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Although the tensorrt version is beyond the requirement, the deepstream-app example works very well . However, When in the deepstream-app I load engine file which was converted from trtexec , there is a strange error:Failed to query video capa..." /> <meta name="twitter:description" content="**• Hardware Platform (Jetson / GPU) GPU **• DeepStream Version 7.1 **• TensorRT Version 10.6.0.26 **• NVIDIA GPU Driver Version (valid for GPU only) 560.35.03 **• Issue Type( questions, new requirements, bugs) bugs **• How to reproduce the issue ? Although the tensorrt version is beyond the requirement, the deepstream-app example works very well . However, When in the deepstream-app I load engine file which was converted from trtexec , there is a strange error:Failed to query video capa..." /> <meta property="og:article:section" content="Accelerated Computing" /> <meta property="og:article:section:color" content="76B900" /> <meta property="og:article:section" content="Intelligent Video Analytics" /> <meta property="og:article:section:color" content="76B900" /> <meta property="og:article:section" content="DeepStream SDK" /> <meta property="og:article:section:color" content="76B900" /> <meta property="og:article:tag" content="deepstream" /> <meta property="article:published_time" content="2024-11-24T14:09:45+00:00" /> <meta property="og:ignore_canonical" content="true" /> <script type="application/ld+json">{"@context":"http://schema.org","@type":"QAPage","name":"Cuda error for converted model from trtexec","mainEntity":{"@type":"Question","name":"Cuda error for converted model from trtexec","text":"**•\tHardware Platform (Jetson / 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content='deepstream'> <div itemprop='publisher' itemscope itemtype="http://schema.org/Organization"> <meta itemprop='name' content='NVIDIA'> <div itemprop='logo' itemscope itemtype="http://schema.org/ImageObject"> <meta itemprop='url' content='https://global.discourse-cdn.com/nvidia/original/3X/a/1/a1ef6e0c1fbd3fad5bf82538b78dfaa9c5fa1a61.png'> </div> </div> <div id='post_1' class='topic-body crawler-post'> <div class='crawler-post-meta'> <span class="creator" itemprop="author" itemscope itemtype="http://schema.org/Person"> <a itemprop="url" href='https://forums.developer.nvidia.com/u/936214531'><span itemprop='name'>936214531</span></a> </span> <link itemprop="mainEntityOfPage" href="https://forums.developer.nvidia.com/t/cuda-error-for-converted-model-from-trtexec/314516"> <span class="crawler-post-infos"> <time datetime='2024-11-24T14:09:45Z' class='post-time'> November 24, 2024, 2:09pm </time> <meta itemprop='dateModified' content='2024-11-24T14:09:45Z'> <span itemprop='position'>1</span> </span> </div> <div class='post' itemprop='text'> <p>**• Hardware Platform (Jetson / GPU) GPU<br> **• DeepStream Version 7.1<br> **• TensorRT Version 10.6.0.26<br> **• NVIDIA GPU Driver Version (valid for GPU only) 560.35.03<br> **• Issue Type( questions, new requirements, bugs) bugs<br> **• How to reproduce the issue ?<br> Although the tensorrt version is beyond the requirement, the deepstream-app example works very well . However, When in the deepstream-app I load engine file which was converted from trtexec , there is a strange error:Failed to query video capabilities: Invalid argument<br> ** INFO: <bus_callback:277>: Pipeline running</p> <p>ERROR: [TRT]: IExecutionContext::enqueueV3: Error Code 1: CuTensor (Internal cuTensor permutate execute failed)<br> ERROR: [TRT]: [checkMacros.cpp::catchCudaError::212] Error Code 1: Cuda Runtime (an illegal memory access was encountered)<br> ERROR: nvdsinfer_backend.cpp:345 Failed to enqueue trt inference batch<br> ERROR: nvdsinfer_context_impl.cpp:1903 Infer context enqueue buffer failed, nvinfer error:NVDSINFER_TENSORRT_ERROR<br> 0:00:01.650998612 29961 0x7fffd4001d60 WARN nvinfer gstnvinfer.cpp:2115:gst_nvinfer_process_tensor_input:<primary_gie1> error: Failed to queue input batch for inferencing<br> ERROR from primary_gie1: Failed to queue input batch for inferencing<br> Debug info: gstnvinfer.cpp(2115): gst_nvinfer_process_tensor_input (): /GstPipeline:pipeline/GstBin:primary_gie_bin1/GstNvInfer:primary_gie1<br> cuCtxCreate failed with error(700) gst_eglglessink_cuda_init<br> Cuda failure: status=700<br> Error(-1) in buffer allocation</p> <p>** (Hysteroscopy:29961): CRITICAL **: 20:23:46.151: gst_nvds_buffer_pool_alloc_buffer: assertion ‘mem’ failed<br> ERROR from tiled_display_tiler: GstNvTiler: FATAL; Failed to allocate buffers</p> <p>Debug info: gstnvtiler.cpp(622): gst_nvmultistreamtiler_decide_allocation (): /GstPipeline:pipeline/GstBin:tiled_display_bin/GstNvMultiStreamTiler:tiled_display_tiler<br> Cuda failure: status=700<br> Error(-1) in buffer allocation</p> <p>** (Hysteroscopy:29961): CRITICAL **: 20:23:46.151: gst_nvds_buffer_pool_alloc_buffer: assertion ‘mem’ failed<br> ERROR from tiled_display_tiler: GstNvTiler: FATAL; Failed to allocate buffers</p> <p>Debug info: gstnvtiler.cpp(622): gst_nvmultistreamtiler_decide_allocation (): /GstPipeline:pipeline/GstBin:tiled_display_bin/GstNvMultiStreamTiler:tiled_display_tiler<br> Cuda failure: status=700<br> Error(-1) in buffer allocation</p> <p>** (Hysteroscopy:29961): CRITICAL **: 20:23:46.151: gst_nvds_buffer_pool_alloc_buffer: assertion ‘mem’ failed<br> ERROR from tiled_display_tiler: GstNvTiler: FATAL; Failed to allocate buffers</p> <p>Debug info: gstnvtiler.cpp(622): gst_nvmultistreamtiler_decide_allocation (): /GstPipeline:pipeline/GstBin:tiled_display_bin/GstNvMultiStreamTiler:tiled_display_tiler<br> Cuda failure: status=700<br> Error(-1) in buffer allocation</p> <p>** (Hysteroscopy:29961): CRITICAL **: 20:23:46.151: gst_nvds_buffer_pool_alloc_buffer: assertion ‘mem’ failed<br> ERROR from tiled_display_tiler: GstNvTiler: FATAL; Failed to allocate buffers</p> <p>Debug info: gstnvtiler.cpp(622): gst_nvmultistreamtiler_decide_allocation (): /GstPipeline:pipeline/GstBin:tiled_display_bin/GstNvMultiStreamTiler:tiled_display_tiler<br> Cuda failure: status=700<br> Error(-1) in buffer allocation</p> <p>** (Hysteroscopy:29961): CRITICAL **: 20:23:46.151: gst_nvds_buffer_pool_alloc_buffer: assertion ‘mem’ failed<br> ERROR from tiled_display_tiler: GstNvTiler: FATAL; Failed to allocate buffers</p> <p>Debug info: gstnvtiler.cpp(622): gst_nvmultistreamtiler_decide_allocation (): /GstPipeline:pipeline/GstBin:tiled_display_bin/GstNvMultiStreamTiler:tiled_display_tiler<br> Cuda failure: status=700<br> Error(-1) in buffer allocation</p> <p>** (Hysteroscopy:29961): CRITICAL **: 20:23:46.152: gst_nvds_buffer_pool_alloc_buffer: assertion ‘mem’ failed<br> ERROR from tiled_display_tiler: GstNvTiler: FATAL; Failed to allocate buffers</p> <p>Debug info: gstnvtiler.cpp(622): gst_nvmultistreamtiler_decide_allocation (): /GstPipeline:pipeline/GstBin:tiled_display_bin/GstNvMultiStreamTiler:tiled_display_tiler<br> Cuda failure: status=700<br> Error(-1) in buffer allocation</p> <p>** (Hysteroscopy:29961): CRITICAL **: 20:23:46.152: gst_nvds_buffer_pool_alloc_buffer: assertion ‘mem’ failed<br> ERROR from tiled_display_tiler: GstNvTiler: FATAL; Failed to allocate buffers</p> <p>Debug info: gstnvtiler.cpp(622): gst_nvmultistreamtiler_decide_allocation (): /GstPipeline:pipeline/GstBin:tiled_display_bin/GstNvMultiStreamTiler:tiled_display_tiler</p> <p>Another way to convert onnx file to tensorrt engine is to use<br> nvdsinfer_model_builder, and it works very well. But I still want to use trtexec because i can set precision for each layer. this is my trtexec command:<br> trtexec --explicitBatch --onnx=sim_cnn.onnx --saveEngine=model.trt --fp16 --precisionConstraints=obey --layerPrecisions=/downsample_layers.0/downsample_layers.0.1/ReduceMean_1:fp32,/downsample_layers.0/downsample_layers.0.1/ReduceMean:fp32,/downsample_layers.0/downsample_layers.0.1/Pow:fp32,/downsample_layers.1/downsample_layers.1.0/ReduceMean:fp32,/downsample_layers.1/downsample_layers.1.0/Pow:fp32,/downsample_layers.1/downsample_layers.1.0/ReduceMean_1:fp32,/downsample_layers.2/downsample_layers.2.0/ReduceMean:fp32,/downsample_layers.2/downsample_layers.2.0/Pow:fp32,/downsample_layers.2/downsample_layers.2.0/ReduceMean_1:fp32,/downsample_layers.3/downsample_layers.3.0/ReduceMean:fp32,/downsample_layers.3/downsample_layers.3.0/Pow:fp32,/downsample_layers.3/downsample_layers.3.0/ReduceMean_1:fp32</p> </div> <div itemprop="interactionStatistic" itemscope 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