Installation

Prerequisites

DeepLens requires:

• Python 3.12 or later
• PyTorch with CUDA support (recommended for GPU acceleration)
• Conda (optional, but recommended for environment management)

Installation Methods

Quick Installation (Tested on Linux, macOS, and Windows)

Clone the repository:

git clone https://github.com/singer-yang/DeepLens
cd DeepLens

Create a conda environment:

conda create -n deeplens_env python=3.12
conda activate deeplens_env

Install PyTorch and dependencies:

For Linux and macOS:

pip install torch torchvision
pip install -r requirements.txt

For Windows:

pip install torch torchvision --index-url https://download.pytorch.org/whl/cu128
pip install -r requirements.txt

Alternative: Using Conda Environment File

Clone the repository:

git clone https://github.com/singer-yang/DeepLens
cd DeepLens

Create a conda environment using the provided environment file:

conda env create -f environment.yml -n deeplens_env
conda activate deeplens_env

Verify Installation

To verify that DeepLens is installed correctly, run the demo script:

python 0_hello_deeplens.py

If the installation is successful, you should see lens visualization and simulation outputs.

GPU Support

For optimal performance, DeepLens requires a CUDA-capable GPU. To check if PyTorch can detect your GPU:

python -c "import torch; print(torch.cuda.is_available())"

If this returns False, you may need to reinstall PyTorch with CUDA support. Visit the PyTorch installation page for instructions.

Additional Dependencies

Some advanced features may require additional packages:

• Matplotlib: For visualization (usually included in requirements.txt)
• OpenCV: For image processing operations
• Pillow: For image I/O operations

These are typically installed automatically with the standard installation methods.

Troubleshooting

Common Issues

Import Errors

If you encounter import errors, ensure that:

1. The conda/virtual environment is activated
2. All dependencies are installed correctly
3. You're running Python from the DeepLens root directory

CUDA/GPU Issues

If you have GPU issues:

1. Check that your NVIDIA drivers are up to date
2. Verify that PyTorch is installed with the correct CUDA version
3. Try running with CPU first to isolate the issue

For more help, join our Slack workspace or contact the developers.