From 1401db08b49d4d472933bdc9b7755ca866c4c156 Mon Sep 17 00:00:00 2001
From: Aleksandr Panchul <apanchul@hotmail.com>
Date: Tue, 20 Oct 2020 15:02:18 -0700
Subject: [PATCH 1/4] adding sample model for ONNX inference

---
 .../04-KFServing/onnx-mnist-input.json        |   1 +
 .../04-KFServing/onnx-mnist-model.onnx        | Bin 0 -> 26454 bytes
 .../04-KFServing/onnx.md                      |  67 +++++++++
 .../04-KFServing/onnx.yaml                    |  11 ++
 .../04-KFServing/onnx_mnist.py                | 128 ++++++++++++++++++
 5 files changed, 207 insertions(+)
 create mode 100644 Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx-mnist-input.json
 create mode 100644 Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx-mnist-model.onnx
 create mode 100644 Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx.md
 create mode 100644 Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx.yaml
 create mode 100644 Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx_mnist.py

diff --git a/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx-mnist-input.json b/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx-mnist-input.json
new file mode 100644
index 000000000..5b69977b6
--- /dev/null
+++ b/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx-mnist-input.json
@@ -0,0 +1 @@
+{"inputs": {"Input3": {"dims": ["1", "1", "28", "28"], "dataType": 1, "rawData": "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"}}, "outputFilter": ["Plus214_Output_0"]}
\ No newline at end of file
diff --git a/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx-mnist-model.onnx b/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx-mnist-model.onnx
new file mode 100644
index 0000000000000000000000000000000000000000..bb189a52ea6ed40f3d5f94f0834f1fdab49022fe
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diff --git a/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx.md b/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx.md
new file mode 100644
index 000000000..e1b42fb73
--- /dev/null
+++ b/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx.md
@@ -0,0 +1,67 @@
+# KFServing of ONNX models
+
+## Building models
+
+ONNX is a format for models, the models themselves could be of any ML framework or your own application
+The artefact of the model build should be in .onnx format.
+
+For example, see how you could [Register and Deploy ONNX Model]( 
+https://github.com/Azure/MachineLearningNotebooks/blob/2aa7c53b0ce84e67565d77e484987714fdaed36e/how-to-use-azureml/deployment/onnx/onnx-model-register-and-deploy.ipynb)
+
+We will be using `onnx-mnist-model.onnx` from that example. You would need to move it to the `pvc` in your cluster as we did in other labs, or
+upload it to your `gs://` or `s3://` storage.
+
+## Deploying model
+
+Deployed model is a CRD `inferenceservice`. You can crete it in the same namespace we created earlier in this lab like so:
+
+    $ kubectl create -f onnx.yaml -n kfserving-test
+    inferenceservice.serving.kubeflow.org/mnist-onnx created
+
+In a few minutes you should see the pods running:
+
+    $ kubectl get pods -n kfserving-test
+    NAME                                                              READY   STATUS    RESTARTS   AGE
+    mnist-onnx-predictor-default-5jk48-deployment-b7c89954c-6s6wn   3/3     Running   0          36s
+
+And, more importantly, the `inferenceservice` in the `READY` state:
+
+    $ kubectl get inferenceservice -n kfserving-test
+    NAME           URL                                                                     READY   DEFAULT TRAFFIC   CANARY TRAFFIC   AGE
+    mnist-onnx   http://mnist-onnx.kfserving-test.example.com/v1/models/mnist-onnx         True    
+
+
+You can now [determine your ingress IP and port](https://github.com/kubeflow/kfserving/blob/master/README.md#determine-the-ingress-ip-and-ports):
+
+For KFServing deployment within Kubeflow:
+
+    $ export INGRESS_HOST=$(kubectl -n istio-system get service kfserving-ingressgateway -o jsonpath='{.status.loadBalancer.ingress[0].ip}')
+    $ export INGRESS_PORT=$(kubectl -n istio-system get service kfserving-ingressgateway -o jsonpath='{.spec.ports[?(@.name=="http2")].port}')
+
+For other stand-alone KFServing deployments:
+
+    $ export INGRESS_HOST=$(kubectl -n istio-system get service istio-ingressgateway -o jsonpath='{.status.loadBalancer.ingress[0].ip}')
+    $ export INGRESS_PORT=$(kubectl -n istio-system get service istio-ingressgateway -o jsonpath='{.spec.ports[?(@.name=="http2")].port}')
+
+Before you run inference on your model, it is useful to define environment variables:
+
+    $ export MODEL_NAME=mnist-onnx
+    $ export SERVICE_HOSTNAME=$(kubectl get inferenceservice ${MODEL_NAME} -n kfserving-test -o jsonpath='{.status.url}' | cut -d "/" -f 3)
+
+## Inference with the deployed model
+
+You need to convert your input into a JSON format, as example, we provide `onnx-mnist-input.json` to show the tags.
+
+    $ export INPUT_PATH=onnx-mnist-input.json
+    $ curl -v -H "Host: ${SERVICE_HOSTNAME}" http://${INGRESS_HOST}:${INGRESS_PORT}/v1/models/$MODEL_NAME:predict -d $INPUT_PATH
+
+In some cases, depending on the model, you would need to do the post-processing of the output.
+
+## Links
+
+- https://www.tensorflow.org/guide/saved_model
+- https://github.com/kubeflow/kfserving/tree/master/docs/samples/onnx
+
+---
+
+[Back](Readme.md)
diff --git a/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx.yaml b/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx.yaml
new file mode 100644
index 000000000..91c9b31d0
--- /dev/null
+++ b/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx.yaml
@@ -0,0 +1,11 @@
+apiVersion: "serving.kubeflow.org/v1alpha2"
+kind: "InferenceService"
+metadata:
+  name: "mnist-onnx"
+spec:
+  default:
+    predictor:
+      onnx:
+        # Another ONNX model, from KFServing samples
+        # storageUri: "gs://kfserving-examples/onnx/style"
+        storageUri: "pvc://pvc://samba-share-claim/mymodels/build_models/mnist-onnx"
diff --git a/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx_mnist.py b/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx_mnist.py
new file mode 100644
index 000000000..7ea580fac
--- /dev/null
+++ b/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx_mnist.py
@@ -0,0 +1,128 @@
+
+# This is a modified version of 
+# https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/deployment/onnx/mnist.py
+# Which is a modified version of https://github.com/pytorch/examples/blob/master/mnist/main.py which is
+# licensed under BSD 3-Clause (https://github.com/pytorch/examples/blob/master/LICENSE)
+
+from __future__ import print_function
+import argparse
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torchvision import datasets, transforms
+import os
+
+
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
+        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
+        self.conv2_drop = nn.Dropout2d()
+        self.fc1 = nn.Linear(320, 50)
+        self.fc2 = nn.Linear(50, 10)
+
+    def forward(self, x):
+        x = F.relu(F.max_pool2d(self.conv1(x), 2))
+        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
+        x = x.view(-1, 320)
+        x = F.relu(self.fc1(x))
+        x = F.dropout(x, training=self.training)
+        x = self.fc2(x)
+        return F.log_softmax(x, dim=1)
+
+
+def train(args, model, device, train_loader, optimizer, epoch, output_dir):
+    model.train()
+    for batch_idx, (data, target) in enumerate(train_loader):
+        data, target = data.to(device), target.to(device)
+        optimizer.zero_grad()
+        output = model(data)
+        loss = F.nll_loss(output, target)
+        loss.backward()
+        optimizer.step()
+        if batch_idx % args.log_interval == 0:
+            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+                epoch, batch_idx * len(data), len(train_loader.dataset),
+                100. * batch_idx / len(train_loader), loss.item()))
+
+
+def test(args, model, device, test_loader):
+    model.eval()
+    test_loss = 0
+    correct = 0
+    with torch.no_grad():
+        for data, target in test_loader:
+            data, target = data.to(device), target.to(device)
+            output = model(data)
+            test_loss += F.nll_loss(output, target, size_average=False, reduce=True).item()  # sum up batch loss
+            pred = output.max(1, keepdim=True)[1]  # get the index of the max log-probability
+            correct += pred.eq(target.view_as(pred)).sum().item()
+
+    test_loss /= len(test_loader.dataset)
+    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
+        test_loss, correct, len(test_loader.dataset),
+        100. * correct / len(test_loader.dataset)))
+
+
+def main():
+    # Training settings
+    parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
+    parser.add_argument('--batch-size', type=int, default=64, metavar='N',
+                        help='input batch size for training (default: 64)')
+    parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
+                        help='input batch size for testing (default: 1000)')
+    parser.add_argument('--epochs', type=int, default=5, metavar='N',
+                        help='number of epochs to train (default: 5)')
+    parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
+                        help='learning rate (default: 0.01)')
+    parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
+                        help='SGD momentum (default: 0.5)')
+    parser.add_argument('--no-cuda', action='store_true', default=False,
+                        help='disables CUDA training')
+    parser.add_argument('--seed', type=int, default=1, metavar='S',
+                        help='random seed (default: 1)')
+    parser.add_argument('--log-interval', type=int, default=10, metavar='N',
+                        help='how many batches to wait before logging training status')
+    parser.add_argument('--output-dir', type=str, default='outputs')
+    args = parser.parse_args()
+    use_cuda = not args.no_cuda and torch.cuda.is_available()
+
+    torch.manual_seed(args.seed)
+
+    device = torch.device("cuda" if use_cuda else "cpu")
+
+    output_dir = args.output_dir
+    os.makedirs(output_dir, exist_ok=True)
+
+    kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
+    train_loader = torch.utils.data.DataLoader(
+        datasets.MNIST('data', train=True, download=True,
+                       transform=transforms.Compose([transforms.ToTensor(),
+                                                    transforms.Normalize((0.1307,), (0.3081,))])
+                       ),
+        batch_size=args.batch_size, shuffle=True, **kwargs)
+    test_loader = torch.utils.data.DataLoader(
+        datasets.MNIST('data', train=False,
+                       transform=transforms.Compose([transforms.ToTensor(),
+                                                    transforms.Normalize((0.1307,), (0.3081,))])
+                       ),
+        batch_size=args.test_batch_size, shuffle=True, **kwargs)
+
+    model = Net().to(device)
+    optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
+
+    for epoch in range(1, args.epochs + 1):
+        train(args, model, device, train_loader, optimizer, epoch, output_dir)
+        test(args, model, device, test_loader)
+
+    # save model
+    dummy_input = torch.randn(1, 1, 28, 28, device=device)
+    model_path = os.path.join(output_dir, 'mnist.onnx')
+    torch.onnx.export(model, dummy_input, model_path)
+
+
+if __name__ == '__main__':
+    main()
+

From fc080a54f0e6d4e4380e44d1827d1f8b4a370030 Mon Sep 17 00:00:00 2001
From: Aleksandr Panchul <apanchul@hotmail.com>
Date: Wed, 21 Oct 2020 11:24:32 -0700
Subject: [PATCH 2/4] correcting a typo in model uri

---
 Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx.yaml b/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx.yaml
index 91c9b31d0..c5a2fd50b 100644
--- a/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx.yaml
+++ b/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx.yaml
@@ -8,4 +8,4 @@ spec:
       onnx:
         # Another ONNX model, from KFServing samples
         # storageUri: "gs://kfserving-examples/onnx/style"
-        storageUri: "pvc://pvc://samba-share-claim/mymodels/build_models/mnist-onnx"
+        storageUri: "pvc://samba-share-claim/mymodels/build_models/mnist-onnx"

From e7746b9b5206e2cbfc4a0c80dd09d92b456954f9 Mon Sep 17 00:00:00 2001
From: Aleksandr Panchul <apanchul@hotmail.com>
Date: Wed, 21 Oct 2020 18:02:58 -0700
Subject: [PATCH 3/4] addinng kubeflow onnx notebook

---
 .../04-KFServing/onnx-mosaic.ipynb            | 247 ++++++++++++++++++
 .../04-KFServing/onnx.md                      |  29 +-
 .../04-KFServing/onnx.yaml                    |   6 +-
 .../04-KFServing/onnx_custom.md               |  62 +++++
 .../04-KFServing/onnx_custom.yaml             |   9 +
 .../04-KFServing/onnx_input_image.jpg         | Bin 0 -> 124814 bytes
 6 files changed, 328 insertions(+), 25 deletions(-)
 create mode 100644 Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx-mosaic.ipynb
 create mode 100644 Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx_custom.md
 create mode 100644 Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx_custom.yaml
 create mode 100644 Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx_input_image.jpg

diff --git a/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx-mosaic.ipynb b/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx-mosaic.ipynb
new file mode 100644
index 000000000..8cb09fa22
--- /dev/null
+++ b/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx-mosaic.ipynb
@@ -0,0 +1,247 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Predict an ONNX InferenceService"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This example assumes you have already deployed the sample ONNX Inference Service. \n",
+    "\n",
+    "Deploy the sample ONNX InferenceSevice by following the instructions in the [README](https://github.com/kubeflow/kfserving/blob/master/docs/samples/onnx/README.md)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: pillow in /home/jovyan/.local/lib/python3.6/site-packages (8.0.0)\n",
+      "\u001b[33mWARNING: You are using pip version 19.1.1, however version 20.2.4 is available.\n",
+      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install pillow --user"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'http://<your_ip_here>:80/v1/models/style-sample:predict'"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# if you dont see values above, see instructions in the readme and replace the values here!\n",
+    "import os\n",
+    "\n",
+    "#model_name = os.environ[\"MODEL_NAME\"]\n",
+    "#service_hostname = os.environ[\"SERVICE_HOSTNAME\"]\n",
+    "#cluster_ip = os.environ[\"CLUSTER_IP\"]\n",
+    "\n",
+    "model_name = \"style-sample\"\n",
+    "service_hostname = \"style-sample.kfserving-test.example.com\"\n",
+    "cluster_ip = \"<your_ip_here>:80\"\n",
+    "\n",
+    "predictor_url = \"http://%s/v1/models/%s:predict\" % (cluster_ip, model_name)\n",
+    "predictor_url"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from PIL import Image\n",
+    "import numpy as np\n",
+    "import google.protobuf.json_format as json_format\n",
+    "import assets.onnx_ml_pb2 as onnx_ml_pb2\n",
+    "import assets.predict_pb2 as predict_pb2\n",
+    "import requests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<PIL.Image.Image image mode=RGB size=244x244 at 0x7FE93C3DE518>"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# load & resize image\n",
+    "image = Image.open(\"onnx_input_image.jpg\")\n",
+    "image = image.resize((244,244), Image.ANTIALIAS)\n",
+    "image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 3, 244, 244)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# preprocess image data\n",
+    "norm_img_data = np.array(image).astype('float32')\n",
+    "norm_img_data = np.transpose(norm_img_data, [2, 0, 1])\n",
+    "norm_img_data = np.expand_dims(norm_img_data, axis=0)\n",
+    "np.shape(norm_img_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create request message to be sent to the predictor\n",
+    "import assets.onnx_ml_pb2 as onnx_ml_pb2\n",
+    "import assets.predict_pb2 as predict_pb2\n",
+    "import requests\n",
+    "\n",
+    "input_tensor = onnx_ml_pb2.TensorProto()\n",
+    "input_tensor.dims.extend(norm_img_data.shape)\n",
+    "input_tensor.data_type = 1\n",
+    "input_tensor.raw_data = norm_img_data.tobytes()\n",
+    "\n",
+    "request_message = predict_pb2.PredictRequest()\n",
+    "request_message.inputs[\"input1\"].data_type = input_tensor.data_type\n",
+    "request_message.inputs[\"input1\"].dims.extend(input_tensor.dims)\n",
+    "request_message.inputs[\"input1\"].raw_data = input_tensor.raw_data\n",
+    "\n",
+    "# write message data to JSON\n",
+    "message_data = json_format.MessageToJson(request_message)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Response [200]>"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Call predictor\n",
+    "request_headers = {'Content-Type': 'application/json', 'Accept': 'application/json', 'Host': service_hostname}\n",
+    "#print(predictor_url)\n",
+    "response = requests.post(predictor_url, headers=request_headers, data=message_data)\n",
+    "response"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parse response message\n",
+    "response_message = json_format.Parse(response.text, predict_pb2.PredictResponse())\n",
+    "output1 = np.frombuffer(response_message.outputs['output1'].raw_data, dtype=np.float32)\n",
+    "output1 = output1.reshape(3,244,244)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<PIL.Image.Image image mode=RGB size=244x244 at 0x7FE93C3C2588>"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# postprocess\n",
+    "result = np.clip(output1, 0, 255)\n",
+    "result = result.transpose(1,2,0).astype(\"uint8\")\n",
+    "img = Image.fromarray(result)\n",
+    "img"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx.md b/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx.md
index e1b42fb73..3194dcbc4 100644
--- a/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx.md
+++ b/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx.md
@@ -1,34 +1,23 @@
-# KFServing of ONNX models
-
-## Building models
-
-ONNX is a format for models, the models themselves could be of any ML framework or your own application
-The artefact of the model build should be in .onnx format.
-
-For example, see how you could [Register and Deploy ONNX Model]( 
-https://github.com/Azure/MachineLearningNotebooks/blob/2aa7c53b0ce84e67565d77e484987714fdaed36e/how-to-use-azureml/deployment/onnx/onnx-model-register-and-deploy.ipynb)
-
-We will be using `onnx-mnist-model.onnx` from that example. You would need to move it to the `pvc` in your cluster as we did in other labs, or
-upload it to your `gs://` or `s3://` storage.
+# KFServing of custom ONNX models
 
 ## Deploying model
 
 Deployed model is a CRD `inferenceservice`. You can crete it in the same namespace we created earlier in this lab like so:
 
     $ kubectl create -f onnx.yaml -n kfserving-test
-    inferenceservice.serving.kubeflow.org/mnist-onnx created
+    inferenceservice.serving.kubeflow.org/style-sample created
 
 In a few minutes you should see the pods running:
 
     $ kubectl get pods -n kfserving-test
     NAME                                                              READY   STATUS    RESTARTS   AGE
-    mnist-onnx-predictor-default-5jk48-deployment-b7c89954c-6s6wn   3/3     Running   0          36s
+    style-sample-predictor-default-5jk48-deployment-b7c89954c-6s6wn   3/3     Running   0          36s
 
 And, more importantly, the `inferenceservice` in the `READY` state:
 
     $ kubectl get inferenceservice -n kfserving-test
     NAME           URL                                                                     READY   DEFAULT TRAFFIC   CANARY TRAFFIC   AGE
-    mnist-onnx   http://mnist-onnx.kfserving-test.example.com/v1/models/mnist-onnx         True    
+    style-sample   http://style-sample.kfserving-test.example.com/v1/models/style-sample   True    
 
 
 You can now [determine your ingress IP and port](https://github.com/kubeflow/kfserving/blob/master/README.md#determine-the-ingress-ip-and-ports):
@@ -45,17 +34,15 @@ For other stand-alone KFServing deployments:
 
 Before you run inference on your model, it is useful to define environment variables:
 
-    $ export MODEL_NAME=mnist-onnx
+    $ export MODEL_NAME=style-sample
     $ export SERVICE_HOSTNAME=$(kubectl get inferenceservice ${MODEL_NAME} -n kfserving-test -o jsonpath='{.status.url}' | cut -d "/" -f 3)
 
 ## Inference with the deployed model
 
-You need to convert your input into a JSON format, as example, we provide `onnx-mnist-input.json` to show the tags.
-
-    $ export INPUT_PATH=onnx-mnist-input.json
-    $ curl -v -H "Host: ${SERVICE_HOSTNAME}" http://${INGRESS_HOST}:${INGRESS_PORT}/v1/models/$MODEL_NAME:predict -d $INPUT_PATH
+From the Kubeflow dashboard of the Azure Stack environment, create Jupyter Server, and open the notebook [onnx-mosaic.ipynb](onnx-mosaic.ipynb).
+Provide the  INGRESS_PORT, INGRESS_HOST, and SERVICE_HOSTNAME to do the inferencing.
 
-In some cases, depending on the model, you would need to do the post-processing of the output.
+For troubleshooting, see the latest versions at [Kubeflow KFServing ONNX](https://github.com/kubeflow/kfserving/tree/master/docs/samples/onnx)
 
 ## Links
 
diff --git a/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx.yaml b/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx.yaml
index c5a2fd50b..fd2ddda71 100644
--- a/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx.yaml
+++ b/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx.yaml
@@ -1,11 +1,9 @@
 apiVersion: "serving.kubeflow.org/v1alpha2"
 kind: "InferenceService"
 metadata:
-  name: "mnist-onnx"
+  name: "style-sample"
 spec:
   default:
     predictor:
       onnx:
-        # Another ONNX model, from KFServing samples
-        # storageUri: "gs://kfserving-examples/onnx/style"
-        storageUri: "pvc://samba-share-claim/mymodels/build_models/mnist-onnx"
+        storageUri: "gs://kfserving-examples/onnx/style"
diff --git a/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx_custom.md b/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx_custom.md
new file mode 100644
index 000000000..6e789a0f7
--- /dev/null
+++ b/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx_custom.md
@@ -0,0 +1,62 @@
+# KFServing of custom ONNX models
+
+For example, see how you could [Register and Deploy ONNX Model]( 
+https://github.com/Azure/MachineLearningNotebooks/blob/2aa7c53b0ce84e67565d77e484987714fdaed36e/how-to-use-azureml/deployment/onnx/onnx-model-register-and-deploy.ipynb)
+
+We will be using `onnx-mnist-model.onnx` from that example. You would need to move it to the `pvc` in your cluster as we did in other labs, or
+upload it to your `gs://` or `s3://` storage.
+
+## Deploying model
+
+Deployed model is a CRD `inferenceservice`. You can crete it in the same namespace we created earlier in this lab like so:
+
+    $ kubectl create -f onnx_custom.yaml -n kfserving-test
+    inferenceservice.serving.kubeflow.org/mnist-onnx created
+
+In a few minutes you should see the pods running:
+
+    $ kubectl get pods -n kfserving-test
+    NAME                                                              READY   STATUS    RESTARTS   AGE
+    mnist-onnx-predictor-default-5jk48-deployment-b7c89954c-6s6wn   3/3     Running   0          36s
+
+And, more importantly, the `inferenceservice` in the `READY` state:
+
+    $ kubectl get inferenceservice -n kfserving-test
+    NAME           URL                                                                     READY   DEFAULT TRAFFIC   CANARY TRAFFIC   AGE
+    mnist-onnx   http://mnist-onnx.kfserving-test.example.com/v1/models/mnist-onnx         True    
+
+
+You can now [determine your ingress IP and port](https://github.com/kubeflow/kfserving/blob/master/README.md#determine-the-ingress-ip-and-ports):
+
+For KFServing deployment within Kubeflow:
+
+    $ export INGRESS_HOST=$(kubectl -n istio-system get service kfserving-ingressgateway -o jsonpath='{.status.loadBalancer.ingress[0].ip}')
+    $ export INGRESS_PORT=$(kubectl -n istio-system get service kfserving-ingressgateway -o jsonpath='{.spec.ports[?(@.name=="http2")].port}')
+
+For other stand-alone KFServing deployments:
+
+    $ export INGRESS_HOST=$(kubectl -n istio-system get service istio-ingressgateway -o jsonpath='{.status.loadBalancer.ingress[0].ip}')
+    $ export INGRESS_PORT=$(kubectl -n istio-system get service istio-ingressgateway -o jsonpath='{.spec.ports[?(@.name=="http2")].port}')
+
+Before you run inference on your model, it is useful to define environment variables:
+
+    $ export MODEL_NAME=mnist-onnx
+    $ export SERVICE_HOSTNAME=$(kubectl get inferenceservice ${MODEL_NAME} -n kfserving-test -o jsonpath='{.status.url}' | cut -d "/" -f 3)
+
+## Inference with the deployed model
+
+You need to convert your input into a JSON format, as example, we provide `onnx-mnist-input.json` to show the tags.
+
+    $ export INPUT_PATH=onnx-mnist-input.json
+    $ curl -v -H "Host: ${SERVICE_HOSTNAME}" http://${INGRESS_HOST}:${INGRESS_PORT}/v1/models/$MODEL_NAME:predict -d $INPUT_PATH
+
+In some cases, depending on the model, you would need to do the post-processing of the output.
+
+## Links
+
+- https://www.tensorflow.org/guide/saved_model
+- https://github.com/kubeflow/kfserving/tree/master/docs/samples/onnx
+
+---
+
+[Back](Readme.md)
diff --git a/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx_custom.yaml b/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx_custom.yaml
new file mode 100644
index 000000000..301a5ea36
--- /dev/null
+++ b/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx_custom.yaml
@@ -0,0 +1,9 @@
+apiVersion: "serving.kubeflow.org/v1alpha2"
+kind: "InferenceService"
+metadata:
+  name: "mnist-onnx"
+spec:
+  default:
+    predictor:
+      onnx:
+        storageUri: "pvc://samba-share-claim/mymodels/build_models/mnist-onnx"
diff --git a/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx_input_image.jpg b/Research/kubeflow-on-azure-stack-lab/04-KFServing/onnx_input_image.jpg
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literal 0
HcmV?d00001


From 0567cf7ea3ad245009fdbbcac6407907df462368 Mon Sep 17 00:00:00 2001
From: Aleksandr Panchul <apanchul@hotmail.com>
Date: Thu, 22 Oct 2020 10:06:32 -0700
Subject: [PATCH 4/4] adding auto-generated protobyf assets for onnx model

---
 .../04-KFServing/assets/onnx_ml_pb2.py        | 1581 +++++++++++++++++
 .../04-KFServing/assets/predict_pb2.py        |  215 +++
 2 files changed, 1796 insertions(+)
 create mode 100644 Research/kubeflow-on-azure-stack-lab/04-KFServing/assets/onnx_ml_pb2.py
 create mode 100644 Research/kubeflow-on-azure-stack-lab/04-KFServing/assets/predict_pb2.py

diff --git a/Research/kubeflow-on-azure-stack-lab/04-KFServing/assets/onnx_ml_pb2.py b/Research/kubeflow-on-azure-stack-lab/04-KFServing/assets/onnx_ml_pb2.py
new file mode 100644
index 000000000..b7d159254
--- /dev/null
+++ b/Research/kubeflow-on-azure-stack-lab/04-KFServing/assets/onnx_ml_pb2.py
@@ -0,0 +1,1581 @@
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: onnx-ml.proto
+
+import sys
+_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
+from google.protobuf.internal import enum_type_wrapper
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import message as _message
+from google.protobuf import reflection as _reflection
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor.FileDescriptor(
+  name='onnx-ml.proto',
+  package='onnx',
+  syntax='proto3',
+  serialized_options=None,
+  serialized_pb=_b('\n\ronnx-ml.proto\x12\x04onnx\"\xe0\x03\n\x0e\x41ttributeProto\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x15\n\rref_attr_name\x18\x15 \x01(\t\x12\x12\n\ndoc_string\x18\r \x01(\t\x12\x30\n\x04type\x18\x14 \x01(\x0e\x32\".onnx.AttributeProto.AttributeType\x12\t\n\x01\x66\x18\x02 \x01(\x02\x12\t\n\x01i\x18\x03 \x01(\x03\x12\t\n\x01s\x18\x04 \x01(\x0c\x12\x1c\n\x01t\x18\x05 \x01(\x0b\x32\x11.onnx.TensorProto\x12\x1b\n\x01g\x18\x06 \x01(\x0b\x32\x10.onnx.GraphProto\x12\x0e\n\x06\x66loats\x18\x07 \x03(\x02\x12\x0c\n\x04ints\x18\x08 \x03(\x03\x12\x0f\n\x07strings\x18\t \x03(\x0c\x12\"\n\x07tensors\x18\n \x03(\x0b\x32\x11.onnx.TensorProto\x12 \n\x06graphs\x18\x0b 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+)
+
+_VERSION = _descriptor.EnumDescriptor(
+  name='Version',
+  full_name='onnx.Version',
+  filename=None,
+  file=DESCRIPTOR,
+  values=[
+    _descriptor.EnumValueDescriptor(
+      name='_START_VERSION', index=0, number=0,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='IR_VERSION_2017_10_10', index=1, number=1,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='IR_VERSION_2017_10_30', index=2, number=2,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='IR_VERSION_2017_11_3', index=3, number=3,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='IR_VERSION_2019_1_22', index=4, number=4,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='IR_VERSION', index=5, number=5,
+      serialized_options=None,
+      type=None),
+  ],
+  containing_type=None,
+  serialized_options=None,
+  serialized_start=3199,
+  serialized_end=3350,
+)
+_sym_db.RegisterEnumDescriptor(_VERSION)
+
+Version = enum_type_wrapper.EnumTypeWrapper(_VERSION)
+_OPERATORSTATUS = _descriptor.EnumDescriptor(
+  name='OperatorStatus',
+  full_name='onnx.OperatorStatus',
+  filename=None,
+  file=DESCRIPTOR,
+  values=[
+    _descriptor.EnumValueDescriptor(
+      name='EXPERIMENTAL', index=0, number=0,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='STABLE', index=1, number=1,
+      serialized_options=None,
+      type=None),
+  ],
+  containing_type=None,
+  serialized_options=None,
+  serialized_start=3352,
+  serialized_end=3398,
+)
+_sym_db.RegisterEnumDescriptor(_OPERATORSTATUS)
+
+OperatorStatus = enum_type_wrapper.EnumTypeWrapper(_OPERATORSTATUS)
+_START_VERSION = 0
+IR_VERSION_2017_10_10 = 1
+IR_VERSION_2017_10_30 = 2
+IR_VERSION_2017_11_3 = 3
+IR_VERSION_2019_1_22 = 4
+IR_VERSION = 5
+EXPERIMENTAL = 0
+STABLE = 1
+
+
+_ATTRIBUTEPROTO_ATTRIBUTETYPE = _descriptor.EnumDescriptor(
+  name='AttributeType',
+  full_name='onnx.AttributeProto.AttributeType',
+  filename=None,
+  file=DESCRIPTOR,
+  values=[
+    _descriptor.EnumValueDescriptor(
+      name='UNDEFINED', index=0, number=0,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='FLOAT', index=1, number=1,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='INT', index=2, number=2,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='STRING', index=3, number=3,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='TENSOR', index=4, number=4,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='GRAPH', index=5, number=5,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='FLOATS', index=6, number=6,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='INTS', index=7, number=7,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='STRINGS', index=8, number=8,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='TENSORS', index=9, number=9,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='GRAPHS', index=10, number=10,
+      serialized_options=None,
+      type=None),
+  ],
+  containing_type=None,
+  serialized_options=None,
+  serialized_start=359,
+  serialized_end=504,
+)
+_sym_db.RegisterEnumDescriptor(_ATTRIBUTEPROTO_ATTRIBUTETYPE)
+
+_TENSORPROTO_DATATYPE = _descriptor.EnumDescriptor(
+  name='DataType',
+  full_name='onnx.TensorProto.DataType',
+  filename=None,
+  file=DESCRIPTOR,
+  values=[
+    _descriptor.EnumValueDescriptor(
+      name='UNDEFINED', index=0, number=0,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='FLOAT', index=1, number=1,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='UINT8', index=2, number=2,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='INT8', index=3, number=3,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='UINT16', index=4, number=4,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='INT16', index=5, number=5,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='INT32', index=6, number=6,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='INT64', index=7, number=7,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='STRING', index=8, number=8,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='BOOL', index=9, number=9,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='FLOAT16', index=10, number=10,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='DOUBLE', index=11, number=11,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='UINT32', index=12, number=12,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='UINT64', index=13, number=13,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='COMPLEX64', index=14, number=14,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='COMPLEX128', index=15, number=15,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='BFLOAT16', index=16, number=16,
+      serialized_options=None,
+      type=None),
+  ],
+  containing_type=None,
+  serialized_options=None,
+  serialized_start=1953,
+  serialized_end=2171,
+)
+_sym_db.RegisterEnumDescriptor(_TENSORPROTO_DATATYPE)
+
+_TENSORPROTO_DATALOCATION = _descriptor.EnumDescriptor(
+  name='DataLocation',
+  full_name='onnx.TensorProto.DataLocation',
+  filename=None,
+  file=DESCRIPTOR,
+  values=[
+    _descriptor.EnumValueDescriptor(
+      name='DEFAULT', index=0, number=0,
+      serialized_options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='EXTERNAL', index=1, number=1,
+      serialized_options=None,
+      type=None),
+  ],
+  containing_type=None,
+  serialized_options=None,
+  serialized_start=2173,
+  serialized_end=2214,
+)
+_sym_db.RegisterEnumDescriptor(_TENSORPROTO_DATALOCATION)
+
+
+_ATTRIBUTEPROTO = _descriptor.Descriptor(
+  name='AttributeProto',
+  full_name='onnx.AttributeProto',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='name', full_name='onnx.AttributeProto.name', index=0,
+      number=1, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='ref_attr_name', full_name='onnx.AttributeProto.ref_attr_name', index=1,
+      number=21, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='doc_string', full_name='onnx.AttributeProto.doc_string', index=2,
+      number=13, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='type', full_name='onnx.AttributeProto.type', index=3,
+      number=20, type=14, cpp_type=8, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='f', full_name='onnx.AttributeProto.f', index=4,
+      number=2, type=2, cpp_type=6, label=1,
+      has_default_value=False, default_value=float(0),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='i', full_name='onnx.AttributeProto.i', index=5,
+      number=3, type=3, cpp_type=2, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='s', full_name='onnx.AttributeProto.s', index=6,
+      number=4, type=12, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b(""),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='t', full_name='onnx.AttributeProto.t', index=7,
+      number=5, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='g', full_name='onnx.AttributeProto.g', index=8,
+      number=6, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='floats', full_name='onnx.AttributeProto.floats', index=9,
+      number=7, type=2, cpp_type=6, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='ints', full_name='onnx.AttributeProto.ints', index=10,
+      number=8, type=3, cpp_type=2, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
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+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
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+      number=10, type=11, cpp_type=10, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='graphs', full_name='onnx.AttributeProto.graphs', index=13,
+      number=11, type=11, cpp_type=10, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+    _ATTRIBUTEPROTO_ATTRIBUTETYPE,
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+  serialized_options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
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+  serialized_start=24,
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+)
+
+
+_VALUEINFOPROTO = _descriptor.Descriptor(
+  name='ValueInfoProto',
+  full_name='onnx.ValueInfoProto',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
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+      number=1, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
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+      number=2, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='doc_string', full_name='onnx.ValueInfoProto.doc_string', index=2,
+      number=3, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  serialized_options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
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+  serialized_start=506,
+  serialized_end=587,
+)
+
+
+_NODEPROTO = _descriptor.Descriptor(
+  name='NodeProto',
+  full_name='onnx.NodeProto',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
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+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='output', full_name='onnx.NodeProto.output', index=1,
+      number=2, type=9, cpp_type=9, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
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+      number=3, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
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+      number=4, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
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+      number=7, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='attribute', full_name='onnx.NodeProto.attribute', index=5,
+      number=5, type=11, cpp_type=10, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='doc_string', full_name='onnx.NodeProto.doc_string', index=6,
+      number=6, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  serialized_options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=590,
+  serialized_end=740,
+)
+
+
+_MODELPROTO = _descriptor.Descriptor(
+  name='ModelProto',
+  full_name='onnx.ModelProto',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
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+      number=1, type=3, cpp_type=2, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='opset_import', full_name='onnx.ModelProto.opset_import', index=1,
+      number=8, type=11, cpp_type=10, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
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+      number=2, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
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+      number=3, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
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+      number=4, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='model_version', full_name='onnx.ModelProto.model_version', index=5,
+      number=5, type=3, cpp_type=2, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='doc_string', full_name='onnx.ModelProto.doc_string', index=6,
+      number=6, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
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+      number=7, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='functions', full_name='onnx.ModelProto.functions', index=8,
+      number=100, type=11, cpp_type=10, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='metadata_props', full_name='onnx.ModelProto.metadata_props', index=9,
+      number=14, type=11, cpp_type=10, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  serialized_options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
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+  serialized_start=743,
+  serialized_end=1058,
+)
+
+
+_STRINGSTRINGENTRYPROTO = _descriptor.Descriptor(
+  name='StringStringEntryProto',
+  full_name='onnx.StringStringEntryProto',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
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+      number=1, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='value', full_name='onnx.StringStringEntryProto.value', index=1,
+      number=2, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
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+  serialized_options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=1060,
+  serialized_end=1112,
+)
+
+
+_TENSORANNOTATION = _descriptor.Descriptor(
+  name='TensorAnnotation',
+  full_name='onnx.TensorAnnotation',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='tensor_name', full_name='onnx.TensorAnnotation.tensor_name', index=0,
+      number=1, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='quant_parameter_tensor_names', full_name='onnx.TensorAnnotation.quant_parameter_tensor_names', index=1,
+      number=2, type=11, cpp_type=10, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
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+  serialized_options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=1114,
+  serialized_end=1221,
+)
+
+
+_GRAPHPROTO = _descriptor.Descriptor(
+  name='GraphProto',
+  full_name='onnx.GraphProto',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
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+      number=1, type=11, cpp_type=10, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='name', full_name='onnx.GraphProto.name', index=1,
+      number=2, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='initializer', full_name='onnx.GraphProto.initializer', index=2,
+      number=5, type=11, cpp_type=10, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='doc_string', full_name='onnx.GraphProto.doc_string', index=3,
+      number=10, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
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+      number=11, type=11, cpp_type=10, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
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+      number=12, type=11, cpp_type=10, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='value_info', full_name='onnx.GraphProto.value_info', index=6,
+      number=13, type=11, cpp_type=10, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='quantization_annotation', full_name='onnx.GraphProto.quantization_annotation', index=7,
+      number=14, type=11, cpp_type=10, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
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+  nested_types=[],
+  enum_types=[
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+  serialized_options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
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+  serialized_start=1224,
+  serialized_end=1515,
+)
+
+
+_TENSORPROTO_SEGMENT = _descriptor.Descriptor(
+  name='Segment',
+  full_name='onnx.TensorProto.Segment',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
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+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
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+      number=2, type=3, cpp_type=2, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
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+  serialized_options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
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+  serialized_start=1913,
+  serialized_end=1950,
+)
+
+_TENSORPROTO = _descriptor.Descriptor(
+  name='TensorProto',
+  full_name='onnx.TensorProto',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
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+      number=1, type=3, cpp_type=2, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
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+      number=2, type=5, cpp_type=1, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
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+      number=3, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
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+      number=4, type=2, cpp_type=6, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=_b('\020\001'), file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
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+      number=5, type=5, cpp_type=1, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=_b('\020\001'), file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
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+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
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+      number=7, type=3, cpp_type=2, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=_b('\020\001'), file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
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+      number=8, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
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+      number=12, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
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+      has_default_value=False, default_value=_b(""),
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+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
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+      has_default_value=False, default_value=[],
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+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='data_location', full_name='onnx.TensorProto.data_location', index=11,
+      number=14, type=14, cpp_type=8, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='double_data', full_name='onnx.TensorProto.double_data', index=12,
+      number=10, type=1, cpp_type=5, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=_b('\020\001'), file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='uint64_data', full_name='onnx.TensorProto.uint64_data', index=13,
+      number=11, type=4, cpp_type=4, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=_b('\020\001'), file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[_TENSORPROTO_SEGMENT, ],
+  enum_types=[
+    _TENSORPROTO_DATATYPE,
+    _TENSORPROTO_DATALOCATION,
+  ],
+  serialized_options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=1518,
+  serialized_end=2214,
+)
+
+
+_TENSORSHAPEPROTO_DIMENSION = _descriptor.Descriptor(
+  name='Dimension',
+  full_name='onnx.TensorShapeProto.Dimension',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='dim_value', full_name='onnx.TensorShapeProto.Dimension.dim_value', index=0,
+      number=1, type=3, cpp_type=2, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='dim_param', full_name='onnx.TensorShapeProto.Dimension.dim_param', index=1,
+      number=2, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='denotation', full_name='onnx.TensorShapeProto.Dimension.denotation', index=2,
+      number=3, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  serialized_options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+    _descriptor.OneofDescriptor(
+      name='value', full_name='onnx.TensorShapeProto.Dimension.value',
+      index=0, containing_type=None, fields=[]),
+  ],
+  serialized_start=2284,
+  serialized_end=2366,
+)
+
+_TENSORSHAPEPROTO = _descriptor.Descriptor(
+  name='TensorShapeProto',
+  full_name='onnx.TensorShapeProto',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='dim', full_name='onnx.TensorShapeProto.dim', index=0,
+      number=1, type=11, cpp_type=10, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[_TENSORSHAPEPROTO_DIMENSION, ],
+  enum_types=[
+  ],
+  serialized_options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=2217,
+  serialized_end=2366,
+)
+
+
+_TYPEPROTO_TENSOR = _descriptor.Descriptor(
+  name='Tensor',
+  full_name='onnx.TypeProto.Tensor',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='elem_type', full_name='onnx.TypeProto.Tensor.elem_type', index=0,
+      number=1, type=5, cpp_type=1, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='shape', full_name='onnx.TypeProto.Tensor.shape', index=1,
+      number=2, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  serialized_options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=2648,
+  serialized_end=2714,
+)
+
+_TYPEPROTO_SEQUENCE = _descriptor.Descriptor(
+  name='Sequence',
+  full_name='onnx.TypeProto.Sequence',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='elem_type', full_name='onnx.TypeProto.Sequence.elem_type', index=0,
+      number=1, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  serialized_options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=2716,
+  serialized_end=2762,
+)
+
+_TYPEPROTO_MAP = _descriptor.Descriptor(
+  name='Map',
+  full_name='onnx.TypeProto.Map',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='key_type', full_name='onnx.TypeProto.Map.key_type', index=0,
+      number=1, type=5, cpp_type=1, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='value_type', full_name='onnx.TypeProto.Map.value_type', index=1,
+      number=2, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  serialized_options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=2764,
+  serialized_end=2824,
+)
+
+_TYPEPROTO_OPAQUE = _descriptor.Descriptor(
+  name='Opaque',
+  full_name='onnx.TypeProto.Opaque',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='domain', full_name='onnx.TypeProto.Opaque.domain', index=0,
+      number=1, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='name', full_name='onnx.TypeProto.Opaque.name', index=1,
+      number=2, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  serialized_options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=2826,
+  serialized_end=2864,
+)
+
+_TYPEPROTO_SPARSETENSOR = _descriptor.Descriptor(
+  name='SparseTensor',
+  full_name='onnx.TypeProto.SparseTensor',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='elem_type', full_name='onnx.TypeProto.SparseTensor.elem_type', index=0,
+      number=1, type=5, cpp_type=1, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='shape', full_name='onnx.TypeProto.SparseTensor.shape', index=1,
+      number=2, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  serialized_options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=2866,
+  serialized_end=2938,
+)
+
+_TYPEPROTO = _descriptor.Descriptor(
+  name='TypeProto',
+  full_name='onnx.TypeProto',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='tensor_type', full_name='onnx.TypeProto.tensor_type', index=0,
+      number=1, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='sequence_type', full_name='onnx.TypeProto.sequence_type', index=1,
+      number=4, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='map_type', full_name='onnx.TypeProto.map_type', index=2,
+      number=5, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='opaque_type', full_name='onnx.TypeProto.opaque_type', index=3,
+      number=7, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='sparse_tensor_type', full_name='onnx.TypeProto.sparse_tensor_type', index=4,
+      number=8, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='denotation', full_name='onnx.TypeProto.denotation', index=5,
+      number=6, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[_TYPEPROTO_TENSOR, _TYPEPROTO_SEQUENCE, _TYPEPROTO_MAP, _TYPEPROTO_OPAQUE, _TYPEPROTO_SPARSETENSOR, ],
+  enum_types=[
+  ],
+  serialized_options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+    _descriptor.OneofDescriptor(
+      name='value', full_name='onnx.TypeProto.value',
+      index=0, containing_type=None, fields=[]),
+  ],
+  serialized_start=2369,
+  serialized_end=2947,
+)
+
+
+_OPERATORSETIDPROTO = _descriptor.Descriptor(
+  name='OperatorSetIdProto',
+  full_name='onnx.OperatorSetIdProto',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='domain', full_name='onnx.OperatorSetIdProto.domain', index=0,
+      number=1, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='version', full_name='onnx.OperatorSetIdProto.version', index=1,
+      number=2, type=3, cpp_type=2, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  serialized_options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=2949,
+  serialized_end=3002,
+)
+
+
+_FUNCTIONPROTO = _descriptor.Descriptor(
+  name='FunctionProto',
+  full_name='onnx.FunctionProto',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='name', full_name='onnx.FunctionProto.name', index=0,
+      number=1, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='since_version', full_name='onnx.FunctionProto.since_version', index=1,
+      number=2, type=3, cpp_type=2, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='status', full_name='onnx.FunctionProto.status', index=2,
+      number=3, type=14, cpp_type=8, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='input', full_name='onnx.FunctionProto.input', index=3,
+      number=4, type=9, cpp_type=9, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='output', full_name='onnx.FunctionProto.output', index=4,
+      number=5, type=9, cpp_type=9, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='attribute', full_name='onnx.FunctionProto.attribute', index=5,
+      number=6, type=9, cpp_type=9, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='node', full_name='onnx.FunctionProto.node', index=6,
+      number=7, type=11, cpp_type=10, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='doc_string', full_name='onnx.FunctionProto.doc_string', index=7,
+      number=8, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  serialized_options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=3005,
+  serialized_end=3196,
+)
+
+_ATTRIBUTEPROTO.fields_by_name['type'].enum_type = _ATTRIBUTEPROTO_ATTRIBUTETYPE
+_ATTRIBUTEPROTO.fields_by_name['t'].message_type = _TENSORPROTO
+_ATTRIBUTEPROTO.fields_by_name['g'].message_type = _GRAPHPROTO
+_ATTRIBUTEPROTO.fields_by_name['tensors'].message_type = _TENSORPROTO
+_ATTRIBUTEPROTO.fields_by_name['graphs'].message_type = _GRAPHPROTO
+_ATTRIBUTEPROTO_ATTRIBUTETYPE.containing_type = _ATTRIBUTEPROTO
+_VALUEINFOPROTO.fields_by_name['type'].message_type = _TYPEPROTO
+_NODEPROTO.fields_by_name['attribute'].message_type = _ATTRIBUTEPROTO
+_MODELPROTO.fields_by_name['opset_import'].message_type = _OPERATORSETIDPROTO
+_MODELPROTO.fields_by_name['graph'].message_type = _GRAPHPROTO
+_MODELPROTO.fields_by_name['functions'].message_type = _FUNCTIONPROTO
+_MODELPROTO.fields_by_name['metadata_props'].message_type = _STRINGSTRINGENTRYPROTO
+_TENSORANNOTATION.fields_by_name['quant_parameter_tensor_names'].message_type = _STRINGSTRINGENTRYPROTO
+_GRAPHPROTO.fields_by_name['node'].message_type = _NODEPROTO
+_GRAPHPROTO.fields_by_name['initializer'].message_type = _TENSORPROTO
+_GRAPHPROTO.fields_by_name['input'].message_type = _VALUEINFOPROTO
+_GRAPHPROTO.fields_by_name['output'].message_type = _VALUEINFOPROTO
+_GRAPHPROTO.fields_by_name['value_info'].message_type = _VALUEINFOPROTO
+_GRAPHPROTO.fields_by_name['quantization_annotation'].message_type = _TENSORANNOTATION
+_TENSORPROTO_SEGMENT.containing_type = _TENSORPROTO
+_TENSORPROTO.fields_by_name['segment'].message_type = _TENSORPROTO_SEGMENT
+_TENSORPROTO.fields_by_name['external_data'].message_type = _STRINGSTRINGENTRYPROTO
+_TENSORPROTO.fields_by_name['data_location'].enum_type = _TENSORPROTO_DATALOCATION
+_TENSORPROTO_DATATYPE.containing_type = _TENSORPROTO
+_TENSORPROTO_DATALOCATION.containing_type = _TENSORPROTO
+_TENSORSHAPEPROTO_DIMENSION.containing_type = _TENSORSHAPEPROTO
+_TENSORSHAPEPROTO_DIMENSION.oneofs_by_name['value'].fields.append(
+  _TENSORSHAPEPROTO_DIMENSION.fields_by_name['dim_value'])
+_TENSORSHAPEPROTO_DIMENSION.fields_by_name['dim_value'].containing_oneof = _TENSORSHAPEPROTO_DIMENSION.oneofs_by_name['value']
+_TENSORSHAPEPROTO_DIMENSION.oneofs_by_name['value'].fields.append(
+  _TENSORSHAPEPROTO_DIMENSION.fields_by_name['dim_param'])
+_TENSORSHAPEPROTO_DIMENSION.fields_by_name['dim_param'].containing_oneof = _TENSORSHAPEPROTO_DIMENSION.oneofs_by_name['value']
+_TENSORSHAPEPROTO.fields_by_name['dim'].message_type = _TENSORSHAPEPROTO_DIMENSION
+_TYPEPROTO_TENSOR.fields_by_name['shape'].message_type = _TENSORSHAPEPROTO
+_TYPEPROTO_TENSOR.containing_type = _TYPEPROTO
+_TYPEPROTO_SEQUENCE.fields_by_name['elem_type'].message_type = _TYPEPROTO
+_TYPEPROTO_SEQUENCE.containing_type = _TYPEPROTO
+_TYPEPROTO_MAP.fields_by_name['value_type'].message_type = _TYPEPROTO
+_TYPEPROTO_MAP.containing_type = _TYPEPROTO
+_TYPEPROTO_OPAQUE.containing_type = _TYPEPROTO
+_TYPEPROTO_SPARSETENSOR.fields_by_name['shape'].message_type = _TENSORSHAPEPROTO
+_TYPEPROTO_SPARSETENSOR.containing_type = _TYPEPROTO
+_TYPEPROTO.fields_by_name['tensor_type'].message_type = _TYPEPROTO_TENSOR
+_TYPEPROTO.fields_by_name['sequence_type'].message_type = _TYPEPROTO_SEQUENCE
+_TYPEPROTO.fields_by_name['map_type'].message_type = _TYPEPROTO_MAP
+_TYPEPROTO.fields_by_name['opaque_type'].message_type = _TYPEPROTO_OPAQUE
+_TYPEPROTO.fields_by_name['sparse_tensor_type'].message_type = _TYPEPROTO_SPARSETENSOR
+_TYPEPROTO.oneofs_by_name['value'].fields.append(
+  _TYPEPROTO.fields_by_name['tensor_type'])
+_TYPEPROTO.fields_by_name['tensor_type'].containing_oneof = _TYPEPROTO.oneofs_by_name['value']
+_TYPEPROTO.oneofs_by_name['value'].fields.append(
+  _TYPEPROTO.fields_by_name['sequence_type'])
+_TYPEPROTO.fields_by_name['sequence_type'].containing_oneof = _TYPEPROTO.oneofs_by_name['value']
+_TYPEPROTO.oneofs_by_name['value'].fields.append(
+  _TYPEPROTO.fields_by_name['map_type'])
+_TYPEPROTO.fields_by_name['map_type'].containing_oneof = _TYPEPROTO.oneofs_by_name['value']
+_TYPEPROTO.oneofs_by_name['value'].fields.append(
+  _TYPEPROTO.fields_by_name['opaque_type'])
+_TYPEPROTO.fields_by_name['opaque_type'].containing_oneof = _TYPEPROTO.oneofs_by_name['value']
+_TYPEPROTO.oneofs_by_name['value'].fields.append(
+  _TYPEPROTO.fields_by_name['sparse_tensor_type'])
+_TYPEPROTO.fields_by_name['sparse_tensor_type'].containing_oneof = _TYPEPROTO.oneofs_by_name['value']
+_FUNCTIONPROTO.fields_by_name['status'].enum_type = _OPERATORSTATUS
+_FUNCTIONPROTO.fields_by_name['node'].message_type = _NODEPROTO
+DESCRIPTOR.message_types_by_name['AttributeProto'] = _ATTRIBUTEPROTO
+DESCRIPTOR.message_types_by_name['ValueInfoProto'] = _VALUEINFOPROTO
+DESCRIPTOR.message_types_by_name['NodeProto'] = _NODEPROTO
+DESCRIPTOR.message_types_by_name['ModelProto'] = _MODELPROTO
+DESCRIPTOR.message_types_by_name['StringStringEntryProto'] = _STRINGSTRINGENTRYPROTO
+DESCRIPTOR.message_types_by_name['TensorAnnotation'] = _TENSORANNOTATION
+DESCRIPTOR.message_types_by_name['GraphProto'] = _GRAPHPROTO
+DESCRIPTOR.message_types_by_name['TensorProto'] = _TENSORPROTO
+DESCRIPTOR.message_types_by_name['TensorShapeProto'] = _TENSORSHAPEPROTO
+DESCRIPTOR.message_types_by_name['TypeProto'] = _TYPEPROTO
+DESCRIPTOR.message_types_by_name['OperatorSetIdProto'] = _OPERATORSETIDPROTO
+DESCRIPTOR.message_types_by_name['FunctionProto'] = _FUNCTIONPROTO
+DESCRIPTOR.enum_types_by_name['Version'] = _VERSION
+DESCRIPTOR.enum_types_by_name['OperatorStatus'] = _OPERATORSTATUS
+_sym_db.RegisterFileDescriptor(DESCRIPTOR)
+
+AttributeProto = _reflection.GeneratedProtocolMessageType('AttributeProto', (_message.Message,), dict(
+  DESCRIPTOR = _ATTRIBUTEPROTO,
+  __module__ = 'onnx_ml_pb2'
+  # @@protoc_insertion_point(class_scope:onnx.AttributeProto)
+  ))
+_sym_db.RegisterMessage(AttributeProto)
+
+ValueInfoProto = _reflection.GeneratedProtocolMessageType('ValueInfoProto', (_message.Message,), dict(
+  DESCRIPTOR = _VALUEINFOPROTO,
+  __module__ = 'onnx_ml_pb2'
+  # @@protoc_insertion_point(class_scope:onnx.ValueInfoProto)
+  ))
+_sym_db.RegisterMessage(ValueInfoProto)
+
+NodeProto = _reflection.GeneratedProtocolMessageType('NodeProto', (_message.Message,), dict(
+  DESCRIPTOR = _NODEPROTO,
+  __module__ = 'onnx_ml_pb2'
+  # @@protoc_insertion_point(class_scope:onnx.NodeProto)
+  ))
+_sym_db.RegisterMessage(NodeProto)
+
+ModelProto = _reflection.GeneratedProtocolMessageType('ModelProto', (_message.Message,), dict(
+  DESCRIPTOR = _MODELPROTO,
+  __module__ = 'onnx_ml_pb2'
+  # @@protoc_insertion_point(class_scope:onnx.ModelProto)
+  ))
+_sym_db.RegisterMessage(ModelProto)
+
+StringStringEntryProto = _reflection.GeneratedProtocolMessageType('StringStringEntryProto', (_message.Message,), dict(
+  DESCRIPTOR = _STRINGSTRINGENTRYPROTO,
+  __module__ = 'onnx_ml_pb2'
+  # @@protoc_insertion_point(class_scope:onnx.StringStringEntryProto)
+  ))
+_sym_db.RegisterMessage(StringStringEntryProto)
+
+TensorAnnotation = _reflection.GeneratedProtocolMessageType('TensorAnnotation', (_message.Message,), dict(
+  DESCRIPTOR = _TENSORANNOTATION,
+  __module__ = 'onnx_ml_pb2'
+  # @@protoc_insertion_point(class_scope:onnx.TensorAnnotation)
+  ))
+_sym_db.RegisterMessage(TensorAnnotation)
+
+GraphProto = _reflection.GeneratedProtocolMessageType('GraphProto', (_message.Message,), dict(
+  DESCRIPTOR = _GRAPHPROTO,
+  __module__ = 'onnx_ml_pb2'
+  # @@protoc_insertion_point(class_scope:onnx.GraphProto)
+  ))
+_sym_db.RegisterMessage(GraphProto)
+
+TensorProto = _reflection.GeneratedProtocolMessageType('TensorProto', (_message.Message,), dict(
+
+  Segment = _reflection.GeneratedProtocolMessageType('Segment', (_message.Message,), dict(
+    DESCRIPTOR = _TENSORPROTO_SEGMENT,
+    __module__ = 'onnx_ml_pb2'
+    # @@protoc_insertion_point(class_scope:onnx.TensorProto.Segment)
+    ))
+  ,
+  DESCRIPTOR = _TENSORPROTO,
+  __module__ = 'onnx_ml_pb2'
+  # @@protoc_insertion_point(class_scope:onnx.TensorProto)
+  ))
+_sym_db.RegisterMessage(TensorProto)
+_sym_db.RegisterMessage(TensorProto.Segment)
+
+TensorShapeProto = _reflection.GeneratedProtocolMessageType('TensorShapeProto', (_message.Message,), dict(
+
+  Dimension = _reflection.GeneratedProtocolMessageType('Dimension', (_message.Message,), dict(
+    DESCRIPTOR = _TENSORSHAPEPROTO_DIMENSION,
+    __module__ = 'onnx_ml_pb2'
+    # @@protoc_insertion_point(class_scope:onnx.TensorShapeProto.Dimension)
+    ))
+  ,
+  DESCRIPTOR = _TENSORSHAPEPROTO,
+  __module__ = 'onnx_ml_pb2'
+  # @@protoc_insertion_point(class_scope:onnx.TensorShapeProto)
+  ))
+_sym_db.RegisterMessage(TensorShapeProto)
+_sym_db.RegisterMessage(TensorShapeProto.Dimension)
+
+TypeProto = _reflection.GeneratedProtocolMessageType('TypeProto', (_message.Message,), dict(
+
+  Tensor = _reflection.GeneratedProtocolMessageType('Tensor', (_message.Message,), dict(
+    DESCRIPTOR = _TYPEPROTO_TENSOR,
+    __module__ = 'onnx_ml_pb2'
+    # @@protoc_insertion_point(class_scope:onnx.TypeProto.Tensor)
+    ))
+  ,
+
+  Sequence = _reflection.GeneratedProtocolMessageType('Sequence', (_message.Message,), dict(
+    DESCRIPTOR = _TYPEPROTO_SEQUENCE,
+    __module__ = 'onnx_ml_pb2'
+    # @@protoc_insertion_point(class_scope:onnx.TypeProto.Sequence)
+    ))
+  ,
+
+  Map = _reflection.GeneratedProtocolMessageType('Map', (_message.Message,), dict(
+    DESCRIPTOR = _TYPEPROTO_MAP,
+    __module__ = 'onnx_ml_pb2'
+    # @@protoc_insertion_point(class_scope:onnx.TypeProto.Map)
+    ))
+  ,
+
+  Opaque = _reflection.GeneratedProtocolMessageType('Opaque', (_message.Message,), dict(
+    DESCRIPTOR = _TYPEPROTO_OPAQUE,
+    __module__ = 'onnx_ml_pb2'
+    # @@protoc_insertion_point(class_scope:onnx.TypeProto.Opaque)
+    ))
+  ,
+
+  SparseTensor = _reflection.GeneratedProtocolMessageType('SparseTensor', (_message.Message,), dict(
+    DESCRIPTOR = _TYPEPROTO_SPARSETENSOR,
+    __module__ = 'onnx_ml_pb2'
+    # @@protoc_insertion_point(class_scope:onnx.TypeProto.SparseTensor)
+    ))
+  ,
+  DESCRIPTOR = _TYPEPROTO,
+  __module__ = 'onnx_ml_pb2'
+  # @@protoc_insertion_point(class_scope:onnx.TypeProto)
+  ))
+_sym_db.RegisterMessage(TypeProto)
+_sym_db.RegisterMessage(TypeProto.Tensor)
+_sym_db.RegisterMessage(TypeProto.Sequence)
+_sym_db.RegisterMessage(TypeProto.Map)
+_sym_db.RegisterMessage(TypeProto.Opaque)
+_sym_db.RegisterMessage(TypeProto.SparseTensor)
+
+OperatorSetIdProto = _reflection.GeneratedProtocolMessageType('OperatorSetIdProto', (_message.Message,), dict(
+  DESCRIPTOR = _OPERATORSETIDPROTO,
+  __module__ = 'onnx_ml_pb2'
+  # @@protoc_insertion_point(class_scope:onnx.OperatorSetIdProto)
+  ))
+_sym_db.RegisterMessage(OperatorSetIdProto)
+
+FunctionProto = _reflection.GeneratedProtocolMessageType('FunctionProto', (_message.Message,), dict(
+  DESCRIPTOR = _FUNCTIONPROTO,
+  __module__ = 'onnx_ml_pb2'
+  # @@protoc_insertion_point(class_scope:onnx.FunctionProto)
+  ))
+_sym_db.RegisterMessage(FunctionProto)
+
+
+_TENSORPROTO.fields_by_name['float_data']._options = None
+_TENSORPROTO.fields_by_name['int32_data']._options = None
+_TENSORPROTO.fields_by_name['int64_data']._options = None
+_TENSORPROTO.fields_by_name['double_data']._options = None
+_TENSORPROTO.fields_by_name['uint64_data']._options = None
+# @@protoc_insertion_point(module_scope)
diff --git a/Research/kubeflow-on-azure-stack-lab/04-KFServing/assets/predict_pb2.py b/Research/kubeflow-on-azure-stack-lab/04-KFServing/assets/predict_pb2.py
new file mode 100644
index 000000000..c71c138ac
--- /dev/null
+++ b/Research/kubeflow-on-azure-stack-lab/04-KFServing/assets/predict_pb2.py
@@ -0,0 +1,215 @@
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: predict.proto
+
+import sys
+_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import message as _message
+from google.protobuf import reflection as _reflection
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+import assets.onnx_ml_pb2 as onnx__ml__pb2
+
+
+DESCRIPTOR = _descriptor.FileDescriptor(
+  name='predict.proto',
+  package='onnxruntime.server',
+  syntax='proto3',
+  serialized_options=None,
+  serialized_pb=_b('\n\rpredict.proto\x12\x12onnxruntime.server\x1a\ronnx-ml.proto\"\xaf\x01\n\x0ePredictRequest\x12>\n\x06inputs\x18\x02 \x03(\x0b\x32..onnxruntime.server.PredictRequest.InputsEntry\x12\x15\n\routput_filter\x18\x03 \x03(\t\x1a@\n\x0bInputsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12 \n\x05value\x18\x02 \x01(\x0b\x32\x11.onnx.TensorProto:\x02\x38\x01J\x04\x08\x01\x10\x02\"\x97\x01\n\x0fPredictResponse\x12\x41\n\x07outputs\x18\x01 \x03(\x0b\x32\x30.onnxruntime.server.PredictResponse.OutputsEntry\x1a\x41\n\x0cOutputsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12 \n\x05value\x18\x02 \x01(\x0b\x32\x11.onnx.TensorProto:\x02\x38\x01\x62\x06proto3')
+  ,
+  dependencies=[onnx__ml__pb2.DESCRIPTOR,])
+
+
+
+
+_PREDICTREQUEST_INPUTSENTRY = _descriptor.Descriptor(
+  name='InputsEntry',
+  full_name='onnxruntime.server.PredictRequest.InputsEntry',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='key', full_name='onnxruntime.server.PredictRequest.InputsEntry.key', index=0,
+      number=1, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='value', full_name='onnxruntime.server.PredictRequest.InputsEntry.value', index=1,
+      number=2, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  serialized_options=_b('8\001'),
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=158,
+  serialized_end=222,
+)
+
+_PREDICTREQUEST = _descriptor.Descriptor(
+  name='PredictRequest',
+  full_name='onnxruntime.server.PredictRequest',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='inputs', full_name='onnxruntime.server.PredictRequest.inputs', index=0,
+      number=2, type=11, cpp_type=10, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='output_filter', full_name='onnxruntime.server.PredictRequest.output_filter', index=1,
+      number=3, type=9, cpp_type=9, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[_PREDICTREQUEST_INPUTSENTRY, ],
+  enum_types=[
+  ],
+  serialized_options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=53,
+  serialized_end=228,
+)
+
+
+_PREDICTRESPONSE_OUTPUTSENTRY = _descriptor.Descriptor(
+  name='OutputsEntry',
+  full_name='onnxruntime.server.PredictResponse.OutputsEntry',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='key', full_name='onnxruntime.server.PredictResponse.OutputsEntry.key', index=0,
+      number=1, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='value', full_name='onnxruntime.server.PredictResponse.OutputsEntry.value', index=1,
+      number=2, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  serialized_options=_b('8\001'),
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=317,
+  serialized_end=382,
+)
+
+_PREDICTRESPONSE = _descriptor.Descriptor(
+  name='PredictResponse',
+  full_name='onnxruntime.server.PredictResponse',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='outputs', full_name='onnxruntime.server.PredictResponse.outputs', index=0,
+      number=1, type=11, cpp_type=10, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[_PREDICTRESPONSE_OUTPUTSENTRY, ],
+  enum_types=[
+  ],
+  serialized_options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=231,
+  serialized_end=382,
+)
+
+_PREDICTREQUEST_INPUTSENTRY.fields_by_name['value'].message_type = onnx__ml__pb2._TENSORPROTO
+_PREDICTREQUEST_INPUTSENTRY.containing_type = _PREDICTREQUEST
+_PREDICTREQUEST.fields_by_name['inputs'].message_type = _PREDICTREQUEST_INPUTSENTRY
+_PREDICTRESPONSE_OUTPUTSENTRY.fields_by_name['value'].message_type = onnx__ml__pb2._TENSORPROTO
+_PREDICTRESPONSE_OUTPUTSENTRY.containing_type = _PREDICTRESPONSE
+_PREDICTRESPONSE.fields_by_name['outputs'].message_type = _PREDICTRESPONSE_OUTPUTSENTRY
+DESCRIPTOR.message_types_by_name['PredictRequest'] = _PREDICTREQUEST
+DESCRIPTOR.message_types_by_name['PredictResponse'] = _PREDICTRESPONSE
+_sym_db.RegisterFileDescriptor(DESCRIPTOR)
+
+PredictRequest = _reflection.GeneratedProtocolMessageType('PredictRequest', (_message.Message,), dict(
+
+  InputsEntry = _reflection.GeneratedProtocolMessageType('InputsEntry', (_message.Message,), dict(
+    DESCRIPTOR = _PREDICTREQUEST_INPUTSENTRY,
+    __module__ = 'predict_pb2'
+    # @@protoc_insertion_point(class_scope:onnxruntime.server.PredictRequest.InputsEntry)
+    ))
+  ,
+  DESCRIPTOR = _PREDICTREQUEST,
+  __module__ = 'predict_pb2'
+  # @@protoc_insertion_point(class_scope:onnxruntime.server.PredictRequest)
+  ))
+_sym_db.RegisterMessage(PredictRequest)
+_sym_db.RegisterMessage(PredictRequest.InputsEntry)
+
+PredictResponse = _reflection.GeneratedProtocolMessageType('PredictResponse', (_message.Message,), dict(
+
+  OutputsEntry = _reflection.GeneratedProtocolMessageType('OutputsEntry', (_message.Message,), dict(
+    DESCRIPTOR = _PREDICTRESPONSE_OUTPUTSENTRY,
+    __module__ = 'predict_pb2'
+    # @@protoc_insertion_point(class_scope:onnxruntime.server.PredictResponse.OutputsEntry)
+    ))
+  ,
+  DESCRIPTOR = _PREDICTRESPONSE,
+  __module__ = 'predict_pb2'
+  # @@protoc_insertion_point(class_scope:onnxruntime.server.PredictResponse)
+  ))
+_sym_db.RegisterMessage(PredictResponse)
+_sym_db.RegisterMessage(PredictResponse.OutputsEntry)
+
+
+_PREDICTREQUEST_INPUTSENTRY._options = None
+_PREDICTRESPONSE_OUTPUTSENTRY._options = None
+# @@protoc_insertion_point(module_scope)