e.g. sampling from gamma distribution is not implemented for GPU).
It can also crash with certain combinations of the DPCTL module.
Other DPNP issues can be found at

Numba’s automatic GPU offloading fails in certain corner cases

For certain corner cases, automatic GPU offloading fails and the code silently falls back to the CPU (refer to The issue has been fixed in current trunk of IntelPython/numba.

DPCTL copy implementation as blocking calls

DPCTL’s data copy operations are currently implemented as blocking calls.

For DPCTL, some functionality is not yet supported

For SYCL DPC++ CUDA and host device queues are not yet supported. The module is also not supported on macOS.

Scikit-learn may produce incorrect shuffling on Windows

The train_test_split in the daal4py backend of scikit-learn can potentially produce incorrect shuffling on the Windows platform

Some daal4py examples do not work on Intel® Iris Xe MAX with float64 compute mode 

The following daal4py examples do not work on Intel® Iris Xe MAX with float64 compute mode:

  • gradient_boosted_regression_batch
  • decision_forest_classification_batch
  • decision_forest_regression_batch
  • bf_knn_classification_batch
  • dbscan_batch
  • svm_batch
  • kmeans_batch

Run daal4py examples using float32 compute mode instead:

  1. Use np.float32 data type for input data. To do this, add parameter t=np.float32 to the readcsv function used in the examples.
  2. Set the parameter fptype to float in the algorithm object constructor: fptype='float'.
  3. Switch on float64 software emulation on Intel® Iris Xe MAX

K-Means example in daal4py (examples/sycl/ produces different results on GPU and CPU.

To avoid failures, comment assert statements that compare GPU results and classic results in the example.

DBSCAN example in daal4py (examples/sycl/ hangs when it is running on CPU with data wrapped in sycl_buffer.

To avoid hangs, do not pass sycl_buffer objects to DBSCAN on CPU.