databay
flyte
Our great sponsors
| databay | flyte | |
|---|---|---|
| 2 | 31 | |
| 185 | 4,761 | |
| - | 3.3% | |
| 0.0 | 9.8 | |
| 9 months ago | about 13 hours 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.
databay
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Hi! I created a library which simplifies and augments usage of Python threads - called SuperLoops. It provides support for thread maintenance, events, failure handling, health status propagation, and graceful termination. Hope you find it useful 👋
ps. I also have a couple of other open source libraries: for scheduled data flow (Databay) and for algo trading (IBeam).
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What is 'the thing' that makes batches? A "Batcher"? Help us come up with a name.
I'm having a discussion with a collaborator of my library, as to what we should call "the object that produces batches". In simple terms - turns a 1D array of items, into a 2D array or arrays of items, for instance: turns [1,2,3,4] into [[1,3], [2,3]] - but you can visualise any implementation that makes sense to you, we only care about the naming here.
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?
hickory - Command line tool for scheduling Python scripts
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
PyFunctional - Python library for creating data pipelines with chain functional programming
argo - Workflow Engine for Kubernetes
cloudflare_ddns - Cloudflare Dynamic DNS schedule daemon
temporal - Temporal service
ibeam - IBeam is an authentication and maintenance tool used for the Interactive Brokers Client Portal Web API Gateway.
kubeflow - Machine Learning Toolkit for Kubernetes
superloops
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!