Chengfang Lu
e72bc061c5
Major changes: - New frontend (platform/web/): Vite + React 18 + TypeScript + Tailwind - 4-module navigation: 数据送标 / 模型管理 / 车队管理 / 系统管理 - Data catalog with charts (DMS/ADAS/Lane 3-tab view) - Quality review workflow (标注质检): Good/Fine/Bad scoring with auto-advance - Audit enhancements: batch operations, rejection categories, Feishu notifications - Operation audit log (操作日志) - World model simulation studio (仿真工坊) - Dataset version management with snapshots and diff - ADAS 7-class dataset integration (138K images organized + compressed) - User management with Feishu integration and pagination - CRUD/search/filter on all pages, card layout redesign - PIL-optimized image overlay rendering - Auto-snapshot on build, in_review workflow stage - Removed embedded algorithm code (now in workspace)
27 lines
1.6 KiB
Plaintext
27 lines
1.6 KiB
Plaintext
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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# Builds ultralytics/ultralytics:latest-export image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
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# Export-optimized derivative of ultralytics/ultralytics:latest for testing and benchmarks
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# Includes all export format dependencies and pre-installed export packages
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FROM ultralytics/ultralytics:latest
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# Install export dependencies and run exports to AutoInstall packages
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# Numpy 1.26.4 required for TensorFlow export compatibility
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# Note tensorrt installed on-demand as depends on runtime environment CUDA version
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RUN uv pip install --system -e ".[export]" "onnxruntime-gpu" paddlepaddle x2paddle numpy==1.26.4 && \
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# Run exports to AutoInstall packages \
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yolo export model=tmp/yolo26n.pt format=edgetpu imgsz=32 && \
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yolo export model=tmp/yolo26n.pt format=ncnn imgsz=32 && \
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# Remove temporary files \
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rm -rf tmp /root/.config/Ultralytics/persistent_cache.json
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# Usage --------------------------------------------------------------------------------------------------------------
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# Production builds: https://github.com/ultralytics/ultralytics/blob/main/.github/workflows/docker.yml
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# Example (build): t=ultralytics/ultralytics:latest-export && docker build -f docker/Dockerfile-export -t $t .
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# Example (push): docker push $t
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# Example (pull): t=ultralytics/ultralytics:latest-export && docker pull $t
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# Example (run): docker run -it --ipc=host --runtime=nvidia --gpus all $t
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# Example (run-with-volume): docker run -it --ipc=host --runtime=nvidia --gpus all -v "$PWD/shared/datasets:/datasets" $t
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