# Node Classification on IPU using Cluster-GCN - Training

In this notebook cluster graph convolutional networks are used for node classification, using cluster sampling. This model has been optimised for Graphcore's IPU.

Run our Cluster GCN training on Paperspace.
<br>
[![Gradient](../../../gradient-badge.svg)](https://ipu.dev/PmAtSw)

| Framework | Domain | Model | Datasets | Tasks | Training | Inference | Reference |
|-----------|--------|-------|----------|-------|----------|-----------|-----------|
| PyTorch | GNNs | CGCN | Reddit | Node Classification | <div style="text-align: center;">✅ <br>Min. 4 IPUs (POD4) required | <p style="text-align: center;">❌ | [Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks](https://arxiv.org/pdf/1905.07953.pdf) |

## Instructions summary

1. Install and enable the Poplar SDK (see [Poplar SDK setup](##poplar-sdk-setup))

2. Install the system and Python requirements (see [Environment setup](##environment-setup))

3. Run the Jupyter notebook (see [Running the notebook](##running-the-notebook))

## 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. Download and unpack the Poplar SDK
2. Navigate inside the unpacked Poplar SDK:
```bash
cd poplar_sdk*
```
2. Enable the Poplar SDK with:
```bash
. enable
```

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 with your chosen name:
```bash
python3 -m venv <venv name>
source <venv name>/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. Install the PopTorch Geometric wheel:
```bash
cd <poplar sdk root dir>
pip3 install poptorch_geometric*.whl
```

5. Navigate to this example's root directory, the directory of this readme.

6. 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 the notebook

1. In your virtual environment, install the Jupyter notebook server:
```bash
pip3 install jupyter
```

2. Launch a Jupyter Server on a specific port:
```bash
jupyter-notebook --no-browser --port <port number>
```

3. Connect via SSH to your remote machine, forwarding your chosen port
```bash
ssh -NL <port number>:localhost:<port number> <your username>@<remote machine>
```

4. Use a browser to navigate to your Jupyter server and open the `node_classification_with_cluster_gcn.ipynb` notebook.

For more details about this process, or if you need troubleshooting, see our
[guide on using IPUs from Jupyter notebooks](https://github.com/graphcore/examples/tree/master/tutorials/tutorials/standard_tools/using_jupyter).

### License

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