Distributed MHAug Training

• Author: Juyeong Shin, Young-Koo Lee

• KSC 2023 Paper

• Paper link: https://www.dbpia.co.kr (not published yet)

• Reference

  • DistDGL paper link: https://ieeexplore.ieee.org/abstract/document/9407264
  • MHAug paper link: https://proceedings.neurips.cc/paper/2021/hash/9e7ba617ad9e69b39bd0c29335b79629-Abstract.html
  • DistDGL code link: https://github.com/dmlc/dgl/tree/master/examples/pytorch/graphsage/dist
  • MHAug code link: https://github.com/hyeonzini/Metropolis-Hastings-Data-Augmentation-for-Graph-Neural-Networks

PyTorch (CPU version)

pip3 install torch==2.0.1 --index-url https://download.pytorch.org/whl/cpu

DGL (CPU version)

pip3 install dgl==1.1.2 -f https://data.dgl.ai/wheels/repo.html

OGB

pip3 install ogb==1.3.6

To train DistMHAug, it has five steps:

Step 0: Setup a Distributed File System

• You may skip this step if your cluster already has folder(s) synchronized across machines.

To perform distributed training, files and codes need to be accessed across multiple machines. A distributed file system would perfectly handle the job (i.e., NFS, Ceph).

Server side setup

Here is an example of how to setup NFS. First, install essential libs on the storage server

sudo apt-get install nfs-kernel-server

Below we assume the user account is ubuntu and we create a directory of workspace in the home directory.

mkdir -p /home/ubuntu/workspace

We assume that the all servers are under a subnet with ip range 192.168.0.0 to 192.168.255.255. The exports configuration needs to be modifed to

sudo vi /etc/exports
# add the following line
/home/ubuntu/workspace  192.168.0.0/16(rw,sync,no_subtree_check)

The server's internal ip can be checked via ifconfig or ip. If the ip does not begin with 192.168, then you may use

/home/ubuntu/workspace  10.0.0.0/8(rw,sync,no_subtree_check)
/home/ubuntu/workspace  172.16.0.0/12(rw,sync,no_subtree_check)

Then restart NFS, the setup on server side is finished.

sudo systemctl restart nfs-kernel-server

For configraution details, please refer to NFS ArchWiki.

Client side setup

To use NFS, clients also require to install essential packages

sudo apt-get install nfs-common

You can either mount the NFS manually

mkdir -p /home/ubuntu/workspace
sudo mount -t nfs <nfs-server-ip>:/home/ubuntu/workspace /home/ubuntu/workspace

or edit the fstab so the folder will be mounted automatically

# vim /etc/fstab
## append the following line to the file
<nfs-server-ip>:/home/ubuntu/workspace   /home/ubuntu/workspace   nfs   defaults	0 0

Then run mount -a.

Now go to /home/ubuntu/workspace and clone the DGL Github repository.

Step 1: set IP configuration file.

User need to set their own IP configuration file ip_config.txt before training. For example, if we have four machines in current cluster, the IP configuration could like this:

172.31.19.1
172.31.23.205
172.31.29.175
172.31.16.98

Users need to make sure that the master node (node-0) has right permission to ssh to all the other nodes without password authentication. This link provides instructions of setting passwordless SSH login.

Step 2: partition the graph.

The example provides a script to partition some builtin graphs such as Reddit and OGB product graph. If we want to train GraphSage on 4 machines, we need to partition the graph into 4 parts.

In this example, we partition the ogbn-products graph into 4 parts with Metis on node-0. The partitions are balanced with respect to the number of nodes, the number of edges and the number of labelled nodes.

python3 partition_graph.py --dataset ogbn-products --num_parts 4 --balance_train --balance_edges

This script generates partitioned graphs and store them in the directory called data.

Step 3: Launch distributed jobs

DGL provides a script to launch the training job in the cluster. part_config and ip_config specify relative paths to the path of the workspace.

The command below launches one process per machine for both sampling and training.

python3 ~/DistMHAug/launch.py \
--workspace ~/DistMHAug/ \
--num_trainers 1 \
--num_samplers 0 \
--num_servers 1 \
--part_config data/ogbn-products.json \
--ip_config ip_config.txt \
"python3 node_classification.py --graph_name ogbn-products --ip_config ip_config.txt --num_epochs 30 --batch_size 1000"

By default, this code will run on CPU. If you have GPU support, you can just add a --num_gpus argument in user command:

python3 ~/DistMHAug/launch.py \
--workspace ~/DistMHAug/ \
--num_trainers 1 \
--num_samplers 0 \
--num_servers 1 \
--part_config data/ogbn-products.json \
--ip_config ip_config.txt \
"python3 node_classification.py --graph_name ogbn-products --ip_config ip_config.txt --num_epochs 30 --batch_size 1000 --num_gpus 4"

LICENSE

© 2023 meongju0o0 uses Apache 2.0 License. Powered by DGL Team.