Build engine file failed

• Hardware Platform (Jetson / GPU) Tesla T4
• DeepStream Version 5.1
• TensorRT Version
• NVIDIA GPU Driver Version (valid for GPU only) 470.42.01

ı m trying to use my own peoplenet model to run config file “config3.txt” through the “ds3.txt”.

when I try running the peoplenet model in deepstream the result is this :

!tlt tlt-converter $USER_EXPERIMENT_DIR/final/peoplenet.etlt \
                   -k "tlt_encode" \
                    -c $USER_EXPERIMENT_DIR/experiment_dir_final/calibration.bin \
                   -o output_cov/Sigmoid,output_bbox/BiasAdd \
                   -d 3,544,960 \
                   -m 64 \
                   -t fp16\
                   -e $USER_EXPERIMENT_DIR/final/peoplenet.trt \
                   -b 1

duplicated:/opt/nvidia/deepstream/deepstream-5.1/sources/apps/sample_apps/deepstream-transfer-learning-app/configs$ deepstream-app -c ‘/opt/nvidia/deepstream/deepstream-5.1/sources/apps/sample_apps/deepstream-transfer-learning-app/configs/ds3.txt’
Warn: ‘threshold’ parameter has been deprecated. Use ‘pre-cluster-threshold’ instead.
gstnvtracker: Loading low-level lib at /opt/nvidia/deepstream/deepstream-5.1/lib/libnvds_mot_klt.so
gstnvtracker: Optional NvMOT_RemoveStreams not implemented
gstnvtracker: Batch processing is OFF
gstnvtracker: Past frame output is OFF
ERROR: …/nvdsinfer/nvdsinfer_func_utils.cpp:33 [TRT]: INVALID_CONFIG: The engine plan file is not compatible with this version of TensorRT, expecting library version 7.2.3 got 7.2.1, please rebuild.
ERROR: …/nvdsinfer/nvdsinfer_func_utils.cpp:33 [TRT]: engine.cpp (1646) - Serialization Error in deserialize: 0 (Core engine deserialization failure)
ERROR: …/nvdsinfer/nvdsinfer_func_utils.cpp:33 [TRT]: INVALID_STATE: std::exception
ERROR: …/nvdsinfer/nvdsinfer_func_utils.cpp:33 [TRT]: INVALID_CONFIG: Deserialize the cuda engine failed.
ERROR: …/nvdsinfer/nvdsinfer_model_builder.cpp:1567 Deserialize engine failed from file: /opt/nvidia/deepstream/deepstream-5.1/samples/models/Primary_Detector/model_head_3.trt
0:00:00.514120929 6217 0x556841e69d80 WARN nvinfer gstnvinfer.cpp:616:gst_nvinfer_logger:<primary_gie> NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::deserializeEngineAndBackend() <nvdsinfer_context_impl.cpp:1691> [UID = 1]: deserialize engine from file :/opt/nvidia/deepstream/deepstream-5.1/samples/models/Primary_Detector/model_head_3.trt failed
0:00:00.514152080 6217 0x556841e69d80 WARN nvinfer gstnvinfer.cpp:616:gst_nvinfer_logger:<primary_gie> NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::generateBackendContext() <nvdsinfer_context_impl.cpp:1798> [UID = 1]: deserialize backend context from engine from file :/opt/nvidia/deepstream/deepstream-5.1/samples/models/Primary_Detector/model_head_3.trt failed, try rebuild
0:00:00.514171360 6217 0x556841e69d80 INFO nvinfer gstnvinfer.cpp:619:gst_nvinfer_logger:<primary_gie> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:1716> [UID = 1]: Trying to create engine from model files
ERROR: …/nvdsinfer/nvdsinfer_model_builder.cpp:187 Uff input blob name is empty
ERROR: …/nvdsinfer/nvdsinfer_model_builder.cpp:797 Failed to create network using custom network creation function
ERROR: …/nvdsinfer/nvdsinfer_model_builder.cpp:862 Failed to get cuda engine from custom library API
0:00:00.514814787 6217 0x556841e69d80 ERROR nvinfer gstnvinfer.cpp:613:gst_nvinfer_logger:<primary_gie> NvDsInferContext[UID 1]: Error in NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:1736> [UID = 1]: build engine file failed
Segmentation fault (core dumped)

ds3.txt

[application]
enable-perf-measurement=1
perf-measurement-interval-sec=5
#gie-kitti-output-dir=streamscl

[tiled-display]
enable=1
rows=1
columns=1
width=1280
height=720
gpu-id=0
#(0): nvbuf-mem-default - Default memory allocated, specific to particular platform
#(1): nvbuf-mem-cuda-pinned - Allocate Pinned/Host cuda memory applicable for Tesla
#(2): nvbuf-mem-cuda-device - Allocate Device cuda memory applicable for Tesla
#(3): nvbuf-mem-cuda-unified - Allocate Unified cuda memory applicable for Tesla
#(4): nvbuf-mem-surface-array - Allocate Surface Array memory, applicable for Jetson
nvbuf-memory-type=0

[source0]
enable=1
#Type - 1=CameraV4L2 2=URI 3=MultiURI 4=RTSP
type=2
uri=file://../../../../../samples/streams/v1.mp4
num-sources=1
#drop-frame-interval=2
gpu-id=0
# (0): memtype_device   - Memory type Device
# (1): memtype_pinned   - Memory type Host Pinned
# (2): memtype_unified  - Memory type Unified
cudadec-memtype=0

[sink0]
enable=1
#Type - 1=FakeSink 2=EglSink 3=File
type=1
sync=1
source-id=0
gpu-id=0
nvbuf-memory-type=0

[osd]
enable=1
gpu-id=0
border-width=1
text-size=15
text-color=1;1;1;1;
text-bg-color=0.3;0.3;0.3;1
font=Serif
show-clock=0
clock-x-offset=800
clock-y-offset=820
clock-text-size=12
clock-color=1;0;0;0
nvbuf-memory-type=0

[streammux]
gpu-id=0
##Boolean property to inform muxer that sources are live
live-source=0
batch-size=1
##time out in usec, to wait after the first buffer is available
##to push the batch even if the complete batch is not formed
batched-push-timeout=40000
## Set muxer output width and height
width=1920
height=1080
##Enable to maintain aspect ratio wrt source, and allow black borders, works
##along with width, height properties
enable-padding=0
nvbuf-memory-type=0
## If set to TRUE, system timestamp will be attached as ntp timestamp
## If set to FALSE, ntp timestamp from rtspsrc, if available, will be attached
# attach-sys-ts-as-ntp=1

# config-file property is mandatory for any gie section.
# Other properties are optional and if set will override the properties set in
# the infer config file.
[primary-gie]
enable=1
gpu-id=0
#model-engine-file=../../../../../samples/models/Primary_Detector/resnet10.caffemodel_b4_gpu0_int8.engine
batch-size=1
#Required by the app for OSD, not a plugin property
bbox-border-color0=1;0;0;1
bbox-border-color1=0;1;1;1
bbox-border-color2=0;0;1;1
bbox-border-color3=0;1;0;1
interval=0
gie-unique-id=1
nvbuf-memory-type=0
config-file=config3.txt

[tracker]
enable=1
# For the case of NvDCF tracker, tracker-width and tracker-height must be a multiple of 32, respectively
tracker-width=640
tracker-height=384
#ll-lib-file=/opt/nvidia/deepstream/deepstream-5.1/lib/libnvds_mot_iou.so
#ll-lib-file=/opt/nvidia/deepstream/deepstream-5.1/lib/libnvds_nvdcf.so
ll-lib-file=/opt/nvidia/deepstream/deepstream-5.1/lib/libnvds_mot_klt.so
#ll-config-file required for DCF/IOU only
#ll-config-file=tracker_config.yml
#ll-config-file=iou_config.txt
gpu-id=0
#enable-batch-process and enable-past-frame applicable to DCF only
enable-batch-process=1
enable-past-frame=0
display-tracking-id=1

[tests]
file-loop=0

[img-save]
enable=1
output-folder-path=./output_x
save-img-cropped-obj=0
save-img-full-frame=1
frame-to-skip-rules-path=capture_time_rules.csv
second-to-skip-interval=3
min-confidence=0.3
max-confidence=1.0
min-box-width=5
min-box-height=5


config:

[property]
gpu-id=0
net-scale-factor=0.0039215697906911373
tlt-model-key=tlt_encode
tlt-encoded-model=../../../../../samples/models/Primary_Detector/model_head_3.etlt 
labelfile-path=../../../../../samples/models/Primary_Detector/head_labels.txt
# GPU Engine File
model-engine-file=../../../../../samples/models/Primary_Detector/model_head_3.trt
#int8-calib-file=../../../../../samples/models/Primary_Detector/cal_trt.bin
batch-size=1
process-mode=1
model-color-format=0
## 0=FP32, 1=INT8, 2=FP16 mode
network-mode=2
num-detected-classes=1
interval=0
gie-unique-id=1
infer-dims=3;544;960
uff-input-blob-name=input_1
output-blob-names=output_bbox/BiasAdd;output_cov/Sigmoid
force-implicit-batch-dim=1
#parse-bbox-func-name=NvDsInferParseCustomResnet
#custom-lib-path=/path/to/libnvdsparsebbox.so
## 0=Group Rectangles, 1=DBSCAN, 2=NMS, 3= DBSCAN+NMS Hybrid, 4 = None(No clustering)
cluster-mode=1
#scaling-filter=0
#scaling-compute-hw=0

[class-attrs-all]
pre-cluster-threshold=0.2
group-threshold=1
## Set eps=0.7 and minBoxes for cluster-mode=1(DBSCAN)
eps=0.7
minBoxes=3
roi-top-offset=0
roi-bottom-offset=0
detected-min-w=0
detected-min-h=0
detected-max-w=0
detected-max-h=0

## Per class configuration
#[class-attrs-2]
threshold=0.9
#eps=0.5
#group-threshold=3
#roi-top-offset=20
#roi-bottom-offset=10
#detected-min-w=40
#detected-min-h=40
#detected-max-w=400
#detected-max-h=800

Sorry for the late response, we will do the investigation and update soon.

Thanks

Can you run your model with TRT unit test:

/usr/src/tensorrt/bin/trtexec