ultralytics 8.4.120

v8.4.120 - Avoid nondeterministic CUDA anchor cumsum (#25806)

on Python PyPI

🌟 Summary

Ultralytics 8.4.120 improves CUDA training determinism and TensorFlow export reliability, while expanding documentation for LLM workflows and AI coding-agent integrations. 🚀

📊 Key Changes

• Deterministic CUDA anchor generation by @glenn-jocher

  • Replaced CUDA cumulative-sum operations with deterministic arange-based generation when creating detection anchors.
  • Removes recurring cumsum_cuda_kernel warnings during deterministic training.
  • Preserves runtime device handling for traced and TorchScript GPU models, avoiding device information being incorrectly fixed during tracing.

• More reliable TensorFlow exports 🛠️

  • Removed the obsolete NVIDIA package index from TensorFlow and non-YOLO export dependency installation.
  • onnx-graphsurgeon can now be installed directly from PyPI, reducing DNS and connectivity issues—especially in CPU-based CI environments and isolated export setups.

• New Ultralytics LLM documentation 🤖

  • Documents the OpenAI-compatible LLM interface for text, image, streaming, asynchronous, provider-specific, and YOLO-combined workflows.
  • Provides examples for OpenAI-compatible services such as DeepSeek, Kimi, Z.AI GLM, OpenRouter, and local servers.
  • Updates the default documented and runtime model to gpt-5.6-luna.

• New Agent Skills integration guide 🧩

  • Documents the official ultralytics/skills repository.
  • Covers AI-agent skills for model selection, datasets, training, tuning, inference, and export.
  • Includes installation guidance for Claude Code, Codex, and other compatible agents.

• Version update

  • Bumped the Ultralytics package version from 8.4.119 to 8.4.120.

🎯 Purpose & Impact

• ✅ Cleaner deterministic training logs: Users no longer see repeated CUDA cumsum warnings that can obscure important training messages.
• ✅ More predictable model tracing: TorchScript and traced GPU models retain runtime device behavior without sacrificing the deterministic anchor-generation fix.
• ✅ Smoother TensorFlow export setup: Fewer external package-index dependencies should improve export reliability in restricted networks, CI pipelines, and CPU-only environments.
• ✅ Better LLM discoverability: Developers can more easily connect YOLO detection results with language and vision models through a consistent interface.
• ✅ Improved AI-assisted development: Agent Skills provide structured, workflow-specific guidance for using Ultralytics tools with supported coding agents.
• ℹ️ No major model architecture changes were introduced in this release; the primary technical improvement is improved determinism and export robustness.

What's Changed

• Document the OpenAI-compatible LLM interface by @onuralpszr in #25789
• Document Ultralytics Agent Skills by @JaviChulvi in #25787
• Avoid nondeterministic CUDA anchor cumsum by @glenn-jocher in #25806

New Contributors

• @JaviChulvi made their first contribution in #25787

Full Changelog: v8.4.119...v8.4.120