YoloV8 with TensorRT Jetpack 6: dependencies? about ultralytics HOT 4 OPEN

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Install

Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

Environments

YOLOv8 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

If this badge is green, all Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLOv8 Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.

from ultralytics.

Hello! It looks like you're encountering dependency conflicts during your build, particularly with google.protobuf. This is a common issue when different packages require different versions of the same dependency.

For Jetpack 6 compatibility, ensure that all your dependencies are aligned with the versions supported by Jetpack 6. Here are a few suggestions:

1. Protobuf Version: It seems there's an issue with the protobuf version. You might need to explicitly specify a compatible version in your installation command. For example:

   pip install protobuf==3.20.*

2. Virtual Environment: Consider using a Python virtual environment to avoid conflicts with system-wide packages. This can help isolate your dependencies:

   python3 -m venv myenv
   source myenv/bin/activate

3. Dependencies Check: After setting up your environment, you can install the ultralytics package and its dependencies within this isolated environment.

4. Rebuild the Container: With the virtual environment and correct versions, rebuild your container. This might resolve the conflicting issues.

If these steps don't resolve the issue, could you please provide the specific versions of the packages you're using? This will help in diagnosing the problem more accurately.

Thank you for reaching out, and I hope this helps! Let us know how it goes.

from ultralytics.

Hi Glenn, thank you very much.

Besides version mismatching, I encountered another issue when compiling yolo inside a container build, which is to get access to the cuda devices. Unless you use really new versions of podman/docker, with the new CDI stack where you add something like: --device nvidia.com/gpu=all
at build time it won't work, so you have to compile it outside the container, and then, copy it inside the resulting image.

from ultralytics.

Hello!

Thank you for sharing this additional insight regarding the CUDA device access during the container build. You're absolutely right; handling GPU access in Docker or Podman can be tricky without the latest features like the CDI stack.

Compiling the YOLO model outside the container and then copying it into the image is a practical workaround. This approach ensures that the compiled model can utilize the GPU resources effectively once the container is deployed.

If you have any more questions or run into further issues, feel free to reach out. Happy coding! 🚀

from ultralytics.