airflow-provider-flyte
flyte
| airflow-provider-flyte | flyte | |
|---|---|---|
| 4 | 31 | |
| 9 | 5,119 | |
| - | 7.0% | |
| 0.0 | 9.8 | |
| almost 2 years ago | 3 days ago | |
| Python | 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.
airflow-provider-flyte
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Airflow :: 資源整理
Flyte Provider for Apache Airflow
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Introducing Flyte (v1.1.0): Orchestrate Your Machine Learning and Data Pipelines with Ease (2.5K Stars on GitHub, Kubernetes-Native)
We have integrated Flyte with tools such as Spark, BigQuery, MPI, Sagemaker, Great Expectations, Pandera, etc. I’ve recently worked on building an Airflow provider for Flyte that enables triggering Flyte workflows from within Airflow; this is helpful if you want to build ETL pipelines in Airflow and machine learning pipelines in Flyte and use the two of them together.
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Is it possible to automatically deploy a ML pipeline to Airflow?
Hey u/eemamedo! Airflow's good for ETL, but may not be the best choice for ML pipelines because ML isn't the same as ETL and has a different set of requirements. I recently published Airflow Flyte Provider that enables you to seamlessly build and maintain your ML pipelines in Flyte from within Airflow. So you can write all your ETL/ELT pipelines in Airflow and ML pipelines in Flyte, and thereby, leverage the best of both worlds!
- Flyte Provider for Apache Airflow
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?
docs - This repository contains all content and code for Astro and Astronomer Software documentation.
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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
kestra - Infinitely scalable, event-driven, language-agnostic orchestration and scheduling platform to manage millions of workflows declaratively in code.
polyaxon - MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
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. 📈