Kedro
trio
Kedro | trio | |
---|---|---|
29 | 19 | |
9,362 | 5,895 | |
0.7% | 1.1% | |
9.7 | 9.5 | |
9 days ago | 4 days ago | |
Python | Python | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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.
trio
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trio VS awaits - a user suggested alternative
2 projects | 9 Dec 2023
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In what ways are channels are better than the traditional await?
Incidentally, the alternative event loop implementation trio in python does not have "gather", you also need channels, and it's a deliberate design choice - there is some discussion about that in this ticket https://github.com/python-trio/trio/issues/2188
- Polyphony: Fine-Grained Concurrency for Ruby
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This Week In Python
trio – a friendly Python library for async concurrency and I/O
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Python projects with best practices on Github?
trio. the best code, the best documentation, awesome community.
- Trio: Structured Concurrency for Python
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The Heisenbug lurking in your async code (Python)
I'll +1 the Trio shoutout [1], but it's worth emphasizing that the core concept of Trio (nurseries) now exists in the stdlib in the form of task groups [2]. The article mentions this very briefly, but it's easy to miss, and I wouldn't describe it as a solution to this bug, anyways. Rather, it's more of a different way of writing multitasking code, which happens to make this class of bug impossible.
[1] https://github.com/python-trio/trio
[2] https://docs.python.org/3/library/asyncio-task.html#task-gro...
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The gotcha of unhandled promise rejections
It's similar to manual memory management.
Structured concurrency is one approach to solving this problem. In a structured concurrency a promise would not go out of scope unhandled. Not sure how you would add APIs for it though.
See Python's trio nurseries idea which uses a python context manager.
https://github.com/python-trio/trio
I'm working on a syntax for state machines and it could be used as a DSL for promises. It looks similar to a bash pipeline but it matches predicates similar to prolog.
In theory you could wire up a tree of structured concurrency with this DSL.
https://github.com/samsquire/ideas4#558-assign-location-mult...
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Python Asyncio: The Complete Guide
Not complete - doesn't include Task Groups [1]
In fairness they were only included in asyncio as of Python 3.11, which was released a couple of weeks ago.
These were an idea originally from Trio [2] where they're called "nurseries" instead of "task groups". My view is that you're better off using Trio, or at least anyio [3] which gives a Trio-like interface to asyncio. One particularly nice thing about Trio (and anyio) is that there's no way to spawn background tasks except to use task groups i.e. there's no analogue of asyncio's create_task() function. That is good because it guarantees that no task is ever left accidentally running in the background and no exception left silently uncaught.
[1] https://docs.python.org/3/library/asyncio-task.html#task-gro...
[2] https://github.com/python-trio/trio
[3] https://anyio.readthedocs.io/en/latest/
- Anyone here able to help with a python issue?
What are some alternatives?
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
uvloop - Ultra fast asyncio event loop.
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.
curio - Good Curio!
Dask - Parallel computing with task scheduling
asyncio
cookiecutter-pytorch - A Cookiecutter template for PyTorch Deep Learning projects.
Twisted - Event-driven networking engine written in Python.
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
LDAP3 - a strictly RFC 4510 conforming LDAP V3 pure Python client. The same codebase works with Python 2. Python 3, PyPy and PyPy3
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!
DearPyGui - Dear PyGui: A fast and powerful Graphical User Interface Toolkit for Python with minimal dependencies