install env setup

Environment Setup#

GraphStorm can be installed as a pip package. However, configuring a GraphStorm environment in various Operation Systems is non-trivial, therefore, GraphStorm provides Docker-based running environment for easy deployment.

1. Setup GraphStorm Docker Environment#

Prerequisites#

1. Docker: You need to install Docker in your environment as the Docker documentation suggests, and the Nvidia Container Toolkit.

For example, in an AWS EC2 instance without Docker preinstalled, you can run the following commands to install Docker.

sudo apt-get update
sudo apt update
sudo apt install Docker.io

If using AWS Deep Learning AMI GPU version, the Nvidia Container Toolkit has been preinstalled.

2. GPU: The current version of GraphStorm requires at least one Nvidia GPU installed in the instance.

Build a GraphStorm Docker image from source code#

Please use the following command to build a Docker image from source:

git clone https://github.com/awslabs/graphstorm.git
cd /path-to-graphstorm/docker/
bash /path-to-graphstorm/docker/build_docker_oss4local.sh /path-to-graphstorm/ docker-name docker-tag

There are three arguments of the build_docker_oss4local.sh:

1. path-to-graphstorm (required), is the absolute path of the “graphstorm” folder, where you clone and download the GraphStorm source code. For example, the path could be /code/graphstorm.

2. docker-name (optional), is the assigned name of the to be built Docker image. Default is graphstorm.

3. docker-tag (optional), is the assigned tag name of the to be built docker image. Default is local.

You can use the below command to check if the new Docker image is created successfully.

docker image ls

If the build succeeds, there should be a new Docker image, named <docker-name>:<docker-tag>, e.g., graphstorm:local.

Create a GraphStorm Container#

First, you need to create a GraphStorm container based on the Docker image built in the previous step.

Run the following command:

nvidia-docker run --network=host -v /dev/shm:/dev/shm/ -d --name test graphstorm:local

This command will create a GraphStorm contained, named test and run the container as a daemon.

Then connect to the container by running the following command:

docker container exec -it test /bin/bash

If succeeds, the command prompt will change to the container’s, like

root@ip-address:/#

2. Setup GraphStorm with pip Packages#

Prerequisites#

1. Linux OS: The current version of GraphStorm supports Linux as the Operation System. We tested GraphStorm on both Ubuntu (22.04 or later version) and Amazon Linux 2.

2. GPU: The current version of GraphStorm requires at least one Nvidia GPU installed in the instance.

3. Python3: The current version of GraphStorm requires Python installed with the version larger than 3.7.

Install GraphStorm#

Users can use pip or pip3 to install GraphStorm.

pip install graphstorm

Install Dependencies#

GraphStorm requires a set of dependencies, which can be installed with the following pip or pip3 commands.

pip install boto3==1.26.126
pip install botocore==1.29.126
pip install h5py==3.8.0
pip install scipy
pip install tqdm==4.65.0
pip install pyarrow==12.0.0
pip install transformers==4.28.1
pip install pandas
pip install scikit-learn
pip install ogb==1.3.6
pip install psutil==5.9.5
pip install torch==1.13.1+cu116 --extra-index-url https://download.pytorch.org/whl/cu116
pip install dgl==1.0.3+cu117 -f https://data.dgl.ai/wheels/cu117/repo.html

Configure SSH No-password login#

Use the following commands to configure a local SSH no-password login that GraphStorm relies on.

ssh-keygen -t rsa -f ~/.ssh/id_rsa -N ''
cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys

Then use this command to test if the SSH no-password login works.

ssh 127.0.0.1

If everything is right, the above command will enter another Linux shell process. Then exit this new shell with the command exit.

Clone GraphStorm Toolkits (Optional)#

GraphStorm provides a set of toolkits, including scripts, tools, and examples, which can facilitate the use of GraphStrom.

• graphstorm/training_scripts/ and graphstorm/inference_scripts/ include examplar configuration yaml files that used in GraphStorm documentations and tutorials.

• graphstorm/examples includes Python code for customized models and customized data preparation.

• graphstorm/tools includes graph partition and related Python code.

• graphstorm/sagemaker include commands and code to run GraphStorm on Amazon SageMaker.

Users can clone GraphStorm source code to obtain these toolkits.

git clone https://github.com/awslabs/graphstorm.git

Warning

If use this method to setup GraphStorm environment, please replace the argument --ssh-port of in launch commands in GraphStorm’s tutorials from 2222 with 22.