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

Temporal graph networks for link prediction in dynamic graphs, based on [`examples/tgn.py`](https://github.com/rusty1s/pytorch_geometric/blob/master/examples/tgn.py) from PyTorch-Geometric, optimised for Graphcore's IPU.

Run our TGN on paperspace.
<br>
[![Gradient](../../../gradient-badge.svg)](https://ipu.dev/jffnhe)

| Framework | Domain | Model | Datasets | Tasks | Training | Inference | Reference |
|-----------|--------|-------|----------|-------|----------|-----------|-----------|
| PyTorch | GNNs | TGN | JODIE | Link prediction | <p style="text-align: center;">✅ <br> Min. 1 IPU (POD4) required | <p style="text-align: center;">❌ | [Temporal Graph Networks for Deep Learning on Dynamic Graphs](https://arxiv.org/abs/2006.10637v3) |


## 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:
```bash
 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:
```bash
cd poplar-<OS version>-<SDK version>-<hash>
. enable.sh
```

3. Additionally, enable PopART with:
```bash
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](https://docs.graphcore.ai/projects/poplar-quick-start).


## Environment setup
To prepare your environment, follow these steps:

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

2. Navigate to the Poplar SDK root directory

3. Install the PopTorch (PyTorch) wheel:
```bash
cd <poplar sdk root dir>
pip3 install poptorch...x86_64.whl
```

4. Navigate to this example's root directory

5. Install the Python requirements:
```bash
pip3 install -r requirements.txt
```

More detailed instructions on setting up your PyTorch environment are available in the [PyTorch quick start guide](https://docs.graphcore.ai/projects/pytorch-quick-start).

## Running and benchmarking

To run a tested and optimised configuration and to reproduce the performance shown on our [performance results page](https://www.graphcore.ai/performance-results), 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:

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

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

```bash
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](https://github.com/graphcore/examples-utils/blob/master/examples_utils/benchmarks/README.md).


### 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