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README.md

Training Dynamic Graphs on IPU using Temporal Graph Networks (TGN)

Temporal graph networks for link prediction in dynamic graphs, based on examples/tgn.py from PyTorch-Geometric, optimised for Graphcore's IPU.

Run our TGN on paperspace.
Gradient

Framework Domain Model Datasets Tasks Training Inference Reference
PyTorch GNNs TGN JODIE Link prediction

✅
Min. 1 IPU (POD4) required

❌

 Temporal Graph Networks for Deep Learning on Dynamic Graphs

Instructions summary

  1. Install and enable the Poplar SDK (see Poplar SDK setup)

  2. Install the system and Python requirements (see Environment setup)

Poplar SDK setup

To check if your Poplar SDK has already been enabled, run:

 echo $POPLAR_SDK_ENABLED

If no path is provided, then follow these steps:

  1. Navigate to your Poplar SDK root directory

  2. Enable the Poplar SDK with:

cd poplar-<OS version>-<SDK version>-<hash>
. enable.sh
  1. Additionally, enable PopART with:
cd popart-<OS version>-<SDK version>-<hash>
. enable.sh

More detailed instructions on setting up your Poplar environment are available in the Poplar quick start guide.

Environment setup

To prepare your environment, follow these steps:

  1. Create and activate a Python3 virtual environment:
python3 -m venv <venv name>
source <venv path>/bin/activate

  1. Navigate to the Poplar SDK root directory

  2. Install the PopTorch (PyTorch) wheel:

cd <poplar sdk root dir>
pip3 install poptorch...x86_64.whl

  1. Navigate to this example's root directory

  2. Install the Python requirements:

pip3 install -r requirements.txt

More detailed instructions on setting up your PyTorch environment are available in the PyTorch quick start guide.

Running and benchmarking

To run a tested and optimised configuration and to reproduce the performance shown on our performance results page, use the examples_utils module (installed automatically as part of the environment setup) to run one or more benchmarks. The benchmarks are provided in the benchmarks.yml file in this example's root directory.

For example:

python3 -m examples_utils benchmark --spec <path to benchmarks.yml file>

Or to run a specific benchmark in the benchmarks.yml file provided:

python3 -m examples_utils benchmark --spec <path to benchmarks.yml file> --benchmark <name of benchmark>

For more information on using the examples-utils benchmarking module, please refer to the README.

License

This application is licensed under the MIT license, see the LICENSE file at the top-level of this repository.

This directory includes derived work from the PyTorch Geometric repository, https://github.com/pyg-team/pytorch_geometric by Matthias Fey and Jiaxuan You, published under the MIT license