flyte VS Celery-Kubernetes-Operator

Compare flyte vs Celery-Kubernetes-Operator and see what are their differences.

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flyte Celery-Kubernetes-Operator
31 1
4,645 77
5.7% -
9.8 3.6
7 days ago 4 months ago
Go Python
Apache License 2.0 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.

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.

Celery-Kubernetes-Operator

Posts with mentions or reviews of Celery-Kubernetes-Operator. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-01-15.
  • Help: from Docker-Compose to Production (EC2, ECS, EKR)
    2 projects | /r/docker | 15 Jan 2021
    The take-out from that course is: don't deploy anything stateful on Kubernetes in production, period. Even disregarding that, don't deploy anything stateful that doesn't come in a form of an operator. For celery, https://github.com/celery/Celery-Kubernetes-Operator is a WIP, so obviously not suitable for anything.

What are some alternatives?

When comparing flyte and Celery-Kubernetes-Operator you can also consider the following projects:

metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!

argo - Workflow Engine for Kubernetes

temporal - Temporal service

kubeflow - Machine Learning Toolkit for Kubernetes

flower - Real-time monitor and web admin for Celery distributed task queue

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.

rq - Simple job queues for Python

hera - Hera is an Argo Python SDK. Hera aims to make construction and submission of various Argo Project resources easy and accessible to everyone! Hera abstracts away low-level setup details while still maintaining a consistent vocabulary with Argo. ⭐️ Remember to star!

pachyderm - Data-Centric Pipelines and Data Versioning

polyaxon - MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle

kestra - Infinitely scalable, event-driven, language-agnostic orchestration and scheduling platform to manage millions of workflows declaratively in code.

whylogs - An open-source data logging library for machine learning models and data pipelines. 📚 Provides visibility into data quality & model performance over time. 🛡️ Supports privacy-preserving data collection, ensuring safety & robustness. 📈