bash_kernel
ploomber
bash_kernel | ploomber | |
---|---|---|
5 | 121 | |
675 | 3,380 | |
- | 0.5% | |
5.0 | 7.4 | |
about 2 months ago | 28 days ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | 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.
bash_kernel
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Runme – Interactive Runbooks Built with Markdown
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
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Ask HN: Is there a Jupyter Notebook for terminal/shell
Something like this? https://github.com/takluyver/bash_kernel
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Simple Jupyter kernel for Crystal with 140 lines
I wrote a Crystal kernel for Jupyter, just a modified bash_kernel, 140 lines of code, but it was tiring because I don't have enough Python ability. icrystal is the widely used Jupyter kernel for Crystal, which uses ICR . On the other hand, this crystal_kernel uses the official crystal interpreter.
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SPyQL – SQL with Python in the Middle
Thank you! One of my main goals was making data processing in the command-line more accessible and intuitive. If you use a shell you can leverage an extensive array of tools. please take a look at the recipes in the Readme. The shell is many times underrated for data processing!
Right now you can use it in Jupiter Notebooks using a shell kernel like: https://github.com/takluyver/bash_kernel
On the mid-term, developing a spyql kernel is appealing because of syntax highlighting, code autocompleting, and more. But unless several people show interest on this, I should tackle other features first.
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How does your team organize/manage their runbooks?
I recently learned of jupyter+bash and it seemed like a step toward rundeck.
ploomber
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Show HN: JupySQL – a SQL client for Jupyter (ipython-SQL successor)
- One-click sharing powered by Ploomber Cloud: https://ploomber.io
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!
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Runme – Interactive Runbooks Built with Markdown
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
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Rant: Jupyter notebooks are trash.
Develop notebook-based pipelines
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Who needs MLflow when you have SQLite?
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.
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New to large SW projects in Python, best practices to organize code
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.
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A three-part series on deploying 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.
- Ploomber Cloud - Parametrizing and running notebooks in the cloud in parallel
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Is Colab still the place to go?
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!
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Alternatives to nextflow?
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
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Saving log files
That's what we do for lineage with https://ploomber.io/
What are some alternatives?
icr - Interactive console for Crystal programming language
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
spyql - Query data on the command line with SQL-like SELECTs powered by Python expressions
papermill - 📚 Parameterize, execute, and analyze notebooks
crystal_kernel - Python wrapper kernel for Crystal
dagster - An orchestration platform for the development, production, and observation of data assets.
yq - yq is a portable command-line YAML, JSON, XML, CSV, TOML and properties processor
dvc - 🦉 ML Experiments and Data Management with Git
runme - DevOps Workflows Built with Markdown
argo - Workflow Engine for Kubernetes
xc - Markdown defined task runner.
MLflow - Open source platform for the machine learning lifecycle