Skip to main content

Python Client for DLTK.

Project description

DLTK logo

Python 3.8


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


  • 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


Installing through pip

pip install qubitai-dltk

Installing from Source

  1. Clone the repo
git clone
  1. Set working directory to qubitai-dltk folder
cd qubitai-dltk
  1. Install requirements from requirements.txt file
pip install -r requirements.txt


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.


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)


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}}


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 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

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

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


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


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


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


Founding Member Mentor Lead Maintainer Core Contributor

For more details you can reach us at QubitAI Email-ID -

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

qubitai-dltk-1.0.9.tar.gz (63.0 kB view hashes)

Uploaded source

Built Distribution

qubitai_dltk-1.0.9-py3-none-any.whl (64.1 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page