Change base image to PyTorch 2.8.0-cuda12.8

[glenn-jocher]

Updated the base image to PyTorch 2.8.0 with CUDA 12.8.

🛠️ PR Summary

_{Made with ❤️ by Ultralytics Actions}

🌟 Summary

Upgrade Docker base image to PyTorch 2.8.0 with CUDA 12.8 and cuDNN 9 for modern GPU compatibility and performance 🚀

📊 Key Changes

• Update Docker base image from pytorch/pytorch:2.0.0-cuda11.7-cudnn8-runtime to pytorch/pytorch:2.8.0-cuda12.8-cudnn9-runtime
• Retain asset downloads (Arial fonts) to the Ultralytics config directory
• Aligns container with current PyTorch/CUDA/cuDNN stack per latest PyTorch Docker tags

🎯 Purpose & Impact

• Improved performance and compatibility on newer NVIDIA GPUs and drivers 💡
• Access to PyTorch 2.8 features, optimizations, and security updates 🔒
• Future-proofing: aligns with modern CUDA/cuDNN, reducing tech debt and easing maintenance 🧱
• Potential breaking change: requires newer NVIDIA drivers (compatible with CUDA 12.x); users on older drivers may need to upgrade ⚠️
• Possible image size/runtime behavior changes; validate workflows (training/inference) after updating ✅

[UltralyticsAssistant]

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[UltralyticsAssistant]

🔍 PR Review

_{Made with ❤️ by Ultralytics Actions}

Please reassess the base-image bump: PyTorch 2.8/CUDA 12.8 drops Maxwell (sm_5x) GPU support, so the new Dockerfile may break inference/training on older cards unless we have an alternative plan.

💬 Posted 1 inline comment

[UltralyticsAssistant]

🎉 Fantastic work, @glenn-jocher — PR merged! Upgrading the Docker base to PyTorch 2.8.0 with CUDA 12.8 and cuDNN 9 is a powerful step toward modern GPU performance and smoother maintenance.

“Inventing the future” starts here; as Alan Kay said, “The best way to predict the future is to invent it.” This change aligns us with the latest PyTorch Docker tags, unlocks new features and optimizations, and keeps YOLOv3 ready for today’s and tomorrow’s hardware.

Thank you for future-proofing the stack and keeping our users moving fast and forward. 🚀