integrations/neural-magic/

[giscus[bot]]

integrations/neural-magic/

Learn how to deploy your YOLOv8 models rapidly using Neural Magic’s DeepSparse. This guide focuses on integrating Ultralytics YOLOv8 with the DeepSparse Engine for high-speed, CPU-based inference, leveraging advanced neural network sparsity techniques.

https://docs.ultralytics.com/integrations/neural-magic/

[sanvir19]

"Can I use DeepSparse on my Jetson Nano Dev Kit? Do I need to convert my YOLO model to TensorRT for that? The blog mentions converting it to ONNX format. Will using DeepSparse make my model run faster on my Jetson?"

[glenn-jocher]

@sanvir19 hey there! 😊

DeepSparse is designed to optimize neural network execution on CPUs, leveraging techniques like sparsity to enhance performance. While it's great for CPU environments, the Jetson Nano Dev Kit primarily benefits from GPU acceleration.

For your Jetson Nano, converting your YOLO model to TensorRT is indeed the recommended path. TensorRT is specifically designed to optimize deep learning inference on NVIDIA GPUs, including those on Jetson devices. Exporting your model to ONNX is a step in the right direction, as ONNX models can be further converted to TensorRT for deployment on Jetson.

Using DeepSparse could potentially make your model run faster on CPU-based systems, but for Jetson Nano, focusing on TensorRT will leverage the GPU for optimal performance. Here's a quick guide to convert your model to TensorRT:

1. Export your YOLO model to ONNX format:

   yolo export model=yolov8n.pt format=onnx

2. Use the onnx2trt tool to convert the ONNX model to TensorRT:

   onnx2trt yolov8n.onnx -o yolov8n.trt

This approach should give you the best inference speed on your Jetson Nano. If you have any more questions or need further assistance, feel free to ask!

[Hemilibeatriz]

Which version of YOLO was DeepSparse tested on? In version 8.2.2, I'm unable to get it to work

[glenn-jocher]

Hi there! It seems you're having trouble with DeepSparse on YOLOv8 version 8.2.2. The integration and testing with Neural Magic’s DeepSparse were primarily conducted on earlier versions of YOLOv8. It's possible that there have been changes in version 8.2.2 that affect compatibility.

I recommend checking the version compatibility in the Neural Magic documentation or trying to export and run your model with an earlier version of YOLOv8. If the issue persists, it might be helpful to reach out to Neural Magic's support or check their community forums for more specific guidance.

Here’s a quick example of how you might export your model to ONNX, which is required for DeepSparse:

yolo task=detect mode=export model=yolov8n.pt format=onnx opset=13

And then deploy it using DeepSparse:

from deepsparse import Pipeline

# Specify the path to your YOLOv8 ONNX model
model_path = "path/to/yolov8n.onnx"

# Set up the DeepSparse Pipeline
yolo_pipeline = Pipeline.create(task="yolov8", model_path=model_path)

# Run the model on your images
images = ["path/to/image.jpg"]
pipeline_outputs = yolo_pipeline(images=images)

Hope this helps! Let me know if there's anything else you need. 😊

[Framecyber]

I'm curious that can I use sparseML with the open ai clip model? is there a code or support for this?