CryoSiam Installation Guide¶
This guide provides instructions for installing CryoSiam using either pip or conda, with full support for GPU-enabled PyTorch.
Requirements¶
- Python >= 3.8
- Conda (for environment-based setup)
- (Optional) NVIDIA GPU with CUDA 11.8+ drivers for GPU acceleration
Step 1: Create the Environment¶
conda env create -f https://github.com/frosinastojanovska/cryosiam/blob/main/environment.yml
conda activate cryosiam
Step 2: Install cryosiam¶
After activating the environment:
git clone https://github.com/frosinastojanovska/cryosiam.git
cd cryosiam
pip install --no-deps .
Verify Installation¶
To verify the CryoSiam installation, run:
cryosiam --version
To check that PyTorch is installed correctly and can detect your GPU:
import torch
print(torch.cuda.is_available()) # Should return True if GPU is available
Troubleshooting¶
- CUDA errors: Make sure your system has the correct NVIDIA drivers and CUDA version.
- Missing packages: Double-check that you followed the correct conda install steps.
- Conflicts: It's recommended to use a clean conda environment.
Need Help?¶
If you run into issues, feel free to open an issue on the GitHub repository.
Don't Have Conda Installed?¶
If you don't have conda installed yet, we recommend using Miniforge (lightweight) or Anaconda (full-featured).
Option 1: Install Miniforge (recommended)¶
Miniforge is a minimal installer for Conda that supports conda-forge by default.
Linux / macOS:
# Download and install Miniforge
wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh
bash Miniforge3-Linux-x86_64.sh
Windows:
-
Download the latest installer from:
https://github.com/conda-forge/miniforge/releases -
Run the
.exeinstaller and follow the setup.
Option 2: Install Anaconda (Full Distribution)¶
Anaconda includes Conda, Python, and hundreds of data science packages.
Download from: https://www.anaconda.com/products/distribution
After installing Miniforge or Anaconda, return to the main Conda installation steps above.