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Kubeflow Pipeline sample that uses custom docker image with private python code inside the image as base image for the pipeline components.

License: MIT License

Shell 2.69% Python 20.50% Dockerfile 3.28% Jupyter Notebook 73.53%

kubeflow-pipelines kubeflow-component kubeflow-pipeline kubeflow kubeflow-demo kubeflow-community

kubeflowpipelinemnist's Introduction

KubeflowPipelineMNIST

Local Jupyter Lap Setup

OPTIONAL : If you don't have docker installed, you can install it by running ./install-docker.sh

GPU

./setup-nvidia-container-toolkit.sh
./build-local-notebook-server.sh
./start-local-notebook-server-gpu.sh
Access jupyter lab via http://127.0.0.1:8889/lab?token=YOUR_TOKEN where YOUR_TOKEN is the token created in step 3. (check the output from step 3 to know your token).
Inside jupyter lab, click File->New->Terminal
Clone this git repo (eg: git clone https://github.com/AlexandreBrown/KubeflowPipelineMNIST.git)

CPU

./build-local-notebook-server.sh
./start-local-notebook-server-cpu.sh
Access jupyter lab via http://127.0.0.1:8889/lab?token=YOUR_TOKEN where YOUR_TOKEN is the token created in step 2. (check the output from step 2 to know your token).
Inside jupyter lab, click File->New->Terminal
Clone this git repo (eg: git clone https://github.com/AlexandreBrown/KubeflowPipelineMNIST.git)

Local Setup Result

Pipeline

Execute the cells from the notebook (see /notebooks)
Upload the generated pipeline yaml file to the Kubeflow pipeline UI

Pipeline Result

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