ploomber
clearml
Our great sponsors
ploomber | clearml | |
---|---|---|
121 | 20 | |
3,355 | 5,169 | |
1.1% | 2.4% | |
7.8 | 8.1 | |
about 1 month ago | 4 days 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.
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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Ploomber Cloud - Parametrizing and running notebooks in the cloud in parallel
We started with an open-source framework to help data practitioners make their work reproducible. However, after months of building and learning from our community, we realized that many needed help with the setup: getting Python installed, getting dependencies, running experiments locally, etc.
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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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"Do I need to know {insert advanced math} to get a Data Science job?" [Rant]
btw, you can export Ploomber to Argo and Airflow!
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Running Jupyter notebooks in parallel
As a second option, we will use Ploomber with serial execution, which also has a Python API that allows us to execute different notebooks using the NotebookRunner function:
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How do you deal with parallelising parts of an ML pipeline especially on Python?
I also recommend checking ploomber out, this open source can help you build code as templates, parallelize it and parameterize it. There are also some reporting and debugging tools in there!
clearml
- FLaNK Stack Weekly 12 February 2024
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clearml VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
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cascade alternatives - clearml and MLflow
3 projects | 1 Nov 2023
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[D] Drop your best open source Deep learning related Project
Hi there. ClearML is our open-source solution which is part of the PyTorch ecosystem. We would really appreciate it if you read our README and starred us if you like what you see!
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[D] Facebook Visdom vs Google Tensorboard for Pytorch
I'm talking about ClearML😅 trying not to shill for open-source but ~5000 teams have already chosen 💪 https://github.com/allegroai/clearml
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[D] What’s the simplest, most lightweight but complete and 100% open source MLOps toolkit? -> MY OWN CONCLUSIONS
There are mainly two solutions that are 100% open source and free to install and use, and that may solve most of the requirements of ML practitioners: Hopsworks and ClearML. Among this two, if I had to chose one right now, it will be ClearML. Hopsworks might be much more complete, but ClearML seems to have a bigger community behind it and to be easier to install and use. So ClearML will be something to take a look at in case we go for an all-in-one package. I also like the idea of having a platform with an UI with all our projects.
- [D] What’s the simplest, most lightweight but complete and 100% open source MLOps toolkit?
What are some alternatives?
MLflow - Open source platform for the machine learning lifecycle
BentoML - Build Production-Grade AI Applications
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.
papermill - 📚 Parameterize, execute, and analyze notebooks
dagster - An orchestration platform for the development, production, and observation of data assets.
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
dvc - 🦉 ML Experiments and Data Management with Git
kedro-great - The easiest way to integrate Kedro and Great Expectations
argo - Workflow Engine for Kubernetes
nbdev - Create delightful software with Jupyter Notebooks
docker-airflow - Docker Apache Airflow