Deploy Paddle model with InferenceService¶
In this example, we use a trained paddle resnet50 model to classify images by running an inference service with Paddle predictor.
Deploy Paddle model with V1 protocol¶
Create the InferenceService¶
apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "paddle-resnet50"
spec:
predictor:
model:
modelFormat:
name: paddle
storageUri: "gs://kfserving-examples/models/paddle/resnet"
apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "paddle-resnet50"
spec:
predictor:
paddle:
storageUri: "gs://kfserving-examples/models/paddle/resnet"
Apply the above yaml to create the InferenceService
kubectl apply -f paddle.yaml
Expected Output
$ inferenceservice.serving.kserve.io/paddle-resnet50 created
Run a Prediction¶
The first step is to determine the ingress IP and ports and set INGRESS_HOST
and INGRESS_PORT
.
You can see an example payload jay.json as the sample input to test the model.
MODEL_NAME=paddle-resnet50
SERVICE_HOSTNAME=$(kubectl get inferenceservice ${MODEL_NAME} -o jsonpath='{.status.url}' | cut -d "/" -f 3)
curl -v -H "Host: ${SERVICE_HOSTNAME}" \
-H "Content-Type: application/json" \
-d @./jay.json \
http://${INGRESS_HOST}:${INGRESS_PORT}/v1/models/${MODEL_NAME}:predict
Expected Output
* Trying 127.0.0.1:80...
* TCP_NODELAY set
* Connected to localhost (127.0.0.1) port 80 (#0)
> POST /v1/models/paddle-resnet50:predict HTTP/1.1
> Host: paddle-resnet50.default.example.com
> User-Agent: curl/7.68.0
> Accept: */*
> Content-Length: 3010209
> Content-Type: application/x-www-form-urlencoded
> Expect: 100-continue
>
* Mark bundle as not supporting multiuse
< HTTP/1.1 100 Continue
* We are completely uploaded and fine
* Mark bundle as not supporting multiuse
< HTTP/1.1 200 OK
< content-length: 23399
< content-type: application/json; charset=UTF-8
< date: Mon, 17 May 2021 03:34:58 GMT
< server: istio-envoy
< x-envoy-upstream-service-time: 511
<
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* Connection #0 to host localhost left intact
Deploy the model with Open Inference Protocol¶
Test the Model locally¶
Once you've got your model serialised model.pdmodel
, we can then use KServe Paddle Server to spin up a local server.
Note
This step is optional and just meant for testing, feel free to jump straight to deploying with InferenceService.
Using KServe PaddleServer¶
Pre-requisites¶
Firstly, to use KServe Paddle server locally, you will first need to install the paddleserver
runtime package in your local environment.
- Clone the KServe repository and navigate into the directory.
git clone https://github.com/kserve/kserve
- Install
paddleserver
runtime. Kserve uses Poetry as the dependency management tool. Make sure you have already installed poetry.cd python/paddleserver poetry install
Serving model locally¶
The paddleserver
package takes two arguments.
--model_dir
: The model directory path where the model is stored.--model_name
: The name of the model deployed in the model server, the default value ismodel
. This is optional.
With the paddleserver
runtime package installed locally, you should now be ready to start our server as:
python3 paddleserver --model_dir /path/to/model_dir --model_name paddle-v2-resnet50
Deploy the Model with REST endpoint through InferenceService¶
Lastly, you will use KServe to deploy the trained model onto Kubernetes.
For this, you will just need to use version v1beta1
of the
InferenceService
CRD and set the protocolVersion
field to v2
.
apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "paddle-v2-resnet50"
spec:
predictor:
model:
modelFormat:
name: paddle
protocolVersion: v2
runtime: kserve-paddleserver
storageUri: "gs://kfserving-examples/models/paddle/resnet"
Apply the InferenceService yaml to get the REST endpoint
kubectl apply -f paddle-v2-resnet.yaml
Test the Deployed Model¶
You can now test your deployed model by sending a sample request.
Note that this request needs to follow the Open Inference Protocol.
You can use the example payload jay-v2.json as the sample input to test the model.
Determine the ingress IP and port and set INGRESS_HOST
and INGRESS_PORT
.
Now, you can use curl
to send the inference request as:
SERVICE_HOSTNAME=$(kubectl get inferenceservice paddle-v2-resnet50 -o jsonpath='{.status.url}' | cut -d "/" -f 3)
curl -v \
-H "Host: ${SERVICE_HOSTNAME}" \
-H "Content-Type: application/json" \
-d @./jay-v2.json \
http://${INGRESS_HOST}:${INGRESS_PORT}/v2/models/paddle-v2-resnet50/infer
Expected Output
{"model_name":"paddle-v2-resnet50","model_version":null,"id":"afa5ec0b-a5c7-454b-a464-53ba931b22df","parameters":null,"outputs":[{"name":"output-0","shape":[1,1000],"datatype":"FP32","parameters":null,"data":[6.736678770380422e-9,1.1535990829258935e-8,5.142250714129659e-8,6.647170636142619e-8,4.094492567219277e-8,1.3402451770616608e-7,9.355561303436843e-8,2.8935891904779965e-8,6.845367295227334e-8,7.680615965455218e-8,0.0000020334689452283783,0.0000011085678579547675,2.3477592492326949e-7,6.582037030966603e-7,0.00012373103527352214,4.2878804151769145e-7,0.000006419959845516132,0.9993496537208557,0.00007372002437477931,0.00003101135735050775,0.0000056028093240456656,0.0000021862508674530545,1.9544044604913324e-8,3.728893887000595e-7,4.2903633357127546e-7,1.8251179767503345e-7,7.159925985433802e-8,9.231618136595898e-9,6.469241498052725e-7,7.031690341108288e-9,4.451231561120039e-8,1.2455971898361895e-7,9.44632745358831e-8,4.347704418705689e-8,4.658220120745682e-7,6.797721141538204e-8,2.1060276367279585e-7,2.2605123106700376e-8,1.4311490303953178e-7,7.951298641728499e-8,1.2341783417468832e-7,0.0000010921713737843675,0.000015243892448779661,3.1173343018053856e-7,2.4152058131221565e-7,6.863762536113427e-8,8.467682022228473e-8,9.4246772164297e-8,1.0219210366813058e-8,3.3770753304906975e-8,3.6928835100979995e-8,1.3694031508748594e-7,1.0674284567357972e-7,2.599483650556067e-7,3.4866405940192635e-7,3.132053549848024e-8,3.574873232992104e-7,6.64843895492595e-8,3.1638955988455564e-7,0.0000012095878219042788,8.66409024524728e-8,4.0144172430700564e-8,1.2544761318622477e-7,3.3201178695208e-8,1.9731444922399533e-7,3.806405572959193e-7,1.3827865075199952e-7,2.300225965257141e-8,7.14422512260171e-8,2.851114544455413e-8,2.982567437470607e-8,8.936032713791064e-8,6.22388370175031e-7,6.478838798784636e-8,1.3663023423760023e-7,9.973181391842445e-8,2.5761554667269593e-8,4.130220077058766e-8,3.9384463690339544e-8,1.2158079698565416e-7,0.000004302821707824478,0.0000018179063090428826,0.0000018520155435908237,0.0000016246107179540559,0.000016448313544970006,0.000010544916221988387,0.000003993061909568496,2.646479799750523e-7,0.000019193475964129902,4.803242745765601e-7,1.696285067964709e-7,0.000004550505764200352,0.00004235929372953251,0.000004443338639248395,0.000005104009687784128,0.000013506396498996764,4.1758724478313525e-7,4.494491463447048e-7,3.156698369366495e-7,0.0000010557599807725637,1.336463917311903e-8,1.3893659556174498e-8,6.770379457066156e-8,1.4129696523923485e-7,7.170518756538513e-8,7.934466594861078e-8,2.639154317307657e-8,2.6134321373660896e-8,7.196725881897237e-9,2.1752363466021052e-8,6.684639686227456e-8,3.417795824134373e-8,1.6228275967478112e-7,4.107114648377319e-7,6.472135396506928e-7,2.951379372007068e-7,5.653474133282543e-9,4.830144462175667e-8,8.887481861563629e-9,3.7306168820805397e-8,1.7784264727538357e-8,4.641905082536368e-9,3.413118676576232e-8,1.937393818707278e-7,0.0000012980176506971475,3.5641004814124244e-8,2.149332445355867e-8,3.055293689158134e-7,1.5532516783878236e-7,0.0000014520978766086046,3.488464628276233e-8,0.00003825438398052938,4.5088432898410247e-7,4.1766969616219285e-7,6.770622462681786e-7,1.4142248971893423e-7,0.000014235997696232516,6.293820433711517e-7,0.000004762866865348769,9.024900577969674e-7,9.058987870957935e-7,0.0000015713684433649178,1.5720647184025438e-7,1.818536503606083e-7,7.193188622522939e-8,0.0000011952824934269302,8.874837362782273e-7,2.0870831463071227e-7,9.906239029078279e-8,7.793621747964607e-9,1.0058498389753368e-7,4.2059440374941914e-7,1.843624630737395e-7,1.6437947181202617e-7,7.025352743994517e-8,2.570448600636155e-7,7.586877615040066e-8,7.841313731660193e-7,2.495309274763713e-7,5.157681925993529e-8,4.0674127177453556e-8,7.531796519799627e-9,4.797485431140558e-8,1.7419973019627832e-8,1.7958679165985814e-7,1.2566392371127222e-8,8.975440124459055e-8,3.26965476915575e-8,1.1208359751435637e-7,3.906746215420753e-8,4.6769045525252295e-8,1.8523553535487736e-7,1.4833052830454108e-7,1.2279349448363064e-7,0.0000010729105497375713,3.6538490011395197e-9,1.6198403329781286e-7,1.6190719875908144e-8,1.2004933580556099e-7,1.4800277448046018e-8,4.02294837442696e-8,2.15060893538066e-7,1.1925696696835075e-7,4.8982514044837444e-8,7.608920071788816e-8,2.3137479487900237e-8,8.521050176568679e-8,9.586213423062873e-8,1.3351650807180704e-7,3.021699157557123e-8,4.423876376336011e-8,2.610667060309879e-8,2.3977091245797055e-7,1.3192564551900432e-7,1.6734931662654162e-8,1.588336999702733e-7,4.0643516285854275e-7,8.753454494581092e-8,8.366999395548191e-7,3.437598650180007e-8,7.847892646850596e-8,8.526394701391382e-9,9.601382799928615e-8,5.258924034023948e-7,1.3557448141909845e-7,1.0307226716577134e-7,1.0429813457335513e-8,5.187714435805901e-8,2.187001335585137e-8,1.1791439824548888e-8,2.98065643278278e-8,4.338393466696289e-8,2.9991046091026874e-8,2.8507610494443725e-8,3.058665143385042e-8,6.441099031917474e-8,1.5364101102477434e-8,1.5973883549236234e-8,2.5736850872704053e-8,1.0903765712555469e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Deploy the Model with GRPC endpoint through InferenceService¶
Create the inference service resource and expose the gRPC port using the below yaml.
Note
Currently, KServe only supports exposing either HTTP or gRPC port. By default, HTTP port is exposed.
apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "paddle-v2-resnet50-grpc"
spec:
predictor:
model:
modelFormat:
name: paddle
protocolVersion: v2
runtime: kserve-paddleserver
storageUri: "gs://kfserving-examples/models/paddle/resnet"
ports:
- name: h2c # knative expects grpc port name to be 'h2c'
protocol: TCP
containerPort: 8081
apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "paddle-v2-resnet50-grpc"
spec:
predictor:
model:
modelFormat:
name: paddle
protocolVersion: v2
runtime: kserve-paddleserver
storageUri: "gs://kfserving-examples/models/paddle/resnet"
ports:
- name: grpc-port # Istio requires the port name to be in the format <protocol>[-<suffix>]
protocol: TCP
containerPort: 8081
Apply the InferenceService yaml to get the gRPC endpoint
kubectl apply -f paddle-v2-grpc.yaml
Test the deployed model with grpcurl¶
After the gRPC InferenceService
becomes ready, grpcurl, can be used to send gRPC requests to the InferenceService
.
# download the proto file
curl -O https://raw.githubusercontent.com/kserve/open-inference-protocol/main/specification/protocol/open_inference_grpc.proto
INPUT_PATH=jay-v2-grpc.json
PROTO_FILE=open_inference_grpc.proto
SERVICE_HOSTNAME=$(kubectl get inferenceservice paddle-v2-resnet50-grpc -o jsonpath='{.status.url}' | cut -d "/" -f 3)
Determine the ingress IP and port and set INGRESS_HOST
and INGRESS_PORT
. Now, you can use curl
to send the inference requests.
The gRPC APIs follows the KServe prediction V2 protocol / Open Inference Protocol.
For example, ServerReady
API can be used to check if the server is ready:
grpcurl \
-plaintext \
-proto ${PROTO_FILE} \
-authority ${SERVICE_HOSTNAME} \
${INGRESS_HOST}:${INGRESS_PORT} \
inference.GRPCInferenceService.ServerReady
Expected Output
{
"ready": true
}
You can use the example payload jay-v2-grpc.json as the sample input to test the model.
Notice that the input format differs from the in the previous REST endpoint
example.
ModelInfer
API takes input following the ModelInferRequest
schema defined in the grpc_predict_v2.proto
file.
grpcurl \
-vv \
-plaintext \
-proto ${PROTO_FILE} \
-authority ${SERVICE_HOSTNAME} \
-d @ \
${INGRESS_HOST}:${INGRESS_PORT} \
inference.GRPCInferenceService.ModelInfer \
<<< $(cat "$INPUT_PATH")
Expected Output
Resolved method descriptor:
// The ModelInfer API performs inference using the specified model. Errors are
// indicated by the google.rpc.Status returned for the request. The OK code
// indicates success and other codes indicate failure.
rpc ModelInfer ( .inference.ModelInferRequest ) returns ( .inference.ModelInferResponse );
Request metadata to send:
(empty)
Response headers received:
content-type: application/grpc
date: Tue, 10 Oct 2023 14:55:27 GMT
grpc-accept-encoding: identity, deflate, gzip
server: istio-envoy
x-envoy-upstream-service-time: 190
Estimated response size: 4093 bytes
Response contents:
{
"modelName": "paddle-v2-resnet50-grpc",
"id": "97db0c56-95d2-4171-afd5-f7609a87032d",
"outputs": [
{
"name": "output-0",
"datatype": "FP32",
"shape": [
"1",
"1000"
],
"contents": {
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}
]
}
Response trailers received:
(empty)
Sent 1 request and received 1 response