Python Client for DLTK.
Project description
DLTK SDK
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
Note: To use third party AI engines please provide your credentials. Instructions on getting credentials and configuring are provided below.
Installation
Installing through pip
pip install qubitai-dltk
Installing from Source
a. Clone the repo
git clone https://github.com/dltk-ai/qubitai-dltk.git
b. Set working directory to qubitai-dltk folder
c. Install requirements from requirements.txt file
pip install -r requirements.txt
Choose any one of the above options for Installation
Usage
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)
Example notebooks for all the services are provided and are accesible when the user installs qubitai-dltk from source.
Important Parameters:
1. API key:
2. base_url: The base_url is the url for the machine where base service is installed by default its localhost, so base_url needs to be http://localhost:8000
Expected Output
{
"spacy": {"emotion": "POSITIVE", "scores": {"neg": 0.0, "neu": 0.653, "pos": 0.347, "compound": 0.7496}}
}
Services
1. Machine Learning
-
ML Wrapper - It parse user request parameters
-
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.
2. NLP
- This microservice provides features like Sentiment analysis, Name Entity Recognition, Tag Extraction using widely used
Spacy
andNLTK
package. It also provide support for various AI engines like Azure & IBM.
3. Computer Vision
-
CV Wrapper - This microservice receives images provided by user and route to right service based on the feature requested by them.
-
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.
Reference
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)
Contact
QubitAI Email-ID - connect@qubitai.tech
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