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SensiML Analytic Suite Python client

Project description

SensiML python client provides access to SensiML Analytics services for building machine learning pipelines including data processing, feature generation and classification for developing smart sensor algorithms optimized to run on embedded devices.

Installation

Download the Analytic Studio which will install a python 3 environment along with all the requirements to run the SensiML Library.

https://sensiml.cloud/downloads

You can also install directly from pypy repository using pip. We recommend having python >= 3.7

pip install sensiml -U

Our library is designed to be used within a jupyter notebook. For our GUI to work correctly you will need to also install nbextension to jupyter notebook. Installation instructions can be found here

https://jupyter-contrib-nbextensions.readthedocs.io/en/latest/install.html

You will need to enable the following extensions. bqplot, ipywidgets and qgrid.

jupyter contrib nbextension install –user

jupyter nbextension enable bqplot

jupyter nbextension enable ipywidgets

jupyter nbextension enable qgrid

Connect to SensiML Analytic Engine

Once you have installed the software, you can connect to the server by running the following in a notebook cell.

from sensiml import *

sml = SensiML()

Connecting to SensiML servers requires and account, you can register at https://sensiml.cloud/accounts/register

Documentation can be found here https://sensiml.atlassian.net/wiki/spaces/SS/overview as well as in the Analytic Studio.

For information about SensiML, to get in touch, or learn more about using our platform to build machine learning models suitable for performing real-time timeseries classification on embedded devices you can reach us at https://sensiml.com/#contact

Project details


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Files for SensiML, version 2019.3.6
Filename, size File type Python version Upload date Hashes
Filename, size SensiML-2019.3.6-py3-none-any.whl (173.0 kB) File type Wheel Python version py3 Upload date Hashes View hashes
Filename, size SensiML-2019.3.6.tar.gz (120.0 kB) File type Source Python version None Upload date Hashes View hashes

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