beneath
flyte
Our great sponsors
| beneath | flyte | |
|---|---|---|
| 2 | 31 | |
| 78 | 4,727 | |
| - | 3.3% | |
| 0.0 | 9.8 | |
| about 2 years ago | 5 days ago | |
| Go | Go | |
| GNU General Public License v3.0 or later | 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.
beneath
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Analyzing the r/wallstreetbets hivemind — August 2021
If you’re interested, here’s the raw Reddit data, my data pipeline, the derived data, and my Jupyter notebook. I’m using Beneath, an open data platform I’m building, to stream and save the data.
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[Self Promotion] Reddit r/wallstreetbets posts and comments in real-time
The scraper (which uses Async PRAW) is open source here: https://github.com/beneath-hq/beneath/tree/master/examples/reddit
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?
Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai [Moved to: https://github.com/activeloopai/deeplake]
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
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. 📈
argo - Workflow Engine for Kubernetes
optimus - Optimus is an easy-to-use, reliable, and performant workflow orchestrator for data transformation, data modeling, pipelines, and data quality management.
temporal - Temporal service
pachyderm - Data-Centric Pipelines and Data Versioning
kubeflow - Machine Learning Toolkit for Kubernetes
sayn - Data processing and modelling framework for automating tasks (incl. Python & SQL transformations).
Celery-Kubernetes-Operator - An operator to manage celery clusters on Kubernetes (Work in Progress)
oomstore - Lightweight and Fast Feature Store Powered by Go (and Rust).
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.