ploomber VS ipyflow

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Documentation: https://jupysql.ploomber.io

Note that JupySQL is a fork of ipython-sql; which is no longer actively developed. Catherine, ipython-sql's creator, was kind enough to pass the project to us (check out ipython-sql's README).

We'd love to learn what you think and what features we can ship for JupySQL to be the best SQL client! Please let us know in the comments!

For those who don't know, Jupyter has a bash kernel: https://github.com/takluyver/bash_kernel

And you can run Jupyter notebooks from the CLI with Ploomber: https://github.com/ploomber/ploomber

Develop notebook-based pipelines

Fair point. MLflow has a lot of features to cover the end-to-end dev cycle. This SQLite tracker only covers the experiment tracking part.

We have another project to cover the orchestration/pipelines aspect: https://github.com/ploomber/ploomber and we have plans to work on the rest of features. For now, we're focusing on those two.

I recommend taking a look at the ploomber open source. It helps you structure your code and parameterize it in a way that's easier to maintain and test. Our blog has lots of resources about it from testing your code to building a data science platform on AWS.

Developing end-to-end data science infrastructure can get complex. For example, many of us might have struggled to try to integrate AWS services and deal with configuration, permissions, etc. At Ploomber, we’ve worked with many companies in a wide range of industries, such as energy, entertainment, computational chemistry, and genomics, so we are constantly looking for simple solutions to get them started with Data Science in the cloud.

If you like working locally with notebooks, you can run via the free tier of ploomber, that'll allow you to get the Ram/Compute you need for the bigger models as part of the free tier. Also, it has the historical executions so you don't need to remember what you executed an hour later!

It really depends on your use cases, I've seen a lot of those tools that lock you into a certain syntax, framework or weird language (for instance Groovy). If you'd like to use core python or Jupyter notebooks I'd recommend Ploomber, the community support is really strong, there's an emphasis on observability and you can deploy it on any executor like Slurm, AWS Batch or Airflow. In addition, there's a free managed compute (cloud edition) where you can run certain bioinformatics flows like Alphafold or Cripresso2

That's what we do for lineage with https://ploomber.io/

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 :)

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

> 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.

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.

You’re looking for reactive kernels: https://github.com/ipyflow/ipyflow