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Getting started

Installing

Python version

HiPlot requires Python version greater than 3.9 (you can check your Python version with python3 --version)

Python virtualenv

We advise that you create a virtualenv for HiPlot, if you don't use one already.

On Linux/MacOS

# Create a virtualenv
python3 -m venv venv_hiplot
# Activate it
. venv_hiplot/bin/activate

On Windows

py -3 -m venv venv_hiplot
venv_hiplot\Scripts\activate

Install HiPlot

Within the activated environment, use the following command to install HiPlot:

pip install -U "hiplot-mm>=0.0.4"

Congratulation, HiPlot is now ready to use! You can either:

  • Use it to render python data in a notebook
  • Or start it as a webserver to track, compare and visualize your experiments

Option 1: Use HiPlot in an ipython notebook

Here we assume that we have a list of several datapoints. HiPlot can only render :class:`hiplot.Experiment` objects, so we create one with :class:`hiplot.Experiment.from_iterable`. Once we have created this object, we can display it with :class:`hiplot.Experiment.display`.

import hiplot as hip
data = [{'dropout':0.1, 'lr': 0.001, 'loss': 10.0, 'optimizer': 'SGD'},
        {'dropout':0.15, 'lr': 0.01, 'loss': 3.5, 'optimizer': 'Adam'},
        {'dropout':0.3, 'lr': 0.1, 'loss': 4.5, 'optimizer': 'Adam'}]
hip.Experiment.from_iterable(data).display()

Learn more in the tutorial: :ref:`tutoNotebook`

Option 2: Use HiPlot webserver

Within the activated environment, use the following command to run HiPlot server:

>>> hiplot

Then open your web browser at http://127.0.0.1:5005/. In the web interface, you can enter an experiment URI - you can enter the path to a CSV file, or just type in demo, or demo_line_xy to see some basic examples.

Note

By default, hiplot only listens on localhost, which prevents anyone else from seeing your experiments. To allow anyone to connect, use

>>> hiplot --host 0.0.0.0

HiPlot webserver can do way more:

Option 3: Create data-apps using Streamlit streamlit_logo

Streamlit allows data scientists and machine learning engineers to create beautiful, performant apps in pure Python.

This is the best way to create custom interfaces with HiPlot. For instance, you can perform dynamic actions based on selected rows inside HiPlot (like plotting or displaying further information), and still have a sharable/deployable interface.

Learn more in the tutorial: :ref:`tutoStreamlit`

../assets/streamlit.png

Here we let the user modify the dataset before displaying it

Option 4: Render standalone HTML files

We provide a CLI tool hiplot-render to render HiPlot experiments into standalone HTML files, containing all HiPlot files, and your data. To render a demo, or your own CSV file, use:

>>> hiplot-render demo > hiplot_demo.html
>>> hiplot-render /path/to/your/file.csv > hiplot.html

If your data is not already in the CSV format, you can either convert it to CSV, or see how to :ref:`tutoWebserverCustomFetcher`.