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Library by QuantumCore: Revolutionary ML Company

Project description


PyQCore is a client in Python to access our high speed reservoir computing engine, WebQore.

We are in an early-release beta. Expect some adventures and rough edges.


2019.06.13 Release 0.2.1: Add sofmax_top option to classification_predict to show the softmax values with predicted classes. 2019.03.08 Release 0.2.0: Add regression_train, regression_test, regression_predict.

More about PyQCore

At a granular level, PyQcore is a library that consists of the following components:

Component Description
pyqcore.client.SimpleQCoreClient a client class that makes model on WebQore Engine
pyqcore.examples.jpvow.load_jpvow a function that loads sample data from UCI repository and return as sklearn format

This library is enable to use the WebQore Engine in your codes. Also you need get license code from QuantumCore website to use this library(QuantumCore Inc. ).

About QuantumCore Engine

WebQore Engine is developed by QuantumCore Inc. This new model is 4Kx faster and lighter than previous LSTM or RNN model without GPU. Also you do not have to adjust parameters anymore.
The Engine you use with this library is currently running on t2.micro on AWS (check out the speck here).
The company also plans to impliment this algorithm on small edge computer.


This library requires Python > 3.5.

Installing PyQcore is not difficult. Just run the command below:

pip install pyqcore

Or clone this repository and run:

python install

Getting Started

Refer our sample scrpts or tutorial notebook.
Make sure that you need get license code from QuantumCore Inc.


  • GitHub issues: bug reports, feature requests, install issues, RFCs, thoughts, etc.
  • Mailing List: please send any issue at


Copyright (c) 2019 QuantumCore Inc.

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