qubitai-dltk 1.0.9

Python Client for DLTK.

About

Our philosophy is to create a Deep Technologies platform with ethical AI for enterprises that offers meaningful insights and actions.

DLTK Unified Deep Learning platform can be leveraged to build solutions that are Application-Specific and Industry-Specific where AI opportunity found by using DLTK SDKs, APIs and Microservices. With best of the breed AI Services from platform pioneers like H2O, Google's TensorFlow, WEKA and a few trusted open-sources models and libraries, we offer custom AI algorithms with co-innovation support.

Getting Started

Pre-requisite

• OpenDLTK : OpenDLTK is collection of open-source docker images, where processing of images, text or structured tabular data is done using state-of-the-art AI models.

  Please follow the below link for instructions on OpenDLTK Installation

Installation

Installing through pip

pip install qubitai-dltk

Installing from Source

1. Clone the repo

git clone https://github.com/dltk-ai/qubitai-dltk.git

2. Set working directory to qubitai-dltk folder

cd qubitai-dltk

3. Install requirements from requirements.txt file

pip install -r requirements.txt

Usage

A detailed documentation is present here, on how to use various services supported by DLTK, to verify whether all setup are done properly, we will be using a sample NLP code to analyze sentiment of the input text.

Example

import dltk_ai
client = dltk_ai.DltkAiClient(base_url='http://localhost:8000')

text = "The product is very easy to use and has got a really good life expectancy."

sentiment_analysis_response = client.sentiment_analysis(text)

print(sentiment_analysis_response)

Important Parameters:

APIkey : a valid API key generated by following steps as shown here

base_url : The base_url is the url for the machine where base service is installed. (default: http://localhost:8000)

Expected Output

{
  "nltk_vader": {"emotion": "POSITIVE", "scores": {"negative": 0.0, "neutral": 0.653, "positive": 0.347, "compound": 0.7496}}
}

Services

Machine Learning

ML Scikit - This Microservice uses widely used Scikit package for training and evaluating classification, regression, clustering models and other ML related tasks on dataset provided by user.

ML H2O - This Microservice uses H2O.ai python SDK for training and evaluating classification, regression, clustering models and other ML related tasks on dataset provided by user.

ML Weka - This Microservice uses WEKA for training and evaluating classification, regression, clustering models and other ML related tasks on dataset provided by user.

Example Notebooks

• ML Classification Colab Notebook
• ML Regrression Colab Notebook
• ML Clustering Colab Notebook

Natural Language Processing (NLP)

• This microservice provides features like Sentiment analysis, Name Entity Recognition, Tag Extraction using widely used Spacy and NLTK package. It also provide support for various AI engines like Azure & IBM.

Example Notebook

• NLP Colab Notebook

Computer Vision

• Image Classification - This microservice classify images into various classes using pretrained model and also using supported AI Engines.

• Object Detection - This microservice detect objects in Images provided by user using pretrained model and using supported AI Engines.

Example Notebooks

• Image Classification Colab Notebook
• Object Detection Colab Notebook
• Face Analytics Colab Notebook

Note

• To use third party AI engines like Microsoft Azure & IBM watson, please ensure that its credentials were configured while setting up openDLTK.

Documentation

For more detail on DLTK features & usage please refer DLTK SDK Client Documentation

License

The content of this project itself is licensed under GNU LGPL, Version 3 (LGPL-3)

Team

Founding Member	Mentor	Lead Maintainer	Core Contributor

For more details you can reach us at QubitAI Email-ID - [e-mail]