The model I trained using tao-toolkit in deepstream can be used, but deepstream cannot automatically save the engine model.
deepstream-app version 6.2.0
DeepStreamSDK 6.2.0
CUDA Driver Version: 11.4
CUDA Runtime Version: 11.4
TensorRT Version: 8.4
cuDNN Version: 8.4
libNVWarp360 Version: 2.0.1d3
config_infer------.txt
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[property]
gpu-id=0
net-scale-factor=0.0039215697906911373
tlt-model-key=tlt_encode
tlt-encoded-model=./resnet34_peoplenet_pruned.etlt
#int8-calib-file=../../../models/peoplenet/resnet34_peoplenet_int8.txt
labelfile-path=./class.txt
model-engine-file=./resnet34_peoplenet_int8.etlt_b1_gpu0_int32.engine
input-dims=3;544;960;0
uff-input-blob-name=input_1
batch-size=1
process-mode=1
model-color-format=0
## 0=FP32, 1=INT8, 2=FP16 mode
network-mode=0
num-detected-classes=3
cluster-mode=2
interval=0
gie-unique-id=1
output-blob-names=output_bbox/BiasAdd;output_cov/Sigmoid
#Use the config params below for dbscan clustering mode
#[class-attrs-all]
#detected-min-w=4
#detected-min-h=4
#minBoxes=3
#eps=0.7
#Use the config params below for NMS clustering mode
[class-attrs-all]
topk=20
nms-iou-threshold=0.5
pre-cluster-threshold=0.2
## Per class configurations
[class-attrs-0]
topk=20
nms-iou-threshold=0.5
pre-cluster-threshold=0.4
#[class-attrs-1]
#pre-cluster-threshold=0.05
#eps=0.7
#dbscan-min-score=0.5