Kedro
rich
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Kedro | rich | |
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29 | 148 | |
9,362 | 47,088 | |
1.6% | 1.1% | |
9.7 | 8.0 | |
3 days ago | 4 days ago | |
Python | Python | |
Apache License 2.0 | MIT 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.
rich
- Rich is a Python library for rich text and beautiful formatting in the terminal
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Neat Parallel Output in Python
There is an open issue [1] on GitHub to make it more modular and get rid of markdown and syntax highlighting but I have no hope for rich to get more minimal.
[1]: https://github.com/Textualize/rich/issues/2277
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Ask HN: Programmers and Technologists in Scotland
I hope he doesn't mind, but the creator of Rich and Textualize is a good guy, and Scottish: https://www.willmcgugan.com/about/
https://www.textualize.io/
https://github.com/Textualize/rich
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Python 3.12
They keep getting improved error messaging and this is one of my favorite features. But I'd love if we could get some real rich text. Idk if anyone else uses rich, but it has infected all my programs now. Not just to print with colors, but because it makes debugging so much easier. Not just print(f"{var=}") but the handler[0,1]. Color is so important to these types of things and so is formatting. Plus, the progress bars are nice and have almost completely replaced tqdm for me[2]. They're just easier and prettier.
[0] https://rich.readthedocs.io/en/stable/logging.html
[1] Try this example: https://github.com/Textualize/rich/blob/master/examples/exce...
[2] Side note: does anyone know how to get these properly working when using DDP with pytorch? I get flickering when using this and I think it is actually down to a pytorch issue and how they're handling their loggers and flushing the screen. I know pytorch doesn't want to depend on rich, but hey, pip uses rich so why shouldn't everyone?
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colors.crumb - first Crumb usable. Extending Crumb with basic terminal styling and RGB, HEX, ANSI conversion functions.
colors.crumb extends Crumb with basic terminal styling functions and RGB, HEX, ANSI conversion functions. It is in the realm of JavaScript's chalk and Python's rich but slightly more functional 😉.
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Textual: Rapid Application Development Framework for Python
I am working on a new python project and one of the first things I added was https://github.com/Textualize/rich because of how easy it is to make things look good in the terminal.
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What are you rewriting in rust?
I am not rewriting anything but I'd love to have a library like `rich` in Rust: https://github.com/textualize/rich
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Things to do with standalone script
Add some cool-looking stuff to your output with rich.
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I made a library for making user terminal input really really pretty!
You might consider taking inspiration from the rich module. In particular, I like how rich supports inline color theming which seems much more cumbersome in your framework, requiring the use of context managers as well as familiarity with how your framework structures color objects. Other than that though, I'm impressed!
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coBib 4.0: a modern UI using Textualize libraries
Today I released coBib 4.0, my console bibliography manager written in Python, which now uses rich and textual to provide a cohesive and modern user experience in both its CLI and TUI.
What are some alternatives?
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
tqdm - :zap: A Fast, Extensible Progress Bar for Python and CLI
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.
colorama - Simple cross-platform colored terminal text in Python
Dask - Parallel computing with task scheduling
python-prompt-toolkit - Library for building powerful interactive command line applications in Python
cookiecutter-pytorch - A Cookiecutter template for PyTorch Deep Learning projects.
textual - The lean application framework for Python. Build sophisticated user interfaces with a simple Python API. Run your apps in the terminal and a web browser.
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
blessed - Blessed is an easy, practical library for making python terminal apps
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!
alive-progress - A new kind of Progress Bar, with real-time throughput, ETA, and very cool animations!