IMAGE RUNNER

Work in progress (WIP)
An image manipulator built on

TRAINING THE MODEL LOCALLY

The training of each model will be handled through the CLI. You can find this in the management/commands directory of each app.
For instance: running python manage.py cat_dog_trainer will run pre-train the model and save it to the mlmodels directory.
This is to be called once, usually during the first setup.
Subsequent requests from the web ui will be routed to the trained model via a service layer.

Due to how resource intensive these operations can be, and depending on your hardware, it is always advisable to use cloud platforms like Google Colab, Sage Maker etc.
Each CLI class has a private method in it called __execute()
You should copy the content of that method, as well as all necessary imports at the top of the class, and run them in your cloud platform.
You should endeavour to change the path of the exported path. For instance, instead of setting your path as this: model_path = os.path.join("image", "mldmodels", "cat_dog_model.pkl"), you can choose to set it as this: model_path = '/content/model.pkl'
If using Google colab, you could choose to connect to your drive and mount the file to it, then export the trained model when completed.
All trained models should be placed in the mlmodels directory of the respective apps.

Install XQuartz (Download Link)
Click on Preferences from the Menu bar and toggle on Allow connections from network clients
Vist the root of the cloned project and run xauth list
Copy the output from the above step
Run echo "<OUTPUT_OF_STEP_3>" | sed -e 's/^..../ffff/' > .docker.xauth
This should create a file .docker.xauth. This file is already mapped and set within the python service
Run xhost +