Added Quick Start Guide

doc/ext_links.txt

@@ -10,7 +10,7 @@
 .. _DPC++: https://www.apress.com/gp/book/9781484255735
 .. _SYCL*: https://www.khronos.org/sycl/
 .. _dpctl: https://intelpython.github.io/dpctl/latest/index.html
-.. _Data Parallel Control: https://intelpython.github.io/dpctl/latest/index.html
+.. _Data Parallel Control Library: https://intelpython.github.io/dpctl/latest/index.html
 .. _Intel oneAPI Base Toolkit: https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html
 .. _Intel Distribution for Python*: https://www.intel.com/content/www/us/en/developer/tools/oneapi/distribution-for-python.html
 .. _Intel AI Analytics Toolkit: https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html

doc/index.rst

@@ -1,7 +1,7 @@
 .. _index:
 .. include:: ./ext_links.txt
 
-Data Parallel Extension for Numpy*
+Data Parallel Extension for NumPy*
 ==================================
 
 .. module:: dpnp
@@ -10,7 +10,7 @@ Data Parallel Extension for Numpy*
    :maxdepth: 2
 
    overview
-   getting_started
+   quick_start_guide
    reference/index
    dpnp_backend_api
    dpctl

doc/overview.rst

@@ -6,12 +6,12 @@ Overview
 
 .. module:: dpnp
 
-The Data Parallel Extensions for Numpy (dpnp package) - a library that
+The Data Parallel Extension for NumPy* (dpnp package) - a library that
 implements a subset of `NumPy*`_ that can be executed on any
 data parallel device. The subset is a drop-in replacement of core `NumPy*`_
 functions and numerical data types.
 
-The Data Parallel Extensions for Numpy is being developed as part of
+The Data Parallel Extension for NumPy* is being developed as part of
 `Intel AI Analytics Toolkit`_ and is distributed with the
 `Intel Distribution for Python*`_. The dpnp package is also available
 on Anaconda cloud. Please refer the :doc:`getting_started` page to learn more.
@@ -26,15 +26,15 @@ The :class:`dpnp.ndarray` class is a compatible alternative of
    >>> x = np.array([1, 2, 3])
 
 ``x`` in the above example is an instance of :class:`dpnp.ndarray` that
-is created identically to ``NumPy``'s one. The key difference of
+is created identically to ``NumPy*``'s one. The key difference of
 :class:`dpnp.ndarray` from :class:`numpy.ndarray` is that the memory
 is allocated on the default `SYCL*`_ device, which is a ``"gpu"`` on systems
 with integrated or discrete GPU (otherwise it is the ``"host"`` device
 on systems that do not have GPU).
 
-Most of the array manipulations are also done in the way similar to NumPy such as:
+Most of the array manipulations are also done in the way similar to `NumPy*`_ such as:
 
    >>> s = np.sum(x)
 
-Please see the :ref:`API Reference <dpnp_reference>` for the complete list of supported NumPy APIs
+Please see the :ref:`API Reference <dpnp_reference>` for the complete list of supported `NumPy*`_ APIs
 along with their limitations.

doc/quick_start_guide.rst

@@ -0,0 +1,123 @@
+.. _quick_start_guide:
+.. include:: ./ext_links.txt
+
+.. |copy| unicode:: U+000A9
+
+.. |trade| unicode:: U+2122
+
+=================
+Quick Start Guide
+=================
+
+Device Drivers
+=================
+
+To start programming data parallel devices beyond CPU, you will need
+an appropriate hardware. The Data Parallel Extension for NumPy* works fine
+on Intel |copy| laptops with integrated graphics. In majority of cases,
+your Windows*-based laptop already has all necessary device drivers installed.
+But if you want the most up-to-date driver, you can always
+`update it to the latest one <https://www.intel.com/content/www/us/en/download-center/home.html>`_.
+Follow device driver installation instructions to complete the step.
+
+
+Python Interpreter
+=================
+
+You will need Python 3.8, 3.9, or 3.10 installed on your system. If you
+do not have one yet the easiest way to do that is to install
+`Intel Distribution for Python*`_. It installs all essential Python numerical
+and machine learning packages optimized for the Intel hardware, including
+Data Parallel Extension for NumPy*.
+If you have Python installation from another vendor, it is fine too. All you
+need is to install Data Parallel Extension for NumPy* manually as shown
+in the next installation section.
+
+
+Installation
+============
+
+Install Package from Anaconda
+---------------------
+
+It is recommended to use conda packages from the ``anaconda.org/intel``
+channel. You will need one of the commands below:
+
+* Conda: ``conda install numba-dpex``
+
+* Pip: ``pip install numba-dpex``
+
+These commands install dpnp package along with its dependencies, including
+``dpctl`` package with `Data Parallel Control Library`_ and all required
+compiler runtimes and OneMKL.
+
+.. note::
+   Before installing with conda or pip it is strongly advised to update ``conda`` and ``pip`` to latest versions
+
+
+Build and Install Conda Package
+-------------------------------
+
+Alternatively you can create and activate a local conda build environment:
+
+.. code-block:: bash
+
+    conda create -n build-env conda-build
+    conda activate build-env
+
+And to build dpnp package from the sources:
+
+.. code-block:: bash
+
+    conda build conda-recipe -c intel -c conda-forge
+
+Finanly, to install the result package:
+
+.. code-block:: bash
+
+    conda install dpnp
+
+
+Build and Install with scikit-build
+-----------------------------------
+
+Another way to build and install dpnp package from the source is to use Python
+``setuptools`` and ``scikit-build``. You will need to create a local conda
+build environment by command below depending on hosting OS.
+
+On Linux:
+
+.. code-block:: bash
+
+    conda create -n build-env dpctl cython dpcpp_linux-64 mkl-devel-dpcpp tbb-devel onedpl-devel cmake scikit-build ninja pytest -c intel -c conda-forge
+    conda activate build-env
+
+On Windows:
+
+.. code-block:: bash
+
+    conda create -n build-env dpctl cython dpcpp_win-64 mkl-devel-dpcpp tbb-devel onedpl-devel cmake scikit-build ninja pytest -c intel -c conda-forge
+    conda activate build-env
+
+To build and install the package on Linux OS, run:
+
+.. code-block:: bash
+
+    python setup.py install -- -G Ninja -DCMAKE_C_COMPILER:PATH=icx -DCMAKE_CXX_COMPILER:PATH=icpx
+
+To build and install the package on Windows OS, run:
+
+.. code-block:: bash
+
+    python setup.py install -- -G Ninja -DCMAKE_C_COMPILER:PATH=icx -DCMAKE_CXX_COMPILER:PATH=icx
+
+
+Testing
+=======
+
+If you want to execute the scope of Python test suites which are available
+by the source, you will need to run a command as below:
+
+.. code-block:: bash
+
+    pytest -s tests