feast
black
feast | black | |
---|---|---|
8 | 322 | |
5,303 | 37,555 | |
1.6% | 0.8% | |
9.5 | 9.4 | |
1 day 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.
feast
- What's Happening with Feast?
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Running The Feast Feature Store With Dragonfly
Feast stands as an exceptional open-source feature store, revolutionizing the efficient management and uninterrupted serving of machine learning (ML) features for real-time applications. At its core, Feast offers a sophisticated interface for storing, discovering, and accessing features—the individual measurable properties or characteristics of data essential for ML modeling. Operating on a distributed architecture, Feast harmoniously integrates several pivotal components, including the Feast Registry, Stream Processor, Batch Materialization Engine, and Stores.
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Ask HN: How to Break into AI Engineering
AI Engineering is basically Data Engineering focused on AI. When in "traditional" Data Engineering you create pipelines that store processed data in something like a Data Lake, in AI Eng. your end storage might be a specialized Feature Storage (like Feast or GCP Vertex AI).
There are some AI Engineers with strong scientific/mathematical background, but that's rare. Usually, you're paired with these ML people that actually develop and evaluate the models.
So my advice is to start with Data Engineering and then find a specialization AI. You should have a VERY solid foundation on scripting and programming, specially Python. Also, a lot of concepts of "data wrangling". Understanding how data flows from point A to point B, how the intermediate storages and streaming engines work, etc. Functional programming is key here.
[0] https://github.com/feast-dev/feast
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In Need of Guidance: Implementing MLOps in a Complex Organization as a Junior Data Engineer
A feature store usually stores features which are used for training ML model. It is a centralized place for collaboration between data engineer, ML engineer, and data scientist, so that data engineer can write to the feature store while ML engineer and data scientist read from it. Hopsworks https://www.hopsworks.ai and feast https://github.com/feast-dev/feast are examples of open source feature store.
- [D] Your 🫵 Preferred Feature Stores?
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[P] Announcing Feast 0.10: The simplest way to serve features in production
Github: https://github.com/feast-dev/feast
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[D] What’s the simplest, most lightweight but complete and 100% open source MLOps toolkit? -> MY OWN CONCLUSIONS
Have you looked at Feats as a Feature Store solution? It seems promising but I haven't really looked into it yet though.
- Feast: OSS Feature Store for Production ML
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?
kedro-great - The easiest way to integrate Kedro and Great Expectations
autopep8 - A tool that automatically formats Python code to conform to the PEP 8 style guide.
featureform - The Virtual Feature Store. Turn your existing data infrastructure into a feature store.
prettier - Prettier is an opinionated code formatter.
Milvus - A cloud-native vector database, storage for next generation AI applications
yapf - A formatter for Python files
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
Pylint - It's not just a linter that annoys you!
great_expectations - Always know what to expect from your data.
ruff - An extremely fast Python linter and code formatter, written in Rust.
mlrun - MLRun is an open source MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications.
isort - A Python utility / library to sort imports.