A workshop, showing how to use Nuclio & MLRun with Kubeflow to deploy end to end machine learning pipelines, with Multiple runtimes (serverless functions, jobs, etc...), Artifacts & Code tracking and Automation directly from your jupyter notebook!
We will install Nuclio and MLRun (Jupyter environment and UI) over docker for easy installation.
- Nuclio [website|github|installation notes]
- MLRun [github|installation notes]
docker pull quay.io/nuclio/dashboard:stable-amd64
docker pull mlrun/jupyter:TMLS
docker pull mlrun/mlrun-ui:0.5.4
You can change the SHARED_DIR to another path for storing the data/artifacts
SHARED_DIR=~/mlrun-data
docker network create mlrun-network
docker run -it -p 8080:8080 -p 8888:8888 --rm -d --network mlrun-network --name jupyter -e NUCLIO_DASHBOARD_URL=http://nuclio:8070 -v ${SHARED_DIR}:/home/jovyan/data mlrun/jupyter:TMLS
docker run -it -p 4000:80 --rm -d --network mlrun-network --name mlrun-ui -e MLRUN_API_PROXY_URL=http://jupyter:8080 mlrun/mlrun-ui:0.5.4
docker run -p 8070:8070 --rm -d --network mlrun-network --name nuclio -v /var/run/docker.sock:/var/run/docker.sock -v /tmp:/tmp quay.io/nuclio/dashboard:stable-amd64
Open the browser pointing to Jupyter, Nuclio, and MLRUn UIs at:
- Jupyter: http://localhost:8888/
- MLRun: http://localhost:4000/
- Nuclio: http://localhost:8070/
- Review some background about the problems of moving to production and how we can use this set of tools to help automate it.
- We will review how Nuclio integrates with Jupyter for easy deployment by using
nuclio-jupyter.
We will deploy an example translation (NLP) endpoint and use this template to create a function of our own. - We will review MLRun with some basics and a local example, a full getting started tutorial to setup a project, tracking results, lunching a pipeline and how to easily create serving endpoints with MLRun's Serving runtime.
Through this process we will share about best practices, how to ease our work and innovate fast!