From f92bd9dea119715ced09d0b6ca6241cdadb8397b Mon Sep 17 00:00:00 2001 From: Lakshantha Dissanayake Date: Tue, 11 Jun 2024 08:04:59 -0700 Subject: [PATCH] Fix `jp` to `jetpack` (#13499) --- docs/en/guides/nvidia-jetson.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/en/guides/nvidia-jetson.md b/docs/en/guides/nvidia-jetson.md index b3cb8c1bbb5..c8d80d13fcd 100644 --- a/docs/en/guides/nvidia-jetson.md +++ b/docs/en/guides/nvidia-jetson.md @@ -68,7 +68,7 @@ The fastest way to get started with Ultralytics YOLOv8 on NVIDIA Jetson is to ru Execute the below command to pull the Docker container and run on Jetson. This is based on [l4t-pytorch](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-pytorch) docker image which contains PyTorch and Torchvision in a Python3 environment. ```bash -t=ultralytics/ultralytics:latest-jetson-jp5 && sudo docker pull $t && sudo docker run -it --ipc=host --runtime=nvidia $t +t=ultralytics/ultralytics:latest-jetson-jetpack5 && sudo docker pull $t && sudo docker run -it --ipc=host --runtime=nvidia $t ``` After this is done, skip to [Use TensorRT on NVIDIA Jetson section](#use-tensorrt-on-nvidia-jetson). @@ -153,7 +153,7 @@ Here we support to run Ultralytics on legacy hardware such as the Jetson Nano. C Execute the below command to pull the Docker container and run on Jetson. This is based on [l4t-cuda](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-cuda) docker image which contains CUDA in a L4T environment. ```bash -t=ultralytics/ultralytics:jetson-jp4 && sudo docker pull $t && sudo docker run -it --ipc=host --runtime=nvidia $t +t=ultralytics/ultralytics:latest-jetson-jetpack4 && sudo docker pull $t && sudo docker run -it --ipc=host --runtime=nvidia $t ``` ## Use TensorRT on NVIDIA Jetson