Installation
This document provides detailed instructions for installing and setting up the SpatialZ project. Follow the steps below to configure your environment and install dependencies.
Step 1: Create a new virtual environment called spatialz with Python 3.9.19
conda create -n spatialz python=3.9.19 -y
Step 2: Activate the spatialz environment
conda activate spatialz
Step 3: Install PyTorch with CUDA 11.7 support
pip install torch==1.13.0+cu117 -f https://download.pytorch.org/whl/cu117/torch_stable.html
Step 5: Install the project dependencies from requirements.txt
pip install -r requirements.txt
Deploying a Docker Image on a New Server
We also provide a Docker image that encapsulates our code and demo data, making it easier for users to directly download and use the provided resources. This image ensures a consistent and reproducible environment, allowing users to seamlessly run the code and explore the demo data without needing to configure dependencies or environments manually.
Step 1: Install Docker on the New Server
The following commands illustrate the basic steps to install Docker on Ubuntu system (Ubuntu system required):
sudo apt-get update
sudo apt-get install docker-ce
Step 2: Pull the Image from Docker Hub
To download the Docker image, execute the following command:
sudo docker pull linsenlin/spatialz:latest
Step 3: Launch the Docker Container on the New Server
Once the image is pulled, users can start the Docker container on the new server. The following command will run the container and map port 8888 of the server to port 8888 of the container:
sudo docker run --gpus all -p 8888:8888 linsenlin/spatialz:latest
Step 4: Access Jupyter Notebook
After launching the Docker container, users can access Jupyter Notebook by navigating to port 8888 on the server. If the server’s IP address is ‘server_ip’, simply enter the following URL in a web browser:
http://server_ip:8888