polyaxon
flyte
Our great sponsors
polyaxon | flyte | |
---|---|---|
9 | 31 | |
3,465 | 4,671 | |
0.8% | 5.7% | |
8.8 | 9.8 | |
10 days ago | about 1 hour 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.
polyaxon
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Any MLOps platform you use?
If you're not concerned about self-hosting, WandB is one of the more fully featured training monitoring tools (I've used it in the past without any issues but the lack of data and training privacy and lack of self-hosting possibilities makes it a hard no for anything that isn't scholastic). Polyaxon is an alternative but rewriting all your variable logging to conform to their requirements makes it very difficult to switch to it in the middle of a project so you have to commit to it from the get-go.
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[D] Kubernetes for ML - how are y'all doing it?
[4]: https://github.com/polyaxon/polyaxon
We use Polyaxon and itβs pretty good
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[D] What MLOps platform do you use, and how helpful are they?
Disclosure - I'm the author of Polyaxon.
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[D] Productionalizing machine learning pipelines for small teams
For running experiments, http://polyaxon.com/ is a really good free open-source package that has lots of nice integrations so you can quickly run experiments in k8s but it might be overkill in some cases.
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Top 5 tools to get started with MLOps !
Polyaxon : https://polyaxon.com
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 π (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/
What are some alternatives?
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
argo - Workflow Engine for Kubernetes
temporal - Temporal service
kubeflow - Machine Learning Toolkit for Kubernetes
MLflow - Open source platform for the machine learning lifecycle
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. βοΈ 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.
whylogs - An open-source data logging library for machine learning models and data pipelines. π Provides visibility into data quality & model performance over time. π‘οΈ Supports privacy-preserving data collection, ensuring safety & robustness. π