kopf
A Python framework to write Kubernetes operators in just a few lines of code (by nolar)
flyte
Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks. (by flyteorg)
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| kopf | flyte | |
|---|---|---|
| 6 | 31 | |
| 1,951 | 4,727 | |
| - | 3.3% | |
| 7.8 | 9.8 | |
| 16 days ago | 6 days ago | |
| Python | Go | |
| MIT License | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
kopf
Posts with mentions or reviews of kopf.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-04-18.
- A Kubernetes Operator in Rust
- I wrote a kubernetes operator for “locust”, should I open source it
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Automate All the Boring Kubernetes Operations with Python
If you're looking for more examples beyond what was shown and referenced above, I recommend exploring other popular tools that make use Python Kubernetes client, such kopf - the library for creating Kubernetes operators. I also find it very useful to take a look at tests of the library itself, as it showcases its intended usage such this client test suite.
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is it possible to have components of a specific namespace run on specific nodes ?
Depending on how you want to configure your selecting logic, it can be solved by mutating admission webhooks for the pods. For example, in Kopf, the simplest approach would be:
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Kopf 1.31 now supports admission webhooks. Feedback is welcome!
Hello. Kopf (a framework to write Kubernetes operators in Python) 1.31 is released and has finally got admission webhooks — https://github.com/nolar/kopf/releases/tag/1.31.0. I would appreciate some feedback from experienced operator developers on how easy or hard it is to write webhooks now, and what is missing and makes it inconvenient. The docs: https://kopf.readthedocs.io/en/stable/admission/ For a brief preview, it looks like this:
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lightkube 0.6.0 - python kubernetes client
Correct, using generic resources with Client.watch should work. In general to create more complex operators I would recommend to check out kopf.
flyte
Posts with mentions or reviews of flyte.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-12-15.
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First 15 Open Source Advent projects
9. Flyte by Union AI | Github | tutorial
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Flyte 1.10: Self-hosted solution to build production-grade data and ML pipelines; now ships with monorepo, new agents and sensors, eager workflows and more 🚀 (4.1k stars on GitHub)
GitHub: https://github.com/flyteorg/flyte
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Flyte: Open-source orchestrator for building production-grade ML pipelines
This is actually but a link to Flyte, this is a link to the documentation for the Flyte integration in LangChain, a separate product.
Flyte's homepage is https://flyte.org/
- Flyte: Advanced workflow orchestration alternative to Apache Airflow
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Orchestration: Thoughts on Dagster, Airflow and Prefect?
Anyone tried Flyte?
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Flyte 1.6.0: Self-hosted solution to build production-grade data and ML pipelines; now ships with PyTorch elastic training, image specification without dockerfile, enhanced task execution insights and more 🚀 (3.4k stars on GitHub)
Website: https://flyte.org/
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Flyte(v1.5.0) - Self-hosted solution to build production-grade data and ML pipelines; now ships with streaming support, pod templates, partial tasks and more 🚀 (3.2k stars on GitHub)
Flyte is an open source orchestration tool for managing the workflow of machine learning and AI projects. It runs on top of Kubernetes.
- Flyte: Open-Source Kubernetes-Native ML Orchestrator Implemented in Go
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What is MLOps and how to get started? | MLOps series | Deploying ML in production
I have a question though, what is your opinion on https://flyte.org. My pipeline uses this and it’ll be interesting to get your perspectives on it’s capabilities.
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Github alternative for ML?
Have you looked at flyte.org. It aims to bring "versioning", "compute" and "reproducibility" together in one package.
What are some alternatives?
When comparing kopf and flyte you can also consider the following projects:
awx-operator - An Ansible AWX operator for Kubernetes built with Operator SDK and Ansible. 🤖
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
argo - Workflow Engine for Kubernetes
fastapi-crudrouter - A dynamic FastAPI router that automatically creates CRUD routes for your models
temporal - Temporal service
Celery-Kubernetes-Operator - An operator to manage celery clusters on Kubernetes (Work in Progress)
kubeflow - Machine Learning Toolkit for Kubernetes
pykorm - A python 🐍 kubernetes ☸️ ORM 🚀. Very useful when writing operators for your CRDs with Kopf.
Dependency Injector - Dependency injection framework for Python
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.