Installation#

This page provides a complete installation guide for DeepSpatial, including environment setup, package installation, verification, and troubleshooting.

System Requirements#

Python: 3.9 to 3.11 recommended
OS: Linux, macOS, or Windows (WSL recommended for GPU workflows on Windows)
GPU (optional but recommended): NVIDIA GPU with CUDA support for faster training

1. Create a Clean Python Environment#

Using Conda:

conda create -n deepspatial python=3.10 -y
conda activate deepspatial

Or using venv:

python -m venv .venv
source .venv/bin/activate  # Linux/macOS
# .venv\Scripts\activate   # Windows PowerShell

2. Install DeepSpatial#

Option A: Install from PyPI#

pip install deepspatial

Option B: Install from Source (Recommended for Development)#

git clone https://github.com/yyh030806/DeepSpatial.git
cd DeepSpatial
pip install -e .

3. PyTorch and GPU Support#

DeepSpatial depends on PyTorch. If you need CUDA acceleration, install the matching PyTorch build for your CUDA version.

Example (CUDA 12.1):

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

For CPU-only environments:

pip install torch torchvision torchaudio

4. Verify Installation#

Run the following check:

python -c "import deepspatial, torch; print('deepspatial', deepspatial.__version__); print('torch', torch.__version__); print('cuda', torch.cuda.is_available())"

Expected outcome:

deepspatial version is printed
PyTorch version is shown
cuda True if GPU is available and configured correctly, otherwise cuda False

Installation

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