flyte
kubeflow
Our great sponsors
flyte | kubeflow | |
---|---|---|
31 | 3 | |
4,671 | 13,552 | |
6.2% | 1.3% | |
9.8 | 8.5 | |
about 17 hours ago | 7 days ago | |
Go | TypeScript | |
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.
flyte
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First 15 Open Source Advent projects
9. Flyte by Union AI | Github | tutorial
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Orchestration: Thoughts on Dagster, Airflow and Prefect?
Anyone tried Flyte?
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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.
GitHub: https://github.com/flyteorg/flyte
- Kubernetes for Data Science with Kubeflow
- Dabbling with Dagster vs. Airflow
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Airflow's Problem
Some of these were the core problems that we wanted to address as part of https://flyte.org. We started with a team first and multi-tenant approach at the core. For example, each team can have separate IAM roles, secrets are restricted to teams, tasks and workflows are shareable across teams, without making libraries. and it is possible to trigger workflows across teams.
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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)
GitHub: https://github.com/flyteorg/flyte
Website: https://flyte.org/
kubeflow
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Machine Learning Orchestration on Kubernetes using Kubeflow
If you are looking for bringing agility, improved management with enterprise-grade features such as RBAC, multi-tenancy and isolation, security, auditability, collaboration for the machine learning operations in your organization, Kubeflow is an excellent option. It is stable, mature and curated with best-in-class tools and framework which can be deployed in any Kubernetes distribution. See Kubeflow roadmap here to look into what's coming in the next version.
What are some alternatives?
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
argo - Workflow Engine for Kubernetes
kserve - Standardized Serverless ML Inference Platform on Kubernetes
temporal - Temporal service
BentoML - Build Production-Grade AI Applications
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.
polyaxon - MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
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.