Will there be an extension for Optimum-NVIDIA?

[williamcboehmjr]

I'm no python developer but this looks like it might be able to hook right into TensorRT? I'm not sure tho
https://github.com/huggingface/optimum-nvidia
I know it's made for LLM interference but surely it can be used with Stable Diffusion?
I might tinker with TensorRT (never used it before) and then give it's code to an LLM and try to tell it to use Opimum-Nvidia for interference... See what happens.

[williamcboehmjr]

https://pypi.org/project/optimum/

[williamcboehmjr]

To use the huggingface/optimum-nvidia library with TensorRT, you can follow the steps below:

1. Install the necessary dependencies: First, make sure you have CUDA and cuDNN installed. Then, install the optimum library with the onnxruntime-gpu option by running the following command:

   pip install optimum[onnxruntime-gpu]
   ```
   
   

2. Uninstall conflicting packages: To avoid conflicts between onnxruntime and onnxruntime-gpu, uninstall the onnxruntime package if it is already installed by running:

   pip uninstall onnxruntime
   ```
   
   

3. Import the necessary modules: In your Python script, import the required modules from optimum.onnxruntime and transformers. For example:

   from optimum.onnxruntime import ORTModelForSequenceClassification
   from transformers import AutoTokenizer
   ```

4. Load the ONNX model: Use the ORTModelForSequenceClassification.from_pretrained() method to load the ONNX model. Specify the provider argument as "TensorrtExecutionProvider" to use TensorRT for accelerated inference. Here's an example:

   ort_model = ORTModelForSequenceClassification.from_pretrained(
       "philschmid/tiny-bert-sst2-distilled",
       export=True,
       provider="TensorrtExecutionProvider",
   )
   ```

5. Use the model for inference: You can now use the loaded model for inference using the common 🤗 Transformers API. For example, you can use the model with a pipeline for text classification:

   tokenizer = AutoTokenizer.from_pretrained("philschmid/tiny-bert-sst2-distilled")
   pipe = pipeline(task="text-classification", model=ort_model, tokenizer=tokenizer)
   result = pipe("Both the music and visuals were astounding.")
   print(result)
   ```

By following these steps, you can leverage the huggingface/optimum-nvidia library with TensorRT for accelerated inference on NVIDIA GPUs.

---

Learn more:

1. Accelerated inference on NVIDIA GPUs
2. Optimum-NVIDIA Unlocking blazingly fast LLM inference in just 1 line of code
3. Optimizing Transformers for GPUs with Optimum

[williamcboehmjr]

Does that look right? I may dable at this later. I'm not in front of my computer.