This document discusses deploying machine learning workloads with Kubernetes and Kubeflow. It covers setting up a Kubeflow cluster, training a model using TFJob, serving the model with Seldon Core, querying the model, and using Ksonnet to generate and apply Kubernetes manifests for Kubeflow components like TF serving.

1. Deploying Machine Learning Workloads
with Kubernetes and Kubeflow
@Ben_Hall
Ben@BenHall.me.uk
Katacoda.com

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11. • Setup a Kubeflow cluster
• Training the model using TFJob
• Serving the model with Seldon Core
• Querying the model
• https://github.com/kubeflow/examples/tree/mast
er/github_issue_summarization

12. Ksonnet
ks registry add kubeflow github.com/kubeflow/kubeflow/tree/master/kubeflow
ks pkg install kubeflow/core
ks pkg install kubeflow/tf-serving
ks pkg install kubeflow/tf-job
ks generate kubeflow-core kubeflow-core --namespace=${NAMESPACE}
ks apply default -c kubeflow-core

13. Ksonnet
MODEL_COMPONENT=model-server
MODEL_NAME=inception
MODEL_PATH=/serving/inception-export
cd ~/my-kubeflow
ks generate tf-serving ${MODEL_COMPONENT} --name=${MODEL_NAME}
ks param set ${MODEL_COMPONENT} modelPath $MODEL_PATH
ks param set ${MODEL_COMPONENT} modelServerImage katacoda/trainedmodel:0.1
ks apply default -c ${MODEL_COMPONENT}

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15. Thank you!
Questions? Feedback?
@Ben_Hall
Ben@BenHall.me.uk
Blog.BenHall.me.uk
www.Katacoda.com