prerequisite.md

Project Prerequisite

Contents

• Setting up
  • Local Machine Setup
    • Install Python
    • Install Git
  • Virtual Environment Setup
    • Conda
    • Pip
  • Project Setup
    • Clone repo
    • Install dependency
• Issues
• Extras

Setting up

Local Machine Setup

To run the project in a linux os environment, you need to have Anaconda or Miniconda installed on your machine. You can download Miniconda from the official website. For windows users, you can download Anaconda from the official website.

You can also use windows subsystem for linux (WSL) to run the project. Visit the official website to install WSL.

Check out bin directory at the project root for miniconda installation.

Install Python

To run the project, you need to have Python installed on your machine. You can download Python from the official website.

Install Git

You need to have Git installed on your machine to clone the project repository. You can download Git from the official website.

Virtual Environment Setup

Conda

To create a conda virtual environment for the project, run the following command:

conda create --name skin-cancer-detection python=3.10

To activate the virtual environment, run the following command:

conda activate skin-cancer-detection

To deactivate the virtual environment, run the following command:

conda deactivate

Pip

To create a pip virtual environment for the project, run the following command:

python -m venv skin-cancer-detection

To activate the virtual environment, run the following command:

source skin-cancer-detection/bin/activate

To deactivate the virtual environment, run the following command:

deactivate

Project Setup

Clone the Repository

To clone the project repository, run the following command:

git clone https://github.com/evans-nyang/skin-cancer-detection.git

Install Dependencies

To install the project dependencies, run the following command:

pip install -r requirements.txt

Issues

We used pydot in the notebook to visualize our keras model. pydot is a Python interface to Graphviz and its DOT language. You can use pydot to create, read, edit, and visualize graphs.

pydot dependencies include:

• pyparsing: used only for loading DOT files, installed automatically during pydot installation.
• GraphViz: used to render graphs in a variety of formats, including PNG, SVG, PDF, and more. Should be installed separately, using your system's package manager, something similar (e.g., MacPorts), or from its source.

pydot may not work correctly if graphviz is installed via pip command in python. To solve this, install graphviz in your linux environment using apt-get as shown below:

sudo apt-get install graphviz

The code below is extracted from the notebook cell, keras leverages pydot for model visualization:

keras.utils.plot_model(model, show_shapes=True, show_layer_names=True, dpi=60)

Extras

• Check out the pydot documentation here: pydot documentation
• To see what Graphviz is capable of, check the Graphviz Gallery!