ubelt
Kedro
ubelt | Kedro | |
---|---|---|
7 | 29 | |
712 | 9,398 | |
- | 1.1% | |
8.3 | 9.7 | |
6 days ago | 5 days ago | |
Python | Python | |
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.
ubelt
-
Python libs that I wish were part of the standard library
I can't give you a stdlib, but I can give you a package with a lot of the basic functionality but still small enough that it installs quickly and has negligable overhead. The ubelt library is a set of 100ish utility functions and classes. It's similar to boltons, but I suppose it reflects a different perspective on what's useful.
-
How do you feel about vendored packages?
Number 3 is the one I feel most conflicted about. Specifically, I tout my ubelt library as having 0 required dependencies. However, it vendors two libraries: progiter and orderedset. The first of which I also maintain and the second of which I don't maintain, but have contributed to. It feels odd to have a single dependency for a library that would otherwise have zero. But at the same time it feels odd to maintain that code myself. Also if I didn't vendor it, it would not be included in the documentation, so there is that. I've recently been thinking I should split ubelt up into many smaller packages and then use ubelt as a "hub" to include them all. However, that's a lot more work than just maintaining one (still quite small) package, and I think having everything broken up with incur a lot of overhead at pip install time, so I'm very conflicted on the whole subject.
- Useful helper libraries
-
Python projects with best practices on Github?
I'm fairly happy with my ubelt library.
-
[D] What is some cool python magic(s) that you've learned over the years?
The ubelt.util_platform module is a good example of including references to similar functionality.
-
[P] best-of-ml-python: A ranked list of awesome machine learning Python libraries
I also have a utility library ubelt with 552 stars and 6.9k downloads / month.
Kedro
-
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).
-
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", ]
-
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.
-
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.
-
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
-
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.
-
Python projects with best practices on Github?
You can also check out Kedro, itโs like the Flask for data science projects and helps apply clean code principles to data science code.
- Data Science/ Analyst Zertifikate fรผr den Job Markt?
- What are examples of well-organized data science project that I can see on Github?
-
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.
What are some alternatives?
best-of-web-python - ๐ A ranked list of awesome python libraries for web development. Updated weekly.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
best-of-python-dev - ๐ A ranked list of awesome python developer tools and libraries. Updated weekly.
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.
best-of-jupyter - ๐ A ranked list of awesome Jupyter Notebook, Hub and Lab projects (extensions, kernels, tools). Updated weekly.
Dask - Parallel computing with task scheduling
best-of-python - ๐ A ranked list of awesome Python open-source libraries and tools. Updated weekly.
cookiecutter-pytorch - A Cookiecutter template for PyTorch Deep Learning projects.
fastcore - Python supercharged for the fastai library
ploomber - The fastest โก๏ธ way to build data pipelines. Develop iteratively, deploy anywhere. โ๏ธ
best-of-ml-python - ๐ A ranked list of awesome machine learning Python libraries. Updated weekly.
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!