updated openvino mode ensemble to 0.2 version

examples/models/openvino_imagenet_ensemble/README.md

@@ -0,0 +1,54 @@
+# Inference pipeline example with models ensemble and python_openvino image
+
+## Overview
+The pipeline presented here includes the following components:
+
+* an [input transformer](resources/transformer) that converts jpeg compressed content into NumPy array
+* two [model components](resources/model) that executes inference requests using ResNet and DenseNet models
+* a [combiner component](resources/combiner) that implements the ensemble of models
+* an [output transformer](resources/transformer) that converts an array of classification probabilities to a human-readable top1 class name
+
+![pipeline graph](pipeline1.png)
+
+The model component is based on [python_openvino](../../../wrappers/s2i/python_openvino) model wrapper
+which includes OpenVINO inference engine python API. It can be used as a base for all kind of models implementation
+using optimized inference execution and OpenVINO models.
+
+
+# Deployment and execution
+Try executing the pipeline by following the steps in the [jupyter notebook](openvino_imagenet_ensemble.ipynb).
+
+It is recommended it run it on Kubernetes nodes with 32vCPU nodes. When less resources is available on the nodes,
+adjust the env variables in the pipeline OMP_THREADS to match the physical cores you allocated (vCPU/2).
+
+This pipeline uses public docker images, public pre-trained models and includes sample images. 
+Only the kubernetes instance is needed to follow it and reproduce in your environment.
+
+You can use included grpc client to connect to the deployed pipeline and submit the inference requests.
+```bash
+python seldon_grpc_client.py --help
+usage: seldon_grpc_client.py [-h] [--repeats REPEATS] [--debug]
+                             [--test-input TEST_INPUT]
+                             [--ambassador AMBASSADOR]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --repeats REPEATS
+  --debug
+  --test-input TEST_INPUT
+  --ambassador AMBASSADOR
+
+example:  
+python seldon_grpc_client.py --ambassador IP:port --test-input input_images.txt
+```
+
+
+## References:
+
+[intel.ai blog post](https://www.intel.ai/inference-performance-boost-with-seldon-on-intel-xeon-scalable-processors)
+
+[seldon model wrapper with openvino python API](../../../wrappers/s2i/python_openvino)
+
+[Inference Engine Dev Guide](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Deep_Learning_Inference_Engine_DevGuide.html)
+
+[OpenVINO model zoo](https://github.com/opencv/open_model_zoo)

examples/models/openvino_imagenet_ensemble/resources/model/Makefile

@@ -1,8 +1,8 @@
-IMAGE_VERSION=0.1
+IMAGE_VERSION=0.2
 IMAGE_NAME=docker.io/seldonio/openvino-demo-prediction
 
 build:
-	s2i build -E environment_grpc . seldonio/seldon-core-s2i-openvino:0.1 $(IMAGE_NAME):$(IMAGE_VERSION)
+	s2i build -E environment_grpc . seldonio/seldon-core-s2i-openvino:0.2 $(IMAGE_NAME):$(IMAGE_VERSION)
 
 push_to_dockerhub:
 	docker push $(IMAGE_NAME):$(IMAGE_VERSION)

examples/models/openvino_imagenet_ensemble/seldon_ov_predict_ensemble.json

@@ -63,7 +63,15 @@
               },
               {
                 "name": "prediction1",
-                "image": "seldonio/openvino-demo-prediction:0.1",
+                "image": "seldonio/openvino-demo-prediction:0.2",
+                "resources": {
+                  "requests": {
+                    "cpu": "1"
+                  },
+                  "limits": {
+                    "cpu": "32"
+                  }
+                },
                 "env": [
                   {
                     "name": "XML_PATH",
@@ -73,6 +81,18 @@
                     "name": "BIN_PATH",
                     "value": "gs://intelai_public_models/densenet_169/1/densenet_169_i8.bin"
                   },
+                  {
+                    "name": "KMP_AFFINITY",
+                    "value": "granularity=fine,verbose,compact,1,0"
+                  },
+                  {
+                    "name": "KMP_BLOCKTIME",
+                    "value": "1"
+                  },
+                  {
+                    "name": "OMP_NUM_THREADS",
+                    "value": "16"
+                  },
                   {
                     "name": "http_proxy",
                     "value": ""
@@ -93,33 +113,53 @@
               },
               {
                 "name": "prediction2",
-                "image": "seldonio/openvino-demo-prediction:0.1",
+                "image": "seldonio/openvino-demo-prediction:0.2",
+                "resources": {
+                  "requests": {
+                    "cpu": "1"
+                  },
+                  "limits": {
+                    "cpu": "32"
+                  }
+                },
                 "env": [
-                   {
-                     "name": "XML_PATH",
-                     "value": "gs://intelai_public_models/resnet_50_i8/1/resnet_50_i8.xml"
-                   },
-                   {
-                    "name": "BIN_PATH",
-                    "value": "gs://intelai_public_models/resnet_50_i8/1/resnet_50_i8.bin"
-                   },
-                   {
-                     "name": "http_proxy",
+                  {
+                    "name": "XML_PATH",
+                    "value": "gs://intelai_public_models/resnet_50_i8/1/resnet_50_i8.xml"
+                  },
+                  {
+                   "name": "BIN_PATH",
+                   "value": "gs://intelai_public_models/resnet_50_i8/1/resnet_50_i8.bin"
+                  },
+                  {
+                    "name": "KMP_AFFINITY",
+                    "value": "granularity=fine,verbose,compact,1,0"
+                  },
+                  {
+                    "name": "KMP_BLOCKTIME",
+                    "value": "1"
+                  },
+                  {
+                    "name": "OMP_NUM_THREADS",
+                    "value": "16"
+                  },
+                  {
+                    "name": "http_proxy",
+                   "value": ""
+                  },
+                  {
+                    "name": "https_proxy",
                     "value": ""
-                   },
-                   {
-                     "name": "https_proxy",
-                     "value": ""
-                   },
-                   {
+                  },
+                  {
                     "name": "TRACING",
                      "value": "1"
-                   },
-                   {
-                     "name": "JAEGER_AGENT_HOST",
-                     "value": "jaeger-agent"
-                   }
-                  ]
+                  },
+                  {
+                    "name": "JAEGER_AGENT_HOST",
+                    "value": "jaeger-agent"
+                  }
+                ]
               }
             ],
             "terminationGracePeriodSeconds": 1