ploomber
dagster-sklearn
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ploomber | dagster-sklearn | |
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121 | 3 | |
3,374 | 40 | |
1.0% | - | |
7.4 | 0.0 | |
20 days ago | about 1 year ago | |
Python | Python | |
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.
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/
dagster-sklearn
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Scheduling tools for ETL and ML flow
I would give dagster a look. It has a built-in native scheduler and is cross-platform. It is general purpose, so your team can grow with it and tackle broader set of use cases if needed. If you struggle to get started after reading their docs/tutorials, you can take a look at my personal repo. Ive gotten a few feedback that my example has been very useful in getting started. I know they revamped their docs recently, but havent looked at their tutorial again or looked to see if they provided an intermediate level full example yet, so I need to get back in there to see.
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Dagster Tutorials/Presentations
Hey! I've recently started to use dagster and it's been great with its 0.11.x releases. I am still a newbie with it and maybe only use 20% of its features and abstractions. Here's my work-in-progress personal Github repo. Not sure if you'll learn much from it.
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Is anyone trying to switch out of data science, and if so, what jobs are you applying for?
I have created a trivial, contrived scikit-learn example using dagster so that people have an idea of how it can be used.
What are some alternatives?
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.
Dask - Parallel computing with task scheduling
papermill - 📚 Parameterize, execute, and analyze notebooks
dagster - An orchestration platform for the development, production, and observation of data assets.
yellowbrick - Visual analysis and diagnostic tools to facilitate machine learning model selection.
dvc - 🦉 ML Experiments and Data Management with Git
best-of-ml-python - 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
argo - Workflow Engine for Kubernetes
dagster-example-pipeline - Template Dagster repo using poetry and a single Docker container; works well with CICD
MLflow - Open source platform for the machine learning lifecycle
nbdev - Create delightful software with Jupyter Notebooks