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https://github.com/fluiddyn/transonic

:rocket: Make your Python code fly at transonic speeds!

https://github.com/fluiddyn/transonic

Science Score: 36.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
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    Committers with academic emails
    2 of 4 committers (50.0%) from academic institutions
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  • ○
    Scientific vocabulary similarity
    Low similarity (12.1%) to scientific vocabulary

Keywords

aot cython extensions jit numba numpy pythran
Last synced: 5 months ago · JSON representation

Repository

:rocket: Make your Python code fly at transonic speeds!

Basic Info
Statistics
  • Stars: 124
  • Watchers: 4
  • Forks: 0
  • Open Issues: 6
  • Releases: 0
Topics
aot cython extensions jit numba numpy pythran
Created about 7 years ago · Last pushed 8 months ago
Metadata Files
Readme Changelog Contributing License Roadmap

README.md

Make your Python code fly at transonic speeds!

Latest version Code coverage Documentation status Supported Python versions Heptapod CI Github Actions mybinder sonarcloud

Documentation: https://transonic.readthedocs.io

Transonic is a pure Python package (requiring Python >= 3.9) to easily accelerate modern Python-Numpy code with different accelerators (currently Cython, Pythran, Numba and JAX, but potentially later Cupy, PyTorch, Weld, Pyccel, etc...).

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 an active development stage (see our roadmap). Remarks and suggestions are very welcome.

However, Transonic is now really usable, useful and used "in production" in FluidSim and FluidFFT (see examples for blocks and @boost).

The long-term project

[!NOTE] The context of the creation of Transonic is presented in these documents:

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 some 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 (Pythran and Cython) can be used as JIT compilers (with a cache mechanism).

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 (functions, methods and blocks of code). The default 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. There are also (more experimental) backends for Cython and Numba.

[!NOTE] Transonic can be used in libraries and applications using MPI (as FluidSim).

Installation and configuration

bash pip install transonic

Transonic is sensible to environment variables:

  • TRANSONIC_DIR can be set to control where the cached files are saved.
  • TRANSONIC_DEBUG triggers a verbose mode.
  • 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 set_compile_at_import.
  • TRANSONIC_NO_REPLACE can be set to disable all code replacements. This is useful to compare execution times and when measuring code coverage.
  • TRANSONIC_COMPILE_JIT can be set to false to disable the compilation of jited functions. This can be useful for unittests.
  • TRANSONIC_BACKEND to choose between the supported backends. The default backend "pythran" is quite robust. There are now 3 other backends: "cython", "numba" and "python" (prototypes).
  • TRANSONIC_MPI_TIMEOUT sets the MPI timeout (default to 5 s).

License

Transonic is distributed under the BSD License.

Owner

  • Name: FluidDyn
  • Login: fluiddyn
  • Kind: organization

FluidDyn project | framework for research and teaching in fluid dynamics - git mirror

GitHub Events

Total
  • Watch event: 3
  • Delete event: 1
  • Push event: 16
  • Create event: 5
Last Year
  • Watch event: 3
  • Delete event: 1
  • Push event: 16
  • Create event: 5

Committers

Last synced: 9 months ago

All Time
  • Total Commits: 751
  • Total Committers: 4
  • Avg Commits per committer: 187.75
  • Development Distribution Score (DDS): 0.108
Past Year
  • Commits: 42
  • Committers: 1
  • Avg Commits per committer: 42.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
paugier p****r@u****r 670
Ashwin Vishnu a****o@k****e 42
Pierre Blanc-fatin p****t@g****m 37
ogiorgis o****s@l****r 2
Committer Domains (Top 20 + Academic)
logilab.fr: 1 kth.se: 1 univ-grenoble-alpes.fr: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 13
  • Total pull requests: 1
  • Average time to close issues: 4 months
  • Average time to close pull requests: 25 minutes
  • Total issue authors: 4
  • Total pull request authors: 1
  • Average comments per issue: 3.77
  • Average comments per pull request: 0.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • jeanlaroche (6)
  • paugier (5)
  • exenGT (1)
  • rgommers (1)
Pull Request Authors
  • ashwinvis (1)
Top Labels
Issue Labels
enhancement (3) question (2) good first issue (1)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 1,755 last-month
  • Total docker downloads: 127
  • Total dependent packages: 6
  • Total dependent repositories: 9
  • Total versions: 50
  • Total maintainers: 1
pypi.org: transonic

Make your Python code fly at transonic speeds!

  • Versions: 50
  • Dependent Packages: 6
  • Dependent Repositories: 9
  • Downloads: 1,755 Last month
  • Docker Downloads: 127
Rankings
Docker downloads count: 2.6%
Dependent packages count: 3.2%
Average: 4.6%
Dependent repos count: 4.9%
Downloads: 7.8%
Maintainers (1)
paugier
Last synced: 6 months ago

Dependencies

environment.yml conda
  • clang
  • cython
  • numba
  • numpy
  • pip
  • python 3.7.*
  • pythran
doc/examples/packages/package_cythran/setup.py pypi
  • transonic *
doc/examples/packages/package_simple/setup.py pypi
  • transonic *