Metadata-Version: 2.1
Name: transonic
Version: 0.1.12
Summary: Make your Python code fly at transonic speeds!
Home-page: UNKNOWN
License: CeCILL-B License
Description: Transonic: Make your code fly at transonic speeds!
        ==================================================
        
        |release| |docs| |coverage| |travis|
        
        .. |release| image:: https://img.shields.io/pypi/v/transonic.svg
           :target: https://pypi.python.org/pypi/transonic/
           :alt: Latest version
        
        .. |docs| image:: https://readthedocs.org/projects/transonic/badge/?version=latest
           :target: http://transonic.readthedocs.org
           :alt: Documentation status
        
        .. |coverage| image:: https://codecov.io/bb/fluiddyn/transonic/branch/default/graph/badge.svg
           :target: https://codecov.io/bb/fluiddyn/transonic/branch/default/
           :alt: Code coverage
        
        .. |travis| image:: https://travis-ci.org/fluiddyn/transonic.svg?branch=master
           :target: https://travis-ci.org/fluiddyn/transonic
           :alt: Travis CI status
        
        Transonic is a fork of `FluidPythran
        <https://bitbucket.org/fluiddyn/fluidpythran>`_ by its authors. It's going to
        replace FluidPythran.
        
        **Documentation**: https://transonic.readthedocs.io
        
        Transonic is a pure Python package (requiring Python >= 3.6) to easily
        accelerate modern Python-Numpy code with different accelerators (like Cython,
        `Pythran <https://github.com/serge-sans-paille/pythran>`_, Numba, Cupy,
        PyTorch, Uarray, etc...) opportunistically (i.e. if/when they are available).
        
        **The accelerators are not hard dependencies of Transonic:** Python codes using
        Transonic run fine without any accelerators installed (of course without
        speedup)!
        
        .. warning ::
        
          Transonic is still in a very early stage. Remarks and suggestions are very
          welcome.
        
          In particular, Transonic is now only able to use the Pythran compiler! So you
          are not going to be able to use for example Numba with this version of
          Transonic.
        
          However, Transonic is now really usable, useful and used "in production" in
          `FluidSim <https://bitbucket.org/fluiddyn/fluidsim>`_ and `FluidFFT
          <https://bitbucket.org/fluiddyn/fluidfft>`_ (see examples for `blocks
          <https://bitbucket.org/fluiddyn/fluidsim/src/default/fluidsim/base/time_stepping/pseudo_spect.py>`_,
          `@boost
          <https://bitbucket.org/fluiddyn/fluidfft/src/default/fluidfft/fft3d/operators.py>`_
          and `@jit
          <https://bitbucket.org/fluiddyn/fluidsim/src/default/fluidsim/solvers/plate2d/output/correlations_freq.py>`_).
        
        
        The long-term project
        ---------------------
        
        Transonic targets Python end-users and library developers.
        
        It is based on the following principles:
        
        - We'd like to write scientific / computing applications / libraries with
          pythonic, readable, modern code (Python >= 3.6).
        
        - In some cases, Python-Numpy is too slow. However, there are tools to
          accelerate such Python-Numpy code which lead to very good performances!
        
        - Let's try to write universal code which express what we want to compute and
          not the special hacks we want to use to make it fast. We just need nice ways
          to express that a function, a method or a block of code has to be accelerated
          (and how it has to be accelerated). We'd like to be able to do this in a
          pythonic way, with decorators and context managers.
        
        - There are many tools to accelerate Python-Numpy code! Let's avoid writting
          code specialized for only one of these tools.
        
        - Let's try to keep the code as it would be written without acceleration. For
          example, with Transonic, we are able to accelerate (simple) methods of
          classes even though most of the accelerators don't support classes.
        
        - Let's accelerate/compile only what needs to be accelerated, i.e. only the
          bottlenecks. Python and its interpreters are good for the rest. In most
          cases, the benefice of writting big compiled extensions (with Cython or in
          other languages) is negligible.
        
        - Adding types is sometimes necessary. In modern Python, we have nice syntaxes
          for type annotations! Let's use them.
        
        - Ahead-of-time (AOT) and just-in-time (JIT) compilation modes are both useful.
          We'd like to have a nice, simple and unified API for these two modes.
        
          * AOT is useful to be able to distribute compiled packages and in some cases,
            more optimizations can be applied.
        
          * JIT is simpler to use (no need for type annotations) and optimizations can
            be more hardware specific.
        
          Note that with Transonic, AOT compilers can be used as JIT compilers (with a
          cache mechanism).
        
          In contrast, some JIT compilers cannot be used as AOT compilers. For these
          tools, the AOT decorators are used in a JIT mode.
        
        To summarize, a **strategy to quickly develop a very efficient scientific
        application/library** with Python could be:
        
        1. Use modern Python coding, standard Numpy/Scipy for the computations and all
           the cool libraries you want.
        
        2. Profile your applications on real cases, detect the bottlenecks and apply
           standard optimizations with Numpy.
        
        3. Add few lines of Transonic to compile the hot spots.
        
        What we have now
        ----------------
        
        We start to have a good API to accelerate Python-Numpy code.
        
        The only implemented Transonic backend uses Pythran and works well.
        
        `Here, we explain why Pythran is so great for Python users and why Transonic is
        great for Pythran users
        <https://transonic.readthedocs.io/en/latest/pythran_backend.html>`_
        
        .. note ::
        
          Transonic can be used in libraries and applications using MPI (as
          `FluidSim <https://bitbucket.org/fluiddyn/fluidsim>`_).
        
        .. _compile-at-import :
        
        Installation and configuration
        ------------------------------
        
        .. code ::
        
           pip install transonic
        
        Transonic is sensible to environment variables:
        
        - :code:`TRANSONIC_DIR` can be set to control where the cached files are
          saved.
        
        - :code:`TRANSONIC_COMPILE_AT_IMPORT` can be set to enable a mode for which
          Transonic compiles at import time the Pythran file associated with the
          imported module. This behavior can also be triggered programmatically by using
          the function :code:`set_compile_at_import`.
        
        - :code:`TRANSONIC_NO_REPLACE` can be set to disable all code replacements.
          This is useful to compare execution times and when measuring code coverage.
        
        - :code:`TRANSONIC_COMPILE_JIT` can be set to false to disable the
          compilation of jited functions. This can be useful for unittests.
        
        
        A short tour of Transonic syntaxes
        -------------------------------------
        
        Decorator :code:`boost` and command :code:`# transonic def`
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        .. code :: python
        
            import h5py
            import mpi4py
        
            from transonic import boost
        
            # transonic def myfunc(int, float)
        
            @boost
            def myfunc(a, b):
                return a * b
        
            ...
        
        Most of this code looks familiar to Pythran users. The differences:
        
        - One can use (for example) h5py and mpi4py (of course not in the Pythran
          functions).
        
        - :code:`# transonic def` instead of :code:`# pythran export`.
        
        - A tiny bit of Python... The decorator :code:`@boost` replaces the
          Python function by the pythranized function if Transonic has been used to
          produced the associated Pythran file.
        
        
        Pythran using type annotations
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        The previous example can be rewritten without :code:`# transonic def`. It is
        the recommended syntaxes for ahead-of-time function acceleration:
        
        .. code :: python
        
            import h5py
            import mpi4py
        
            from transonic import boost
        
            @boost
            def myfunc(a: int, b: float):
                return a * b
        
            ...
        
        Nice (shorter and clearer than with the Pythran command) but very limited... So
        one can also elegantly define many Pythran signatures using in the annotations
        type variables and Pythran types in strings (see `these examples
        <https://transonic.readthedocs.io/en/latest/examples/type_hints.html>`_).
        Moreover, it is possible to mix type hints and :code:`# transonic def` commands.
        
        Just-In-Time compilation
        ~~~~~~~~~~~~~~~~~~~~~~~~
        
        With Transonic, one can use the Ahead-Of-Time compiler Pythran in a
        Just-In-Time mode. It is really the **easiest way to speedup a function with
        Pythran**, just by adding a decorator! And it also works `in notebooks
        <https://transonic.readthedocs.io/en/latest/ipynb/executed/demo_jit.html>`_!
        
        It is a "work in progress" so the API is not great, but it is a good start!
        
        .. code :: python
        
            import numpy as np
        
            # transonic import numpy as numpy
        
            from transonic import jit, include
        
            @include(used_by="func1")
            def func0(a, b):
                return a + b
        
            @jit
            def func1(a, b):
                return np.exp(a) * b * func0(a, b)
        
        Note that the :code:`@jit` decorator takes into account type hints (see
        `the example in the documentation
        <https://transonic.readthedocs.io/en/latest/examples/using_jit.html>`_).
        
        **Implementation details for just-in-time compilation:** A Pythran file is
        produced for each "JITed" function (function decorated with :code:`@jit`). The
        file is compiled at the first call of the function and the compiled version is
        used as soon as it is ready. The warmup can be quite long but the compiled
        version is saved and can be reused (without warmup!) by another process.
        
        
        Define accelerated blocks
        ~~~~~~~~~~~~~~~~~~~~~~~~~
        
        Transonic blocks can be used with classes and more generally in functions
        with lines that cannot be compiled by Pythran.
        
        .. code :: python
        
            from transonic import Transonic
        
            ts = Transonic()
        
            class MyClass:
        
                ...
        
                def func(self, n):
                    a, b = self.something_that_cannot_be_pythranized()
        
                    if ts.is_transpiled:
                        result = ts.use_block("name_block")
                    else:
                        # transonic block (
                        #     float a, b;
                        #     int n
                        # ) -> result
        
                        # transonic block (
                        #     complex a, b;
                        #     int n
                        # ) -> result
        
                        result = a**n + b**n
        
                    return self.another_func_that_cannot_be_pythranized(result)
        
        For blocks, we need a little bit more of Python.
        
        - At import time, we have :code:`ts = Transonic()`, which detects which
          Pythran module should be used and imports it. This is done at import time
          since we want to be very fast at run time.
        
        - In the function, we define a block with three lines of Python and special
          Pythran annotations (:code:`# transonic block`). The 3 lines of Python are used
          (i) at run time to choose between the two branches (:code:`is_transpiled` or
          not) and (ii) at compile time to detect the blocks.
        
        Note that the annotations in the command :code:`# transonic block` are different
        (and somehow easier to write) than in the standard command :code:`# pythran
        export`.
        
        `Blocks can now also be defined with type hints!
        <https://transonic.readthedocs.io/en/latest/examples/blocks.html>`_
        
        .. warning ::
        
           I'm not satisfied by the syntax for Pythran blocks so I (PA) proposed an
           alternative syntax in `issue #29
           <https://bitbucket.org/fluiddyn/fluidpythran/issues/29>`_.
        
        Python classes: :code:`@boost` and :code:`@jit` for methods
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        For simple methods **only using attributes**, we can write:
        
        .. code :: python
        
            import numpy as np
        
            from transonic import boost
        
            A = "float[:]"
        
            @boost
            class MyClass:
        
                arr0: A
                arr1: A
        
                def __init__(self, n):
                    self.arr0 = np.zeros(n)
                    self.arr1 = np.zeros(n)
        
                @boost
                def compute(self, alpha: float):
                    return (self.arr0 + self.arr1).mean() ** alpha
        
        .. warning ::
        
           Calling another method in a Pythranized method is not yet supported!
        
        More examples of how to use Transonic for Object Oriented Programing are
        given `here
        <https://transonic.readthedocs.io/en/latest/examples/methods.html>`__.
        
        
        Make the Pythran files
        ----------------------
        
        There is a command-line tool :code:`transonic` which makes the associated
        Pythran files from Python files with annotations and transonic code. By
        default and if Pythran is available, the Pythran files are compiled.
        
        There is also a function :code:`make_backend_files` that can be used in a
        setup.py like this:
        
        .. code ::
        
            from pathlib import Path
        
            from transonic.dist import make_backend_files
        
            here = Path(__file__).parent.absolute()
        
            paths = ["fluidsim/base/time_stepping/pseudo_spect.py"]
            make_backend_files([here / path for path in paths], mocked_modules=["h5py"])
        
        Note that the function :code:`make_backend_files` does not use compile the file
        produced. The compilation has to be done after the call of this function (see
        for example how it is done in the example package `example_package_fluidpythran
        <https://bitbucket.org/fluiddyn/example_package_fluidpythran>`_ or in
        `fluidsim's setup.py
        <https://bitbucket.org/fluiddyn/fluidsim/src/default/setup.py>`_).
        
        License
        -------
        
        Transonic is distributed under the CeCILL-B_ License, a BSD compatible
        french license.
        
        .. _CeCILL-B: http://www.cecill.info/index.en.html
        
Keywords: pythran
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >= 3.6
Provides-Extra: dev
Provides-Extra: test
