Pyston 2.3.5 Released As Speedy Alternative To CPython
Pyston as what was born at Dropbox as a speedy open-source Python implementation is out with a new update as it continues to focus on maximizing performance. Dropbox ceased work on Pyston years ago while the developers continued and since last year joined Anaconda to continue their performant Python quest.
Pyston 2.3.5 was released on Monday as the newest version of this CPython 3.8 fork that aims to deliver double digit percentage gains over upstream while remaining a highly-compatible Python implementation. With Pyston 2.3.5 the focus is on delivering yet higher performance and increased compatibility with upstream CPython.
The Pyston developers have yet to publish any blog post or more detailed information on the v2.3.5 performance work, but the new release is available for download from GitHub. There is the open-source code as well as binaries for portable Linux x86_64 / AArch64, Ubuntu LTS releases, and Conda builds.
Back in June I did some early Python 3.11 benchmarks against older releases and Pyston and PyPi. Those benchmarks showed Pyston still having advantages even over the upcoming Python 3.11 that has seen significant performance optimizations.
Pyston 2.3.5 was released on Monday as the newest version of this CPython 3.8 fork that aims to deliver double digit percentage gains over upstream while remaining a highly-compatible Python implementation. With Pyston 2.3.5 the focus is on delivering yet higher performance and increased compatibility with upstream CPython.
The Pyston developers have yet to publish any blog post or more detailed information on the v2.3.5 performance work, but the new release is available for download from GitHub. There is the open-source code as well as binaries for portable Linux x86_64 / AArch64, Ubuntu LTS releases, and Conda builds.
Back in June I did some early Python 3.11 benchmarks against older releases and Pyston and PyPi. Those benchmarks showed Pyston still having advantages even over the upcoming Python 3.11 that has seen significant performance optimizations.
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