nbdime
ploomber
nbdime | ploomber | |
---|---|---|
7 | 121 | |
2,596 | 3,380 | |
0.3% | 0.5% | |
8.4 | 7.4 | |
3 days ago | 28 days ago | |
TypeScript | Python | |
GNU General Public License v3.0 or later | 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.
nbdime
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Stuff I Learned during Hanukkah of Data 2023
I remember hearing about nbdime and thinking it sounded useful, but I've never really needed it since I rarely use Jupyter in the first place. But then I made some changes to my Hanukkah of Data 2023 notebook to work with the follow-up "speed run" challenge (a new dataset and slightly tweaked clues), and the native Git diff was too noisy to be useful. nbdime came to the rescue! Here are the changes I had to make for days 2 and 3 during the speed run:
- The Jupyter+Git problem is now solved
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Ask HN: Are there any good Diff tools for Jupyter Notebooks?
[5] ReviewNB for reviewing & diff'ing notebook PRs / Commits on GitHub
Disclaimer: While I’m the author of last two (GitPlus & ReviewNB), I’ve represented the overall landscape in an unbiased way. I've been working on this specific problem for 3+ years & regularly talk to teams who use GitHub with notebooks.
[1] https://nbdime.readthedocs.io
- Notebooks suck: change my mind
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What if Git worked with Programming Languages?
Interesting they mentioned Jupyter Notebooks but not NBDime https://github.com/jupyter/nbdime which is a Jupyter plugin specifically to address this problem. Without it, diffing notebooks is not feasible.
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Jupyter diff in Magit
A bit off-topic but someone might know; I'm working with jupyter notebook files (ipynb) which are basically json files. Git diff is very noisy so there's nbdime which works great in the CLI. Is there a way to have Magit aware of its integration with git diff?
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The Notepad++
I use nbdime which allows you to ignore parts of a notebook (e.g. outputs) when diffing.
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?
jupytext - Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts
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.
poetry-dynamic-versioning - Plugin for Poetry to enable dynamic versioning based on VCS tags
papermill - 📚 Parameterize, execute, and analyze notebooks
nvim-treesitter-context - Show code context
dagster - An orchestration platform for the development, production, and observation of data assets.
webdiff - Two-column web-based git difftool
dvc - 🦉 ML Experiments and Data Management with Git
locust - "git diff" over abstract syntax trees
argo - Workflow Engine for Kubernetes
unison - A friendly programming language from the future
MLflow - Open source platform for the machine learning lifecycle