polyaxon
NLNS
| polyaxon | NLNS | |
|---|---|---|
| 9 | 1 | |
| 3,486 | 68 | |
| 0.5% | - | |
| 8.7 | 1.8 | |
| 16 days ago | over 3 years ago | |
| Python | Python | |
| Apache License 2.0 | GNU General Public License v3.0 only |
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?
We use Polyaxon and it’s 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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Does anyone have experience with polyaxon?
I just came across https://github.com/polyaxon/polyaxon because mlflow gives me a hard time and costs my company money by the day because it is not working as expected.
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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
- Open source alternative to AWS Sagemaker, Google AI Platform, and Azure ML
NLNS
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[D] What type of machine learning can be used to solve timetable optimisation problems?
I do not believe anyone has tried ML methods for solving timetable problems yet, so this would be new. My group has come up with several different options for ML+Optimization, but probably our approach "Neural Large Neighborhood Search" will be the most promising here. See our ECAI paper: https://ecai2020.eu/papers/786_paper.pdf, medium post explaining the method: https://dot-bielefeld.medium.com/learning-improvement-heuristics-for-vehicle-routing-problems-with-neural-large-neighborhood-search-6e19252e85f4 and source code: https://github.com/ahottung/NLNS.
What are some alternatives?
MLflow - Open source platform for the machine learning lifecycle
VeRyPy - A python library with implementations of 15 classical heuristics for the capacitated vehicle routing problem.
kubeflow - Machine Learning Toolkit for Kubernetes
or-gym - Environments for OR and RL Research
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
ml4vrp - Geometric Deep Learning Models for Vehicle Routing Problem
dvc - 🦉 ML Experiments and Data Management with Git
tensor2tensor - Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.
onepanel - The open source, end-to-end computer vision platform. Label, build, train, tune, deploy and automate in a unified platform that runs on any cloud and on-premises.
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
mmlspark - Simple and Distributed Machine Learning [Moved to: https://github.com/microsoft/SynapseML]
d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.