Getting Started with DeepStack

DeepStack is distributed as a docker image. In this tutorial, we shall go through the complete process of setting up docker and DeepStack and using it to build a Face Recognition system.

Setting Up Docker

Docker is a container platform that allows developers to distribute applications as self-contained packages that ships every dependency from the operating system to the app dependences. It is similar to virtual machines but is more lightweight and easier to manage.

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.

Installing DeepStack

DeepStack is an AI Server that provides AI features as APIs consumable via basic web requests. It works entirely offline and can be installed anywhere docker runs both on premise and in the cloud.

DeepStack is developed and maintained by DeepQuest AI

Run the command below to install DeepStack

docker pull deepquestai/deepstack

To install the GPU Accelerated Version, follow Using DeepStack with NVIDIA GPU

Starting DeepStack

Below we shall run DeepStack with only the FACE features enabled

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

Basic Parameters

-e VISION-FACE=True This enables the face recognition APIs, all apis are disabled by default.

-v localstorage:/datastore This specifies the local volume where deepstack will store all data.

-p 80:5000 This makes deepstack accessible via port 80 of the machine.


If you installed the GPU Version, remmember to add the args args –rm –runtime=nvidia The equivalent run command for the gpu version is

sudo docker run --rm --runtime=nvidia -e VISION-FACE=True -v localstorage:/datastore \
-p 80:5000 deepquestai/deepstack:gpu

Face Recognition

Think of a software that can identity known people by their names. Face Recognition does exactly that. Register a picture of a number of people and the system will be able to recognize them again anytime. Face Recognition is a two step process: The first is to register a known face and second is to recognize unknown faces.


Here we are building an application that can tell the names of a number of popular celebrities. First we collect pictures of a number of celebrities and we register them with deepstack


Below we will register the faces with their names

const request = require("request")
const fs = require("fs")

run_prediction("cruise.jpg","Tom Cruise")
run_prediction("elba.jpg","Idris Elba")
run_prediction("perri.jpg","Christina Perri")

function run_prediction(image_path,userid){

    image_stream = fs.createReadStream(image_path)

    var form = {"image":image_stream,"userid":userid}{url:"http://localhost:80/v1/vision/face/register", formData:form},function(err,res,body){

        response = JSON.parse(body)




{ success: true, message: 'face added' }
{ success: true, message: 'face added' }
{ success: true, message: 'face added' }

{ success: true, message: ‘face updated’ }


Now we shall attempt to recognize any of these celebrities using DeepStack. Below we will send in a whole new picture of Adele and DeepStack will attempt to predict the name.


Prediction code

const request = require("request")
const fs = require("fs")

image_stream = fs.createReadStream("test-image.jpg")

var form = {"image":image_stream}{url:"http://localhost:80/v1/vision/face/recognize", formData:form},function(err,res,body){

    response = JSON.parse(body)
    predictions = response["predictions"]
    for(var i =0; i < predictions.length; i++){






We have just created a face recognition system. You can try with different people and test on different pictures of them.

The next tutorial is dedicated to the full power of the face recognition api as well as best practices to make the best out of it.