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Jian Zhang (James) edited this page Aug 6, 2023 · 10 revisions

Environment Setup#

GraphStorm supports two environment setup methods:

  • Install GraphStorm using as a pip packcage. This method works well for development and test on a signle machine.

  • Setup a GraphStorm Docker image. This method is good for using GraphStorm in distributed environments that commonly used in production.

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

  3. (Optional) GraphStorm supports Nvidia GPUs for using GraphStorm in GPU environments.

Install GraphStorm#

Users can use pip or pip3 to install GraphStorm.

pip install graphstorm

Install Dependencies#

Users should install DGL that is the core dependency of GraphStorm using the following commands.

For Nvidia GPU environment:

pip install dgl==1.0.4+cu117 -f https://data.dgl.ai/wheels/cu117/repo.html

For CPU environment:

pip install dgl==1.0.4 -f https://data.dgl.ai/wheels-internal/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.

  • If use this method to setup GraphStorm environment, you may need to replace the python3 command with python, depending on your Python versions.

2. 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.

  1. (Optional) GraphStorm supports Nvidia GPUs for using GraphStorm in GPU environments.

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 cloned 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 container, 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>:/#
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