Getting Started

Installation

pip install hiplot

Once installed, you can use Hiplot in two ways.

As a python library in Jupyter Notebooks

import hiplot as hip

As a Flask web app by launching the webserver in terminal/cmd:

(To launch as localhost)

>>> hiplot (To enable sharing plot URLs)

>>> hiplot --host 0.0.0.0

Note: For using the web app, the implementation of accompanying experiment fetchers is essential which are outlined in the Advanced Capabilities section later.

—

A simple everyday scenario in the Notebooks

Below is an everyday use case of leveraging HiPlot to analyze how various learning rates, dropouts, and optimizers affected the training loss.

import hiplot as hip

data = [

{'dropout':0.1,

'learning_rate': 0.001,

'optimizer': 'SGD',

'loss': 10.0

},

{'dropout':0.15,

'learning_rate': 0.01,

'optimizer': 'Adam',

'loss': 3.5

},

{'dropout':0.3,

'learning_rate': 0.1,

'optimizer': 'Adam',

'loss': 4.5

}] hip.Experiment.from_iterable(data).display(force_full_width=True)

This yields the following plot along with the tabular view underneath.

Yup, that’s how simple it is.