guides/nvidia-jetson/

[giscus[bot]]

guides/nvidia-jetson/

Quick start guide to setting up YOLOv8 on a NVIDIA Jetson device with comprehensive benchmarks.

https://docs.ultralytics.com/guides/nvidia-jetson/

[RasoulZardeyni]

Which one reduce inference time using TensorRT or deepstream?

[pderrenger]

In terms of reducing inference time on your NVIDIA Jetson device, using TensorRT is generally recommended. TensorRT is specifically designed to provide high performance for NVIDIA hardware by optimizing deep learning models for inference. It accelerates models by fusing layers, pruning unused layers, and quantizing variables, among other optimizations. 🚀

DeepStream, on the other hand, is more focused on streaming analytics and can utilize TensorRT-optimized models. For most cases, directly using TensorRT for model optimization is your go-to for minimizing inference times.

Here's a quick example of exporting a YOLO model to TensorRT and running inference, tailored for your convenience:

from ultralytics import YOLO

# Load your YOLO model
model = YOLO('yolov8n.pt')

# Export to TensorRT format
model.export(format='engine')

# Load the TensorRT model
trt_model = YOLO('yolov8n.engine')

# Run inference using the TensorRT model
results = trt_model('your_image_or_video.jpg')

Feel free to explore more about deploying on NVIDIA Jetson with our Quick Start Guide for NVIDIA Jetson. Happy inferencing! 🌟

[RasoulZardeyni]

Thanks!

[pderrenger]

Hey there! 😊 It looks like you're interested in speeding up your inference times. Between TensorRT and DeepStream on NVIDIA Jetson devices, TensorRT typically offers more direct optimization for reducing inference time for individual models by providing lower-level optimizations and quantization options.

DeepStream is a comprehensive framework for building end-to-end video analytics applications, which utilizes TensorRT under the hood for model inference. DeepStream excels in scenarios involving video streams, multi-stream input, and complex pipelines involving multiple AI models and analytics. If your primary goal is solely to minimize inference time for a specific model, direct optimization via TensorRT can provide more focused improvements.

A quick example of exporting a YOLO model to TensorRT:

from ultralytics import YOLO

# Load your YOLO model
model = YOLO('yolov8n.pt')

# Export to TensorRT format
model.export(format='engine')  # This creates 'yolov8n.engine'

# Done! Now you can use 'yolov8n.engine' for faster inference.

However, if your application benefits from a broader video analytics pipeline, incorporating DeepStream into your workflow can optimize and streamline these processes on top of efficiently handling inference with TensorRT optimized models. 🚀

Hope this gives you a clearer picture! If you have more questions, feel free to ask.

[derrick20000]

Hi, for this docker that run on jetpack 4x, I need to use csi camera for my project, and I face a lot of problems about it. Without docker, I can open the camera. With docker, I cant open it although I already add this command --volume /tmp/argus_socket:/tmp/argus_socket. This is my command to activate ultralytics docker:
sudo docker run -it --runtime nvidia
--device /dev/video0
--device /dev/nvhost-ctrl
--device /dev/nvhost-ctrl-gpu
--device /dev/nvhost-prof-gpu
--device /dev/nvmap
--device /dev/nvhost-gpu
--device /dev/nvhost-vic
--device /dev/nvhost-nvdec
--device /dev/nvhost-msenc
--device /dev/nvhost-isp
--device /dev/nvhost-vi
--volume /tmp/argus_socket:/tmp/argus_socket
-v ~/myenv/FYP:/workspace
-e DISPLAY=$DISPLAY
-e QT_X11_NO_MITSHM=1
-v /tmp/.X11-unix:/tmp/.X11-unix
--privileged
ultralytics/ultralytics:latest-jetson-jetpack4

I not sure is the torch and torchvision version that beyond the Jetpack 4 which is 1.11.0 that suppose for Jetpack 5 affect the csi camera. Please help me, thanks.

[glenn-jocher]

@derrick20000 hi there! 😊

Thank you for reaching out with your issue regarding the CSI camera in Docker on JetPack 4.x. It sounds like you're on the right track with your Docker setup, but let's see if we can pinpoint the problem.

Firstly, ensure that you have the latest versions of torch and ultralytics installed. If not, please update them and try again. This can sometimes resolve compatibility issues.

Regarding the CSI camera, the command you provided looks mostly correct. However, there are a few additional steps and checks you can perform:

1. Ensure Camera Access in Docker:
   Make sure the Docker container has the necessary permissions to access the camera. You might need to add more device mappings or permissions. Here's an enhanced version of your Docker run command:

   sudo docker run -it --runtime nvidia \
     --device /dev/video0 \
     --device /dev/nvhost-ctrl \
     --device /dev/nvhost-ctrl-gpu \
     --device /dev/nvhost-prof-gpu \
     --device /dev/nvmap \
     --device /dev/nvhost-gpu \
     --device /dev/nvhost-vic \
     --device /dev/nvhost-nvdec \
     --device /dev/nvhost-msenc \
     --device /dev/nvhost-isp \
     --device /dev/nvhost-vi \
     --volume /tmp/argus_socket:/tmp/argus_socket \
     -v ~/myenv/FYP:/workspace \
     -e DISPLAY=$DISPLAY \
     -e QT_X11_NO_MITSHM=1 \
     -v /tmp/.X11-unix:/tmp/.X11-unix \
     --privileged \
     ultralytics/ultralytics:latest-jetson-jetpack4

2. Check Camera Access Outside Docker:
   Verify that the camera works correctly outside of Docker. You can use tools like v4l2-ctl to check if the camera is accessible:

   v4l2-ctl --list-devices

3. Install Necessary Libraries:
   Ensure that all necessary libraries for camera access are installed within the Docker container. You might need to install v4l-utils and other dependencies:

   sudo apt-get update
   sudo apt-get install v4l-utils

4. Test Camera Access Inside Docker:
   Once inside the Docker container, test if you can access the camera using a simple script or tool. For example, you can use OpenCV to test camera access:

   import cv2
   
   cap = cv2.VideoCapture(0)
   if not cap.isOpened():
       print("Cannot open camera")
       exit()
   while True:
       ret, frame = cap.read()
       if not ret:
           print("Can't receive frame (stream end?). Exiting ...")
           break
       cv2.imshow('frame', frame)
       if cv2.waitKey(1) == ord('q'):
           break
   cap.release()
   cv2.destroyAllWindows()

If the issue persists, please provide a minimum reproducible example of your code and setup. This will help us investigate further. You can find more details on creating a minimum reproducible example here.

For more detailed guidance, please refer to our NVIDIA Jetson Quick Start Guide.

I hope this helps! Let us know how it goes or if you have any other questions. 🚀

[jirivchi1]

Hi, I am using jetson nano with jetpack4 and jetson orin nano with jetpack5.1.3. I am trying to use webcamera inside the docker and with the follow argument it works for jetson nano jetpack4-->

sudo docker run -it --runtime nvidia
--device /dev/video0
--device /dev/nvhost-ctrl
--device /dev/nvhost-ctrl-gpu
--device /dev/nvhost-prof-gpu
--device /dev/nvmap
--device /dev/nvhost-gpu
--device /dev/nvhost-vic
--device /dev/nvhost-nvdec
--device /dev/nvhost-msenc
--device /dev/nvhost-isp
--device /dev/nvhost-vi
--volume /tmp/argus_socket:/tmp/argus_socket
-v ~/myenv/FYP:/workspace
-e DISPLAY=$DISPLAY
-e QT_X11_NO_MITSHM=1
-v /tmp/.X11-unix:/tmp/.X11-unix
--privileged
ultralytics/ultralytics:latest-jetson-jetpack4

My question is: How it change for jetson orin nano? how can I change the last line? " ultralytics/ultralytics:latest-jetson-jetpack4
"

Thanks in advanced

[pderrenger]

@jirivchi1 hi there!

Thank you for reaching out. To run your setup on the Jetson Orin Nano with JetPack 5.1.3, you can modify the Docker command to use the appropriate Docker image for JetPack 5. Here’s how you can adjust the last line of your command:

sudo docker run -it --runtime nvidia \
  --device /dev/video0 \
  --device /dev/nvhost-ctrl \
  --device /dev/nvhost-ctrl-gpu \
  --device /dev/nvhost-prof-gpu \
  --device /dev/nvmap \
  --device /dev/nvhost-gpu \
  --device /dev/nvhost-vic \
  --device /dev/nvhost-nvdec \
  --device /dev/nvhost-msenc \
  --device /dev/nvhost-isp \
  --device /dev/nvhost-vi \
  --volume /tmp/argus_socket:/tmp/argus_socket \
  -v ~/myenv/FYP:/workspace \
  -e DISPLAY=$DISPLAY \
  -e QT_X11_NO_MITSHM=1 \
  -v /tmp/.X11-unix:/tmp/.X11-unix \
  --privileged \
  ultralytics/ultralytics:latest-jetson-jetpack5

This command uses the ultralytics/ultralytics:latest-jetson-jetpack5 image, which is optimized for JetPack 5.x.

For more detailed instructions and additional setup options, you can refer to our Quick Start Guide: NVIDIA Jetson with Ultralytics YOLOv8.

If you encounter any issues or need further assistance, please ensure you are using the latest versions of torch and ultralytics. If the problem persists, providing a minimum reproducible example will help us investigate further. You can find more information on creating a reproducible example here.

Best of luck with your project! 🚀

[jirivchi1]

thanks, it works!

[pderrenger]

@jirivchi1 you're welcome! 😊 I'm glad to hear it worked for you.

If you have any more questions or need further assistance, feel free to ask. For more detailed information on deploying Ultralytics YOLOv8 on NVIDIA Jetson devices, you can refer to our Quick Start Guide: NVIDIA Jetson with Ultralytics YOLOv8.

Happy coding! 🚀

[AgbajeAyomipo]

Good day, as i can see in one of the above pictures, there's 15G of memory available which is quite alright.
i have 3.3G available and i'm detecting in real time, i keep getting a "Low memory" warning after detecting for about 20 minutes. What could the issue be?
I have confirmed that it runs on the gpu.
I have a jetson orin nano.

PS: I intend to detect in real time for hours.

[pderrenger]

Good day!

Thank you for reaching out. The "Low memory" warning you're encountering after 20 minutes of real-time detection on your Jetson Orin Nano could be due to several factors, including memory leaks or inefficient memory management in your code.

Here are a few steps to help diagnose and potentially resolve the issue:

1. Reproducible Example: To better assist you, could you provide a minimum reproducible example of your code? This will help us pinpoint the issue more effectively. You can find guidance on creating a reproducible example here: Minimum Reproducible Example.

2. Update Packages: Ensure you are using the latest versions of all relevant packages, including the Ultralytics YOLOv8 package. Sometimes, updates include important bug fixes and performance improvements.

3. Memory Management: Monitor your memory usage over time to identify any potential leaks. You can use tools like jtop to monitor system resources on your Jetson device:

   sudo apt update
   sudo pip install jetson-stats
   sudo reboot
   jtop

4. Optimize Inference: Consider using TensorRT for optimized inference performance. TensorRT can significantly reduce memory usage and improve inference speed. Here's a quick example of how to export your model to TensorRT and run inference:

   from ultralytics import YOLO
   
   # Load a YOLOv8n PyTorch model
   model = YOLO("yolov8n.pt")
   
   # Export the model to TensorRT format
   model.export(format="engine")  # creates 'yolov8n.engine'
   
   # Load the exported TensorRT model
   trt_model = YOLO("yolov8n.engine")
   
   # Run inference
   results = trt_model("https://ultralytics.com/images/bus.jpg")

5. Best Practices: Follow best practices for maximizing performance on NVIDIA Jetson devices:

   • Enable MAX Power Mode:

     sudo nvpmodel -m 0

   • Enable Jetson Clocks:

     sudo jetson_clocks

For more detailed guidance, you can refer to our Quick Start Guide: NVIDIA Jetson with Ultralytics YOLOv8.

I hope this helps! Please let us know if you have any further questions or need additional assistance.

[AgbajeAyomipo]

import ultralytics
from ultralytics import YOLO

model = YOLO("yolov8n.pt")

Dependencies:
* torch==2.1.0a0+41361538.nv23.6
* ultralytics==8.2.38

Now, in the other discussion i made, i was asked to export the model:
i ran the following code:


model.export(format = "engine", workspace = 2, int8 = True)

Error Message after running this:
[06/26/2024-12:24:43] [TRT] [W] Tactic Device request: 150MB Available: 149MB. Device memory is insufficient to use tactic.
[06/26/2024-12:24:43] [TRT] [W] Skipping tactic 2 due to insufficient memory on requested size of 150 detected for tactic 0x000000000000001e.

Thank You.

[pderrenger]

Hello,

Thank you for providing the details of your setup and the error message you're encountering. It looks like the issue is related to insufficient memory for the TensorRT tactic during the export process.

Here are a few steps to help resolve this issue:

1. Reduce Workspace Size: The workspace parameter specifies the maximum workspace size in GiB for TensorRT optimizations. You might want to try reducing this value further to fit within your available memory. For example:

   model.export(format="engine", workspace=1, int8=True)

2. Check Memory Usage: Monitor your system's memory usage to ensure there are no other processes consuming significant memory. You can use the jtop tool for this:

   sudo apt update
   sudo pip install jetson-stats
   sudo reboot
   jtop

3. Enable MAX Power Mode and Jetson Clocks: Ensure your Jetson device is running at its maximum performance settings:

   sudo nvpmodel -m 0
   sudo jetson_clocks

4. Verify Latest Versions: Make sure you are using the latest versions of all relevant packages. Sometimes, updates include important bug fixes and performance improvements.

If the issue persists, providing a minimum reproducible example can be very helpful for further diagnosis. You can find guidance on creating a reproducible example here: Minimum Reproducible Example.

Feel free to reach out if you have any further questions or need additional assistance. 😊

[jirivchi1]

I tried ultralytics for jetson orin nano "Start without Docker" and I am getting the follow error:

(env3.8) jetson-orin@jetsonorin-desktop:~/Desktop/gpu$ python3
Python 3.8.10 (default, Nov 22 2023, 10:22:35)
[GCC 9.4.0] on linux
Type “help”, “copyright”, “credits” or “license” for more information

Traceback (most recent call last):
File “”, line 1, in
File “/home/jetson-orin/Desktop/gpu_cocacola/env3.8/lib/python3.8/site-packages/ultralytics/init.py”, line 10, in
from ultralytics.data.explorer.explorer import Explorer
File “/home/jetson-orin/Desktop/gpu_cocacola/env3.8/lib/python3.8/site-packages/ultralytics/data/init.py”, line 3, in
from .base import BaseDataset
File “/home/jetson-orin/Desktop/gpu_cocacola/env3.8/lib/python3.8/site-packages/ultralytics/data/base.py”, line 17, in
from ultralytics.data.utils import FORMATS_HELP_MSG, HELP_URL, IMG_FORMATS
File “/home/jetson-orin/Desktop/gpu_cocacola/env3.8/lib/python3.8/site-packages/ultralytics/data/utils.py”, line 19, in
from ultralytics.nn.autobackend import check_class_names
File “/home/jetson-orin/Desktop/gpu_cocacola/env3.8/lib/python3.8/site-packages/ultralytics/nn/init.py”, line 3, in
from .tasks import (
File “/home/jetson-orin/Desktop/gpu_cocacola/env3.8/lib/python3.8/site-packages/ultralytics/nn/tasks.py”, line 10, in
from ultralytics.nn.modules import (
File “/home/jetson-orin/Desktop/gpu_cocacola/env3.8/lib/python3.8/site-packages/ultralytics/nn/modules/init.py”, line 20, in
from .block import (
File “/home/jetson-orin/Desktop/gpu_cocacola/env3.8/lib/python3.8/site-packages/ultralytics/nn/modules/block.py”, line 8, in
from ultralytics.utils.torch_utils import fuse_conv_and_bn
File “/home/jetson-orin/Desktop/gpu_cocacola/env3.8/lib/python3.8/site-packages/ultralytics/utils/init.py”, line 47, in
TORCHVISION_VERSION = importlib.metadata.version(“torchvision”) # faster than importing torchvision
File “/usr/lib/python3.8/importlib/metadata.py”, line 530, in version
return distribution(distribution_name).version
File “/usr/lib/python3.8/importlib/metadata.py”, line 503, in distribution
return Distribution.from_name(distribution_name)
File “/usr/lib/python3.8/importlib/metadata.py”, line 177, in from_name
raise PackageNotFoundError(name)
importlib.metadata.PackageNotFoundError: torchvision

I am seeing is not only my problem and there is a post in NVIDIA forum: https://forums.developer.nvidia.com/t/pytorch-installation-on-jetson-agx-orin/292846

help: last line of installation: python3 setup.py install --user
is saying this kinf of warning --> torchvision/csrc/ops/cuda/ps_roi_pool_kernel.cu(283)

[pderrenger]

Hello @jirivchi1,

Thank you for reaching out and providing detailed information about the issue you're encountering. It appears that the error is related to the torchvision package not being found, which is a common issue on Jetson devices due to the ARM64 architecture.

To resolve this, please follow these steps to ensure that torch and torchvision are correctly installed for your Jetson Orin Nano:

1. Uninstall the existing torch and torchvision packages:

   pip uninstall torch torchvision

2. Install the required dependencies:

   sudo apt-get install -y libopenblas-base libopenmpi-dev libjpeg-dev zlib1g-dev

3. Download and install the compatible PyTorch wheel:

   wget https://developer.download.nvidia.com/compute/redist/jp/v512/pytorch/torch-2.1.0a0+41361538.nv23.06-cp38-cp38-linux_aarch64.whl -O torch-2.1.0a0+41361538.nv23.06-cp38-cp38-linux_aarch64.whl
   pip install torch-2.1.0a0+41361538.nv23.06-cp38-cp38-linux_aarch64.whl

4. Clone and install torchvision from source:

   git clone https://github.com/pytorch/vision torchvision
   cd torchvision
   git checkout v0.16.2
   python3 setup.py install --user

5. Ensure onnxruntime-gpu is installed correctly:

   wget https://nvidia.box.com/shared/static/zostg6agm00fb6t5uisw51qi6kpcuwzd.whl -O onnxruntime_gpu-1.17.0-cp38-cp38-linux_aarch64.whl
   pip install onnxruntime_gpu-1.17.0-cp38-cp38-linux_aarch64.whl

6. Reinstall numpy to the compatible version:

   pip install numpy==1.23.5

After completing these steps, please try running your script again. This should resolve the torchvision import error and allow you to proceed with your project.

If the issue persists, please ensure you are using the latest versions of all packages and provide a minimum reproducible example as outlined here to help us diagnose further.

For more detailed guidance, you can refer to our NVIDIA Jetson Quick Start Guide.

Best regards and happy coding! 🚀

Ultralytics Team

[sophiapeckner]

Hello! I'm running YOLOv8 on my Jetson Nano JetPack 4.x. I took the Docker approach.
I've run yolo predict on bus.jpg ('https://ultralytics.com/images/bus.jpg') using TorchScript and TensorRT format which I exported using yolo export:

yolo predict model=yolov8n.torchscript source=bus.jpg
Speed: 93.6ms preprocess, 215ms inference, 151.7ms postprocess per image at shape (1, 3, 640, 640)

yolo predict model=yolov8n.engine source=bus.jpg
Speed: 7243.4ms preprocess, 3195.6ms inference, 11259.1ms postprocess per image at shape (1, 3, 640, 640)

These results are unexpected. You'd think TensorRT/.engine would be faster but in fact it's much slower. I'm wondering if this should raise any red flags? And if so, how might I proceed? I also ask because I've been having a lot of trouble exporting YOLOv8 on the Nano so just wanted to be on the lookout for any potential problems so that I can catch them early on. Thanks in advance!

[glenn-jocher]

@sophiapeckner it seems unusual that your TensorRT model is performing slower than the TorchScript model. This could be due to several factors, including the export process or runtime environment. First, ensure you are using the latest versions of the Ultralytics package and JetPack. Additionally, verify that TensorRT is properly installed and configured. You might also want to check if the Jetson Nano is running in MAXN mode and that Jetson clocks are enabled. For further details, you can refer to our NVIDIA Jetson guide. If the issue persists, please provide more details about your setup.

[sophiapeckner]

Ultralytics package is 8.2.57 (I checked in Docker container)
JetPack version is 4.6.5 which is the highest version supported by the Nano
TensorRT is installed; could you provide more guidance on how to check if it's properly configured?
MAXN mode and Jetson clocks are enabled
I looked at Ultralytic's Docker Support Guide. When I run nvidia-smi I get command not found. According to this forum:

Ubuntu 18.04 ships the nvidia-smi binary in the nvidia-utils-390 package. Unfortunately this isn't built for the arm64 architecture

I have Ubuntu 18.04 and getting a newer image that supports Ubuntu 22.04 isn't an option. Do you have any thoughts on how I should proceed?

[glenn-jocher]

Thank you for the detailed information. Given your setup, the significant slowdown with TensorRT is indeed unexpected. First, ensure that the TensorRT engine was correctly optimized during export. You can try re-exporting the model with the simplify and dynamic arguments to see if it improves performance:

yolo export model=yolov8n.pt format=engine simplify=True dynamic=True

Additionally, verify that TensorRT is properly installed and configured by running a simple TensorRT sample provided in the NVIDIA TensorRT documentation. If the issue persists, consider checking for any potential bottlenecks in your Docker setup or system configuration. For further guidance, you can refer to our NVIDIA Jetson guide.

[MadhavKrishna1]

when i run this command on Jetson Xavier NX (Jetpack 5.1.2)
"sudo apt install -y libjpeg-dev zlib1g-dev
git clone https://github.com/pytorch/vision torchvision
cd torchvision
git checkout v0.16.2
python3 setup.py install --user"
i get this error:
"python3 setup.py install --user
Traceback (most recent call last):
File "setup.py", line 9, in
import torch
ModuleNotFoundError: No module named 'torch'"

What should i do?

[MadhavKrishna1]

i got it solved, actually i missed one download and a installation was missing

"wget https://developer.download.nvidia.com/compute/redist/jp/v512/pytorch/torch-2.1.0a0+41361538.nv23.06-cp38-cp38-linux_aarch64.whl -O torch-2.1.0a0+41361538.nv23.06-cp38-cp38-linux_aarch64.whl
pip install torch-2.1.0a0+41361538.nv23.06-cp38-cp38-linux_aarch64.whl".

[glenn-jocher]

@MadhavKrishna1 glad to hear you resolved the issue! Installing the correct PyTorch version is crucial for compatibility on Jetson devices. If you encounter any further issues or need additional guidance, feel free to refer to our NVIDIA Jetson Quick Start Guide. It provides detailed steps and troubleshooting tips for setting up YOLOv8 on Jetson devices.

[MadhavKrishna1]

WARNING ⚠️ TensorRT requires GPU export, automatically assigning device=0
Ultralytics YOLOv8.2.60 🚀 Python-3.8.10 torch-2.1.0a0+41361538.nv23.06 CUDA:0 (Xavier, 14898MiB)

I am getting this warning when i ran these code
"from ultralytics import YOLO

Load a YOLOv8n PyTorch model

model = YOLO("yolov8n.pt")

Export the model

model.export(format="engine") "

Even though i have my torch cuda enabled.
torch.cuda.is_available() shows output True.

[pderrenger]

@MadhavKrishna1 the warning indicates that TensorRT requires GPU export, which is why it automatically assigns device=0. Even though torch.cuda.is_available() returns True, the export process specifically needs to ensure that the GPU is utilized. Please verify that your NVIDIA Jetson device is correctly set up with the latest JetPack and CUDA drivers. If the issue persists, try running the export command within a Docker container as described in the NVIDIA Jetson setup guide. This can help ensure all dependencies are correctly configured.

[DJT777]

Is there any intention to update the documentation for Jetpack 6?

https://docs.ultralytics.com/guides/nvidia-jetson

[glenn-jocher]

Thank you for your interest. Currently, there are no immediate plans to update the documentation specifically for JetPack 6. However, we continually review and update our resources to ensure compatibility with the latest technologies. Please keep an eye on our documentation page for any future updates. If you have any specific issues or questions, feel free to ask!

[knowee97]

Hi, I plan on using the docker that'll run on jetpack 4.x and would need to use a CSI camera (IMX519) for my project. As it is my first time using a docker, are there any videos that will demonstrate a step process on using a docker on a Jetson Nano?

[glenn-jocher]

@knowee97 for a step-by-step guide on using Docker with Jetson Nano, I recommend watching the video linked in the Quick Start Guide: "How to Setup NVIDIA Jetson with Ultralytics YOLOv8." This guide covers Docker setup and usage on Jetson devices, including camera integration. For detailed instructions, visit the Ultralytics documentation at https://docs.ultralytics.com/guides/nvidia-jetson/. This should help you get started with your CSI camera and Docker on JetPack 4.x. If you encounter specific issues, feel free to ask!

[knowee97]

I was able to run the Docker under the "Run on JetPack 4.x" section on my Jetson Nano Dev Kit. After mounting files from the Jetson to the docker, I wasn't able to access my CSI camera (IMX159) using a quick test code from previous comments. It worked as usual outside the docker, however wouldn't in the docker. I checked using "ls -l /dev/video0" in the docker and detected it. This is my command to activate the ultralytics docker:

sudo docker run -it --ipc=host
--runtime nvidia
--device /dev/video0:/dev/video0
-v /home/noe/Desktop/pyProg:/usr/src/ultralytics/myProg
ultralytics/ultralytics:latest-jetson-jetpack4

[glenn-jocher]

It sounds like you've set up the Docker container correctly. However, accessing the CSI camera within Docker can sometimes be tricky. Ensure that you have the necessary permissions and that the camera is correctly mapped. You might also want to try adding --privileged to your Docker run command to grant the container additional privileges. If the issue persists, please verify that all necessary drivers and libraries for the camera are available within the Docker environment. For further guidance, refer to the detailed steps in the Ultralytics Jetson Guide.

[knowee97]

An update to using the docker was made below. However, I still haven't been able to access the cameras (CSI & Webcam) even when using --privileged. Outside the docker works great and inside the docker is recognized, yet no able to access it and output frame. What are some possibilities I can try and take?

sudo docker run -it --ipc=host --runtime=nvidia
--gpus all
--device /dev/video0:/dev/video0
--device /dev/video1:/dev/video1
-v /path/to/code:/usr/src/ultralytics/myProg
-v /path/to/Models:/usr/src/ultralytics/Models
--privileged
ultralytics/ultralytics:latest-jetson-jetpack4

[glenn-jocher]

It seems you've set up the Docker container correctly, but accessing the CSI and webcam cameras can be tricky. Ensure that all necessary drivers and libraries for the cameras are available within the Docker environment. You might also try running the container with --privileged and --device /dev/video0:/dev/video0 --device /dev/video1:/dev/video1 to map the devices correctly. Additionally, verify that the container has access to the necessary video group permissions. If the issue persists, consider checking the Docker logs for any specific errors related to camera access. For more detailed guidance, refer to the Ultralytics Jetson Guide.

[Faker-T1-NO1]

hi , i have error: torchvision 0.16.2 requires torch==2.1.2, but you have torch 2.1.0a0+41361538.nv23.6 which is incompatible.
how to slove this prolem

[glenn-jocher]

@Faker-T1-NO1 to resolve the compatibility issue between torchvision and torch, you need to ensure both packages are compatible with each other. Uninstall the current versions and install the compatible versions as follows:

1. Uninstall the existing packages:

   pip uninstall torch torchvision

2. Install the compatible versions:

   sudo apt-get install -y libopenblas-base libopenmpi-dev
   wget https://developer.download.nvidia.com/compute/redist/jp/v512/pytorch/torch-2.1.0a0+41361538.nv23.06-cp38-cp38-linux_aarch64.whl
   pip install torch-2.1.0a0+41361538.nv23.06-cp38-cp38-linux_aarch64.whl
   sudo apt install -y libjpeg-dev zlib1g-dev
   git clone https://github.com/pytorch/vision torchvision
   cd torchvision
   git checkout v0.16.2
   python3 setup.py install --user

This should resolve the version mismatch. For more details, refer to the NVIDIA Jetson setup guide.

[Faker-T1-NO1]

Yes, I follow this but have this error Vào Th 6, 2 thg 8, 2024 lúc 12:11 Glenn Jocher ***@***.***> đã viết:

…

@Faker-T1-NO1 <https://github.com/Faker-T1-NO1> to resolve the compatibility issue between torchvision and torch, you need to ensure both packages are compatible with each other. Uninstall the current versions and install the compatible versions as follows: 1. Uninstall the existing packages: pip uninstall torch torchvision 2. Install the compatible versions: sudo apt-get install -y libopenblas-base libopenmpi-dev wget https://developer.download.nvidia.com/compute/redist/jp/v512/pytorch/torch-2.1.0a0+41361538.nv23.06-cp38-cp38-linux_aarch64.whl pip install torch-2.1.0a0+41361538.nv23.06-cp38-cp38-linux_aarch64.whl sudo apt install -y libjpeg-dev zlib1g-dev git clone https://github.com/pytorch/vision torchvisioncd torchvision git checkout v0.16.2 python3 setup.py install --user This should resolve the version mismatch. For more details, refer to the NVIDIA Jetson setup guide <https://docs.ultralytics.com/guides/nvidia-jetson/>. — Reply to this email directly, view it on GitHub <#9942 (reply in thread)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/BKIS5BJS3DHXAF74PQRH5FDZPMIINAVCNFSM6AAAAABGAK2IGGVHI2DSMVQWIX3LMV43URDJONRXK43TNFXW4Q3PNVWWK3TUHMYTAMRRHE2DQNQ> . You are receiving this because you were mentioned.Message ID: ***@***.*** com>

[glenn-jocher]

@Faker-T1-NO1 it seems like you're encountering an error despite following the instructions. Please ensure you are using the latest versions of the packages. If the issue persists, try reinstalling PyTorch and Torchvision as described. For further guidance, you can refer to the detailed setup instructions in the NVIDIA Jetson setup guide. If the problem continues, please provide more details about the error message for further assistance.