examples
flyte
Our great sponsors
| examples | flyte | |
|---|---|---|
| 1 | 31 | |
| 1,373 | 4,761 | |
| 0.7% | 3.3% | |
| 4.4 | 9.8 | |
| 14 days ago | about 18 hours ago | |
| Jsonnet | 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.
examples
-
Kubernetes for Data Science with Kubeflow
Wow you're not kidding, I had a look at some samples and this is not something I want my datasc people to have to deal with.
flyte
-
First 15 Open Source Advent projects
9. Flyte by Union AI | Github | tutorial
-
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
-
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
-
Orchestration: Thoughts on Dagster, Airflow and Prefect?
Anyone tried Flyte?
-
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/
-
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
-
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.
-
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?
aim - Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
envd - 🏕️ Reproducible development environment
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.