graph-notebook VS ML-Workspace

We will now configure a Neptune graph notebook to access the cluster, so we can run queries and generate interactive visualisations. Neptune Workbench allows users to run fully managed jupyter notebook environment in Sagemaker with the latest release of the open source graph Neptune project. This has the benefit of offering in-built capabilities like visualisation of queries

This blog follows on from the setup of Neptune DB with Worldwide Events data in the first part. Here we will run some queries and investigate node relationships in the Neptune Notebook. We can use some of the magic commands available in graph notebook open sourced by AWS and available to use in the Neptune Notebook instance configured in the first part of this blog. We can configure the visualisation options available in the notebook when we execute the query, using the%graph_notebook_vis_options command. This will output a json containing the default configuration options for rendering the graphs.

AWS Graph Notebooks

Also check out: https://github.com/ml-tooling/ml-workspace, it a nice open source project with lots of packages ready to use.

The other option - no reason why you couldn't host something on the desktop machine - web based IDE like R-Studio or Python - have a look at ml-workspace - https://github.com/ml-tooling/ml-workspace that runs in Docker and would provide interfaces for both Python and R, VSCode as well as a GPU accelerated variant for doing Tensorflow etc - either Windows or Linux can support Docker containers (Linux is less trouble apparently - I only have played with it in Linux personally)

It will be a web based IDE dev kit (like Jupyter Hub, or JupyterLab) if you are familiar with them)

I'll stick with https://github.com/ml-tooling/ml-workspace, is a docker with all tools installed, also the option of using GPU, so I think is better than only for debian. This way anyone can use it.