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Resolve typos in the job for Clean up anaconda packages 8d95c8f 
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‎pull/2331/.buildinfo

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# Sphinx build info version 1
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# This file records the configuration used when building these files. When it is not found, a full rebuild will be done.
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config: 03b0fbffe48d8d2f3890766a428c8e8b
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tags: 645f666f9bcd5a90fca523b33c5a78b7

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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<title>Overview: module code &mdash; Data Parallel Extension for NumPy 0.17.0dev7+7.g8d95c8fcbc5 documentation</title>
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Data Parallel Extension for NumPy
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<li class="toctree-l1"><a class="reference internal" href="../overview.html">Overview</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../reference/index.html">API Reference</a></li>
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<p class="caption" role="heading"><span class="caption-text">Development information</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../dpnp_backend_api.html">C++ backend API Reference</a></li>
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<h1>All modules for which code is available</h1>
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<ul><li><a href="dpnp/dpnp_array.html">dpnp.dpnp_array</a></li>
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<li><a href="dpnp/fft/dpnp_iface_fft.html">dpnp.fft.dpnp_iface_fft</a></li>
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‎pull/2331/_sources/dpctl.rst.txt

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.. _dptcl:
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.. include:: ./ext_links.txt
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Interplay with the Data Parallel Control Library
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================================================
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`Data Parallel Control Library`_ provides API to manage specific
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`SYCL*`_ resources for SYCL-based Python packages.
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An example below demonstrates how the Data Parallel Extension for NumPy* can be
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easily combined with the device management interface provided by dpctl package.
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.. code-block:: python
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:linenos:
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import dpctl
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import dpnp
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d = dpctl.select_cpu_device()
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x = dpnp.array([1, 2, 3], device=d)
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s = dpnp.sum(x)
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y = dpnp.linspace(0, dpnp.pi, num=10**6, device="gpu")
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f = 1 + y * dpnp.sin(y)
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# locate argument where function attains global maximum
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max_arg = x[dpnp.argmax(f)]
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max_val = dpnp.max(f)
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For more information please refer to `Data Parallel Control Library`_
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documentation.
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Example
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-------
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.. literalinclude:: ../examples/example10.py
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:linenos:
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:language: python
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:lines: 35-

‎pull/2331/_sources/dpnp_backend_api.rst.txt

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.. _dpnp_backend_reference:
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*************************
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C++ backend API Reference
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*************************
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`C++ backend of Data Parallel Extension for NumPy* <backend_doc/>`_

‎pull/2331/_sources/index.rst.txt

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.. _index:
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.. include:: ./ext_links.txt
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Data Parallel Extension for NumPy*
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==================================
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.. module:: dpnp
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:no-index:
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.. toctree::
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:maxdepth: 2
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overview
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quick_start_guide
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reference/index
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.. toctree::
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:maxdepth: 1
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:caption: Development information
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dpnp_backend_api

‎pull/2331/_sources/overview.rst.txt

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.. _overview:
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.. include:: ./ext_links.txt
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Overview
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========
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.. module:: dpnp
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The Data Parallel Extension for NumPy* (dpnp package) - a library that
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implements a subset of `NumPy*`_ that can be executed on any
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data parallel device. The subset is a drop-in replacement of core `NumPy*`_
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functions and numerical data types.
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The Data Parallel Extension for NumPy* is being developed as part of
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`Intel AI Analytics Toolkit`_ and is distributed with the
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`Intel Distribution for Python*`_. The dpnp package is also available
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on Anaconda cloud. Please refer the :doc:`quick_start_guide` page to learn more.
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Being drop-in replacement for `NumPy*`_ means that the usage is very similar:
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>>> import dpnp as np
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The :class:`dpnp.ndarray` class is a compatible alternative of
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:class:`numpy.ndarray`.
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>>> x = np.array([1, 2, 3])
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``x`` in the above example is an instance of :class:`dpnp.ndarray` that
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is created identically to ``NumPy*``'s one. The key difference of
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:class:`dpnp.ndarray` from :class:`numpy.ndarray` is that the memory
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is allocated on the default `SYCL*`_ device, which is a ``"gpu"`` on systems
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with integrated or discrete GPU (otherwise it is the ``"host"`` device
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on systems that do not have GPU).
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Most of the array manipulations are also done in the way similar to `NumPy*`_ such as:
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>>> s = np.sum(x)
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Please see the :ref:`API Reference <dpnp_reference>` for the complete list of supported `NumPy*`_ APIs
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along with their limitations.

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