Deep Learning as a Cloud API Service.

Last update: Jan 06, 2023

Overview

Deep API

Deep Learning as Cloud APIs.

This project provides pre-trained deep learning models as a cloud API service. A web interface is available as well.

Quick Start
API Client
- Using curl
- Using Python
Concurrent clients
Full APIs

Quick Start

Python 3:

$ pip3 install -r requirements.txt
$ python main.py

Anaconda:

$ conda env create -f environment.yml
$ conda activate cloudapi
$ python main.py

Using Docker:

docker run -p 8080:8080 wuhanstudio/deep-api

Navigate to https://localhost:8080

API Client

It's possible to get predictions by sending a POST request to http://127.0.0.1:8080/vgg16_cifar10.

Using curl:

```
export IMAGE_FILE=test/cat.jpg
(echo -n '{"file": "'; base64 $IMAGE_FILE; echo '"}') | \
curl -H "Content-Type: application/json" \
     -d @- http://127.0.0.1:8080/vgg16_cifar10
```

Using Python:

def classification(url, file):
    # Load the input image and construct the payload for the request
    image = Image.open(file)
    buff = BytesIO()
    image.save(buff, format="JPEG")

    data = {'file': base64.b64encode(buff.getvalue()).decode("utf-8")}
    return requests.post(url, json=data).json()

res = classification('http://127.0.0.1:8080/vgg', 'cat.jpg')

This python script is available in the test folder. You should see prediction results by running python3 minimal.py:

cat            0.99804
deer           0.00156
truck          0.00012
airplane       0.00010
dog            0.00009
bird           0.00005
ship           0.00003
frog           0.00001
horse          0.00001
automobile     0.00001

Concurrent clients

Sending 5 concurrent requests to the api server:

$ python3 multi-client.py --num_workers 5 cat.jpg

You should see the result:

----- start -----
Sending requests
Sending requests
Sending requests
Sending requests
Sending requests
------ end ------
Concurrent Requests: 5
Total Runtime: 2.441638708114624

Full APIs

Post URLs:

Model	Dataset	Post URL
VGG-16	Cifar10	http://127.0.0.1:8080/vgg16_cifar10
VGG-16	ImageNet	http://127.0.0.1:8080/vgg16
Resnet-50	ImageNet	http://127.0.0.1:8080/resnet50
Inception v3	ImageNet	http://127.0.0.1:8080/inceptionv3

Post Data (JSON):

{
  "file": ""
}

Query Parameters:

Name	Type	Default	Value
top	integer	10	One of [1, 3, 5, 10], top=5 returns top 5 predictions.
no-prob	integer	0	no-prob=1 returns labels without probabilities. no-prob=0 returns labels and probabilities.

Example post urls (returns top 10 predictions with probabilities):

http://127.0.0.1:8080/vgg16?top=10&no-prob=0

Returns (JSON):

Key	Value
success	True / False
Predictions	Array of prediction results, each element contains {"labels": "cat", "probability": 0.99}
error	The error message if any

Example returned json:

{
  "success": true,
  "predictions": [
    {
      "label": "cat",
      "probability": 0.9996376037597656
    },
    {
      "label": "dog",
      "probability": 0.0002855948405340314
    },
    {
      "label": "deer",
      "probability": 0.000021985460989526473
    },
    {
      "label": "bird",
      "probability": 0.000021391952031990513
    },
    {
      "label": "horse",
      "probability": 0.000013297495570441242
    },
    {
      "label": "airplane",
      "probability": 0.000006046993803465739
    },
    {
      "label": "ship",
      "probability": 0.0000044226785576029215
    },
    {
      "label": "frog",
      "probability": 0.0000036349929359857924
    },
    {
      "label": "truck",
      "probability": 0.0000035354278224986047
    },
    {
      "label": "automobile",
      "probability": 0.000002384880417594104
    }
  ],
}

References

You might also like...

Deep Learning: Architectures & Methods Project: Deep Learning for Audio Super-Resolution

Deep Learning: Architectures & Methods Project: Deep Learning for Audio Super-Resolution Figure: Example visualization of the method and baseline as a

16 Dec 23, 2022

A simple rest api serving a deep learning model that classifies human gender based on their faces. (vgg16 transfare learning)

this is a simple rest api serving a deep learning model that classifies human gender based on their faces. (vgg16 transfare learning)

5 Dec 9, 2021

Pytorch implementation of Straight Sampling Network For Point Cloud Learning (ICIP2021).

Pytorch code for SS-Net This is a pytorch implementation of Straight Sampling Network For Point Cloud Learning (ICIP2021). Environment Code is tested

1 May 18, 2022

Deploy a ML inference service on a budget in less than 10 lines of code.

BudgetML is perfect for practitioners who would like to quickly deploy their models to an endpoint, but not waste a lot of time, money, and effort trying to figure out how to do this end-to-end.

1.3k Dec 25, 2022

An air quality monitoring service with a Raspberry Pi and a SDS011 sensor.

Raspberry Pi Air Quality Monitor A simple air quality monitoring service for the Raspberry Pi. Installation Clone the repository and run the following

24 Dec 9, 2022

Web service for facial landmark detection, head pose estimation, facial action unit recognition, and eye-gaze estimation based on OpenFace 2.0

OpenGaze: Web Service for OpenFace Facial Behaviour Analysis Toolkit Overview OpenFace is a fantastic tool intended for computer vision and machine le

4 Nov 3, 2022

Space-event-trace - Tracing service for spaceteam events

space-event-trace Tracing service for TU Wien Spaceteam events. This service is

2 Jan 4, 2022

Black-Box-Tuning - Black-Box Tuning for Language-Model-as-a-Service

Black-Box-Tuning Source code for paper "Black-Box Tuning for Language-Model-as-a

149 Jan 4, 2023

PyTorch implementation of the Deep SLDA method from our CVPRW-2020 paper "Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis"

Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis This is a PyTorch implementation of the Deep Streaming Linear Discriminant

41 Dec 25, 2022

Releases(v0.1.0)

v0.1.0(Oct 26, 2021)
Deep Learning as a Cloud API Service that supports:

Pretrained VGG16 model on Cifar10 dataset

Pretrained VGG16 model on ImageNet dataset

Pretrained Resnet50 model on ImageNet dataset

Pretrained Inceptionv3 model on ImageNet dataset

Automatic python client code generation

Automatic curl client code generation

A web interface for the api service

A minimal version is deployed here:

http://api.wuhanstudio.uk/
Source code(tar.gz)
Source code(zip)

Deep Learning as a Cloud API Service.

Related tags

Overview

Deep API

Quick Start

Python 3:

Anaconda:

Using Docker:

API Client

Using curl:

Using Python:

Concurrent clients

Full APIs

Post URLs:

Post Data (JSON):

Query Parameters:

Returns (JSON):

References

You might also like...

Deep Learning: Architectures & Methods Project: Deep Learning for Audio Super-Resolution

A simple rest api serving a deep learning model that classifies human gender based on their faces. (vgg16 transfare learning)

Pytorch implementation of Straight Sampling Network For Point Cloud Learning (ICIP2021).

Deploy a ML inference service on a budget in less than 10 lines of code.

An air quality monitoring service with a Raspberry Pi and a SDS011 sensor.

Web service for facial landmark detection, head pose estimation, facial action unit recognition, and eye-gaze estimation based on OpenFace 2.0

Space-event-trace - Tracing service for spaceteam events

Black-Box-Tuning - Black-Box Tuning for Language-Model-as-a-Service

PyTorch implementation of the Deep SLDA method from our CVPRW-2020 paper "Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis"

Releases(v0.1.0)

v0.1.0(Oct 26, 2021)

Owner

Wu Han

Enigma-Plus - Python based Enigma machine simulator with some extra features

Image restoration with neural networks but without learning.

End-to-End Object Detection with Fully Convolutional Network

Real-time pose estimation accelerated with NVIDIA TensorRT

Large-Scale Unsupervised Object Discovery

Code for Multiple Instance Active Learning for Object Detection, CVPR 2021

OBBDetection: an oriented object detection toolbox modified from MMdetection

A Domain-Agnostic Benchmark for Self-Supervised Learning

The sixth place winning solution (6/220) in 2021 Gaofen Challenge.

On the Adversarial Robustness of Visual Transformer

The reference baseline of final exam for XMU machine learning course

The PyTorch implementation of DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision.

DFM: A Performance Baseline for Deep Feature Matching

Modified prey-predator system - Modified prey–predator model describes the rate of change for each species by adding coupling terms.

Model Serving Made Easy

An imperfect information game is a type of game with asymmetric information

Coarse implement of the paper "A Simultaneous Denoising and Dereverberation Framework with Target Decoupling", On DNS-2020 dataset, the DNSMOS of first stage is 3.42 and second stage is 3.47.

PoseViz – Multi-person, multi-camera 3D human pose visualization tool built using Mayavi.

This repository contains the implementation of the paper: Federated Distillation of Natural Language Understanding with Confident Sinkhorns

This is the repository for the NeurIPS-21 paper [Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited Labels].