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[![Build Sphinx](https://github.com/IntelPython/dpnp/workflows/Build%20Sphinx/badge.svg)](https://intelpython.github.io/dpnp)
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[![OpenSSF Scorecard](https://api.securityscorecards.dev/projects/github.com/IntelPython/dpnp/badge)](https://securityscorecards.dev/viewer/?uri=github.com/IntelPython/dpnp)
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<img align="left" src="https://spec.oneapi.io/oneapi-logo-white-scaled.jpg" alt="oneAPI logo" width="75"/>
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# DPNP - Data Parallel Extension for NumPy*
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Data Parallel Extension for NumPy* or `dpnp` is a Python library that
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implements a subset of NumPy* that can be executed on any data parallel device.
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The subset is a drop-in replacement of core NumPy* functions and numerical data types.
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[API coverage summary](https://intelpython.github.io/dpnp/reference/comparison.html#summary)
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[Full documentation](https://intelpython.github.io/dpnp/)
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[DPNP C++ backend documentation](https://intelpython.github.io/dpnp/backend_doc/)
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`Dpnp` is the core part of a larger family of [data-parallel Python libraries and tools](https://www.intel.com/content/www/us/en/developer/tools/oneapi/distribution-for-python.html)
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to program on XPUs.
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## Build from source:
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Ensure you have the following prerequisite packages installed:
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- `cython`
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- `cmake >=3.21`
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- `dpcpp_linux-64` or `dpcpp_win-64` (depending on your OS)
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- `dpctl`
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- `mkl-devel-dpcpp`
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- `onedpl-devel`
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- `ninja`
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- `numpy >=1.19,<1.25a0`
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- `python`
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- `scikit-build`
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- `setuptools`
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- `sysroot_linux-64 >=2.28` (only on Linux OS)
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- `tbb-devel`
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# Installing
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After these steps, `dpnp` can be built in debug mode as follows:
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You can install the library using `conda` or [pip](https://pypi.org/project/dpnp/)
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package managers. It is also available as part of the [Intel(R) Distribution for Python](https://www.intel.com/content/www/us/en/developer/tools/oneapi/distribution-for-python.html)
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(IDP).
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```bash
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git clone https://github.com/IntelPython/dpnp
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cd dpnp
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python scripts/build_locally.py
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```
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## Intel(R) oneAPI
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## Install Wheel Package via pip
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Install DPNP
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```cmd
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python -m pip install --index-url https://software.repos.intel.com/python/pypi dpnp
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```
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You can find the most recent release of `dpnp` every quarter as part of the Intel(R) oneAPI releases.
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Set path to Performance Libraries in case of using venv or system Python:
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```cmd
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export LD_LIBRARY_PATH=<path_to_your_env>/lib
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```
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To get the library from the latest oneAPI release, follow the
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instructions from Intel(R) [oneAPI installation guide](https://www.intel.com/content/www/us/en/developer/articles/guide/installation-guide-for-oneapi-toolkits.html).
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It is also required to set following environment variables:
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```cmd
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export OCL_ICD_FILENAMES_RESET=1
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export OCL_ICD_FILENAMES=libintelocl.so
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```
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> **NOTE:** You need to install the Intel(R) oneAPI AI Analytics Toolkit to get
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>IDP and `dpnp`.
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## Run test
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```bash
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pytest
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# or
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pytest tests/test_matmul.py -s -v
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# or
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python -m unittest tests/test_mixins.py
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```
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## Conda
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To install `dpnp` from the Intel(R) conda channel, use the following command:
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## Run numpy external test
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```bash
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. ./0.env.sh
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python -m tests.third_party.numpy_ext
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# or
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python -m tests.third_party.numpy_ext core/tests/test_umath.py
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# or
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python -m tests.third_party.numpy_ext core/tests/test_umath.py::TestHypot::test_simple
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conda install dpnp -c https://software.repos.intel.com/python/conda/ -c conda-forge
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```
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### Building documentation:
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## Pip
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The `dpnp` can be installed using `pip` obtaining wheel packages either from
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PyPi or from Intel(R) channel. To install `dpnp` wheel package from Intel(R)
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channel, run the following command:
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```bash
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Prerequisites:
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$ conda install sphinx sphinx_rtd_theme
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Building:
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1. Install dpnp into your python environment
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2. $ cd doc && make html
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3. The documentation will be in doc/_build/html
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python -m pip install --index-url https://software.repos.intel.com/python/pypi dpnp
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```
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## Packaging:
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## Installing the bleeding edge
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To try out the latest features, install `dpnp` using our development channel on
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Anaconda cloud:
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```bash
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. ./0.env.sh
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conda-build conda-recipe/
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conda install dpnp -c dppy/label/dev -c https://software.repos.intel.com/python/conda/ -c conda-forge
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```
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## Run benchmark:
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```bash
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cd benchmarks/
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asv run --python=python --bench <filename without .py>
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# example:
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asv run --python=python --bench bench_elementwise
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# Building
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# or
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Refer to our [Documentation](https://intelpython.github.io/dpnp/quick_start_guide.html)
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for more information on setting up a development environment and building `dpnp`
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from the source.
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asv run --python=python --bench <class>.<bench>
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# example:
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asv run --python=python --bench Elementwise.time_square
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# add --quick option to run every case once but looks like first execution has additional overheads and takes a lot of time (need to be investigated)
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```
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# Running Tests
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Tests are located in folder [dpnp/tests](dpnp/tests).
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## Tests matrix:
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| # |Name |OS |distributive|interpreter|python used from|SYCL queue manager|build commands set |forced environment |
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|---|------------------------------------|-----|------------|-----------|:--------------:|:----------------:|------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------|
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|1 |Ubuntu 20.04 Python37 |Linux|Ubuntu 20.04|Python 3.7 | IntelOneAPI | local |export DPNP_DEBUG=1 python setup.py clean python setup.py build_clib python setup.py build_ext --inplace pytest |cmake-3.19.2, valgrind, pytest-valgrind, conda-build, pytest, hypothesis |
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|2 |Ubuntu 20.04 Python38 |Linux|Ubuntu 20.04|Python 3.8 | IntelOneAPI | local |export DPNP_DEBUG=1 python setup.py clean python setup.py build_clib python setup.py build_ext --inplace pytest |cmake-3.19.2, valgrind, pytest-valgrind, conda-build, pytest, hypothesis |
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|3 |Ubuntu 20.04 Python39 |Linux|Ubuntu 20.04|Python 3.9 | IntelOneAPI | local |export DPNP_DEBUG=1 python setup.py clean python setup.py build_clib python setup.py build_ext --inplace pytest |cmake-3.19.2, valgrind, pytest-valgrind, conda-build, pytest, hypothesis |
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|4 |Ubuntu 20.04 External Tests Python37|Linux|Ubuntu 20.04|Python 3.7 | IntelOneAPI | local |export DPNP_DEBUG=1 python setup.py clean python setup.py build_clib python setup.py build_ext --inplace python -m tests_external.numpy.runtests|cmake-3.19.2, valgrind, pytest-valgrind, conda-build, pytest, hypothesis |
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|5 |Ubuntu 20.04 External Tests Python38|Linux|Ubuntu 20.04|Python 3.8 | IntelOneAPI | local |export DPNP_DEBUG=1 python setup.py clean python setup.py build_clib python setup.py build_ext --inplace python -m tests_external.numpy.runtests|cmake-3.19.2, valgrind, pytest-valgrind, conda-build, pytest, hypothesis |
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|6 |Ubuntu 20.04 External Tests Python39|Linux|Ubuntu 20.04|Python 3.9 | IntelOneAPI | local |export DPNP_DEBUG=1 python setup.py clean python setup.py build_clib python setup.py build_ext --inplace python -m tests_external.numpy.runtests|cmake-3.19.2, valgrind, pytest-valgrind, conda-build, pytest, hypothesis |
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|7 |Code style |Linux|Ubuntu 20.04|Python 3.8 | IntelOneAPI | local |python ./setup.py style |cmake-3.19.2, valgrind, pytest-valgrind, conda-build, pytest, hypothesis, conda-verify, pycodestyle, autopep8, black |
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|8 |Valgrind |Linux|Ubuntu 20.04| | IntelOneAPI | local |export DPNP_DEBUG=1 python setup.py clean python setup.py build_clib python setup.py build_ext --inplace |cmake-3.19.2, valgrind, pytest-valgrind, conda-build, pytest, hypothesis |
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|9 |Code coverage |Linux|Ubuntu 20.04|Python 3.8 | IntelOneAPI | local |export DPNP_DEBUG=1 python setup.py clean python setup.py build_clib python setup.py build_ext --inplace |cmake-3.19.2, valgrind, pytest-valgrind, conda-build, pytest, hypothesis, conda-verify, pycodestyle, autopep8, pytest-cov|
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To run the tests, use:
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```bash
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python -m pytest --pyargs dpnp
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```

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