Environment Setup Guide¶

Prerequisites¶

Python 3.10+ (tested with Python 3.13.7)
Git
16GB+ RAM recommended
(Optional) NVIDIA GPU with CUDA support for training

Installation Instructions¶

Windows¶

Clone the repository:

git clone <repository-url>
cd "skin cancer"

Create virtual environment:
```
python -m venv venv
```

Activate virtual environment:

# Command Prompt
venv\Scripts\activate.bat

# PowerShell
venv\Scripts\Activate.ps1

# Git Bash
source venv/Scripts/activate

Upgrade pip:

pip install --upgrade pip setuptools wheel

Install dependencies:
```
pip install -r requirements.txt
```

Linux / macOS¶

Clone the repository:

git clone <repository-url>
cd skin-cancer-classification

Create virtual environment:
```
python3 -m venv venv
```
Activate virtual environment:
```
source venv/bin/activate
```

Upgrade pip:

pip install --upgrade pip setuptools wheel

Install dependencies:
```
pip install -r requirements.txt
```

GPU Setup (CUDA)¶

NVIDIA GPU Requirements¶

CUDA 12.1+ compatible GPU
NVIDIA Driver 530.30.02+
CUDA Toolkit 12.1+
cuDNN 8.x

Installing PyTorch with CUDA Support¶

Replace the CPU version with GPU-enabled PyTorch:

# Uninstall CPU version
pip uninstall torch torchvision

# Install CUDA 12.1 version
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121

For CUDA 11.8:

pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118

Verify GPU Installation¶

python -c "import torch; print(f'CUDA available: {torch.cuda.is_available()}'); print(f'GPU: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else \"N/A\"}')"

Expected output (with GPU):

CUDA available: True
GPU: NVIDIA GeForce RTX 3090

Verification Commands¶

Check Python Version¶

python --version
# Should show: Python 3.10.x or higher

Verify Core Libraries¶

# PyTorch
python -c "import torch; print(f'PyTorch {torch.__version__}')"

# timm (model architectures)
python -c "import timm; print(f'timm {timm.__version__}')"

# Fairness libraries
python -c "import fairlearn; print(f'Fairlearn {fairlearn.__version__}')"
python -c "import aif360; print('AIF360 installed')"

# Data science stack
python -c "import numpy, pandas, sklearn; print('Data science stack OK')"

# Computer vision
python -c "import cv2, albumentations; print('CV libraries OK')"

# Experiment tracking
python -c "import tensorboard, wandb; print('Tracking tools OK')"

Run All Verifications¶

python -c "
import sys
import torch
import timm
import fairlearn
import aif360
import numpy
import pandas
import sklearn
import cv2
import albumentations
import tensorboard
import wandb

print('=== Environment Verification ===')
print(f'Python: {sys.version}')
print(f'PyTorch: {torch.__version__}')
print(f'CUDA available: {torch.cuda.is_available()}')
print(f'timm: {timm.__version__}')
print(f'Fairlearn: {fairlearn.__version__}')
print(f'NumPy: {numpy.__version__}')
print(f'Pandas: {pandas.__version__}')
print(f'scikit-learn: {sklearn.__version__}')
print('All critical dependencies installed successfully!')
"

Troubleshooting¶

Issue: `ModuleNotFoundError` after installation¶

Solution: Ensure virtual environment is activated:

# Check which Python is being used
which python   # Linux/macOS
where python   # Windows

# Should point to venv directory

Issue: CUDA not available despite GPU present¶

Solution: 1. Verify NVIDIA driver: nvidia-smi 2. Check CUDA toolkit: nvcc --version 3. Reinstall PyTorch with correct CUDA version (see GPU Setup above) 4. Verify with: python -c "import torch; print(torch.cuda.is_available())"

Issue: Out of memory during training¶

Solutions: - Reduce batch size in configuration files - Enable gradient checkpointing (already configured in model configs) - Use mixed precision training (FP16) - Consider using a smaller model architecture

Issue: Slow data loading¶

Solutions: - Increase num_workers in DataLoader (default: 4) - Use SSD for dataset storage - Pre-process and cache augmentations

Issue: `ImportError: DLL load failed` (Windows)¶

Solution: 1. Install Visual C++ Redistributable: https://aka.ms/vs/17/release/vc_redist.x64.exe 2. Reinstall PyTorch 3. Restart terminal

Issue: Permission denied when creating directories¶

Solution:

# Linux/macOS: Use sudo or change ownership
sudo chown -R $USER:$USER .

# Windows: Run terminal as Administrator

Issue: Pre-commit hooks failing¶

Solution:

# Reinstall pre-commit hooks
pre-commit clean
pre-commit install

# Run manually to test
pre-commit run --all-files

Environment Variables (Optional)¶

Create .env file in project root:

# Weights & Biases (optional)
WANDB_API_KEY=your_api_key_here
WANDB_PROJECT=skin-cancer-classification

# Data directories
DATA_ROOT=./data
EXPERIMENTS_ROOT=./experiments

# Training configuration
CUDA_VISIBLE_DEVICES=0  # GPU device ID
OMP_NUM_THREADS=4       # CPU threads for data loading

Development Tools¶

Jupyter Notebook (Optional)¶

pip install jupyter notebook ipykernel
python -m ipykernel install --user --name=skin-cancer-env

VS Code Extensions (Recommended)¶

Python (Microsoft)
Pylance
Black Formatter
Jupyter
GitLens

PyCharm Configuration¶

File > Settings > Project > Python Interpreter
Add Interpreter > Existing Environment
Select venv/bin/python (Linux/macOS) or venv\Scripts\python.exe (Windows)

Next Steps¶

After successful environment setup:

Review project structure: README.md
Set up data directories: docs/data_setup.md
Configure experiments: docs/experiment_tracking.md
Run baseline experiments: experiments/baseline/README.md

System Requirements¶

Minimum¶

CPU: 4 cores
RAM: 16GB
Storage: 50GB
Python: 3.10+

Recommended (for training)¶

CPU: 8+ cores
RAM: 32GB+
GPU: NVIDIA RTX 3090 or better (24GB VRAM)
Storage: 100GB+ SSD
Python: 3.10-3.12

Cloud Options¶

Google Colab (Free GPU tier available)
Kaggle Notebooks (Free GPU: 30hrs/week)
AWS SageMaker
Azure ML
Lambda Labs

Support¶

For issues not covered here: 1. Check GitHub Issues 2. Review PyTorch documentation: https://pytorch.org/docs/ 3. Consult timm documentation: https://huggingface.co/docs/timm 4. Fairlearn docs: https://fairlearn.org/

Last Updated: 2025-10-13 Python Version Tested: 3.13.7 PyTorch Version: 2.8.0