Final Project Scripts - SYS843

Note: The scripts used in this project are only designed to work with GPUs if Apple Silicon is present.

Virtual environment

Use conda to create new virtual environment:

conda create --name sys843_project

Activate virtual environment:

conda activate sys843_project

Install Python at version 3.12 to ensure compability with Pytorch libraries:

conda install python=3.12

Next, install Pythorch libraries:

pip install torch torchvision

Then install timm (library that has various pre-trained models):

pip install timm

Then install matplotlib to plot graphs:

pip install matplotlib

Follow instalation of coremltools as described in Installing Core ML Tools guide:

pip install coremltools

Hyperparameter tuning script

You can run the Python file train_hyperparameter_tuning.py under the virtual environment by making sure the environment is active and then:

python train_hyperparameter_tuning.py --model fastvit_t8

To find a set of optiomal hyperparameters for FastViT-T8.

Train script script

You can run the Python file train.py under the virtual environment by making sure the environment is active and then:

python train.py --model fastvit_t8

To train FastViT-T8.

Plot script script

You can run the Python file train_plots.py under the virtual environment by making sure the environment is active and then:

python train_plots.py

Convert model to Core ML script

You can run the Python file convert_coreml.py under the virtual environment by making sure the environment is active and then:

python convert_coreml.py --model fastvit_t8

To convert the model FastViT-T8 to Core ML.