/ Machine Learning

Elyra extends JupyterLab Notebooks with an AI centric approach

Elyra extends JupyterLab Notebooks with an AI centric approach


Elyra is a set of AI-centric extensions to JupyterLab Notebooks.

Elyra currently includes:

Notebook Pipelines visual editor

Building an AI pipeline for a model is hard, breaking down and modularizing a pipeline is harder.
A typical machine/deep learning pipeline begins as a series of preprocessing steps followed by
experimentation/optimization and finally deployment. Each of these steps represent a challenge in
the model development lifecycle.

Elyra provides a Notebook Pipeline visual editor for building Notebook-based AI pipelines,
simplifying the conversion of multiple notebooks into batch jobs or workflow.

Currently, the only supported pipeline runtime is
Kubeflow Pipelines,
but others can be easily added.


The pipeline visual editor also enables detailed customization of your pipeline, allowing
users to choose which docker image to use when executing your notebook, setup environment
variables required to properly run your notebook, as well as configuring dependency files
that need to be available to child notebooks.


Ability to run a notebook as a batch job

Elyra also extends the notebook UI to simplify the submission of a single notebook as a batch job


Hybrid runtime support

Elyra leverages Jupyter Enterprise Gateway to enable Jupyter Notebooks
to share resources across distributed clusters such as Apache Spark, Kubernetes, OpenShift, and the like.

It simplifies the task of running notebooks interactively on cloud machines,
seamlessly leveraging the power of cloud-based resources such as GPUs and TPUs.

Python script execution support

Elyra exposes Python Scripts as first-class citizens, introducing the ability to
create python scripts directly from the workspace launcher, and leveraging the
Hybrid Runtime Support to allow users to locally edit their scripts and execute
them against local or cloud-based resources seamlessly.


Notebook versioning based on git integration

The integrated support for git repositories simplify tracking changes, allowing rollback to working versions
of the code, backups and, most importantly, sharing among team members - fostering productivity by
enabling a collaborative working environment.


Notebook navigation using auto-generated Table of Contents

The enhanced notebook navigation recognizes markdown titles, subtitles, etc to auto-generate
a Notebook Table of Contents providing enhanced navigation capabilities.


Reusable configuration for runtimes

Elyra introduces a 'shared configuration service' that simplifies workspace configuration management,
enabling things like external runtime access details to be configured once and shared
across multiple components.


Elyra can be installed via PyPi:

Prerequisites :

Optional :

via PyPi:

pip install elyra && jupyter lab build

Note: Ubuntu and CentOS users may need to use pip3 install elyra

Verify Installation

jupyter serverextension list

Should output:

config dir: /usr/local/etc/jupyter
    elyra  enabled
    - Validating...
      elyra  OK
    jupyterlab  enabled
    - Validating...
      jupyterlab 1.2.7 OK
    jupyterlab_git  enabled
    - Validating...
      jupyterlab_git  OK
    nbdime  enabled
    - Validating...
      nbdime 1.1.0 OK
jupyter labextension list

Should output:

Known labextensions:
   app dir: /usr/local/share/jupyter/lab
        @elyra/application v0.6.1  enabled  OK
        @elyra/notebook-scheduler-extension v0.6.1  enabled  OK
        @elyra/pipeline-editor-extension v0.6.1  enabled  OK
        @elyra/python-runner-extension v0.6.1  enabled  OK
        @jupyterlab/git v0.9.0  enabled  OK
        @jupyterlab/toc v2.0.0  enabled  OK
        nbdime-jupyterlab v1.0.0  enabled  OK

NOTE: If you don't see the elyra server extension enabled, you may need to explicitly enable
it with jupyter serverextension enable elyra

Starting Elyra

After verifying Elyra has been installed, start Elyra with:

jupyter lab

Runtime Configuration


  • A Kubeflow Pipelines Endpoint
  • IBM Cloud Object Storage or other S3 Based Object Store (Optional)

Configuring Runtime Metadata

AI Pipelines require configuring a pipeline runtime to enable its full potential.
AI Pipelines currently only support Kubeflow Pipelines with plans to expand support for other runtimes
in the future.

To configure runtime metadata for Kubeflow Pipelines use the jupyter runtimes install kfp command providing appropriate options. This command will create a json file in your local Jupyter Data directory under its metadata/runtimes subdirectories. If not known, the Jupyter Data directory can be discovered by issuing a jupyter --data-dir
command on your terminal.

Here's an example invocation of jupyter runtimes install kfp to create runtime metadata for use by Kubeflow Pipelines corresponding to the example values in the table below. Following its invocation, a file containing the runtime metadata can be found in [JUPYTER DATA DIR]/metadata/runtimes/my_kfp.json.

jupyter runtimes install kfp --name=my_kfp \
       --display_name="My Kubeflow Pipeline Runtime" \
       --api_endpoint=https://kubernetes-service.ibm.com/pipeline \
       --cos_endpoint=minio-service.kubeflow:9000 \
       --cos_username=minio \
       --cos_password=minio123 \

This produces the following content in my_kfp.json:

    "display_name": "My Kubeflow Pipeline Runtime",
    "schema_name": "kfp",
    "metadata": {
        "api_endpoint": "https://kubernetes-service.ibm.com/pipeline",
        "cos_endpoint": "minio-service.kubeflow:9000",
        "cos_username": "minio",
        "cos_password": "minio123",
        "cos_bucket": "test-bucket"

NOTE: In case of typing a custom bucket name using minio cloud storage, make sure the bucket name has no underscores

To validate your new configuration is available, run:

jupyter runtimes list

Available metadata for external runtimes:
  my_kfp    /Users/jdoe/Library/Jupyter/metadata/runtimes/my_kfp.json

Existing runtime metadata configurations can be removed via jupyter runtimes remove --name=[runtime]:

jupyter runtimes remove --name=my_kfp

Elyra depends on its runtime metadata to determine how to communicate with your KubeFlow Pipelines
Server and with your chosen Object Store to store artifacts.

Parameter Description Example
api_endpoint The KubeFlow Pipelines API Endpoint you wish to run your Pipeline. https://kubernetes-service.ibm.com/pipeline
cos_endpoint This should be the URL address of your S3 Object Storage. If running an Object Storage Service within a kubernetes cluster (Minio), you can use the kubernetes local DNS address. minio-service.kubeflow:9000
cos_username Username used to access the Object Store. SEE NOTE. minio
cos_password Password used to access the Object Store. SEE NOTE. minio123
cos_bucket Name of the bucket you want your artifacts in. If the bucket doesn't exist, it will be created test-bucket

NOTE: If using IBM Cloud Object Storage, you must generate a set of HMAC Credentials
and grant that key at least Writer level privileges.
Your access_key_id and secret_access_key will be used as your cos_username and cos_password respectively.

Development Workflow


Elyra is divided in two parts, a collection of Jupyter Notebook backend extensions,
and their respective JupyterLab UI extensions. Our JupyterLab extensions are located in our packages



make clean install

You can check that the notebook server extension was successful installed with:

jupyter serverextension list

You can check that the JupyterLab extension was successful installed with:

jupyter labextension list