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:
- 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
- on a Mac, the easiest way is probably
- 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
- tip: if you don't have
- Change to this repository's directory:
cd ml-workflows-notebook
- Install the dependencies:
pipenv install --skip-lock
- Run the notebooks:
pipenv run jupyter notebook
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!