JUPYTERLAB VARIABLE INSPECTOR INSTALL
JUPYTERLAB VARIABLE INSPECTOR UPGRADE

The Variable Inspector extension allows inspection of variables for both consoles and notebooks metrics in a datagrid-viewer and allows the inline and interactive inspection of Jupyter Widgets. The goal is to provide a tool similar to the variable inspector in RStudio. This Jupyterlab extension features the variables currently in use and displays their values. Several bugs in ADS have been fixed including the upsampling recommendation, visualizations issues for model evaluation, and so on. jupyterlabvariableinspector Jupyterlab extension that shows currently used variables and their values.copied from cf-staging / jupyterlab-variableinspector. Variable Inspector extension for Jupyterlab.

JUPYTERLAB VARIABLE INSPECTOR UPGRADE

Dask version upgrade (version 2.10.1) with support for OCI Object Storage. conda-forge / packages / jupyterlab-variableinspector 3.0.91.

You can now profile Dask operations in ADS, which allows you to monitor the CPU and memory consumption in your notebook session.Instead the user needs to use import h2o to own and start their own cluster. ADS no longer instantiates an H2O cluster on behalf of the user.Allows an inline and interactive inspection of Jupyter Widgets. Allows inspection of matrices in a datagrid-viewer.

JUPYTERLAB VARIABLE INSPECTOR INSTALL

Contributions in any form are welcome Features Allows inspection of variables for both consoles and notebooks. (Optional) Installing Variable Inspector If you want, you can install a special extension for JupyterLab called Variable Inspector, which will allow you to view currently active Python variables basically, it will give you a better window into what’s going on behind the scenes when you’re coding.

DatasetFactory.open() with format='sql' no longer requires index_col to be specified. Additionally, the table parameter can be either a table or a sql expression in DatasetFactory.open(). Jupyterlab extension that shows currently used variables and their values.

Performance improvements of the ADW dataset loader resulting in a significant speedup.

The Accelerated Data Science (ADS) SDK is enhanced as follows:

Renders geojson, fasta, plotly, bokeh, and json files natively.

The JupyterLab notebook session interface is enhanced so that the JupyterLab environment now supports:

Jupyterlab variable inspector