flyte
dvc
| flyte | dvc | |
|---|---|---|
| 31 | 109 | |
| 4,761 | 13,139 | |
| 1.9% | 0.6% | |
| 9.8 | 9.6 | |
| 7 days ago | 3 days ago | |
| Go | Python | |
| 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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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.
dvc
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My Favorite DevTools to Build AI/ML Applications!
Collaboration and version control are crucial in AI/ML development projects due to the iterative nature of model development and the need for reproducibility. GitHub is the leading platform for source code management, allowing teams to collaborate on code, track issues, and manage project milestones. DVC (Data Version Control) complements Git by handling large data files, data sets, and machine learning models that Git can't manage effectively, enabling version control for the data and model files used in AI projects.
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Why bad scientific code beats code following "best practices"
What you’re describing sounds like DVC (at a higher-ish—80%-solution level).
https://dvc.org/
See pachyderm too.
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First 15 Open Source Advent projects
10. DVC by Iterative | Github | tutorial
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Platforms such as MLflow monitor the development stages of machine learning models. In parallel, Data Version Control (DVC) brings version control system-like functions to the realm of data sets and models.
- ML Experiments Management with Git
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Git Version Controlled Datasets in S3
I was using DVC (https://dvc.org/) for some time to help solve this but it was getting hard to manage the storage connections and I would run into cache issues a lot, but this solves it using git-lfs itself.
- Ask HN: How do your ML teams version datasets and models?
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Exploring MLOps Tools and Frameworks: Enhancing Machine Learning Operations
DVC (Data Version Control):
- Evaluate and Track Your LLM Experiments: Introducing TruLens for LLMs
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[D] Is there a tool to keep track of my ML experiments?
I have been using DVC and MLflow since then DVC had only data tracking and MLflow only model tracking. I can say both are awesome now and maybe the only factor I would like to mention is that IMO, MLflow is a bit harder to learn while DVC is just a git practically.
What are some alternatives?
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
MLflow - Open source platform for the machine learning lifecycle
argo - Workflow Engine for Kubernetes
lakeFS - lakeFS - Data version control for your data lake | Git for data
temporal - Temporal service
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]
kubeflow - Machine Learning Toolkit for Kubernetes
delta - An open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs
Celery-Kubernetes-Operator - An operator to manage celery clusters on Kubernetes (Work in Progress)
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
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.
aim - Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.