Installation

Setting up the Environment

To use spacer, it is recommended to create a clean Conda environment.

# Create environment
$ conda create -n spacer python=3.8
$ conda activate spacer

Install the required Python dependencies:

(spacer) $ pip install torch==2.3.1 numpy==1.26.4 pandas==2.2.2 \
                 scanpy==1.10.2 scikit-learn==1.5.1 scipy==1.13.1 tqdm==4.66.4

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Installing Spacer

Clone the official spacer repository and install the package in editable mode:

(spacer) $ git clone https://github.com/yaober/SPACER.git
(spacer) $ cd SPACER

After downloading, spacer can be imported:

from model.dataset import BagsDataset, custom_collate_fn
from model.model import MIL, EarlyStopping

dataset = BagsDataset( ... )
model = MIL( ... )

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Note

spacer requires Python ≥3.8 and PyTorch ≥2.3. GPU acceleration is recommended for model training. However, GPU usage is not mandatory — the model can also be trained on CPUs. We recommend having at least 256 GB of system memory (RAM) if running on CPU, to ensure smooth data loading and model optimization during training.