Docs link fix

README.md

@@ -1,9 +1,9 @@
 ![Isquare deploy logo](docs/imgs/deploy_logo.png)
 # Isquare client for Python
 
-This repository contains the official python client for [ISquare](isquare.ai) 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. 
+This repository contains the official python client for [ISquare](http://isquare.ai) 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](docs.isquare.ai).
+The complete documentation for ISquare can be found [here](http://docs.isquare.ai).
 
 ## Installation
 
@@ -25,7 +25,7 @@ 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](isquare.ai)) 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](isquare.ai).
+The client can be used to verify your model build (e.g. checking if they will properly run on [ISquare](http://isquare.ai)) 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](http://isquare.ai).
 
 Commands and their usage are described [here](docs/commands.md).
 
@@ -36,12 +36,12 @@ End-to-end guidelines on the code adaptation required to deploy a model on isqua
 ### Command line interface
 
 #### Test if your model repository is Isquare-compatible
-To verify if your code will run smoothly on [ISquare](isquare.ai), 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:
+To verify if your code will run smoothly on [ISquare](http://isquare.ai), 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:
 
 ```bash
  i2py build examples/tasks/mirror.py
 ```
-When you deploy a model with [ISquare](isquare.ai), 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:
+When you deploy a model with [ISquare](http://isquare.ai), 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:
 
 
 ```bash

docs/isquare_tutorial.md

@@ -121,7 +121,7 @@ We strongly recommend that you implement batched inference for your model, since
 And that's all you need to get going from the code point of view. Let's proceed, setup our environment and deploy our model!
 
 ### Step 1.2: Set up your environment
-Most deep learning models are not coded from scratch and depend on external libraries (e.g. python, tensorflow). With isquare.ai, all requirements are handled by a Dockerfile, which is basically a set of instructions which sets up an environment. If you’re new to Docker, check the [documentation](https://docs.docker.com/engine/reference/builder/). We need to create a file containing Docker instructions (usually called Dockerfile):
+Most deep learning models are not coded from scratch and depend on external libraries (e.g. pytorch, tensorflow). With isquare.ai, all requirements are handled by a Dockerfile, which is basically a set of instructions which sets up an environment. If you’re new to Docker, check the [documentation](https://docs.docker.com/engine/reference/builder/). We need to create a file containing Docker instructions (usually called Dockerfile):
 ```dockerfile
 FROM alpineintuition/archipel-base-cpu:latest
 

examples/README.md

@@ -1,5 +1,5 @@
 # Examples 
-This directory shows 3 sample integrations of the [ISquare](isquare.ai) client for image inference, with 3 levels of complexity:
+This directory shows 3 sample integrations of the [ISquare](http://isquare.ai) client for image inference, with 3 levels of complexity:
 - How to perform inference with an image
 - How to perform inference with a video
 - How to stream a camera to your model