--- /dev/null
+# ==================================================================================
+#
+# Copyright (c) 2025 Samsung Electronics Co., Ltd. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==================================================================================
+
+# sudo nerdctl --namespace k8s.io rmi -f myapplication:dev
+echo "Building New Image & Loading"
+sudo buildctl --addr=nerdctl-container://buildkitd build \
+ --frontend dockerfile.v0 \
+ --opt filename=Dockerfile \
+ --local dockerfile=myapplication \
+ --local context=myapplication \
+ --output type=oci,name=myapplication:dev | sudo nerdctl load --namespace k8s.io
\ No newline at end of file
--- /dev/null
+# ==================================================================================
+#
+# Copyright (c) 2025 Samsung Electronics Co., Ltd. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==================================================================================
+FROM python:3.10-slim
+# Set environment variables to avoid user prompts during package installation
+ENV DEBIAN_FRONTEND=noninteractive
+# Install necessary packages and clean up
+RUN apt-get update && apt-get install -y bash && apt-get clean && rm -rf /var/lib/apt/lists/*
+COPY app app
+# Location in the container
+WORKDIR /app
+RUN pip3 install -r requirements.txt
+# CMD ["tail", "-f", "/dev/null"]
\ No newline at end of file
--- /dev/null
+# ==================================================================================
+#
+# Copyright (c) 2025 Samsung Electronics Co., Ltd. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==================================================================================
+import simplejson as json
+import os
+import requests
+import time
+
+KSERVE_HOST = os.environ['KSERVE_HOST']
+MODEL_NAME = os.environ['MODEL_NAME']
+PREDICTION_URL = f"{KSERVE_HOST}/v1/models/{MODEL_NAME}:predict"
+
+def predict_single_at_time(model_input : list):
+ '''
+ model_input must be list
+ '''
+ headers = {
+ "Content-Type": "application/json"
+ }
+
+ data = {
+ "signature_name": "serving_default",
+ "instances" : [model_input]
+ }
+ response = requests.post(PREDICTION_URL, headers=headers, json=data)
+ if response.status_code != 200:
+ print("Error| Status-code is not 200| ", response.text)
+ return -1
+ predictions = json.loads(response.text)
+
+ # Since we predicting for single dataPoint
+ return predictions['predictions'][0]
+
+def make_requests():
+ data = [[2.56, 2.56],
+ [2.56, 2.56],
+ [2.56, 2.56],
+ [2.56, 2.56],
+ [2.56, 2.56],
+ [2.56, 2.56],
+ [2.56, 2.56],
+ [2.56, 2.56],
+ [2.56, 2.56],
+ [2.56, 2.56]]
+ print("Input-data : ", data)
+ while True:
+ try:
+ predicted = predict_single_at_time(data)
+ print(f"Predicted-Values : {predicted}")
+ print("--------------------------------------------------------")
+ time.sleep(5)
+ except Exception as err:
+ print("Recieved Error while make prediction requests | Error : ", err)
+ # Keep-trying after 5 seconds
+ time.sleep(5)
+
+
+if __name__ == '__main__':
+ make_requests()
+
+
\ No newline at end of file
--- /dev/null
+# ==================================================================================
+#
+# Copyright (c) 2025 Samsung Electronics Co., Ltd. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==================================================================================
+
+simplejson
+requests
\ No newline at end of file
--- /dev/null
+# ==================================================================================
+#
+# Copyright (c) 2025 Samsung Electronics Co., Ltd. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==================================================================================
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+ name: myapplication
+ namespace: default
+spec:
+ replicas: 1
+ selector:
+ matchLabels:
+ app: myapplication
+ template:
+ metadata:
+ labels:
+ app: myapplication
+ spec:
+ containers:
+ - name: myapplication-container
+ image: myapplication:dev
+ imagePullPolicy: IfNotPresent
+ command: ["python3", "-u","main.py"]
+ resources:
+ requests:
+ memory: "128Mi"
+ cpu: "250m"
+ limits:
+ memory: "256Mi"
+ cpu: "500m"
+ env:
+ - name: KSERVE_HOST
+ value: "http://qoe-model.kserve-test.svc.cluster.local"
+ - name: MODEL_NAME
+ value: "qoe-model"
\ No newline at end of file
--- /dev/null
+# ==================================================================================
+#
+# Copyright (c) 2025 Samsung Electronics Co., Ltd. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==================================================================================
+apiVersion: "serving.kserve.io/v1beta1"
+kind: "InferenceService"
+metadata:
+ name: "qoe-model"
+ namespace: "kserve-test"
+spec:
+ predictor:
+ model:
+ modelFormat:
+ name: tensorflow
+ storageUri: "<MODEL_URL>"
+
\ No newline at end of file
Obtain Model URL for deploying trained models
---------------------------------------------
-URL for deployment can be obainted from AIMFW dashboard (Training Jobs-> Training Job status -> Select Info for a training job -> Model URL)
-In case of using AIMLFW Model management service, URL for downloading and deploying model using Model Management Service will be the following:
-
.. code:: bash
- http://<VM IP where AIMLFW is deployed>:32006/downloadModel/<model name>/model.zip
+ http://<VM IP where AIMLFW is deployed>:32002/model/<MODEL_NAME>/<MODEL_VERSION>/<MODEL_ARTIFACT_VERSION>/Model.zip
+
+
+.. _reference4:
-Install only Kserve for deploying models
+Model-Deployment
----------------------------------------
-To install Kserve run the below commands
+1. Installing Kserve
.. code:: bash
./bin/install_kserve.sh
-
-Uninstall only Kserve
----------------------
-
-To uninstall Kserve run the below commands
+2. Verify Installation
.. code:: bash
- ./bin/uninstall_kserve.sh
+ ~$ kubectl get pods -n kserve
+ NAME READY STATUS RESTARTS AGE
+ kserve-controller-manager-5d995bd58-9pf6x 2/2 Running 0 6d18h
-
-.. _reference4:
-
-Deploy trained qoe prediction model on Kserve
----------------------------------------------
-
-Create namespace using command below
+3. Deploy trained qoe prediction model on Kserve
.. code:: bash
+ # Create namespace
kubectl create namespace kserve-test
+
Create :file:`qoe.yaml` file with below contents
.. code-block:: yaml
apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
- name: qoe-model
+ name: "qoe-model"
+ namespace: kserve-test
spec:
predictor:
- tensorflow:
- storageUri: "<update Model URL here>"
- runtimeVersion: "2.5.1"
- resources:
- requests:
- cpu: 0.1
- memory: 0.5Gi
- limits:
- cpu: 0.1
- memory: 0.5Gi
+ model:
+ modelFormat:
+ name: tensorflow
+ storageUri: "<MODEL URL>"
To deploy model update the Model URL in the :file:`qoe.yaml` file and execute below command to deploy model
.. code:: bash
- kubectl apply -f qoe.yaml -n kserve-test
-
-Check running state of pod using below command
-
-.. code:: bash
+ kubectl apply -f qoe.yaml
- kubectl get pods -n kserve-test
+
+Verify Model-Deployment
-Test predictions using model deployed on Kserve
------------------------------------------------
+.. code:: bash
-Use below command to obtain Ingress port for Kserve.
+ ~$ kubectl get InferenceService -n kserve-test
-.. code:: bash
+ NAME URL READY PREV LATEST PREVROLLEDOUTREVISION LATESTREADYREVISION AGE
+ qoe-model http://qoe-model.kserve-test.svc.cluster.local True 100 qoe-model-predictor-00001 42s
- kubectl get svc istio-ingressgateway -n istio-system
-Obtain nodeport corresponding to port 80.
-In the below example, the port is 31206.
+ ~$ kubectl get pods -n kserve-test
-.. code::
+ NAME READY STATUS RESTARTS AGE
+ qoe-model-predictor-00001-deployment-86d9db6cb-5r8st 2/2 Running 0 93s
- NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
- istio-ingressgateway LoadBalancer 10.105.222.242 <pending> 15021:31423/TCP,80:31206/TCP,443:32145/TCP,31400:32338/TCP,15443:31846/TCP 4h15m
+4. Test predictions using model deployed on Kserve
-Create predict.sh file with following contents
+In order to test our deployed-model, we will query the InferenceService from a curl-pod.
.. code:: bash
- model_name=qoe-model
- curl -v -H "Host: $model_name.kserve-test.example.com" http://<IP of where Kserve is deployed>:<ingress port for Kserve>/v1/models/$model_name:predict -d @./input_qoe.json
+ # Deploy a curl-pod
+ kubectl run curl-pod --image=curlimages/curl:latest --command sleep 3600
+ # Query Inference-Service
+ kubectl exec -it curl-pod -- \
+ curl \
+ --location http://qoe-model.kserve-test.svc.cluster.local/v1/models/qoe-model:predict \
+ --header "Content-Type: application/json" \
+ --data '{
+ "signature_name": "serving_default",
+ "instances": [[
+ [2.56, 2.56],
+ [2.56, 2.56],
+ [2.56, 2.56],
+ [2.56, 2.56],
+ [2.56, 2.56],
+ [2.56, 2.56],
+ [2.56, 2.56],
+ [2.56, 2.56],
+ [2.56, 2.56],
+ [2.56, 2.56]]
+ ]
+ }'
-Update the ``IP`` of host where Kserve is deployed and ingress port of Kserve obtained using above method.
+| Note: We can change which deployed-model to query by changing the location as:
+| location = <KSERVE_HOST>/v1/models/<MODEL_NAME>:predict, where
+| a. MODEL_NAME: Refers to the Name of Inference-Service
+| b. KSERVE_HOST: Refers to the URL of Inference-Service
-Create sample data for predictions in file :file:`input_qoe.json`. Add the following content in :file:`input_qoe.json` file.
-.. code:: bash
- {"signature_name": "serving_default", "instances": [[[2.56, 2.56],
- [2.56, 2.56],
- [2.56, 2.56],
- [2.56, 2.56],
- [2.56, 2.56],
- [2.56, 2.56],
- [2.56, 2.56],
- [2.56, 2.56],
- [2.56, 2.56],
- [2.56, 2.56]]]}
+5. Uninstall Kserve
+.. code:: bash
-Use command below to trigger predictions
+ ./bin/uninstall_kserve.sh
-.. code:: bash
- source predict.sh
+For Advanced usecases, Please refer to official kserve-documentation `here <https://kserve.github.io/website/0.8/get_started/first_isvc/#1-create-a-namespace>`__
Install both Kserve and Kserve adapter for deploying models
Code Review / aiml-fw / aimlfw-dep.git / commitdiff