unionml
ploomber
unionml | ploomber | |
---|---|---|
6 | 121 | |
330 | 3,392 | |
1.2% | 0.9% | |
4.0 | 7.4 | |
7 months ago | about 1 month ago | |
Python | Python | |
Apache License 2.0 | 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.
unionml
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Who needs MLflow when you have SQLite?
Checkout Flyte.org and it’s sibling project https://www.union.ai/unionml
- UnionML: the easiest way to build and deploy machine learning microservices
- GitHub - unionai-oss/unionml: UnionML: the easiest way to build and deploy machine learning microservices
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Show HN: UnionML – a Python framework for building ML microservices
Hi HN!
Niels here. I'm the creator of *UnionML*, a Python MLOps framework that removes the boilerplate and friction associated with building and deploying machine learning systems to production.
I've been training and deploying models for almost a decade now, and one pain-point I've consistently had is managing the complexity of building and maintaining an ML stack that works for the entire model development lifecycle - from prototyping to production.
UnionML is built on top of Flyte (https://www.flyte.org) and exposes a functional interface for defining the building blocks of your ML application via decorators -- think Flask or FastAPI method endpoints -- and UnionML takes care of bundling them into microservices for different use cases such as:
- model training
- batch prediction
- online prediction
- (more coming soon!)
This project aims to unify the rich ecosystem of data, ML, and MLOps tools that have emerged over the last decade or so (e.g. MLFlow, Sagemaker, Spark, etc.) to provide a nice UX for model developers, in both individual and team settings.
It's very early days for this project, so if you're interested in getting involved or learning more, you can go to the:
- Docs: https://unionml.readthedocs.io/en/latest/
- Repo: https://github.com/unionai-oss/unionml
- Slack: https://flyte-org.slack.com/archives/C03JL38L65V
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?
ploomber-engine - A toolbox 🧰 for Jupyter notebooks 📙: testing, experiment tracking, debugging, profiling, and more!
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.
rubicon-ml - Capture all information throughout your model's development in a reproducible way and tie results directly to the model code!
papermill - 📚 Parameterize, execute, and analyze notebooks
dagster - An orchestration platform for the development, production, and observation of data assets.
neptune-client - 📘 The MLOps stack component for experiment tracking
dvc - 🦉 ML Experiments and Data Management with Git
argo - Workflow Engine for Kubernetes
MLflow - Open source platform for the machine learning lifecycle
nbdev - Create delightful software with Jupyter Notebooks
docker-airflow - Docker Apache Airflow