fashion-mnist-kfp-lab
kubeflow
| fashion-mnist-kfp-lab | kubeflow | |
|---|---|---|
| 1 | 3 | |
| 0 | 13,683 | |
| - | 0.7% | |
| 0.0 | 8.3 | |
| about 3 years ago | 12 days ago | |
| Jupyter Notebook | TypeScript | |
| MIT License | Apache License 2.0 |
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fashion-mnist-kfp-lab
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Machine Learning Orchestration on Kubernetes using Kubeflow
Let's try to learn Kubeflow with an example. In this demo, we will try Kubeflow on a local Kind cluster. You should have at least 16GB of RAM, 8 CPUs modern machine to try it on your local machine, otherwise use a VM in cloud. We will use Zalando's fashion MNIST dataset and this notebook by manceps for demo.
kubeflow
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Is it possible to store the username in a config file inside the jupyter notebook spawned by kubeflow?
I'm not 100% sure this will work but sounds like PodDefault is what you need.
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Machine Learning Orchestration on Kubernetes using Kubeflow
If you are looking for bringing agility, improved management with enterprise-grade features such as RBAC, multi-tenancy and isolation, security, auditability, collaboration for the machine learning operations in your organization, Kubeflow is an excellent option. It is stable, mature and curated with best-in-class tools and framework which can be deployed in any Kubernetes distribution. See Kubeflow roadmap here to look into what's coming in the next version.
- Jupyter notebooks in kubeflow
What are some alternatives?
fashion-mnist - A MNIST-like fashion product database. Benchmark :point_down:
kserve - Standardized Serverless ML Inference Platform on Kubernetes
pipelines - Machine Learning Pipelines for Kubeflow
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
kfctl - kfctl is a CLI for deploying and managing Kubeflow
BentoML - The most flexible way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Inference Graph/Pipelines, Compound AI systems, Multi-Modal, RAG as a Service, and more!
polyaxon - MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
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.
kube-manifests - A collection of misc Kubernetes configs for various jobs, as used in Bitnami's production clusters.