When I deploy a model using DLA, Can I also use cuda graph to capture?

[willie.xing]

the question as the title

[carolyuu]

Hi,
Here are some suggestions for the common issues:

1. Performance

Please run the below command before benchmarking deep learning use case:

$ sudo nvpmodel -m 0
$ sudo jetson_clocks

2. Installation

Installation guide of deep learning frameworks on Jetson:

• TensorFlow: https://docs.nvidia.com/deeplearning/frameworks/install-tf-jetson-platform/index.html
• PyTorch: Installing PyTorch for Jetson Platform - NVIDIA Docs
  We also have containers that have frameworks preinstalled:
  Data Science, Machine Learning, AI, HPC Containers | NVIDIA NGC

3. Tutorial

Startup deep learning tutorial:

• Jetson-inference: Hello AI World guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson
• TensorRT sample: Jetson/L4T/TRT Customized Example - eLinux.org

4. Report issue

If these suggestions don’t help and you want to report an issue to us, please attach the model, command/step, and the customized app (if any) with us to reproduce locally.

Thanks!

[willie.xing]

Can I use cuda graph when i use dla to accelator my model inference?

[AastaLLL]

Hi,

DLA doesn’t support CUDA graph.

Please divide the model into different parts, capture the GPU model with CUDA Graph, and run the DLA model in the way that there’s no GPU fallback inside.

Thanks.