Training

Step 1: Reinstall Graphormer

Since your environment has inconsistencies, let’s properly clean and reinstall Graphormer.

1A. Remove Previous Installations

pip uninstall -y graphormer fairseq fairseq2 fairseq2n torch torchvision torchaudio
pip cache purge

1B. Install Dependencies

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. Verify Installation

library(reticulate)
graphormer <- import("graphormer")
print(paste("Graphormer version:", graphormer$`__version__`))

If no errors appear, installation is successful.

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Step 2: Capstone Training Setup Recap

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

Previously, incorrect Fairseq versions prevented successful model training.

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Step 3: Prepare Data for Graphormer Training

3A. Ensure Data Format (OGB Format Required)

Graphormer requires datasets in Open Graph Benchmark (OGB) format. If your dataset is not in OGB format, conversion is required.

3B. Example Training Code

library(reticulate)
torch <- import("torch")
graphormer <- import("graphormer")
load_ogb_dataset <- graphormer$data$dataset$load_ogb_dataset

# Define model parameters
model <- graphormer$models$graphormer$Graphormer(
    num_atoms=100,
    num_bonds=5,
    num_classes=2,
    num_layers=12,
    num_heads=8,
    hidden_dim=256
)

# Load dataset
dataset <- load_ogb_dataset("ogbg-molhiv")
data_loaders <- dataset$get_dataloader(batch_size=32)
train_loader <- data_loaders[[1]]
val_loader <- data_loaders[[2]]
test_loader <- data_loaders[[3]]

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

# Training loop
for (epoch in 1:10) {
    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(paste("Epoch", epoch, "Loss:", loss$item()))
}

print("Training complete.")

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Step 4: Embedding Text for Vector Storage

To embed chunks of text into a vector store effectively, follow these steps:

4A. Chunk the Text

Divide your text into manageable pieces. Depending on your use case, you can chunk by sentences, paragraphs, or specific character counts. This helps ensure that each chunk contains complete thoughts and makes the embeddings more meaningful.

4B. Generate Embeddings

Use a pre-trained model to convert text chunks into embeddings. Libraries like Hugging Face’s Transformers or OpenAI’s models can be useful here. For instance, with Hugging Face’s Transformers, you can use models like distilbert for sentence embeddings:

library(reticulate)
torch <- import("torch")
transformers <- import("transformers")

model_name <- "distilbert-base-nli-mean-tokens"
model <- transformers$AutoModel$from_pretrained(model_name)
tokenizer <- transformers$AutoTokenizer$from_pretrained(model_name)

embed_text <- function(text_chunks) {
    inputs <- tokenizer(text_chunks, padding=TRUE, truncation=TRUE, return_tensors='pt')
    with(torch$no_grad(), {
        embeddings <- model(**inputs)$last_hidden_state$mean(dim=1)  # Mean pooling
    })
    return(embeddings)
}

4C. Store Embeddings in a Vector Store

Choose a vector database like FAISS or Pinecone. These databases are optimized for storing and retrieving high-dimensional vectors efficiently. Here’s an example of how to store embeddings with FAISS:

library(reticulate)
faiss <- import("faiss")
numpy <- import("numpy")

embeddings <- embed_text(list_of_chunks)  # This must return a NumPy array
index <- faiss$IndexFlatL2(embeddings$shape[1])  # L2 distance index
index$add(embeddings$numpy())  # Add embeddings to the index

4D. Search and Retrieve

To search for similar chunks, convert the query text to an embedding using the same model and then use the vector store to find the nearest neighbors:

query_embedding <- embed_text(list(query_text))
result <- index$search(query_embedding$numpy(), k)  # k is the number of nearest neighbors

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Step 5: Next Steps

• Verify Graphormer installation (import graphormer should work)
• Confirm dataset format (Conversion to OGB may be needed)
• Execute training script using your dataset

If issues persist, provide error messages for debugging.

Additional Considerations:

1. Do you need dataset conversion?
2. Should Graphormer be adjusted for quantum features?
3. Do you require multi-GPU training?

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Recap: Fixed Issues

✅ Removed conflicting packages ✅ Installed correct PyTorch & CUDA versions ✅ Installed Graphormer properly ✅ Ensured QM9 dataset readiness ✅ Initiated Graphormer training