Machine learning workflows for developers

This repository has materials from a hands-on tutorial on machine learning workflows (and using OpenShift for these). We'll have more documentation for how to run the lab on your own time in the future, but for now you can run the notebooks and build pipelines.

Here's how to run the interactive notebooks:

Install the prerequisites

Make sure you have Python 3.7 installed, installing it if necessary
- If you have a favorite package manager, use that
- if not, python.org has binaries for many platforms
Make sure you have git installed, installing it if necessary
- If you have a favorite package manager, use that
- if not, git-scm.com has binaries for many platforms (you won't need a GUI)
Install pipenv
- on a Mac, the easiest way is probably brew install pipenv
- on a Fedora Linux machine, the easiest way is probably dnf install pipenv
- on Windows, if you have Python installed already, the easiest way is probably to use pip

Install the notebooks and dependencies

Clone this repository: git clone https://github.com/willb/ml-workflows-notebook/
- tip: if you don't have git installed, you can also download an archive of this repository
Change to this repository's directory: cd ml-workflows-notebook
Install the dependencies: pipenv install --skip-lock
Run the notebooks: pipenv run jupyter notebook

Running the lab on an OpenShift cluster:

Alternatively, to run our lab on an OpenShift cluster (including on your personal computer with minishift or oc cluster), run the following command:

oc create -f https://raw.githubusercontent.com/willb/ml-workflows-for-developers/summit2019/resources.yaml

Our slides from presenting the lab at Red Hat Summit 2019 are online.

Contact willb@redhat.com with any questions!

DevOps-SmartApps/ml-workflows-notebook

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