Kedro
Hugo
Kedro | Hugo | |
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29 | 549 | |
9,362 | 72,558 | |
0.7% | 0.8% | |
9.7 | 9.8 | |
10 days ago | 7 days ago | |
Python | Go | |
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.
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.
Hugo
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Building static websites
At one point though I realized there is a scaling problem with my build minutes. I knew that golang has considerably faster builds and in my case the easy fix is swapping over to Hugo.
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Creating excerpts in Astro
This blog is running on Hugo. It had previously been running on Jekyll. Both these SSGs ship with the ability to create excerpts from your markdown content in 1 line or thereabouts.
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Craft Your GitHub Profile Page in 60 Seconds with Zero Code, Absolutely Free
Hugo
- Release v0.123.0 · Gohugoio/Hugo
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Top 5 Open-Source Documentation Development Platforms of 2024
Hugo is a popular static site generator specifically designed to create websites and documentation lightning-fast. Its minimalist approach, emphasis on speed, and ease of use have made it popular among developers, technical writers, and anybody looking to construct high-quality websites without the complexity of typical CMS platforms.
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Ask HN: Looking for lightweight personal blogging platform
As per many other comments, it sounds like a static site generator like Hugo (https://gohugo.io/) or Jekyll (https://jekyllrb.com/), hosted on GitHub Pages (https://pages.github.com/) or GitLab Pages (https://about.gitlab.com/stages-devops-lifecycle/pages/), would be a good match. If you set up GitHub Actions or GitLab CI/CD to do the build and deploy (see e.g. https://gohugo.io/hosting-and-deployment/hosting-on-github/), your normal workflow will simply be to edit markdown and do a git push to make your changes live. There are a number of pre-built themes (e.g. https://themes.gohugo.io/) you can use, and these are realtively straightforward to tweak to your requirements.
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Get People Interested in Contributing to Your Open Project
Create the technical documentation of your project You can use any of the following options: * A wiki, like the ArchWiki that uses MediaWiki * Read the Docs, used by projects like Setuptools. Check Awesome Read the Docs for more examples. * Create a website * Create a blog, like the documentation of Blowfish, a theme for Hugo.
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Writing a SSG in Go
Doing this made me appreciate existing SSGs like Hugo and Next.js even more👏👏
- Hugo 0.122 supports LaTeX or TeX typesetting syntax directly from Markdown
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Why Blogging Platforms Suck
I suggest hugo: https://gohugo.io/
Generates a completely static website from MD (and other formats) files; also handles themes (including a lot of them rendering well on mobile), and different types of content - posts, articles, etc. - depending on the theme.
It's open source and, being completely static, cheap as fuck to self host.
What are some alternatives?
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
astro - The web framework for content-driven websites. ⭐️ Star to support our work!
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.
MkDocs - Project documentation with Markdown.
Dask - Parallel computing with task scheduling
Pelican - Static site generator that supports Markdown and reST syntax. Powered by Python.
cookiecutter-pytorch - A Cookiecutter template for PyTorch Deep Learning projects.
eleventy 🕚⚡️ - A simpler site generator. Transforms a directory of templates (of varying types) into HTML.
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
Hexo - A fast, simple & powerful blog framework, powered by Node.js.
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!
obsidian-export - Rust library and CLI to export an Obsidian vault to regular Markdown