Python Packages Project Generator VS ploomber

- python-package-template

Hey HN, this would be more of an early release (still planning on some tweaks before a release) -- would love to hear your thoughts on this!

For some back-story, this is more of a side-side-project (made this while working on another side-project).

When I switched to using Go for my projects (from Python), the lack of a template generator similar to python-package-template[1] was very annoying. I would copy the basic files (Makefile, Github actions, PR templates, etc) from the previous project only to realize I forgot to change some stuff, and now would need to rewrite git history.

By the third project, I decided to create a template generator for Go! I've tried to keep the generated project as flexible as possible - you can decide to skip the of it and go for a simple project, or take the bloat (pre-commit would need Python for one).

While making go-template, one of my side goals has been to keep the project beginner-friendly. I remember stumbling upon python-package-template[1] as a novice, and learning more than I had in a semester - Makefiles, linters, code-formatters, semantic versioning, pipelines, and so much more! With go-template, I hope to give that same experience to some other newbie who might stumble upon my repo (or a project generated using go-template).

As a fun fact, go-template has an option to remove Github-specific-features (pull request templates, workflows, etc). This was inspired by a comment on HN[2] pointing out that many open-source projects were on Github simply because of FOMO, which in-turn promoted Github's dominance!

[1]: https://github.com/TezRomacH/python-package-template

Oh, and if I am not mistaken, you have also used the python-package-template itself to generate goli structure 🔥

CookieCutter or Kedro are the winners. I still think we will stick to Kedro template, because it offers extra functionality, and I like to think of each project as a set of pipelines to be run. Anyway, some cookiecutter templates are very good, like this one. In case we use both Kedro and ClearML, we'll have to figure out how to integrate its pipelines with ClearML tasks. But in the slack channel of ClearML there are other teams doing the same, so at least it's possible.

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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/