Kedro
jupyter
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Kedro | jupyter | |
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29 | 13 | |
9,353 | 14,727 | |
1.5% | 0.5% | |
9.7 | 7.5 | |
6 days ago | about 1 month ago | |
Python | Python | |
Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
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.
Kedro
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Nextflow: Data-Driven Computational Pipelines
Interesting, thanks for sharing. I'll definitely take a look, although at this point I am so comfortable with Snakemake, it is a bit hard to imagine what would convince me to move to another tool. But I like the idea of composable pipelines: I am building a tool (too early to share) that would allow to lay Snakemake pipelines on top of each other using semi-automatic data annotations similar to how it is done in kedro (https://github.com/kedro-org/kedro).
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A Polars exploration into Kedro
# pyproject.toml [project] dependencies = [ "kedro @ git+https://github.com/kedro-org/kedro@3ea7231", "kedro-datasets[pandas.CSVDataSet,polars.CSVDataSet] @ git+https://github.com/kedro-org/kedro-plugins@3b42fae#subdirectory=kedro-datasets", ]
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What are some open-source ML pipeline managers that are easy to use?
So there's 2 sides to pipeline management: the actual definition of the pipelines (in code) and how/when/where you run them. Some tools like prefect or airflow do both of them at once, but for the actual pipeline definition I'm a fan of https://kedro.org. You can then use most available orchestrators to run those pipelines on whatever schedule and architecture you want.
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How do data scientists combine Kedro and Databricks?
We have set up a milestone on GitHub so you can check in on our progress and contribute if you want to. To suggest features to us, report bugs, or just see what we're working on right now, visit the Kedro projects on GitHub.
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How do you organize yourself during projects?
you could use a project framework like kedro to force you to be more disciplined about how you structure your projects. I'd also recommend checking out this book: Edna Ridge - Guerrilla Analytics: A Practical Approach to Working with Data
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Futuristic documentation systems in Python, part 1: aiming for more
Recently I started a position as Developer Advocate for Kedro, an opinionated data science framework, and one of the things we're doing is exploring what are the best open source tools we can use to create our documentation.
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Python projects with best practices on Github?
You can also check out Kedro, it’s like the Flask for data science projects and helps apply clean code principles to data science code.
- Data Science/ Analyst Zertifikate für den Job Markt?
- What are examples of well-organized data science project that I can see on Github?
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Dabbling with Dagster vs. Airflow
An often overlooked framework used by NASA among others is Kedro https://github.com/kedro-org/kedro. Kedro is probably the simplest set of abstractions for building pipelines but it doesn't attempt to kill Airflow. It even has an Airflow plugin that allows it to be used as a DSL for building Airflow pipelines or plug into whichever production orchestration system is needed.
jupyter
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Mastering Data Science: Top 10 GitHub Repos You Need to Know
6. Jupyter Jupyter is a collection of tools and applications designed for interactive computing and data visualization. At the heart of the Jupyter ecosystem is the Jupyter Notebook, an interactive web-based platform that allows you to create and share documents containing live code, equations, visualizations, and narrative text. It’s an excellent tool for exploratory data analysis, model prototyping, and creating reproducible data science workflows.
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You can run Rust code in a Jupyter notebook
How cool. This motivated a quick search - this could be fun:
How to write your own kernel
https://jupyter-client.readthedocs.io/en/stable/kernels.html
All the language kernels (a lot of abandoned ones - the mariaDB one ('binder') will take a while to load but SQL in Jupyter!)
https://github.com/jupyter/jupyter/wiki/Jupyter-kernels
- Resource for interesting data science project notebooks
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Mathics: A free, open-source alternative to Mathematica
There are Jupyter kernels for Python, Mathics, Wolfram, R, Octave, Matlab, xeus-cling, allthekernels (the polyglot kernel). https://github.com/jupyter/jupyter/wiki/Jupyter-kernels
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How does 3[a] gives the element at index 3 in an array?
Not only there is. But it is only a simple Google search away... But to make it simpler... There are 3 😁 https://github.com/jupyter/jupyter/wiki/Jupyter-kernels
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How to use Jupyter notebooks in a conda environment?
As it seems, this is not quite straight forward and manyusers have similar troubles.
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Semi-Weekly Discussion Thread - February 21, 2022
Community maintained kernels : https://github.com/jupyter/jupyter/wiki/Jupyter-kernels
- Node.js Notebooks
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Python Tutorials using Jupyter Notebook
Derek Banas on YouTube is doing a "Python for Finance" course at ghe moment using Jupyter, and is making the files available. I believe he's done others too.Failing that, there's this Git repo: A gallery of interesting jupyter notebooks
- Github Discussion: What is your favorite Data Science Repo?
What are some alternatives?
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
nteract - 📘 The interactive computing suite for you! ✨
luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
cookiecutter-data-science - A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
Dask - Parallel computing with task scheduling
pyodide - Pyodide is a Python distribution for the browser and Node.js based on WebAssembly
cookiecutter-pytorch - A Cookiecutter template for PyTorch Deep Learning projects.
vscode-python - Python extension for Visual Studio Code
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
quokka - Repository for Quokka.js questions and issues
BentoML - The most flexible way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Inference Graph/Pipelines, Compound AI systems, Multi-Modal, RAG as a Service, and more!
notebook - Jupyter Interactive Notebook