uvloop
modin
uvloop | modin | |
---|---|---|
14 | 11 | |
10,025 | 9,486 | |
0.7% | 0.6% | |
5.1 | 9.6 | |
4 days ago | 1 day ago | |
Cython | Python | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
uvloop
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APIs in Go with Huma 2.0
I wound up on a different team with pre-existing Python code so temporarily shelved my use of Go for a bit, and we used Sanic (an async Python framework built on top of the excellent uvloop & libuv that also powers Node.js) to build some APIs for live channel management & operations. We hand-wrote our OpenAPI and used it to generate documentation and a CLI, which was an improvement over what was there (or not) before. Other teams used the OpenAPI document to generate SDKs to interact with our service.
- Python Is Easy. Go Is Simple. Simple = Easy
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will requests-html library work as selenium
If you're looking for maximum requests per second you can change the asyncio event loop with one like UVLoop.
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Benchmark asyncio vs gevent vs native epoll
An optional package uvloop can also be install if working on Linux:
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A Look on Python Web Performance at the end of 2022
The source code from the project resides in the github, with more than 8.6k stars and 596 forks is a very popular github, but no new releases are made since 2018, looks pure much not maintained anymore, no PR's are accepted no Issues are closed, still without windows or macOS Silicon, or PyPy3 support. Japronto it self uses uvloop with more than 9k stars and 521 forks and different from japronto is seems to be well maintained.
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Modern Python Performance Considerations
If you are building server-side applications using Python 3 and async API and if you didn't use https://github.com/MagicStack/uvloop, you are missing out on performance big time.
Also, if you happen to build microservices, don't forget to try PyPy, that's another easy performance booster (if it's compatible to your app).
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So it begins.
Not that bad actually, with a different event loop implementation (such as https://github.com/MagicStack/uvloop). Not sure how well it will perform in a browser though
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SearX On Windows: A Short(ish) Tech Journey
And so I did some searching, and found that SearX isn't officially supported on Windows. Not to be deterred, I did another quick search and found that with pip and/or docker, you should be able to install SearX straightforwardly on Windows. After trying this for a bit, I realized that uvloop, a (questionably optional dependency of SearX) is not supported on Windows. I tried a couple things to get it to work, but they didn't end up working for me either through user error, ignorance, or plain old not working.
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EdgeDB 1.0
they also wrote uvloop [0] which is fantastic and advances the cutting edge of what can be done with modern asyncio-based Python. I saw a ~3x improvement in the throughput of a microservice I wrote when I first tried it out years ago. currently at $dayjob we just use it by default in every Python service, whether or not we expect that service to be performance-critical.
0: https://github.com/MagicStack/uvloop
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How does asynchronous code work in programming languages?
If you manage to grok how uvloop works as well as Python's default asyncio loop scheduler, you'll understand this style. It is not by itself a parallelism enabler, but network I/O the coroutines triggered would run in parallel nevertheless, though CPU bound computations would not by default.
modin
- The Distributed Tensor Algebra Compiler (2022)
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A Polars exploration into Kedro
The interesting thing about Polars is that it does not try to be a drop-in replacement to pandas, like Dask, cuDF, or Modin, and instead has its own expressive API. Despite being a young project, it quickly got popular thanks to its easy installation process and its “lightning fast” performance.
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Modern Polars: an extensive side-by-side comparison of Polars and Pandas
Yeah, tried Polars a couple of times: the API seems worse than Pandas to me too. eg the decision only to support autoincrementing integer indexes seems like it would make debugging "hmmm, that answer is wrong, what exactly did I select?" bugs much more annoying. Polars docs write "blazingly fast" all over them but I doubt that is a compelling point for people using single-node dataframe libraries. It isn't for me.
Modin (https://github.com/modin-project/modin) seems more promising at this point, particularly since a migration path for standing Pandas code is highly desirable.
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Polars: The Next Big Python Data Science Library... written in RUST?
If anyone wants a faster version of pandas it’s not hard to find, modin for example uses multiple cores to speed it up, so if you have 4 cores it’s about 4 times faster than pandas, and has the same API as pandas.
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Working with more than 10gb csv
Modin should fit. It implements Pandas APIs with e.g. Ray as backend. https://github.com/modin-project/modin
- Modern Python Performance Considerations
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I made a video about efficient memory use in pandas dataframes!
If you really want speed you should try modin.pandas which makes pandas multi-threaded.
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Almost no one knows how easily you can optimize your AI models
I am guessing XGB is fairly optimised as it is. If you would want to use the sklearn libraries with pandas, look into Modin
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TIL about modin.pandas which significantly speeds up pandas if you import modin.pandas instead of pandas.
Source
- How to Speed Up Pandas with 1 Line of Code
What are some alternatives?
asyncio
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
trio - Trio – a friendly Python library for async concurrency and I/O
swifter - A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner
Twisted - Event-driven networking engine written in Python.
fugue - A unified interface for distributed computing. Fugue executes SQL, Python, Pandas, and Polars code on Spark, Dask and Ray without any rewrites.
uvicorn - An ASGI web server, for Python. 🦄
mars - Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
asyncio - asyncio is a c++20 library to write concurrent code using the async/await syntax.
PandasGUI - A GUI for Pandas DataFrames
pyzmq - PyZMQ: Python bindings for zeromq
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.