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Here is the step-by-step guide to install MLCube® library and run simple MLCube cubes.

Create a python environment

conda create -n mlcube python=3.8
conda activate mlcube
virtualenv --python=3.8 .mlcube
source .mlcube/bin/activate

Install MLCube Runners

Reference MLCube runners are distributed in separate python packages.

pip install mlcube-docker    
pip install mlcube-singularity    
pip install mlcube-gcp    
pip install mlcube-k8s    
pip install mlcube-kubeflow    
pip install mlcube-ssh    


GCP (Google Cloud Platform), K8S (Kubernetes), Kubeflow and SSH runners are in early stages of development.

Check that the docker runner has been installed.

mlcube config --get runners

Show MLCube system settings.

mlcube config --list


This system settings file (~/mlcube.yaml) configures local MLCube runners. Documentation for MLCube runners describes each of these parameters in details. A typical first step for enterprise environments that are usually behind a firewall is to configure proxy servers.

      http_proxy: http://ADDRESS:PORT
      https_proxy: https://ADDRESS:PORT
      http_proxy: http://ADDRESS:PORT
      https_proxy: https://ADDRESS:PORT

Explore with examples

A great way to learn about MLCube is try out the example MLCube cubes located in the mlcube_examples GitHub repository.

git clone 
cd ./mlcube_examples
mlcube describe --mlcube ./mnist