🌟 Summary
YOLO models can now be exported to Apple Core AI format from x86_64 Linux, making Linux servers and CI environments useful for preparing models for Apple devices.
📊 Key Changes
- 🍎 Core AI export on Linux: Export
.aimodelfiles on x86_64 Linux as well as Apple silicon Macs running macOS 26 or later. The exported models target iOS 27 and macOS 27; running them still requires Apple hardware. - 🔧 Updated Core AI compatibility: Supports
coreai-torch0.4.3, which performs optimization during conversion. The dependency now supports Python 3.11–3.14. - 📈 Clearer benchmarks: Linux can report Core AI export results, while inference is correctly skipped because it requires macOS.
- 🛠️ More reliable exports: PyTorch functions patched for certain ONNX exports are now restored even when an export fails, preventing unexpected effects on later operations in the same process.
- 🐳 Leaner Docker images: Docker builds avoid retaining package-install caches and remove bundled PyTorch test files, reducing image contents without removing needed runtime tools.
🎯 Purpose & Impact
- 🌐 Teams can build Apple-targeted models in Linux-based cloud, server, and CI workflows instead of needing a Mac for export.
- 📱 Apple hardware is still required to run
.aimodelfiles, and Core ML remains a separate option. - ✅ Export failures are less likely to leave a Python process in a modified state, while smaller Docker images use less storage and are quicker to distribute.
What's Changed
- Skip the uv cache and torch test files in Docker images, and fix Core AI export by @glenn-jocher in #26326
- Support coreai-torch 0.4.3, which removed
AIProgram.optimize()by @cdeil in #26327 - Restore PyTorch functions after failed exports by @Nikhi00718 in #26325
ultralytics 8.4.163Export Apple Core AI models on Linux by @glenn-jocher in #26333
Full Changelog: v8.4.162...v8.4.163