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manifest.json
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manifest.json
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{
"name": "aeye",
"label": "AEye: Segmentation of eye tissues from T1w MR images",
"description": "AEye performs inference from a trained model to new T1w MR images to segment the eye tissues (lens, vitreous, optic nerve, rectus muscles, and fat) using a U-Net architecture. The model was trained on a large dataset of T1w MR images from the Study of Health in Pomerania (SHIP).",
"version": "0.0.1",
"author": "Jaime Barranco",
"maintainer": "Jaime Barranco <[email protected]>",
"cite": "",
"license": "Other",
"url": "https://github.com/jaimebarran/aeye_flywheel",
"source": "",
"environment": {
"FLYWHEEL": "/flywheel/v0",
"NPP_VERSION": "12.2.3.2",
"SHELL": "/bin/bash",
"NVIDIA_VISIBLE_DEVICES": "all",
"DALI_BUILD": "12152788",
"CUSOLVER_VERSION": "11.5.4.101",
"CUBLAS_VERSION": "12.3.4.1",
"CUFFT_VERSION": "11.0.12.1",
"NVIDIA_REQUIRE_CUDA": "cuda>=9.0",
"CUDA_CACHE_DISABLE": "1",
"TENSORBOARD_PORT": "6006",
"TORCH_CUDA_ARCH_LIST": "5.2 6.0 6.1 7.0 7.2 7.5 8.0 8.6 8.7 9.0+PTX",
"NCCL_VERSION": "2.19.stable.20231214+cuda12.3",
"CUSPARSE_VERSION": "12.2.0.103",
"ENV": "/etc/shinit_v2",
"PWD": "/flywheel/v0",
"OPENUCX_VERSION": "1.15.0",
"NSIGHT_SYSTEMS_VERSION": "2023.4.1.97",
"NVIDIA_DRIVER_CAPABILITIES": "compute,utility,video",
"POLYGRAPHY_VERSION": "0.49.4",
"UCC_CL_BASIC_TLS": "^sharp",
"TRT_VERSION": "8.6.3.1+cuda12.2.2.009",
"NVIDIA_PRODUCT_NAME": "PyTorch",
"RDMACORE_VERSION": "39.0",
"COCOAPI_VERSION": "2.0+nv0.8.0",
"CUDA_VERSION": "12.3.2.001",
"PYTORCH_VERSION": "2.3.0a0+ebedce2",
"CURAND_VERSION": "10.3.4.107",
"PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION": "python",
"PYTORCH_BUILD_NUMBER": "0",
"USE_EXPERIMENTAL_CUDNN_V8_API": "1",
"CUTENSOR_VERSION": "2.0.0.7",
"PIP_DEFAULT_TIMEOUT": "100",
"NVFUSER_BUILD_VERSION": "d0bb811",
"HPCX_VERSION": "2.16rc4",
"TORCH_CUDNN_V8_API_ENABLED": "1",
"NVM_DIR": "/usr/local/nvm",
"GDRCOPY_VERSION": "2.3",
"NVFUSER_VERSION": "d0bb811",
"OPENMPI_VERSION": "4.1.5rc2",
"NVJPEG_VERSION": "12.3.0.81",
"LIBRARY_PATH": "/usr/local/cuda/lib64/stubs:",
"PYTHONIOENCODING": "utf-8",
"SHLVL": "0",
"BASH_ENV": "/etc/bash.bashrc",
"CUDNN_VERSION": "9.0.0.306",
"NSIGHT_COMPUTE_VERSION": "2023.3.1.1",
"DALI_VERSION": "1.34.0",
"JUPYTER_PORT": "8888",
"PYTORCH_HOME": "/opt/pytorch/pytorch",
"LD_LIBRARY_PATH": "/usr/local/lib/python3.10/dist-packages/torch/lib:/usr/local/lib/python3.10/dist-packages/torch_tensorrt/lib:/usr/local/cuda/compat/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64",
"NVIDIA_BUILD_ID": "82611821",
"OMPI_MCA_coll_hcoll_enable": "0",
"OPAL_PREFIX": "/opt/hpcx/ompi",
"CUDA_DRIVER_VERSION": "545.23.08",
"LC_ALL": "C.UTF-8",
"TRANSFORMER_ENGINE_VERSION": "1.3",
"PYTORCH_BUILD_VERSION": "2.3.0a0+ebedce2",
"_CUDA_COMPAT_PATH": "/usr/local/cuda/compat",
"CUDA_HOME": "/usr/local/cuda",
"CUDA_MODULE_LOADING": "LAZY",
"PATH": "/usr/local/lib/python3.10/dist-packages/torch_tensorrt/bin:/usr/local/mpi/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/local/ucx/bin:/opt/tensorrt/bin",
"MOFED_VERSION": "5.4-rdmacore39.0",
"NVIDIA_PYTORCH_VERSION": "24.02",
"TRTOSS_VERSION": "23.11",
"TORCH_ALLOW_TF32_CUBLAS_OVERRIDE": "1",
"_": "/usr/bin/printenv",
"nnUNet_raw_data_base": "/opt/nnunet_resources/nnUNet_raw_data_base",
"nnUNet_preprocessed": "/opt/nnunet_resources/nnUNet_preprocessed",
"RESULTS_FOLDER": "/opt/nnunet_resources/nnUNet_trained_models"
},
"custom": {
"gear-builder": {
"category": "analysis",
"image": "jaimebarran/fw_gear_aeye:0.0.1"
},
"flywheel": {
"suite": "Image Processing - Segmentation"
}
},
"inputs": {
"nifti": {
"description": "MRI NIfTI file. Input must be a structural image (T1) NIfTI file.",
"base": "file",
"type": {
"enum": [
"nifti"
]
}
}
},
"config": {
"measurement": {
"default": "T1",
"description": "Measurement/Intent of input image. Must be 'T1'(default='auto-detect' - gear will attempt to automatically detect the type of input image. If input does not have a classification value, making auto-detection impossible, the gear will exit with code=XX).",
"type": "string",
"enum": [
"auto-detect",
"T1"
]
},
"debug": {
"type": "boolean",
"default": true,
"description": "Enable debug mode."
}
},
"command": "python run.py"
}