CUDA Setup and Configuration

Loading CUDA Modules

# List available CUDA versions
module avail cuda

# Load a specific version
module load cuda

# Verify CUDA installation
nvcc --version

Environment Variables

CUDA sets these automatically when you load the module:

echo $CUDA_HOME      # CUDA installation path
echo $PATH           # Includes CUDA bin
echo $LD_LIBRARY_PATH # Includes CUDA libs

Installing Additional Packages

# Install PyTorch with CUDA support
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121

# Install TensorFlow with GPU support
pip install tensorflow

# Install cuDNN
module load cudnn

Verifying GPU Access

# Check GPU status
nvidia-smi

# Check CUDA device
python -c "import torch; print(torch.cuda.is_available())"
python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"

Common CUDA Issues

CUDA out of memory

# In PyTorch, clear cache
torch.cuda.empty_cache()

# Monitor memory
nvidia-smi --query-gpu=memory.used,memory.free --format=csv

Wrong CUDA version

# Check your code's CUDA requirement
# Match with available modules
module avail cuda