PyTorch Geometric

PyTorch Geometric or PyG is one of the most popular libraries for geometric deep learning and W&B works extremely well with it for visualizing graphs and tracking experiments.

After you have installed Pytorch Geometric, follow these steps to get started.

Sign up and create an API key

An API key authenticates your machine to W&B. You can generate an API key from your user profile.

For a more streamlined approach, you can generate an API key by going directly to the W&B authorization page. Copy the displayed API key and save it in a secure location such as a password manager.

1. Click your user profile icon in the upper right corner.
2. Select User Settings, then scroll to the API Keys section.
3. Click Reveal. Copy the displayed API key. To hide the API key, reload the page.

Install the wandb library and log in

To install the wandb library locally and log in:

Command Line

1. Set the WANDB_API_KEY environment variable to your API key.

   export WANDB_API_KEY=<your_api_key>
   

2. Install the wandb library and log in.

   pip install wandb
   
   wandb login
   

Visualize the graphs

You can save details about the input graphs including number of edges, number of nodes and more. W&B supports logging plotly charts and HTML panels so any visualizations you create for your graph can then also be logged to W&B.

Use PyVis

The following snippet shows how you could do that with PyVis and HTML.

from pyvis.network import Network
import wandb

with wandb.init(project=’graph_vis’) as run:
    net = Network(height="750px", width="100%", bgcolor="#222222", font_color="white")

    # Add the edges from the PyG graph to the PyVis network
    for e in tqdm(g.edge_index.T):
        src = e[0].item()
        dst = e[1].item()

        net.add_node(dst)
        net.add_node(src)
        
        net.add_edge(src, dst, value=0.1)

    # Save the PyVis visualisation to a HTML file
    net.show("graph.html")
    run.log({"eda/graph": wandb.Html("graph.html")})

Use Plotly

To use plotly to create a graph visualization, first you need to convert the PyG graph to a networkx object. Following this you will need to create Plotly scatter plots for both nodes and edges. The snippet below can be used for this task.

def create_vis(graph):
    G = to_networkx(graph)
    pos = nx.spring_layout(G)

    edge_x = []
    edge_y = []
    for edge in G.edges():
        x0, y0 = pos[edge[0]]
        x1, y1 = pos[edge[1]]
        edge_x.append(x0)
        edge_x.append(x1)
        edge_x.append(None)
        edge_y.append(y0)
        edge_y.append(y1)
        edge_y.append(None)

    edge_trace = go.Scatter(
        x=edge_x, y=edge_y,
        line=dict(width=0.5, color='#888'),
        hoverinfo='none',
        mode='lines'
    )

    node_x = []
    node_y = []
    for node in G.nodes():
        x, y = pos[node]
        node_x.append(x)
        node_y.append(y)

    node_trace = go.Scatter(
        x=node_x, y=node_y,
        mode='markers',
        hoverinfo='text',
        line_width=2
    )

    fig = go.Figure(data=[edge_trace, node_trace], layout=go.Layout())

    return fig


with wandb.init(project=’visualize_graph’) as run:
    run.log({‘graph’: wandb.Plotly(create_vis(graph))})

Log metrics

You can use W&B to track your experiments and related metrics, such as loss functions, accuracy, and more. Add the following line to your training loop:

with wandb.init(project="my_project", entity="my_entity") as run:
    run.log({
        'train/loss': training_loss,
        'train/acc': training_acc,
        'val/loss': validation_loss,
        'val/acc': validation_acc
        })

More resources

• Recommending Amazon Products using Graph Neural Networks in PyTorch Geometric
• Point Cloud Classification using PyTorch Geometric
• Point Cloud Segmentation using PyTorch Geometric