lineapy
ipyflow
lineapy | ipyflow | |
---|---|---|
7 | 20 | |
656 | 1,079 | |
0.5% | 1.0% | |
2.0 | 9.5 | |
9 months ago | 4 days ago | |
Jupyter Notebook | Python | |
Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
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.
lineapy
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Rant: Jupyter notebooks are trash.
There are a few projects that can help close this gap between notebook prototype -> production. One of them is ipyflow (https://github.com/ipyflow/ipyflow), another is lineapy (https://github.com/linealabs/lineapy).
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The hand-picked selection of the best Python libraries and tools of 2022
LineaPy — notebooks in production
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Model artifacts mess and how to deal with it?
If you are mainly using python, there is a library called lineapy that is pretty much trying to solve all the challenges you just listed.
- lineapy: Data engineering, simplified. LineaPy creates a frictionless path for taking your data science artifact from development to production.
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Overwhelmed about consolidating code
Hi, I'm a contributor of LineaPy. We're building a tool that solves this problem. Our goal is to reduce the friction between developing Jupyter notebooks(or python scripts) and production codes.
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When to use Jupyter Notebooks vs. “Organized” Python Code?
I think you might want to give LineaPy a try! It is a tool trying to bridge the gap between Jupyter notebooks and production pipelines. One of the feature it provides is extracting codes only related to objects(you've selected) from your notebook into a python script and I think it is helpful for anyone who is using both Jupyter notebooks and python scripts.
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Introducing LineaPy!
GitHub
ipyflow
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Show HN: Marimo – an open-source reactive notebook for Python
You're probably referring to nbgather (https://github.com/microsoft/gather), which shipped with VSCode for a while.
nbgather used static slicing to get all the code necessary to reconstruct some cell. I actually worked with Andrew Head (original nbgather author) and Shreya Shankar to implement something similar in ipyflow (but with dynamic slicing and a not-as-nice interface): https://github.com/ipyflow/ipyflow?tab=readme-ov-file#state-...
I have no doubt something like this will make its way into marimo's roadmap at some point :)
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React Jam just started, making a game in 13 days with React
Np.
From https://news.ycombinator.com/context?id=35887168 re: ipyflow I learned about ReactiveX for Python (RxPY) https://rxpy.readthedocs.io/en/latest/ .
https://github.com/ipyflow/ipyflow :
> IPyflow is a next-generation Python kernel for Jupyter and other notebook interfaces that tracks dataflow relationships between symbols and cells during a given interactive session, thereby making it easier to reason about notebook state.
FWIU e.g. panda3d does not have a react or rxpy-like API, but probably does have a component tree model?
https://news.ycombinator.com/item?id=38527552 :
>> It actually looks like pygame-web (pygbag) supports panda3d and harfang in WASM
> Harfang and panda3d do 3D with WebGL, but FWIU not yet agents in SSBO/VBO/GPUBuffer
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The GitHub Black Market That Helps Coders Cheat the Popularity Contest
> Another giveaway is the ratio of stars to watchers / forks. I remember one project with thousands of stars but only 10 users "watching" it. They went on to raise a sizable seed round too.
Not necessarily indicative of foul play. I have two projects like this (https://github.com/smacke/ffsubsync and https://github.com/ipyflow/ipyflow) and I attribute it to not having great developer documentation.
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Python 3.12
It's not in the highlights, but one of the things that excites me most is this: https://docs.python.org/dev/whatsnew/3.12.html#pep-669-low-i...
> PEP 669 defines a new API for profilers, debuggers, and other tools to monitor events in CPython. It covers a wide range of events, including calls, returns, lines, exceptions, jumps, and more. This means that you only pay for what you use, providing support for near-zero overhead debuggers and coverage tools. See sys.monitoring for details.
Low-overhead instrumentation opens up a whole bunch of interesting interactive use cases (i.e. Jupyter etc.), and as the author of one library that relies heavily on instrumentation (https://github.com/ipyflow/ipyflow), I'm very keen to explore the possibilities here.
- Excel Labs, a Microsoft Garage Project
- GitHub - ipyflow/ipyflow: A reactive Python kernel for Jupyter notebooks
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IPython kernel alternatives
You’re looking for reactive kernels: https://github.com/ipyflow/ipyflow
- IPyflow: Reactive Python Notebooks in Jupyter(Lab)
What are some alternatives?
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nopdb - NoPdb: Non-interactive Python Debugger
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subtls - A proof-of-concept TypeScript TLS 1.3 client
fugue - A unified interface for distributed computing. Fugue executes SQL, Python, Pandas, and Polars code on Spark, Dask and Ray without any rewrites.
quarto-cli - Open-source scientific and technical publishing system built on Pandoc.