Install GraphScope on Local#
This guide will walk you through the process of installing GraphScope on your local machine.
Ubuntu 20.04 or later, CentOS 7 or later, or macOS 11 (Intel) / macOS 12 (Apple silicon) or later
Python 3.7 ~ 3.11, and pip >= 19.3
JDK 11 (If you want to use GIE, both JDK 8 and 20 have known compatibility issues)
Install from packages#
Install stable version of GraphScope#
You can use
pip to install latest stable graphscope package
python3 -m pip install graphscope --upgrade
If you occur a very low downloading speed, try to use a mirror site for the pip.
python3 -m pip install graphscope --upgrade \ -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host=mirrors.aliyun.com
If you encounter errors like
Could not find a version that satisfies the requirement graphscope (from versions: ),
please check if you are working with an old pip, and ensure pip’s version >= 19.3, as graphscope wheels are
compatible with the manylinux2014 ABI.
python3 -m pip install --upgrade pip
The above command will download all the components required for running GraphScope in standalone mode on your local machine.
Install preview version of GraphScope#
If you wish to experience the latest features, you can install the preview version, which is built and released in a nightly manner.
python3 -m pip install graphscope --pre
To get a clean and tidy environment, you can also use a Docker environment to get started quickly.
docker run --name dev -it --shm-size=4096m ubuntu:latest # inside the docker apt-get update -y && apt-get install python3-pip default-jdk -y python3 -m pip install graphscope ipython tensorflow
Install from source#
Optionally, You can build GraphScope from source and install it on your machine.
Setup build environment for Linux and macOS#
Build GraphScope from source needs many libraries and tools as dependencies. We provide a utility script
to help you install all the dependencies.
# Download source code git clone https://github.com/alibaba/graphscope cd graphscope python3 gsctl.py install-deps dev # use --help to get more usage.
Use dev image with all dependencies installed#
To make our life easier, we provide a pre-built docker image with all the dependencies installed. You can use pull it and work in a container with this image to build GraphScope.
docker pull registry.cn-hongkong.aliyuncs.com/graphscope/graphscope-dev:latest docker run --name dev -it --shm-size=4096m registry.cn-hongkong.aliyuncs.com/graphscope/graphscope-dev:latest # In the container, download the source code. git clone https://github.com/alibaba/graphscope
Build and install#
After the dependencies are installed on your local or in the container, you can build and install GraphScope in root directory of the source code.
make make install
Analytical engine(GAE) may require on-the-fly compilation, which needs clang or g++. Hence additional setup steps may in need to install build-essentials. e.g., on ubuntu:
apt update -y && apt install cmake build-essential -y
Learning engine (GLE) doesn’t support running on Python 3.11 currently, cause TensorFlow lacks supports for this version.
Currently, the support for arm-based platforms is very preliminary, and not ready for production yet.