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

Create a python environment


Option 1: use python virtual environment virtualenv.

virtualenv -p python3.6 ./env_mlcube && source ./env_mlcube/bin/activate

Option 2: use conda.

conda create -n mlcube python=3.6 && conda activate mlcube ```

Install MLCube Runners


Install MLCube Docker runner.

pip install mlcube-docker

Optionally, install other runners.

pip install mlcube-gcp

pip install mlcube-k8s

pip install mlcube-kubeflow

pip install mlcube-singularity

pip install mlcube-ssh

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