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‎docs/Documentation_Guidelines.txt‎

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This file provides guidelines for developers on how to update the documentation. The content of the file will be added to the developers' document later. If you have any questions about this file or Intel(R) SDC documentation please contact Rujal Desai.
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This file provides guidelines for developers on how to update the documentation. The content of the file will be added to the developers' document later. If you have any questions about this file or Intel® SDC documentation please contact Rujal Desai.
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How to update the documentation
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Intel(R) SDC documentation is built using Sphinx. Building the documentation is not part of Intel(R) SDC build as of now(that may change later). So you have to follow few extra steps to build new documentation.
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Intel® SDC documentation is built using Sphinx. Building the documentation is not part of Intel® SDC build as of now(that may change later). So you have to follow few extra steps to build new documentation.
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Sphinx uses RST files to generate html/pdf output. Therefore the new documentation should either be in RST file or standardized docstring(provided with the function/class definition inside source code).
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There are two ways to contribute to the documentation:
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1. Add documentation for a Pandas API overloaded in Intel(R) SDC
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1. Add documentation for a Pandas API overloaded in Intel® SDC
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2. Add/Change content of one of the section of the existing documentation
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Pandas API Documentation
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If you have implemented a Pandas function/operator in Intel(R) SDC follow these steps to add it to the new documentation:
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If you have implemented a Pandas function/operator in Intel® SDC follow these steps to add it to the new documentation:
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1. Write a docstring inside the function/operator definition and make sure it is in the standardized format that Intel(R) SDC follows
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1. Write a docstring inside the function/operator definition and make sure it is in the standardized format that Intel® SDC follows
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If you are not aware of the standardized format of docstring refer to pandas.Series.append method in hpat/datatypes/hpat_series_functions.py . Also, make sure the function definition has all the arguments included in it even though HPAt doesn't support that argument. For example, Pandas Series.ne(not equal) method has the following signature:
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Series.ne(self, other, level=None, fill_value=None, axis=0)
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Intel(R) SDC doesn't support 'level' , 'fill_value' and 'axis' but the function definition in Intel(R) SDC should looik like this:
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Intel® SDC doesn't support 'level' , 'fill_value' and 'axis' but the function definition in Intel® SDC should looik like this:
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new_funcname(self, other, level=None, fill_value=None, axis=0)
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where new_funcname : function name given by developer
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2. Edit the script rename_function.py
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This step is part of a work around. Hence it is specific to Intel(R) SDC. Sphinx uses autodoc to automatically generate documentation from a function docstring. However, if we use that directly the function name displayed in User's Guide for Intel(R) SDC will be the function name that developer has used to overload Pandas API instead of the actual name of that Pandas API that is overloaded. For example,
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This step is part of a work around. Hence it is specific to Intel® SDC. Sphinx uses autodoc to automatically generate documentation from a function docstring. However, if we use that directly the function name displayed in User's Guide for Intel® SDC will be the function name that developer has used to overload Pandas API instead of the actual name of that Pandas API that is overloaded. For example,
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Pandas.Series.append method is overloaded in Intel(R) SDC by this function definition:
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Pandas.Series.append method is overloaded in Intel® SDC by this function definition:
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@overload_method(SeriesType, 'append')
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def hpat_pandas_series_append(self, to_append, ignore_index=False, verify_integrity=False)

‎docs/devsource/DevelopersGuide.rst‎

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Developer's Guide
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=================
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Intel(R) SDC implements Pandas and Numpy API as a DSL.
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Intel® SDC implements Pandas and Numpy API as a DSL.
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Data structures are implemented as Numba extensions, and
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compiler stages are responsible for different levels of abstraction.
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For example, `Series data type support <https://github.com/IntelLabs/hpat/blob/master/hpat/hiframes/pd_series_ext.py>`_
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Follow the pipeline for a simple function like `Series.sum()`
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for initial understanding of the transformations.
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Intel(R) SDC Technology Overview
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Intel® SDC Technology Overview
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------------------------
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This `slide deck <https://drive.google.com/open?id=1jLikSEAqOFf8kKO8vgT7ru6dKU1LGiDR>`_
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provides an overview of Intel(R) SDC technology and software architecture.
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provides an overview of Intel® SDC technology and software architecture.
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These papers provide deeper dive in technical ideas (might not be necessary for many developers):
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Numba
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-----
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Intel(R) SDC sits on top of Numba and is heavily tied to many of its features.
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Intel® SDC sits on top of Numba and is heavily tied to many of its features.
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Therefore, understanding Numba's internal details and being able to develop Numba extensions
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is necessary.
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- Start with `basic overview of Numba use <http://numba.pydata.org/numba-doc/latest/user/5minguide.html>`_ and try the examples.
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- `User documentation <http://numba.pydata.org/numba-doc/latest/user/index.html>`_ is generally helpful for overview of features.
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- | `ParallelAccelerator documentation <http://numba.pydata.org/numba-doc/latest/user/parallel.html>`_
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provides overview of parallel analysis and transformations in Numba (also used in Intel(R) SDC).
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provides overview of parallel analysis and transformations in Numba (also used in Intel® SDC).
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- `Setting up Numba for development <http://numba.pydata.org/numba-doc/latest/developer/contributing.html>`_
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- | `Numba architecture page <http://numba.pydata.org/numba-doc/latest/developer/architecture.html>`_
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is a good starting point for understanding the internals.
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- | Learning Numba IR is crucial for understanding transformations.
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See the `IR classes <https://github.com/numba/numba/blob/master/numba/ir.py>`_.
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Setting `NUMBA_DEBUG_ARRAY_OPT=1` shows the IR at different stages
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of ParallelAccelerator and Intel(R) SDC transformations. Run `a simple parallel
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of ParallelAccelerator and Intel® SDC transformations. Run `a simple parallel
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example <http://numba.pydata.org/numba-doc/latest/user/parallel.html#explicit-parallel-loops>`_
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and make sure you understad the IR at different stages.
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- | `Exending Numba page <http://numba.pydata.org/numba-doc/latest/extending/index.html>`_
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Numba <https://github.com/numba/numba/blob/master/numba/dictobject.py>`_ (documentation planned).
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It has examples of calling into C code which is implemented as
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`a C extension library <https://github.com/numba/numba/blob/master/numba/_dictobject.c>`_.
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For a simpler example of calling into C library, see Intel(R) SDC's I/O features like
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For a simpler example of calling into C library, see Intel® SDC's I/O features like
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`get_file_size <https://github.com/IntelLabs/hpat/blob/master/hpat/io.py#L12>`_.
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- | `Developer reference manual <http://numba.pydata.org/numba-doc/latest/developer/index.html>`_
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provides more details if necessary.
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How to update the documentation
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--------------------------------
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The Intel(R) SDC documentation is generated from RST files using Sphinx. If you want to add to the documentation follow these steps:
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The Intel® SDC documentation is generated from RST files using Sphinx. If you want to add to the documentation follow these steps:
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1. If you don't have sphinx installed then install sphinx in your conda environment::
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‎docs/devsource/conf.py‎

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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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#
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# Intel(R) SDC documentation build configuration file, created by
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# Intel® SDC documentation build configuration file, created by
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# sphinx-quickstart on Wed Sep 6 09:29:19 2017.
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#
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# This file is execfile()d with the current directory set to its
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master_doc = 'index'
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# General information about the project.
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project = 'Intel(R) SDC'
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project = 'Intel® SDC'
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#copyright = '2017, Ehsan Totoni'
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author = 'Intel'
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‎docs/devsource/index.rst‎

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Intel(R) SDC Developer's Guide
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Intel® SDC Developer's Guide
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=======================
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.. toctree::
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DevelopersGuide
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api
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To download Intel(R) SDC documentation PDF click here :download:`pdf <../_builddev/latex/SDC.pdf>`
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To download Intel® SDC documentation PDF click here :download:`pdf <../_builddev/latex/SDC.pdf>`

‎docs/devsource/overview.rst‎

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.. _overview:
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Quick Introduction to Intel(R) SDC
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Quick Introduction to Intel® SDC
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==========================
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.. todo:: Change content here considering Developer's perspective
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High Performance Analytics Toolkit (Intel(R) SDC) is a big data analytics and machine
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High Performance Analytics Toolkit (Intel® SDC) is a big data analytics and machine
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learning framework that provides Python's ease of use but is extremely fast.
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Intel(R) SDC scales analytics programs in python to cluster/cloud environments
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Intel® SDC scales analytics programs in python to cluster/cloud environments
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automatically, requiring only minimal code changes. Here is a logistic
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regression program using Intel(R) SDC::
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regression program using Intel® SDC::
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Intel(R) SDC compiles the source code to efficient native parallel code
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Intel® SDC compiles the source code to efficient native parallel code
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(with `MPI <https://en.wikipedia.org/wiki/Message_Passing_Interface>`_).
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This is in contrast to other frameworks such as Apache Spark which are
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master-executor libraries. Hence, Intel(R) SDC is typically 100x or more faster.
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Intel(R) SDC is built on top of `Numba <https://github.com/numba/numba>`_
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master-executor libraries. Hence, Intel® SDC is typically 100x or more faster.
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Intel® SDC is built on top of `Numba <https://github.com/numba/numba>`_
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‎docs/usersource/GettingStarted.rst‎

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.. _GettingStarted:
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Getting Started with Intel(R) SDC
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Getting Started with Intel® SDC
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~~~~~~~~~~~~~~~~~~~~~~~~~
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Intel(R) SDC is useful to accelerate a subset of `Python <https://docs.python.org/3/>`_ operations working with `Pandas Series <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html>`_ and `Dataframes <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html>`_ as well as with `Numpy Arrays <https://docs.scipy.org/doc/numpy/reference/generated/numpy.array.html>`_ . Being the just-in-time compiler built on top of `Numba <http://numba.pydata.org/numba-doc/latest/index.html>`_ Intel(R) SDC will compile a subset of Pandas and Numpy codes into the native code. The compilation is controlled by a set of `Numba decorators <http://numba.pydata.org/numba-doc/0.8/modules/decorators.html>`_ and **Intel(R) SDC decorators** that can be applied to a function.
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Intel® SDC is useful to accelerate a subset of `Python <https://docs.python.org/3/>`_ operations working with `Pandas Series <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html>`_ and `Dataframes <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html>`_ as well as with `Numpy Arrays <https://docs.scipy.org/doc/numpy/reference/generated/numpy.array.html>`_ . Being the just-in-time compiler built on top of `Numba <http://numba.pydata.org/numba-doc/latest/index.html>`_ Intel® SDC will compile a subset of Pandas and Numpy codes into the native code. The compilation is controlled by a set of `Numba decorators <http://numba.pydata.org/numba-doc/0.8/modules/decorators.html>`_ and **Intel® SDC decorators** that can be applied to a function.
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The code below illustrates a typical workflow that Intel(R) SDC is intended to compile
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The code below illustrates a typical workflow that Intel® SDC is intended to compile
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We also recommend to read `A ~5 minute guide to Numba <https://numba.pydata.org/numba-doc/dev/user/5minguide.html>`_ .
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Installing Intel(R) SDC
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Installing Intel® SDC
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===============
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instructions how to install hpat using 1) conda, 2) pip
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Experienced users can also compile Intel(R) SDC from sources<link to github build instructions for hpat>
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Experienced users can also compile Intel® SDC from sources<link to github build instructions for hpat>
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How to use Intel(R) SDC
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How to use Intel® SDC
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================
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Provide a few code snapshots illustrating typical usages of Intel(R) SDC:
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Provide a few code snapshots illustrating typical usages of Intel® SDC:
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• Reading a file
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=================================
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Need to give basic information that hpat and numba do not support full set of Pandas and Numpy APIs, provide the link to the API Reference section for Intel® SDC, relevant reference to Numba documentation.
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Also give very short introduction to what kind of code Numba/Intel® SDC can compile and what cannot, i.e. type stability etc. Provide the links to relevant sections in Intel® SDC and Numba documentations focusing on compilation issues/limitations
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Measuring Intel(R) SDC performance
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Measuring Intel® SDC performance
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===========================
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Reference to relevant sections of Intel(R) SDC and Numba documentation for detailed discussion
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Reference to relevant sections of Intel® SDC and Numba documentation for detailed discussion

‎docs/usersource/apireference.rst‎

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API Reference: Supported Pandas APIs
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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This page gives an overview of all Pandas APIs supported currently by Intel(R) SDC
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This page gives an overview of all Pandas APIs supported currently by Intel® SDC
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.. toctree::
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:maxdepth: 2

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