kfctl
kubeflow
| kfctl | kubeflow | |
|---|---|---|
| 2 | 3 | |
| 174 | 13,700 | |
| - | 0.8% | |
| 0.0 | 8.3 | |
| 9 months ago | 4 days ago | |
| Go | TypeScript | |
| 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.
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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.
kfctl
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I am attempting to install kubeflow locally on my machine. After a certain point in the installation process I am getting: "scheme missing". I am not sure what this means.
it returns: https://github.com/kubeflow/kfctl/releases/download/v1.2.0/kfctl_v1.2.0-0-gbc038f9_darwin.tar.gz
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Machine Learning Orchestration on Kubernetes using Kubeflow
Due to some issue, I had to enable few feature gates and extra API server arguments to make it work. Please use the following Kind configuration to create the cluster.
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
fashion-mnist-kfp-lab - A notebook showing how to easily convert a current notebook you have to a notebook that can be run on Kubeflow Pipelines.
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
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
pipelines - Machine Learning Pipelines for Kubeflow
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.
mpi-operator - Kubernetes Operator for MPI-based applications (distributed training, HPC, etc.)