/examples/pytorch/sampling/dis_sampling

Stochastic Training for Graph Convolutional Networks Using Distributed Sampler

• Paper: Control Variate
• Paper: Skip Connection
• Author's code: https://github.com/thu-ml/stochastic_gcn

Dependencies

• PyTorch 0.4.1+
• requests

bash pip install torch requests

Usage Guide

Assume that the user has already launched two instances (instance_0 & instance_1) on AWS EC2, and also these two instances have the correct authority to access each other by TCP/IP protocol. Now we can treat instance_0 as Trainer and instance_1 as Sampler. Then, the user can start the trainer process and sampler process on these two instances separately. We have already provided a set of scripts to start the trainer and sampler process and users just need to change the --ip to their own IP address.

Once we start the trainer process, users will see the following logging output:

[04:48:20] .../socket_communicator.cc:68: Bind to 127.0.0.1:2049
[04:48:20] .../socket_communicator.cc:74: Listen on 127.0.0.1:2049, wait sender connect ...

After that user can start the sampler process. For the sampler instance_0, users can change the --num-sampler option to set the number of the sampler. The sampler.py script will start --num-sampler processes concurrently to maximalize the system utilization. Users can also launch many samplers in parallel across a set of machines. For example, if we have 10 sampler instance and for each instance, we set the --num-sampler to 2, we need to set the --num-sampler of the trainer instance to 20.

Neighbor Sampling & Skip Connection

cora

Test accuracy ~83% with --num-neighbors 2, ~84% by training on the full graph

Trainer side:

DGLBACKEND=pytorch python3 gcn_ns_sc_train.py --dataset cora --self-loop --num-neighbors 2 --batch-size 1000000 --test-batch-size 1000000 --ip 127.0.0.1:50051 --num-sampler 1

Sampler side:

OMP_NUM_THREADS=1 DGLBACKEND=pytorch python3 sampler.py --model gcn_ns --dataset cora --self-loop --num-neighbors 2 --batch-size 1000000 --ip 127.0.0.1:50051 --num-sampler 1

citeseer

Test accuracy ~69% with --num-neighbors 2, ~70% by training on the full graph

Trainer side:

DGLBACKEND=pytorch python3 gcn_ns_sc_train.py --dataset citeseer --self-loop --num-neighbors 2 --batch-size 1000000 --test-batch-size 1000000 --ip 127.0.0.1:50051 --num-sampler 1

Sampler side:

OMP_NUM_THREADS=1 DGLBACKEND=pytorch python3 sampler.py --model gcn_ns --dataset citeseer --self-loop --num-neighbors 2 --batch-size 1000000 --ip 127.0.0.1:50051 --num-sampler 1

pubmed

Test accuracy ~76% with --num-neighbors 3, ~77% by training on the full graph

Trainer side:

DGLBACKEND=pytorch python3 gcn_ns_sc_train.py --dataset pubmed --self-loop --num-neighbors 3 --batch-size 1000000 --test-batch-size 1000000 --ip 127.0.0.1:50051 --num-sampler 1

Sampler side:

OMP_NUM_THREADS=1 DGLBACKEND=pytorch python3 sampler.py --model gcn_ns --dataset pubmed --self-loop --num-neighbors 3 --batch-size 1000000 --ip 127.0.0.1:50051 --num-sampler 1

Control Variate & Skip Connection

cora

Test accuracy ~84% with --num-neighbors 1, ~84% by training on the full graph

Trainer side:

DGLBACKEND=pytorch python3 gcn_cv_sc_train.py --dataset cora --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000 --ip 127.0.0.1:50051 --num-sampler 1

Sampler side:

OMP_NUM_THREADS=1 DGLBACKEND=pytorch python3 sampler.py --model gcn_cv --dataset cora --self-loop --num-neighbors 1 --batch-size 1000000 --ip 127.0.0.1:50051 --num-sampler 1

citeseer

Test accuracy ~69% with --num-neighbors 1, ~70% by training on the full graph

Trainer side:

DGLBACKEND=pytorch python3 gcn_cv_sc_train.py --dataset citeseer --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000 --ip 127.0.0.1:50051 --num-sampler 1

Sampler side:

OMP_NUM_THREADS=1 DGLBACKEND=pytorch python3 sampler.py --model gcn_cv --dataset citeseer --self-loop --num-neighbors 1 --batch-size 1000000 --ip 127.0.0.1:50051 --num-sampler 1

pubmed

Test accuracy ~77% with --num-neighbors 1, ~77% by training on the full graph

Trainer side:

DGLBACKEND=pytorch python3 gcn_cv_sc_train.py --dataset pubmed --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000 --ip 127.0.0.1:50051 --num-sampler 1

Sampler side:

OMP_NUM_THREADS=1 DGLBACKEND=pytorch python3 sampler.py --model gcn_cv --dataset pubmed --self-loop --num-neighbors 1 --batch-size 1000000 --ip 127.0.0.1:50051 --num-sampler 1