# Triton inference server is sending back "HTTP/1.1 400 Bad Request"

**URL:** <https://forums.developer.nvidia.com/t/triton-inference-server-is-sending-back-http-1-1-400-bad-request/146244>\
**Category:** TAO Toolkit\
**Created:** [August 10, 2020, 4:27pm UTC](https://forums.developer.nvidia.com/t/triton-inference-server-is-sending-back-http-1-1-400-bad-request/146244 "2020-08-10T16:27:39Z")\
**Posts on this page:** 7\
**Page:** 1

<div class="post-metadata">

**Author:** ![ghazni](https://developer.download.nvidia.com/images/forums/profile-default-devtalk-84.png) [@ghazni](https://forums.developer.nvidia.com/u/ghazni)\
**Post date:** [August 10, 2020, 4:27pm UTC](https://forums.developer.nvidia.com/t/triton-inference-server-is-sending-back-http-1-1-400-bad-request/146244/1 "2020-08-10T16:27:39Z")

</div>

Hi

I am setting up the inference server as per instructions on the guide:

[https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/quickstart.html](https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/quickstart.html)

My setup is as:

GPU: RTX 2080 Ti  
Cuda Version: 10.2  
Driver: 450.57  
cudnn: libcudnn8\_8.0.2.39-1+cuda10.2\_amd64

Apparently inference server starts and listens as describe in the guide however when I run the command using curl to test then don’t receive 200 from server and receive following message:

Command: curl -v localhost:8000/v2/health/ready

- Trying 127.0.0.1…
- TCP\_NODELAY set
- Connected to localhost (127.0.0.1) port 8000 (#0)

> GET /v2/health/ready HTTP/1.1  
> Host: localhost:8000  
> User-Agent: curl/7.58.0  
> Accept: _/_

\< HTTP/1.1 400 Bad Request  
\< Content-Length: 0  
\< Content-Type: text/plain  
\<

- Connection #0 to host localhost left intact

Please help me with this issue. Thanks.

Regards,  
Ghazni

# ============================= == Execution Command to start inference server

docker run --gpus=1 --rm --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 -p8000:8000 -p8001:8001 -p8002:8002 -v/home/mgsaeed/wd500gb/github/triton-inference-server/docs/examples/model\_repository:/models [nvcr.io/nvidia/tritonserver:20.07-v1-py3](http://nvcr.io/nvidia/tritonserver:20.07-v1-py3) tritonserver --model-repository=/models

# ============================= == Triton Inference Server ==

NVIDIA Release 20.07 (build 14602913)

Copyright (c) 2018-2020, NVIDIA CORPORATION. All rights reserved.

Various files include modifications (c) NVIDIA CORPORATION. All rights reserved.  
NVIDIA modifications are covered by the license terms that apply to the underlying  
project or file.

2020-08-10 16:11:10.765523: I tensorflow/stream\_executor/platform/default/dso\_loader.cc:48] Successfully opened dynamic library libcudart.so.11.0  
I0810 16:11:10.792665 1 metrics.cc:164] found 1 GPUs supporting NVML metrics  
I0810 16:11:10.798194 1 metrics.cc:173] GPU 0: GeForce RTX 2080 Ti  
I0810 16:11:10.798388 1 server.cc:127] Initializing Triton Inference Server  
I0810 16:11:10.955257 1 server\_status.cc:55] New status tracking for model ‘densenet\_onnx’  
I0810 16:11:10.955277 1 server\_status.cc:55] New status tracking for model ‘inception\_graphdef’  
I0810 16:11:10.955281 1 server\_status.cc:55] New status tracking for model ‘resnet50\_netdef’  
I0810 16:11:10.955285 1 server\_status.cc:55] New status tracking for model ‘simple’  
I0810 16:11:10.955288 1 server\_status.cc:55] New status tracking for model ‘simple\_string’  
I0810 16:11:10.955312 1 model\_repository\_manager.cc:723] loading: simple:1  
I0810 16:11:10.955387 1 model\_repository\_manager.cc:723] loading: simple\_string:1  
I0810 16:11:10.955491 1 model\_repository\_manager.cc:723] loading: resnet50\_netdef:1  
I0810 16:11:10.955541 1 model\_repository\_manager.cc:723] loading: inception\_graphdef:1  
I0810 16:11:10.955609 1 model\_repository\_manager.cc:723] loading: densenet\_onnx:1  
I0810 16:11:10.957881 1 base\_backend.cc:176] Creating instance simple\_0\_gpu0 on GPU 0 (7.5) using model.graphdef  
I0810 16:11:10.957958 1 base\_backend.cc:176] Creating instance simple\_string\_0\_gpu0 on GPU 0 (7.5) using model.graphdef  
I0810 16:11:10.958099 1 base\_backend.cc:176] Creating instance inception\_graphdef\_0\_gpu0 on GPU 0 (7.5) using model.graphdef  
I0810 16:11:10.984917 1 onnx\_backend.cc:203] Creating instance densenet\_onnx\_0\_gpu0 on GPU 0 (7.5) using model.onnx  
2020-08-10 16:11:10.988128: I tensorflow/core/platform/profile\_utils/cpu\_utils.cc:94] CPU Frequency: 3699850000 Hz  
2020-08-10 16:11:10.989941: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f2a34088380 initialized for platform Host (this does not guarantee that XLA will be used). Devices:  
2020-08-10 16:11:10.989983: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version  
2020-08-10 16:11:10.990153: I tensorflow/stream\_executor/platform/default/dso\_loader.cc:48] Successfully opened dynamic library libcuda.so.1  
2020-08-10 16:11:10.991876: I tensorflow/core/common\_runtime/gpu/gpu\_device.cc:1634] Found device 0 with properties:  
name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.545  
pciBusID: 0000:67:00.0  
2020-08-10 16:11:10.991917: I tensorflow/stream\_executor/platform/default/dso\_loader.cc:48] Successfully opened dynamic library libcudart.so.11.0  
2020-08-10 16:11:10.991964: I tensorflow/stream\_executor/platform/default/dso\_loader.cc:48] Successfully opened dynamic library libcublas.so.11  
2020-08-10 16:11:10.991997: I tensorflow/stream\_executor/platform/default/dso\_loader.cc:48] Successfully opened dynamic library libcufft.so.10  
2020-08-10 16:11:10.992066: I tensorflow/stream\_executor/platform/default/dso\_loader.cc:48] Successfully opened dynamic library libcurand.so.10  
2020-08-10 16:11:10.998282: I tensorflow/stream\_executor/platform/default/dso\_loader.cc:48] Successfully opened dynamic library libcusolver.so.10  
2020-08-10 16:11:10.998368: I tensorflow/stream\_executor/platform/default/dso\_loader.cc:48] Successfully opened dynamic library libcusparse.so.11  
2020-08-10 16:11:10.998399: I tensorflow/stream\_executor/platform/default/dso\_loader.cc:48] Successfully opened dynamic library libcudnn.so.8  
2020-08-10 16:11:11.003025: I tensorflow/core/common\_runtime/gpu/gpu\_device.cc:1762] Adding visible gpu devices: 0  
I0810 16:11:11.064634 1 netdef\_backend.cc:206] Creating instance resnet50\_netdef\_0\_gpu0 on GPU 0 (7.5) using init\_model.netdef and model.netdef  
[E init\_intrinsics\_check.cc:43] CPU feature avx is present on your machine, but the Caffe2 binary is not compiled with it. It means you may not get the full speed of your CPU.  
[E init\_intrinsics\_check.cc:43] CPU feature avx2 is present on your machine, but the Caffe2 binary is not compiled with it. It means you may not get the full speed of your CPU.  
[E init\_intrinsics\_check.cc:43] CPU feature fma is present on your machine, but the Caffe2 binary is not compiled with it. It means you may not get the full speed of your CPU.  
2020-08-10 16:11:12.082177: I tensorflow/core/common\_runtime/gpu/gpu\_device.cc:1175] Device interconnect StreamExecutor with strength 1 edge matrix:  
2020-08-10 16:11:12.082227: I tensorflow/core/common\_runtime/gpu/gpu\_device.cc:1181] 0  
2020-08-10 16:11:12.082238: I tensorflow/core/common\_runtime/gpu/gpu\_device.cc:1194] 0: N  
2020-08-10 16:11:12.088323: I tensorflow/core/common\_runtime/gpu/gpu\_device.cc:1320] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9610 MB memory) → physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:67:00.0, compute capability: 7.5)  
2020-08-10 16:11:12.092148: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f2a34766940 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:  
2020-08-10 16:11:12.092182: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): GeForce RTX 2080 Ti, Compute Capability 7.5  
2020-08-10 16:11:12.094081: I tensorflow/core/common\_runtime/gpu/gpu\_device.cc:1634] Found device 0 with properties:  
name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.545  
pciBusID: 0000:67:00.0  
2020-08-10 16:11:12.094142: I tensorflow/stream\_executor/platform/default/dso\_loader.cc:48] Successfully opened dynamic library libcudart.so.11.0  
2020-08-10 16:11:12.094158: I tensorflow/stream\_executor/platform/default/dso\_loader.cc:48] Successfully opened dynamic library libcublas.so.11  
2020-08-10 16:11:12.094173: I tensorflow/stream\_executor/platform/default/dso\_loader.cc:48] Successfully opened dynamic library libcufft.so.10  
2020-08-10 16:11:12.094187: I tensorflow/stream\_executor/platform/default/dso\_loader.cc:48] Successfully opened dynamic library libcurand.so.10  
2020-08-10 16:11:12.094215: I tensorflow/stream\_executor/platform/default/dso\_loader.cc:48] Successfully opened dynamic library libcusolver.so.10  
2020-08-10 16:11:12.094249: I tensorflow/stream\_executor/platform/default/dso\_loader.cc:48] Successfully opened dynamic library libcusparse.so.11  
2020-08-10 16:11:12.094261: I tensorflow/stream\_executor/platform/default/dso\_loader.cc:48] Successfully opened dynamic library libcudnn.so.8  
2020-08-10 16:11:12.097381: I tensorflow/core/common\_runtime/gpu/gpu\_device.cc:1762] Adding visible gpu devices: 0  
2020-08-10 16:11:12.097420: I tensorflow/core/common\_runtime/gpu/gpu\_device.cc:1175] Device interconnect StreamExecutor with strength 1 edge matrix:  
2020-08-10 16:11:12.097431: I tensorflow/core/common\_runtime/gpu/gpu\_device.cc:1181] 0  
2020-08-10 16:11:12.097440: I tensorflow/core/common\_runtime/gpu/gpu\_device.cc:1194] 0: N  
2020-08-10 16:11:12.100445: I tensorflow/core/common\_runtime/gpu/gpu\_device.cc:1320] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9610 MB memory) → physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:67:00.0, compute capability: 7.5)  
2020-08-10 16:11:12.102552: I tensorflow/core/common\_runtime/gpu/gpu\_device.cc:1634] Found device 0 with properties:  
name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.545  
pciBusID: 0000:67:00.0  
2020-08-10 16:11:12.102626: I tensorflow/stream\_executor/platform/default/dso\_loader.cc:48] Successfully opened dynamic library libcudart.so.11.0  
2020-08-10 16:11:12.102656: I tensorflow/stream\_executor/platform/default/dso\_loader.cc:48] Successfully opened dynamic library libcublas.so.11  
2020-08-10 16:11:12.102684: I tensorflow/stream\_executor/platform/default/dso\_loader.cc:48] Successfully opened dynamic library libcufft.so.10  
2020-08-10 16:11:12.102705: I tensorflow/stream\_executor/platform/default/dso\_loader.cc:48] Successfully opened dynamic library libcurand.so.10  
2020-08-10 16:11:12.102749: I tensorflow/stream\_executor/platform/default/dso\_loader.cc:48] Successfully opened dynamic library libcusolver.so.10  
2020-08-10 16:11:12.102770: I tensorflow/stream\_executor/platform/default/dso\_loader.cc:48] Successfully opened dynamic library libcusparse.so.11  
2020-08-10 16:11:12.102789: I tensorflow/stream\_executor/platform/default/dso\_loader.cc:48] Successfully opened dynamic library libcudnn.so.8  
2020-08-10 16:11:12.106010: I tensorflow/core/common\_runtime/gpu/gpu\_device.cc:1762] Adding visible gpu devices: 0  
2020-08-10 16:11:12.106055: I tensorflow/core/common\_runtime/gpu/gpu\_device.cc:1175] Device interconnect StreamExecutor with strength 1 edge matrix:  
2020-08-10 16:11:12.106072: I tensorflow/core/common\_runtime/gpu/gpu\_device.cc:1181] 0  
2020-08-10 16:11:12.106091: I tensorflow/core/common\_runtime/gpu/gpu\_device.cc:1194] 0: N  
2020-08-10 16:11:12.109675: I tensorflow/core/common\_runtime/gpu/gpu\_device.cc:1320] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9610 MB memory) → physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:67:00.0, compute capability: 7.5)  
I0810 16:11:12.110803 1 model\_repository\_manager.cc:888] successfully loaded ‘simple’ version 1  
I0810 16:11:12.110835 1 model\_repository\_manager.cc:888] successfully loaded ‘simple\_string’ version 1  
I0810 16:11:12.203993 1 model\_repository\_manager.cc:888] successfully loaded ‘inception\_graphdef’ version 1  
I0810 16:11:12.813593 1 model\_repository\_manager.cc:888] successfully loaded ‘densenet\_onnx’ version 1  
I0810 16:11:12.892981 1 model\_repository\_manager.cc:888] successfully loaded ‘resnet50\_netdef’ version 1  
Starting endpoints, ‘inference:0’ listening on  
I0810 16:11:12.895164 1 grpc\_server.cc:1942] Started GRPCService at 0.0.0.0:8001  
I0810 16:11:12.895179 1 http\_server.cc:1428] Starting HTTPService at 0.0.0.0:8000  
I0810 16:11:12.936628 1 http\_server.cc:1443] Starting Metrics Service at 0.0.0.0:8002

---

<div class="post-metadata">

**Author:** ![Morganh](https://sea2.discourse-cdn.com/nvidia/user_avatar/forums.developer.nvidia.com/morganh/32/9748_2.png) [@Morganh](https://forums.developer.nvidia.com/u/Morganh)\
**Post date:** [August 11, 2020, 3:48am UTC](https://forums.developer.nvidia.com/t/triton-inference-server-is-sending-back-http-1-1-400-bad-request/146244/3 "2020-08-11T03:48:37Z")

</div>

Please try  
$ curl localhost:8000/api/status

---

<div class="post-metadata">

**Author:** ![ghazni](https://developer.download.nvidia.com/images/forums/profile-default-devtalk-84.png) [@ghazni](https://forums.developer.nvidia.com/u/ghazni)\
**Post date:** [August 11, 2020, 6:43am UTC](https://forums.developer.nvidia.com/t/triton-inference-server-is-sending-back-http-1-1-400-bad-request/146244/4 "2020-08-11T06:43:59Z")

</div>

Thanks. I have tried which seems to be working though the response message is not same. There is no “HTTP/1.1 200 OK” however print the status of the various models.

After printing a lot of text it does print out SERVER\_READY. Is it the correct/expected response? Thanks.

Regards,  
ghazni

curl localhost:8000/api/status  
id: “inference:0”  
version: “1.15.0”  
uptime\_ns: 62975350798  
model\_status {  
key: “densenet\_onnx”  
value {  
config {  
name: “densenet\_onnx”  
platform: “onnxruntime\_onnx”  
version\_policy {  
latest {  
num\_versions: 1  
}  
}  
input {  
name: “data\_0”  
data\_type: TYPE\_FP32  
format: FORMAT\_NCHW  
dims: 3  
dims: 224  
dims: 224  
reshape {  
shape: 1  
shape: 3  
shape: 224  
shape: 224  
}  
}  
output {  
name: “fc6\_1”  
data\_type: TYPE\_FP32  
dims: 1000  
label\_filename: “densenet\_labels.txt”  
reshape {  
shape: 1  
shape: 1000  
shape: 1  
shape: 1  
}  
}  
instance\_group {  
name: “densenet\_onnx”  
count: 1  
gpus: 0  
gpus: 1  
kind: KIND\_GPU  
}  
default\_model\_filename: “model.onnx”  
optimization {  
input\_pinned\_memory {  
enable: true  
}  
output\_pinned\_memory {  
enable: true  
}  
}  
}  
version\_status {  
key: 1  
value {  
ready\_state: MODEL\_READY  
ready\_state\_reason {  
}  
}  
}  
}  
}  
model\_status {  
key: “inception\_graphdef”  
value {  
config {  
name: “inception\_graphdef”  
platform: “tensorflow\_graphdef”  
version\_policy {  
latest {  
num\_versions: 1  
}  
}  
max\_batch\_size: 128  
input {  
name: “input”  
data\_type: TYPE\_FP32  
format: FORMAT\_NHWC  
dims: 299  
dims: 299  
dims: 3  
}  
output {  
name: “InceptionV3/Predictions/Softmax”  
data\_type: TYPE\_FP32  
dims: 1001  
label\_filename: “inception\_labels.txt”  
}  
instance\_group {  
name: “inception\_graphdef”  
count: 1  
gpus: 0  
gpus: 1  
kind: KIND\_GPU  
}  
default\_model\_filename: “model.graphdef”  
optimization {  
input\_pinned\_memory {  
enable: true  
}  
output\_pinned\_memory {  
enable: true  
}  
}  
}  
version\_status {  
key: 1  
value {  
ready\_state: MODEL\_READY  
ready\_state\_reason {  
}  
}  
}  
}  
}  
model\_status {  
key: “resnet50\_netdef”  
value {  
config {  
name: “resnet50\_netdef”  
platform: “caffe2\_netdef”  
version\_policy {  
latest {  
num\_versions: 1  
}  
}  
max\_batch\_size: 128  
input {  
name: “gpu\_0/data”  
data\_type: TYPE\_FP32  
format: FORMAT\_NCHW  
dims: 3  
dims: 224  
dims: 224  
}  
output {  
name: “gpu\_0/softmax”  
data\_type: TYPE\_FP32  
dims: 1000  
label\_filename: “resnet50\_labels.txt”  
}  
instance\_group {  
name: “resnet50\_netdef”  
count: 1  
gpus: 0  
gpus: 1  
kind: KIND\_GPU  
}  
default\_model\_filename: “model.netdef”  
optimization {  
input\_pinned\_memory {  
enable: true  
}  
output\_pinned\_memory {  
enable: true  
}  
}  
}  
version\_status {  
key: 1  
value {  
ready\_state: MODEL\_READY  
ready\_state\_reason {  
}  
}  
}  
}  
}  
model\_status {  
key: “simple”  
value {  
config {  
name: “simple”  
platform: “tensorflow\_graphdef”  
version\_policy {  
latest {  
num\_versions: 1  
}  
}  
max\_batch\_size: 8  
input {  
name: “INPUT0”  
data\_type: TYPE\_INT32  
dims: 16  
}  
input {  
name: “INPUT1”  
data\_type: TYPE\_INT32  
dims: 16  
}  
output {  
name: “OUTPUT0”  
data\_type: TYPE\_INT32  
dims: 16  
}  
output {  
name: “OUTPUT1”  
data\_type: TYPE\_INT32  
dims: 16  
}  
instance\_group {  
name: “simple”  
count: 1  
gpus: 0  
gpus: 1  
kind: KIND\_GPU  
}  
default\_model\_filename: “model.graphdef”  
optimization {  
input\_pinned\_memory {  
enable: true  
}  
output\_pinned\_memory {  
enable: true  
}  
}  
}  
version\_status {  
key: 1  
value {  
ready\_state: MODEL\_READY  
ready\_state\_reason {  
}  
}  
}  
}  
}  
model\_status {  
key: “simple\_string”  
value {  
config {  
name: “simple\_string”  
platform: “tensorflow\_graphdef”  
version\_policy {  
latest {  
num\_versions: 1  
}  
}  
max\_batch\_size: 8  
input {  
name: “INPUT0”  
data\_type: TYPE\_STRING  
dims: 16  
}  
input {  
name: “INPUT1”  
data\_type: TYPE\_STRING  
dims: 16  
}  
output {  
name: “OUTPUT0”  
data\_type: TYPE\_STRING  
dims: 16  
}  
output {  
name: “OUTPUT1”  
data\_type: TYPE\_STRING  
dims: 16  
}  
instance\_group {  
name: “simple\_string”  
count: 1  
gpus: 0  
gpus: 1  
kind: KIND\_GPU  
}  
default\_model\_filename: “model.graphdef”  
optimization {  
input\_pinned\_memory {  
enable: true  
}  
output\_pinned\_memory {  
enable: true  
}  
}  
}  
version\_status {  
key: 1  
value {  
ready\_state: MODEL\_READY  
ready\_state\_reason {  
}  
}  
}  
}  
}  
ready\_state: SERVER\_READY

---

<div class="post-metadata">

**Author:** ![ghazni](https://developer.download.nvidia.com/images/forums/profile-default-devtalk-84.png) [@ghazni](https://forums.developer.nvidia.com/u/ghazni)\
**Post date:** [August 11, 2020, 7:39am UTC](https://forums.developer.nvidia.com/t/triton-inference-server-is-sending-back-http-1-1-400-bad-request/146244/5 "2020-08-11T07:39:00Z")

</div>

OK I further went ahead and pulled/run clientsdk example as (edited this post as well after finding new information).

docker pull [nvcr.io/nvidia/tritonserver:20.07-py3-clientsdk](http://nvcr.io/nvidia/tritonserver:20.07-py3-clientsdk)  
docker run -it --rm --net=host [nvcr.io/nvidia/tritonserver:20.07-py3-clientsdk](http://nvcr.io/nvidia/tritonserver:20.07-py3-clientsdk)

after this from inside the container  
/workspace/install/bin/image\_client -m resnet50\_netdef -s INCEPTION /workspace/images/mug.jpg

However got below errors:  
error: failed to get model metadata: failed to parse the request JSON buffer: The document is empty. at 0 …

Ok this issue was resolved by pulling the previous image associated with tensorrtserver as  
docker pull [nvcr.io/nvidia/tensorrtserver:20.02-py3-clientsdk](http://nvcr.io/nvidia/tensorrtserver:20.02-py3-clientsdk)

So In summary:  
1: curl -v localhost:8000/v2/health/ready (not working; getting 400)  
2: curl localhost:8000/api/status (apparently working - details in my previous message)  
3: /workspace/install/bin/image\_client -m resnet50\_netdef -s INCEPTION /workspace/images/mug.jpg (not working with tritonserver:20.07-py3-clientsdk)  
4: /workspace/install/bin/image\_client -m resnet50\_netdef -s INCEPTION /workspace/images/mug.jpg (working with [nvcr.io/nvidia/tensorrtserver:20.02-py3-clientsdk](http://nvcr.io/nvidia/tensorrtserver:20.02-py3-clientsdk))

Are those expected results? Many thanks.

Regards,  
Ghazni

---

<div class="post-metadata">

**Author:** ![Morganh](https://sea2.discourse-cdn.com/nvidia/user_avatar/forums.developer.nvidia.com/morganh/32/9748_2.png) [@Morganh](https://forums.developer.nvidia.com/u/Morganh)\
**Post date:** [August 11, 2020, 11:26am UTC](https://forums.developer.nvidia.com/t/triton-inference-server-is-sending-back-http-1-1-400-bad-request/146244/6 "2020-08-11T11:26:37Z")

</div>

For server status.,if it is ready, it should be fine.  
For item 3 or 4,actually you have narrowed down. So please check the difference of the sdks.

---

<div class="post-metadata">

**Author:** ![ghazni](https://developer.download.nvidia.com/images/forums/profile-default-devtalk-84.png) [@ghazni](https://forums.developer.nvidia.com/u/ghazni)\
**Post date:** [August 11, 2020, 11:50am UTC](https://forums.developer.nvidia.com/t/triton-inference-server-is-sending-back-http-1-1-400-bad-request/146244/7 "2020-08-11T11:50:13Z")

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Many thanks, Morganh.

Regards,  
Ghazni

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**Author:** ![TomNVIDIA](https://sea2.discourse-cdn.com/nvidia/user_avatar/forums.developer.nvidia.com/tomnvidia/32/14181_2.png) [@TomNVIDIA](https://forums.developer.nvidia.com/u/TomNVIDIA)\
**Post date:** [October 12, 2021, 4:24pm UTC](https://forums.developer.nvidia.com/t/triton-inference-server-is-sending-back-http-1-1-400-bad-request/146244/9 "2021-10-12T16:24:29Z")

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