https://github.com/saidsef/kubeflow-on-k8s
Kubeflow running on Kubernetes - Machine Learning Toolkit for Kubernetes
https://github.com/saidsef/kubeflow-on-k8s
kubeflow kubernetes machine-learning ml notebook-jupyter workflow-engine
Last synced: 8 days ago
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Kubeflow running on Kubernetes - Machine Learning Toolkit for Kubernetes
- Host: GitHub
- URL: https://github.com/saidsef/kubeflow-on-k8s
- Owner: saidsef
- License: mit
- Created: 2018-11-24T01:11:09.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2019-03-18T23:30:38.000Z (over 6 years ago)
- Last Synced: 2025-04-12T03:37:22.352Z (6 months ago)
- Topics: kubeflow, kubernetes, machine-learning, ml, notebook-jupyter, workflow-engine
- Language: Jsonnet
- Homepage:
- Size: 735 KB
- Stars: 4
- Watchers: 3
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# Kubeflow on Kubernetes Cluster
Kubeflow Pipelines are a new component of Kubeflow that can help you compose, deploy, and manage end-to-end (optionally hybrid) machine learning workflows. Because they are a useful component of Kubeflow, they give you a no lock-in way to advance from prototyping to production. Kubeflow Pipelines also support rapid and reliable experimentation, so users can try many ML techniques to identify what works best for their application.
## Useful Links
- [Getting Started Kubeflow Pipelines](https://cloud.google.com/blog/products/ai-machine-learning/getting-started-kubeflow-pipelines)
- [Introducing Kubeflow Composable](https://kubernetes.io/blog/2017/12/introducing-kubeflow-composable/)
# Kubeflow Version
- Kubeflow v0.4.1
# Prerequisites
- Git
- Ksonnet
- Golang
- Kubernetes Cluster
- Kubectl
# Components
- ambassador
- argo
- centraldashboard
- jupyterhub
- katib
- params.libsonnet
- pytorch-operator
- seldon
- spartakus
- tf-job-operator
# Deployment
```shell
export KUBEFLOW_TAG=0.4.1
export NAMESPACE=kubeflow
git clone https://github.com/saidsef/kubeflow-on-k8s.git
cd kubeflow-on-k8s/
git submodule foreach git pull origin master
mkdir -p /mnt/{katib-mysql,kf-ml-data,kf-openvino,kf-minio}
kubectl apply -f ./deployment --namespace ${NAMESPACE}
ks apply default --namespace ${NAMESPACE} # append `--dry-run` for dry run
kubectl get all -n ${NAMESPACE}
```