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.
platforms: docker: env_args: http_proxy: http://ADDRESS:PORT https_proxy: https://ADDRESS:PORT build_args: 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 https://github.com/mlcommons/mlcube_examples.git cd ./mlcube_examples mlcube describe --mlcube ./mnist