Kedro
black
Kedro | black | |
---|---|---|
29 | 322 | |
9,362 | 37,425 | |
0.7% | 0.6% | |
9.7 | 9.4 | |
10 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.
black
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How to setup Black and pre-commit in python for auto text-formatting on commit
$ git commit -m "add pre-commit configuration" [INFO] Initializing environment for https://github.com/psf/black. [INFO] Installing environment for https://github.com/psf/black. [INFO] Once installed this environment will be reused. [INFO] This may take a few minutes... black................................................(no files to check)Skipped [main 6e21eab] add pre-commit configuration 1 file changed, 7 insertions(+)
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Enhance Your Project Quality with These Top Python Libraries
Black: Known as “The Uncompromising Code Formatter”, Black automatically formats your Python code to conform to the PEP 8 style guide. It takes away the hassle of having to manually adjust your code style.
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Uv: Python Packaging in Rust
black @ git+https://github.com/psf/black
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Let's meet Black: Python Code Formatting
In the realm of Python development, there is a multitude of code formatters that adhere to PEP 8 guidelines. Today, we will briefly discuss how to install and utilize black.
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Show HN: Visualize the Entropy of a Codebase with a 3D Force-Directed Graph
Perfect, that worked, thank you!
I thought this could be solved by changing the directory to src/ and then executing that command, but this didn't work.
This also seems to be an issue with the web app, e.g. the repository for the formatter black is only one white dot https://dep-tree-explorer.vercel.app/api?repo=https://github...
- Introducing Flask-Muck: How To Build a Comprehensive Flask REST API in 5 Minutes
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Embracing Modern Python for Web Development
Ruff is not only much faster, but it is also very convenient to have an all-in-one solution that replaces multiple other widely used tools: Flake8 (linter), isort (imports sorting), Black (code formatter), autoflake, many Flake8 plugins and more. And it has drop-in parity with these tools, so it is really straightforward to migrate from them to Ruff.
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Auto-formater for Android (Kotlin)
What I am looking for is something like Black for Python, which is opinionated, with reasonable defaults, and auto-fixes most/all issues.
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Releasing my Python Project
1. LICENSE: This file contains information about the rights and permissions granted to users regarding the use, modification, distribution, and sharing of the software. I already had an MIT License in my project. 2. pyproject.toml: It is a configuration file typically used for specifying build requirements and backend build systems for Python projects. I was already using this file for Black code formatter configuration. 3. README.md: Used as a documentation file for your project, typically includes project overview, installation instructions and optionally, contribution instructions. 4. example_package_YOUR_USERNAME_HERE: One big change I had to face was restructuring my project, essentially packaging all files in this directory. The name of this directory should be what you want to name your package and shoud not conflict with any of the existing packages. Of course, since its a Python Package, it needs to have an __init__.py. 5. tests/: This is where you put all your unit and integration tests, I think its optional as not all projects will have tests. The rest of the project remains as is.
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Lute v3 - installed software for learning foreign languages through reading
using pylint and black ("the uncompromising code formatter")
What are some alternatives?
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
autopep8 - A tool that automatically formats Python code to conform to the PEP 8 style guide.
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.
prettier - Prettier is an opinionated code formatter.
Dask - Parallel computing with task scheduling
yapf - A formatter for Python files
cookiecutter-pytorch - A Cookiecutter template for PyTorch Deep Learning projects.
Pylint - It's not just a linter that annoys you!
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
ruff - An extremely fast Python linter and code formatter, written in Rust.
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!
isort - A Python utility / library to sort imports.