Forwarded from Binary Tree
Diagrams lets you draw the cloud system architecture in Python code. It was born for prototyping a new system architecture design without any design tools. You can also describe or visualize the existing system architecture as well. Diagrams currently supports main major providers including: AWS, Azure, GCP, Kubernetes, Alibaba Cloud, Oracle Cloud etc... It also supports On-Premise nodes, SaaS and major Programming frameworks and languages.
#python, #diagram, #drawing, #prototyping, #architecture
#python, #diagram, #drawing, #prototyping, #architecture
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Forwarded from Binary Tree
Python 3.9.0 is released!
Major new features of the 3.9 series, compared to 3.8
Some of the new major new features and changes in Python 3.9 are:
- PEP 573, Module State Access from C Extension Methods
- PEP 584, Union Operators in dict
- PEP 585, Type Hinting Generics In Standard Collections
- PEP 593, Flexible function and variable annotations
- PEP 602, Python adopts a stable annual release cadence
- PEP 614, Relaxing Grammar Restrictions On Decorators
- PEP 615, Support for the IANA Time Zone Database in the Standard Library
- PEP 616, String methods to remove prefixes and suffixes
- PEP 617, New PEG parser for CPython
- BPO 38379, garbage collection does not block on resurrected objects;
- BPO 38692, os.pidfd_open added that allows process management without races and signals;
- BPO 39926, Unicode support updated to version 13.0.0;
- BPO 1635741, when Python is initialized multiple times in the same process, it does not leak memory anymore;
- A number of Python builtins (range, tuple, set, frozenset, list, dict) are now sped up using PEP 590 vectorcall;
- A number of Python modules (_abc, audioop, _bz2, _codecs, _contextvars, _crypt, _functools, _json, _locale, operator, resource, time, _weakref) now use multiphase initialization as defined by PEP 489;
- A number of standard library modules (audioop, ast, grp, _hashlib, pwd, _posixsubprocess, random, select, struct, termios, zlib) are now using the stable ABI defined by PEP 384.
#python, #release
Major new features of the 3.9 series, compared to 3.8
Some of the new major new features and changes in Python 3.9 are:
- PEP 573, Module State Access from C Extension Methods
- PEP 584, Union Operators in dict
- PEP 585, Type Hinting Generics In Standard Collections
- PEP 593, Flexible function and variable annotations
- PEP 602, Python adopts a stable annual release cadence
- PEP 614, Relaxing Grammar Restrictions On Decorators
- PEP 615, Support for the IANA Time Zone Database in the Standard Library
- PEP 616, String methods to remove prefixes and suffixes
- PEP 617, New PEG parser for CPython
- BPO 38379, garbage collection does not block on resurrected objects;
- BPO 38692, os.pidfd_open added that allows process management without races and signals;
- BPO 39926, Unicode support updated to version 13.0.0;
- BPO 1635741, when Python is initialized multiple times in the same process, it does not leak memory anymore;
- A number of Python builtins (range, tuple, set, frozenset, list, dict) are now sped up using PEP 590 vectorcall;
- A number of Python modules (_abc, audioop, _bz2, _codecs, _contextvars, _crypt, _functools, _json, _locale, operator, resource, time, _weakref) now use multiphase initialization as defined by PEP 489;
- A number of standard library modules (audioop, ast, grp, _hashlib, pwd, _posixsubprocess, random, select, struct, termios, zlib) are now using the stable ABI defined by PEP 384.
#python, #release
Forwarded from Находки в опенсорсе
> How to make CPython faster.
> We want to speed up CPython by a factor of 5 over the next four releases.
> See the plan for how this can be done.
> Making CPython faster by this amount will require funding. Relying on the goodwill and spare time of the core developers is not sufficient.
This includes JIT and other awesome features for #python
https://github.com/markshannon/faster-cpython
We need this!
> We want to speed up CPython by a factor of 5 over the next four releases.
> See the plan for how this can be done.
> Making CPython faster by this amount will require funding. Relying on the goodwill and spare time of the core developers is not sufficient.
This includes JIT and other awesome features for #python
https://github.com/markshannon/faster-cpython
We need this!
GitHub
GitHub - markshannon/faster-cpython: How to make CPython faster.
How to make CPython faster. Contribute to markshannon/faster-cpython development by creating an account on GitHub.
Forwarded from Находки в опенсорсе
Higher Kinded Types in #python!
I am happy to announce that the first version of Higher Kinded Types
emulation is released with full mypy support as a part of
Source code and docs: https://github.com/dry-python/returns
Try it: https://gist.github.com/sobolevn/7f8ffd885aec70e55dd47928a1fb3e61
In the nearest future, we will share some practical libraries using HKTs in Python, including type-safe business-validation, smart and simple framework-agnostic REST framework, and so on! This is a big day for Python and its ecosystem.
In this article I am going to show how HKT works and why it is useful.
I am happy to announce that the first version of Higher Kinded Types
emulation is released with full mypy support as a part of
dry-python/[email protected].Source code and docs: https://github.com/dry-python/returns
Try it: https://gist.github.com/sobolevn/7f8ffd885aec70e55dd47928a1fb3e61
In the nearest future, we will share some practical libraries using HKTs in Python, including type-safe business-validation, smart and simple framework-agnostic REST framework, and so on! This is a big day for Python and its ecosystem.
In this article I am going to show how HKT works and why it is useful.
Open Software Packaging for Science
#opensource alternative to #conda.
Mamba (drop-in replacement) direct link: https://github.com/TheSnakePit/mamba
Link: https://medium.com/@QuantStack/open-software-packaging-for-science-61cecee7fc23
#python #packagemanagement
#opensource alternative to #conda.
Mamba (drop-in replacement) direct link: https://github.com/TheSnakePit/mamba
Link: https://medium.com/@QuantStack/open-software-packaging-for-science-61cecee7fc23
#python #packagemanagement
GitHub
GitHub - mamba-org/mamba: The Fast Cross-Platform Package Manager
The Fast Cross-Platform Package Manager. Contribute to mamba-org/mamba development by creating an account on GitHub.
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Forwarded from Binary Tree
Testing Python Applications with Pytest.
Pytest is a testing framework and test runner for Python. In this guide we will have a look at the most useful and common configuration and usage, including several pytest plugins and external libraries. Although Python comes with a unittest module in the standard library and there are other Python test frameworks like nose2 or Ward, pytest remains my favourite. The beauty of using simple functions instead of class hierarchies, one simple
#guide #testing #python #pytest
Pytest is a testing framework and test runner for Python. In this guide we will have a look at the most useful and common configuration and usage, including several pytest plugins and external libraries. Although Python comes with a unittest module in the standard library and there are other Python test frameworks like nose2 or Ward, pytest remains my favourite. The beauty of using simple functions instead of class hierarchies, one simple
assert instead of many different assert functions, built-in parametrized testing, a nice system of fixtures and the number of available plugins makes it a pleasure to use.#guide #testing #python #pytest
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Forwarded from Binary Tree
Today mimesis has been designated as a critical project on PyPI.
It's ain't much, but I feel warm when I think about how many people use think I built.
Thank you everyone!
P.S If you don't know what the hell mimesis is, then go and check it out. Maybe you'll find it useful for you.
#mimesis #pypi #python
It's ain't much, but I feel warm when I think about how many people use think I built.
Thank you everyone!
P.S If you don't know what the hell mimesis is, then go and check it out. Maybe you'll find it useful for you.
#mimesis #pypi #python
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Top Python libraries `22
by @tryolabs
link: https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022
#python #tools
by @tryolabs
link: https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022
#python #tools
Tryolabs
Top Python libraries of 2022
There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.
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Forwarded from Machinelearning
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Все мы любим scikit-learn за его простоту и мощь. Но что если ваши модели обучаются слишком долго на больших данных? 🤔 NVIDIA предлагает решение!
Вы берете свой обычный скрипт cо scikit-learn, добавляете всего две строки в начало, и он начинает работать в 10, 50, а то и 100+ раз быстрее на NVIDIA GPU!
✨ Как это работает?
Библиотека cuml от NVIDIA содержит супероптимизированные для GPU версии многих алгоритмов машинного обучения. С помощью простого вызова
cuml.patch.apply() вы "патчите" установленный у вас scikit-learn прямо в памяти.Теперь, когда вы вызываете, например,
KNeighborsClassifier или PCA из sklearn:Ключевые преимущества:
2 строчки:import cuml.patch и cuml.patch.apply().Топ инструмент для всех, кто работает с scikit-learn на задачах, требующих значительных вычислений, и у кого есть GPU от NVIDIA.
👇 Как использовать:
Установите RAPIDS cuml (лучше через conda, см. сайт RAPIDS):
python
conda install -c rapidsai -c conda-forge -c nvidia cuml rapids-build-backend
Добавьте в начало скрипта:
import cuml.patch
cuml.patch.apply()
Используйте scikit-learn как обычно!
Попробуйте и почувствуйте разницу! 😉
▪Блог-пост
▪Colab
▪Github
▪Ускоряем Pandas
@ai_machinelearning_big_data
#python #datascience #machinelearning #scikitlearn #rapids #cuml #gpu #nvidia #ускорение #машинноеобучение #анализданных
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