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Most of the machine learning frameworks allow to export models to ONNX format."]}),"\n",(0,o.jsxs)(t.ul,{children:["\n",(0,o.jsxs)(t.li,{children:["Runtime key: ",(0,o.jsx)(t.code,{children:"onnx"})]}),"\n",(0,o.jsxs)(t.li,{children:["Parameters:","\n",(0,o.jsxs)(t.ul,{children:["\n",(0,o.jsxs)(t.li,{children:[(0,o.jsx)(t.code,{children:"execution_provider"}),": The execution provider to use. Allowed values: ",(0,o.jsx)(t.code,{children:"cpu"}),", ",(0,o.jsx)(t.code,{children:"CUDA"})," (Linux and Windows only), ",(0,o.jsx)(t.code,{children:"TensorRT"})," (Linux and Windows only), ",(0,o.jsx)(t.code,{children:"OpenVINO"}),", ",(0,o.jsx)(t.code,{children:"CoreML"})," (macOS only)."]}),"\n",(0,o.jsxs)(t.li,{children:[(0,o.jsx)(t.code,{children:"execution_mode"}),": Either ",(0,o.jsx)(t.code,{children:"Parallel"})," or ",(0,o.jsx)(t.code,{children:"Sequential"}),". Check the ",(0,o.jsx)(t.a,{href:"https://onnxruntime.ai/docs/performance/tune-performance/threading.html",children:"ONNX threading docs"}),"."]}),"\n",(0,o.jsxs)(t.li,{children:[(0,o.jsx)(t.code,{children:"inter_threads"}),": A integer number. Only takes effect if ",(0,o.jsx)(t.code,{children:"execution_mode"})," is ",(0,o.jsx)(t.code,{children:"parallel"})," (and the model nodes can be run in parallel). Indicates the maximum number of threads to use to run them in parallel."]}),"\n",(0,o.jsxs)(t.li,{children:[(0,o.jsx)(t.code,{children:"intra_threads"}),": A integer number, Indicates the number of threads to parallelize the execution within nodes."]}),"\n"]}),"\n"]}),"\n"]}),"\n",(0,o.jsx)(t.h2,{id:"general-troubleshooting",children:"General Troubleshooting"}),"\n",(0,o.jsx)(t.h3,{id:"cudaerrornokernelimagefordevice",children:"cudaErrorNoKernelImageForDevice"}),"\n",(0,o.jsx)(t.p,{children:"You may find a message like the following when CUDA was not compiled for your target GPU architecture:"}),"\n",(0,o.jsx)(t.pre,{"data-language":"text","data-theme":"default",children:(0,o.jsx)(t.code,{"data-language":"text","data-theme":"default",children:(0,o.jsx)(t.span,{className:"line",children:(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:"Status Message: CUDA error cudaErrorNoKernelImageForDevice:no kernel image is available for execution on the device"})})})}),"\n",(0,o.jsx)(t.p,{children:"Consider building CUDA yourself or using a different GPU. This error has been reported in some Nvidia 940MX (Maxwell architecture)."}),"\n",(0,o.jsx)(t.h3,{id:"seems-to-get-stuck",children:"Seems to get stuck"}),"\n",(0,o.jsxs)(t.p,{children:["If running ",(0,o.jsx)(t.code,{children:"pipeless run"})," using the ONNX inference it seems to get stuck and there is not a clear error shown, try running the worker only to see the error with ",(0,o.jsx)(t.code,{children:"pipeless run worker"}),"."]}),"\n",(0,o.jsx)(t.h3,{id:"i-have-cuda-and-cudnn-installed-but-onnx-runtime-is-not-using-cuda",children:"I have CUDA and Cudnn installed but ONNX Runtime is not using CUDA"}),"\n",(0,o.jsx)(t.p,{children:"In this case, you usually get a warning like:"}),"\n",(0,o.jsx)(t.pre,{"data-language":"text","data-theme":"default",children:(0,o.jsx)(t.code,{"data-language":"text","data-theme":"default",children:(0,o.jsx)(t.span,{className:"line",children:(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:"[W:onnxruntime:Default, onnxruntime_pybind_state.cc:640 CreateExecutionProviderInstance] Failed to create CUDAExecutionProvider. Please reference https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#requirements to ensure all dependencies are met."})})})}),"\n",(0,o.jsxs)(t.p,{children:["Ensure you have the proper CUDA and Cudnn versions installed. Check the ",(0,o.jsx)(t.a,{href:"https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html",children:"ONNX CUDA requirements"})," to ensure your versions are compatible. We recommend using CUDA version ",(0,o.jsx)(t.code,{children:"11.7"})," since ",(0,o.jsx)(t.code,{children:"12.X"})," is not properly recognized by the latest version of the ONNX Runtime."]}),"\n",(0,o.jsxs)(t.p,{children:["Optionally, you can use the Pipeless CUDA container image. Find the docs ",(0,o.jsx)(t.a,{href:"/v1/container",children:"here"}),"."]}),"\n",(0,o.jsx)(t.h3,{id:"segmentation-fault-core-dumped",children:"Segmentation fault (core dumped)"}),"\n",(0,o.jsxs)(t.p,{children:["The most common error that leads to a segmentation fault is to provide wrong input data to the model. 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