Isquare client for Python

This repository contains the official python client for ISquare deploy. It is available under the form of python classes which are ready to use in your code, as well as a command-line-interface. We currently support inference with image, text & json files, as well as any numpy array or python dictionnary or string, both for input and output.

The complete documentation for ISquare can be found here.

Installation

From pip

TODO

From source

pip install --editable .

Additional requirements

To be able to test your model builds, you need the following packages: Docker >= 19.03.13

Note: If you only need the client for inference, this is not required.

Usage

The client can be used to verify your model build (e.g. checking if they will properly run on ISquare) and to perform inference calls to your deployed models. To use this client for inference, you need to have a model up and running on ISquare.

Commands and their usage are described here.

End-to-end guidelines on the code adaptation required to deploy a model on isquare.ai can be found here.

Examples

Command line interface

Test if your model repository is Isquare-compatible

To verify if your code will run smoothly on ISquare, you can perform a local build & unit test. This will build a container image with all your specific dependencies and perform an inference test. We've included an example of a simple computer vision model which returns the mirrored image it is given, and it can be tested by running:

 i2py build examples/tasks/mirror.py

When you deploy a model with ISquare, you will be provided a url for the model, and requested to create access keys. Using a valid url & access keys (the one displayed are an example), you can perform an inference with an Image model (e.g. the Mirror) and a .png image by running:

i2py infer \
  --url wss://archipel-beta1.isquare.ai/43465956-8d6f-492f-ad45-91da69da44d0 \
  --access_uuid 48c1d60a-60fd-4643-90e4-cd0187b4fd9d \
  examples/test.png

Other examples can be found here.

Using a model inside your python code

As you probably want to automate your model calls by integrating them directly into your code, we've provided you with several python classes you can directly use in your code. The main class to use for that is the I2Client class. A simple inference can be performed as follows:

from i2_client import I2Client
import cv2

# You need your url, access key and an image
url = "wss://archipel-beta1.isquare.ai/43465956-8d6f-492f-ad45-91da69da44d0"
access_key = "472f9457-072c-4a1a-800b-75ecdd6041e1"
img = cv2.imread("test.jpg")

# Initialize the client & perform inference
inference_client = I2Client(url,access_key)
success, output = inference_client.inference(img)[0]

A more complex example, showing how to stream a camera to your model, can be found here.