DeepStack Beta - Python Guide

DeepStack is an AI server that empowers every developer in the world to easily build state-of-the-art AI systems both on premise and in the cloud. The promises of Artificial Intelligence are huge but becoming a machine learning engineer is hard. DeepStack runs on the docker platform and can be used from any programming language.

You can learn more about Docker on Docker’s Website Visit Docker Getting Started for instructions on setting up and using Docker for the first time.

DeepStack is developed and maintained by DeepQuest AI

_images/image.jpg

Below, using DeepStack we attempt to classify the scene of the above image

import requests

image_data = open("image.jpg","rb").read()

response = requests.post("http://localhost:80/v1/vision/scene",files={"image":image_data}).json()

print(response)

Result

{'label': 'highway', 'success': True, 'confidence': 0.63377845}

You simply send in an image by POST and deepstack returns a JSON response detailing the label of the image as well as the confidence of the prediction on a scale of 0 - 1.

Installing DeepStack

The code above demonstrates using DeepStack to predict the scene of an image, to run this, you can install DeepStack and start it with a single docker command.

If you are not familiar with docker, you can learn how to use Docker here.

To install DeepStack on Docker, simply run the docker command below

docker pull deepquestai/deepstack

Once installed, you can run DeepStack with the command below

docker run -e VISION-SCENE=True -v localstorage:/datastore -p 80:5000 deepquestai/deepstack

The command above runs deepstack with the scene recognition activated, once this is running, you can run the example above.

GPU Accelerated Version

DeepStack runs many times faster on machines with NVIDIA GPUS, to install and use the GPU Version, read Using DeepStack with NVIDIA GPU

HARDWARE AND SOFTWARE REQUIREMENTS

DeepStack runs on any platform with Docker installed. However, for best performance, the following minimum requirements are highly recommended.

  • Intel Core i5 processor
  • 8 GB RAM
  • 10 GB Disk Space
  • Linux or Windows 10 Pro

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