Hello, I use YoloV8 on my Orin 16G device The converted model automatically shuts down after running for a while

[Today-fine]

My project is based on Deepstream-test2 modifications

Pipeline flow:

appsrc ! h264parse ! nvv4l2decoder ! nvstreammux ! nvinfer ! nvtracker ! nvvidconv ! nvdsosd ! nvvideoconvert ! video/x-raw(memory:NVMM),format=I420 ! nvv4l2h264enc ! video/x-h264,stream-format=byte-stream ! h264parse ! flvmux ! rtmpsink

1. dstest2_config.yml

   streammux:
    batch-size: 1
    batched-push-timeout: 40000
    width: 1920
    height: 1440
    live-source: 1
   
   # /root/shared_develop/develop/sqn-iot-dji-spdk/source_object/ai/deep_test2_file/dstest2_config.yml
   
   tracker:
    tracker-width: 640
    tracker-height: 384
    # tracker-width: 1280
    # tracker-height: 736
    gpu-id: 0
    ll-lib-file: ../source_object/ai/deep_test2_file/tracker_file/libnvds_nvmultiobjecttracker.so
    # ll-lib-file: /root/shared_package/ByteTrack/deploy/DeepStream/lib/libByteTracker.so
    # ll-config-file required to set different tracker types
    # ll-config-file: ../ai/deep_test2_file/tracker_file/config_tracker_IOU.yml
    # ll-config-file: ../ai/deep_test2_file/tracker_file/config_tracker_NvSORT.yml
    ll-config-file: ../source_object/ai/deep_test2_file/tracker_file/config_tracker_NvDCF_perf.yml
    # ll-config-file: ../ai/deep_test2_file/tracker_file/config_tracker_NvDCF_accuracy.yml
    # ll-config-file: ../ai/deep_test2_file/tracker_file/config_tracker_NvDeepSORT.yml
    enable-batch-process: 1
   
   # Inference using nvinfer:
   primary-gie:
    config-file-path: dstest2_pgie_config.yml

2. dstest2_pgie_config.yml

   # Official model
   
   property:
    gpu-id:  0
    net-scale-factor: 0.0039215697906911373
    model-file: Primary_Detector/resnet10.caffemodel
    proto-file: Primary_Detector/resnet10.prototxt
    model-engine-file: Primary_Detector/resnet10.caffemodel_b1_gpu0_int8.engine
    labelfile-path: Primary_Detector/labels.txt
    int8-calib-file: Primary_Detector/cal_trt.bin
    force-implicit-batch-dim: 1
    batch-size: 1
    network-mode: 1
    process-mode: 1
    model-color-format: 0
    num-detected-classes: 4
    interval: 0
    gie-unique-id: 1
    output-blob-names: conv2d_bbox;conv2d_cov/Sigmoid
    #scaling-filter: 0
    #scaling-compute-hw: 0
    cluster-mode: 2
   
   class-attrs-all:
   pre-cluster-threshold: 0.2
   topk: 20
   nms-iou-threshold: 0.5
   
   # Use the yoloV8s model
   
   # property:
   #   gpu-id: 0
   #   net-scale-factor: 0.0039215697906911373
   #   model-color-format: 0
   #   onnx-file: yolov8s.onnx
   #   model-engine-file: ../yolov8s.onnx_b1_gpu0_fp32.engine
   #   #int8-calib-file: calib.table
   #   labelfile-path: ../labels.txt
   #   batch-size: 1
   #   network-mode: 0
   #   num-detected-classes: 80
   #   interval: 0
   #   gie-unique-id: 1
   #   process-mode: 1
   #   network-type: 0
   #   cluster-mode: 2
   #   maintain-aspect-ratio: 1
   #   symmetric-padding: 1
   #   #force-implicit-batch-dim: 1
   #   #workspace-size: 1000
   #   parse-bbox-func-name: NvDsInferParseYolo
   #   #parse-bbox-func-name: NvDsInferParseYoloCuda
   #   custom-lib-path: ../libnvdsinfer_custom_impl_Yolo.so
   #   engine-create-func-name: NvDsInferYoloCudaEngineGet
   
   # class-attrs-all:
   #   nms-iou-threshold: 0.45
   #   pre-cluster-threshold: 0.25
   #   topk: 300

Run the flow

1. When the program runs the official model in dstest2_pgie_config.yml, the device will not break the point during the running process, and when the model of yoloV8s is run, the device will automatically break the point when inference and push for a period of time

Operating environment

Distribution： ubuntu 20.04 focal
Python ： 3.8.10
CUDA：11.4.315
cuDNN ：8.6.0.166
TensorRT ：8.5.2.2
VPI：2.2.7
OpenCV：4.5.4
Model：NVIDIA Orin NX Developer Kit
Module：NVIDIA Jetson Oriin NX(16GB ram)
Jetpack：5.1.1
DeepStream: 6.2

[marcoslucianops]

Can you send the output from the terminal?

[Today-fine]

Hello, the device is shut down directly during the running of the program, and no inspection can be done

[marcoslucianops]

Probably, it's a problem with your power supply.

[Today-fine]

After investigation, it is indeed a power supply problem.