JupyterLab Extension #
The JupyterLab Mount Extension enables you to quickly iterate and validate a pipeline specification’s transform.image user code and input spec without having to build and push a new docker image each time you make a change or discover a bug.
Notebooks are connected directly to your HPE Machine Learning Data Management projects, repos, branches, and data, allowing you to explore and manipulate your data in the same environment where you are developing your pipeline code.
Before You Start #
Install the Extension #
There are two main ways to install the Jupyter Lab extension:
- ⭐ Via Docker: Fastest implementation!
- 🧪 Locally: Great for development and testing
Examples #
Make sure to check our data science notebook examples running on HPE Machine Learning Data Management, from a market sentiment NLP implementation using a FinBERT model to pipelines training a regression model on the Boston Housing Dataset. You will also find integration examples with open-source products, such as labeling or model serving applications.