DistTGL: Distributed Memory-based Temporal Graph Neural Network Training

Overview

This repo is the open-sourced code for our work DistTGL: Distributed Memory-based Temporal Graph Neural Network Training.

Requirements

• python >= 3.8.13
• pytorch >= 1.11.0
• pandas >= 1.1.5
• numpy >= 1.19.5
• dgl >= 0.8.2
• pyyaml >= 5.4.1
• tqdm >= 4.61.0
• pybind11 >= 2.6.2
• g++ >= 7.5.0
• openmp >= 201511

Dataset

Download the dataset using the down.sh script. Note that we do not release the Flights dataset due to license restriction. You can download the Flight dataset directly from this link.

Mini-batch Preparation

DistTGL pre-compute mini-batches before training. To ensure fast mini-batch loading from disk, please store the mini-batches in a fast SSD. In the paper, we use RAID0 array of two NVMe SSDs.

We first compile the sampler from TGL by

python setup.py build_ext --inplace

Then generate the mini-batches using

python gen_minibatch.py --data <DatasetName> --gen_eval --minibatch_parallelism <NumberofMinibatchParallelism>

where <NumberofMinibatchParallelism> is the i in (i x j x k) in the paper.

Run

On each machine, execute

torchrun --nnodes=<NumberofMachines> --nproc_per_node=<NumberofGPUPerMachine> --rdzv_id=<JobID> --rdzv_backend=c10d --rdzv_endpoint=<HostNodeIPAddress>:<HostNodePort> train5.py --data <DatsetName> --group <NumberofGroupParallelism>

where <NumberofGroupParallelism> is the k in (i x j x k) in the paper.

Security

See CONTRIBUTING for more information.

License

This project is licensed under the Apache-2.0 License.