To address the issue of 0% GPU utilization during the indexing process with your NVIDIA A40 GPU, follow these targeted steps:

1. Ensure CUDA Compatibility: Confirm that CUDA is correctly installed and that its version is compatible with your GPU. This is crucial for enabling GPU acceleration in Python.

2. Framework Version Check: Make sure you're using a version of PyTorch or TensorFlow that supports your CUDA version. Compatibility between these components is essential for GPU utilization.

3. Optimize with LocalTensorRTLLM: For efficient NVIDIA GPU utilization, particularly with TensorRT for language models, use the LocalTensorRTLLM class from llama_index.legacy.llms.nvidia_tensorrt. This class is specifically designed to leverage NVIDIA GPUs:

   from llama_index.legacy.llms.nvidia_tensorrt import LocalTensorRTLLM
   
   tensorrt_llm = LocalTensorRTLLM(
       model_path="path/to/your/model",
       tokenizer_dir="path/to/your/tokenizer",
       temperature=0.1,
       max_new_tokens=512,
       context_window=2048,
       verbose=True
   )

4. Verify GPU Device Identifier: Double-check that cuda:7 correctly identifies your NVIDIA A40 GPU. Use nvidia-smi to list all CUDA-capable devices and ensure the identifier matches.

5. Monitor GPU Utilization: Utilize nvidia-smi to monitor GPU utilization in real-time, confirming that your GPU is being engaged during the indexing operation.

6. Debugging: If the issue persists, simplify your code to a basic operation that should engage the GPU, helping to isolate the problem.

This approach focuses on ensuring compatibility and proper configuration for GPU utilization, leveraging specific classes designed for NVIDIA GPUs, and monitoring to confirm active GPU engagement.

To continue the conversation, mention @dosu.

50GB of data is a ton of data to load into memory. I wonder if it's getting stuck there? If you hit ctrl+c, the traceback would tell us where it is in the code.

I would also suggest testing the embedding model on its own first to ensure it uses GPU
embeddings = embed_model.get_text_embedding("Hello World!") is an easy test

If you have the memory, you can also increase the batch size: HuggingFaceEmbedding(..., embed_batch_size=10) (default is 10)

Also, by specifying transformations=[embed_model], you are removing any text splitting (not sure if intended or not)

You also didn't specify a vector DB, so all the embeddings (50GB of text of embeddings!!) will be in memory -- probably also not ideal

If I was to re-write this, it might look like:

splitter = SentenceSplitter(chunk_size=1024, chunk_overlap=128)
vector_store = <some remote/hosted/optimized vector store, qdrant, chroma, weaviate, etc.>
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex(nodes=[], storage_context=storage_context, embed_model=embed_model)

# iter and insert in batches
cur_batch = []
for doc in SimpleDirectoryReader(data_path).iter_data():
  cur_batch.append(doc)
  if len(cur_batch) == 100:
    nodes = splitter(cur_batch)
    index.insert_nodes(nodes)
    cur_batch = []

# get the last batch
nodes = splitter(cur_batch)
index.insert_nodes(nodes)

Thanks for ur help, really apperciate it. I realized it was stuck and split it into small batches then. thanks a lot.