k8s-cleaner
flyte
| k8s-cleaner | flyte | |
|---|---|---|
| 2 | 31 | |
| 156 | 4,820 | |
| - | 3.1% | |
| 8.7 | 9.8 | |
| 23 days ago | 4 days ago | |
| Go | Go | |
| 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.
k8s-cleaner
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A controller to identify unused and unhealthy Kubernetes resources
As Kubernetes deployments grow in complexity and scale, maintaining a clean and efficient cluster becomes increasingly important. While Kubernetes provides tools for resource management, such as garbage collection, it can still be challenging to identify and remove unused or stale resources manually. This is where k8s-cleaner comes in.
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Eliminate Stale Kubernetes Resources with Cleaner
So I created a Cleaner: https://github.com/gianlucam76/k8s-cleaner controller to automate the removal of stale Kubernetes resources.
flyte
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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?
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
Celery-Kubernetes-Operator - An operator to manage celery clusters on Kubernetes (Work in Progress)
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.
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. 📈
microservices-demo - Sample cloud-first application with 10 microservices showcasing Kubernetes, Istio, and gRPC.