dvclive
flyte
| dvclive | flyte | |
|---|---|---|
| 5 | 31 | |
| 153 | 4,853 | |
| 2.0% | 3.8% | |
| 8.9 | 9.8 | |
| 9 days ago | 6 days 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.
dvclive
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First 15 Open Source Advent projects
10. DVC by Iterative | Github | tutorial
- Log and track ML metrics, parameters, models with Git and DVC
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[P] Extension for VS Code to track ML experiments
There is no designated way to dump metrics. In the case of data for plots, we have a simple logger that might help: https://github.com/iterative/dvclive
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Show HN: DVC Studio – Git-Based ML Experiments Management
DVC has metrics logger similar to other experiment management tool: https://github.com/iterative/dvclive/
Also, metrics & params section of the docs explains this (but yes, it is not perfect yet): https://dvc.org/doc/start/metrics-parameters-plots
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?
phoenix - AI Observability & Evaluation
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
pytest-visual - A visual testing framework for ML with automated change detection
argo - Workflow Engine for Kubernetes
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
temporal - Temporal service
dvc - 🦉 ML Experiments and Data Management with Git
kubeflow - Machine Learning Toolkit for Kubernetes
OpenLLM - Run any open-source LLMs, such as Llama 2, Mistral, as OpenAI compatible API endpoint in the cloud.
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!