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Which development tool should you use for Python development?

Jupyter Notebook is sure to be one of the many answers, and its interactive debugging, rich text support and other features have made it popular in teaching, scientific computing and more. Jupyter Notebook has become a popular development tool on GitHub, as frequent visitors to the site know.

Jupyter Notebook will also be mentioned as another tool, JupyterLab, which is being touted as the next generation of Jupyter Notebook.

It not only inherits the advantages of Jupyter Notebook, but also integrates more practical and efficient functions on the basis of Jupyter Notebook.

Therefore, JupyterLab these two years also gradually began to be recognized, welcome.

Elyra is a powerful extension for JupyterLab.

Elyra is a set of AI-centric extensions to JupyterLab with the following features:

  • Visualize pipelining and editors
  • The ability to run Notebook as a batch job
  • Support for mixed operation
  • The ability to execute Python scripts simultaneously in the editor
  • Reusable code snippets
  • Integrate Git version control
  • Automatically generate directory navigation

Here’s a look at each of Elyra’s features:

Visualize pipelining and editors

In an AI project, this breaks down into several steps:

  • Data preprocessing
  • Feature extraction
  • training
  • Model to evaluate
  • The deployment of

Many mature machine learning platforms have introduced a visual Pipline to link these processes together and clearly see the state of each execution step.

Elyra provides a Notebook Pipeline visual editor for building a Notebook based AI Pipeline, simplifying the process of converting multiple Notebooks into batch jobs or workflows.

The visual editor can also customize the pipeline in detail, allowing the user to choose which Docker image to use when executing the notebook, set the environment variables needed to run the notebook correctly, and the dependency files needed to configure the child notebook.

The ability to run Notebook as a batch job

Elyra has also extended the JupyterLab user interface to simplify the submission of a single Notebook as a batch job.

Support for mixed operation

Elyra uses the Jupyter Enterprise Gateway to share Jupyter Notebook resources across distributed clusters such as Apache Spark, Kubernetes, OpenShift, and more.

It seamlessly leverages the capabilities of cloud-based resources such as Gpus and TPUS, simplifying the task of interactively running a notebook on a cloud computer.

The ability to execute Python scripts simultaneously in the editor

Elyra introduces the ability to create Python scripts directly from the Workspace launcher, and utilizes Hybrid Runtime Support to allow users to edit their scripts locally and execute them seamlessly for local or cloud-based resources.

Reusable code snippets

Elyra supports snippets in the Beta version. This allows users to add reusable custom snippets of code to improve programming efficiency in JupyterLab by reducing rework.

Integrate Git version control

Elyra integrates Git version control, which supports code rollback, backup, collaboration, and more to improve development efficiency.

Automatically generate directory navigation

Enhanced Notebook navigation automatically generates Notebook directories that provide enhanced navigation.

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