training

Alright, here’s the step-by-step fix to reinstall Graphormer and start training for your Capstone project.

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Step 1: Reinstall Graphormer

Since your environment is messy after hours of installing Fairseq, let’s clean install Graphormer properly.

1A. Remove Any Old Graphormer Installation

Run this first to remove old installations:

pip uninstall -y graphormer fairseq fairseq2 fairseq2n

1B. Install Dependencies for Graphormer

pip install torch torchvision torchaudio
pip install numpy networkx rdkit-pypi
pip install --no-cache-dir --force-reinstall git+https://github.com/microsoft/Graphormer.git

1C. Confirm Installation

import graphormer
print(f"Graphormer version: {graphormer.__version__}")

If this runs without errors, you’re good.

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Step 2: Recap Your Capstone Training Setup

Your Capstone project involves: - Quantum Computing + GIS
- Molecular, fossil fuel, biological particle datasets
- Autoencoders, anomaly detection, time-series GIS generation
- Tracking molecular migration, fossil fuel movement
- Goal: Predict geological/environmental shifts using Graphormer

Yesterday’s Issues: - Fairseq/Fairseq2 wasted your time - Graphormer wasn’t installed properly - No model training started

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Step 3: Training Graphormer on Your Data

3A. Setup Data Format

Graphormer expects datasets in OGB format (Open Graph Benchmark).
- Do you have your dataset in OGB format? - If not, I can help convert your dataset into OGB format.

3B. Example Training Code

Here’s a basic training script for Graphormer:

import torch
from graphormer.models.graphormer import Graphormer

# Define model parameters
model = Graphormer(
    num_atoms=100,  # Adjust based on dataset
    num_bonds=5,
    num_classes=2,  # Adjust based on task
    num_layers=12,
    num_heads=8,
    hidden_dim=256
)

# Load dataset (adjust path)
from graphormer.data.dataset import load_ogb_dataset
dataset = load_ogb_dataset("ogbg-molhiv")

# Split dataset
train_loader, val_loader, test_loader = dataset.get_dataloader(batch_size=32)

# Define optimizer
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)

# Training loop
for epoch in range(10):  # Adjust epochs
    model.train()
    for batch in train_loader:
        optimizer.zero_grad()
        output = model(batch)
        loss = torch.nn.functional.cross_entropy(output, batch.y)
        loss.backward()
        optimizer.step()
    print(f"Epoch {epoch+1}, Loss: {loss.item()}")

print("Training complete.")

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Step 4: Next Steps

• Confirm Graphormer installed
  • Run import graphormer and check version
• Confirm dataset format
  • If you need to convert it to OGB format, I can help
• Run training
  • Use the script above with your dataset

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Let me know: 1. Do you need dataset conversion?
2. Do you need to adjust Graphormer for quantum features?
3. Do you need multi-GPU training?

No more wasted time—let’s train this model now.