Fixed installation of YOLOv7 on GPU

[bsekachev]

Motivation and context

Resolved #8503

older onnxruntime-gpu is not compatible with numpy > 2.0
one solution is to fix numpy to older version
another solution (in pull request) is to update onnxruntime-gpu

Newest version of onnxruntime-gpu available in PyPI requires Cuda 12

If you have difficulties with setup, try:

• To update NVIDIA driver (mine is 566.36)
• To update Docker version (mine is 27.4.0)
• To update Nvidia Toolkit (mine is 1.17.3)

How has this been tested?

Checklist

• I submit my changes into the develop branch
• I have created a changelog fragment
• I have updated the documentation accordingly
• I have added tests to cover my changes
• I have linked related issues (see GitHub docs)
• I have increased versions of npm packages if it is necessary
  (cvat-canvas,
  cvat-core,
  cvat-data and
  cvat-ui)

License

• I submit my code changes under the same MIT License that covers the project.
  Feel free to contact the maintainers if that's a concern.

Summary by CodeRabbit

• New Features

  • Added a changelog entry for the installation of YOLOv7 on GPU.

• Bug Fixes

  • Updated the configuration for the serverless function to improve performance and compatibility with the latest CUDA and ONNX runtime versions.

[coderabbitai]

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Walkthrough

This pull request introduces a changelog entry documenting a fix for the installation of YOLOv7 on GPU and modifies the configuration of a serverless function. The changes include an upgrade of the CUDA base image and the onnxruntime-gpu package version in the function-gpu.yaml file. The updates aim to enhance the performance and compatibility of the YOLOv7 model without altering its core functionality.

Changes

File	Change Summary
changelog.d/20241212_212647_sekachev.bs_fixed_serverless_func.md	Added changelog entry for "Installation of YOLOv7 on GPU".
serverless/onnx/WongKinYiu/yolov7/nuclio/function-gpu.yaml	Updated baseImage to nvidia/cuda:12.6.3-cudnn-runtime-ubuntu22.04 and onnxruntime-gpu version to 1.20.*.

Assessment against linked issues

Objective	Addressed	Explanation
Resolution of segmentation fault and compatibility issues for auto-annotation (#8503)	❌	The changes do not address the segmentation fault or compatibility with NumPy.

🐰 In the meadow where the bunnies play,
A fix for YOLOv7 came our way!
With CUDA upgraded, oh what a sight,
Our models will run with all their might!
Hopping along, we celebrate this cheer,
For smoother annotations, we hold dear! 🥕✨

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[sonarqubecloud]

Quality Gate passed

Issues
0 New issues
0 Accepted issues

Measures
0 Security Hotspots
0.0% Coverage on New Code
0.0% Duplication on New Code

See analysis details on SonarQube Cloud

[codecov-commenter]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 73.90%. Comparing base (289ad43) to head (877504d).

Additional details and impacted files

@@             Coverage Diff             @@
##           develop    #8824      +/-   ##
===========================================
- Coverage    73.93%   73.90%   -0.03%     
===========================================
  Files          409      409              
  Lines        43930    43930              
  Branches      3986     3986              
===========================================
- Hits         32478    32465      -13     
- Misses       11452    11465      +13     

Components	Coverage Δ
cvat-ui	78.36% <ø> (+0.02%)	⬆️
cvat-server	70.07% <ø> (-0.08%)	⬇️