Add tensorRT models

go/tests/models.yaml

@@ -5,38 +5,71 @@
   email: [email protected]
   password: password
 
+# - '@type': /modelzoo.Model
+#   model_name: ImageNet Classification ResNet50
+#   metadata:
+#   - key: category
+#     value: vision classification
+#   - key: author
+#     value: Microsoft Research
+#   - key: service_type
+#     value: clipper
+#   - key: clipper_url
+#     value: http://localhost:10000/vision_classification/predict
+#   - key: input_type
+#     value: image
+#   - key: output_type
+#     value: table
+#   - key: framework
+#     value: PyTorch
+#   - key: paper_link
+#     value: https://arxiv.org/pdf/1512.03385.pdf
+#   - key: metric_ImageNet_top_5_error_%
+#     value: "5.25"
+
+# - '@type': /modelzoo.Model
+#   model_name: Text Dumy
+#   metadata:
+#   - key: category
+#     value: text generation
+#   - key: service_type
+#     value: clipper
+#   - key: clipper_url
+#     value: http://localhost:10000/text_generation/predict
+#   - key: input_type
+#     value: text
+#   - key: output_type
+#     value: text
+
 - '@type': /modelzoo.Model
-  model_name: ImageNet Classification ResNet50
+  model_name: Resnet101-ONNX
   metadata:
   - key: category
-    value: vision classification
-  - key: author
-    value: Microsoft Research
+    value: image classification
   - key: service_type
     value: clipper
   - key: clipper_url
-    value: http://localhost:10000/vision_classification/predict
+    value: http://34.218.240.189:3000/onnxrtresnet/predict
   - key: input_type
     value: image
   - key: output_type
-    value: table
+    value: table  
   - key: framework
-    value: PyTorch
-  - key: paper_link
-    value: https://arxiv.org/pdf/1512.03385.pdf
-  - key: metric_ImageNet_top_5_error_%
-    value: "5.25"
+    value: ONNXRuntime + TensorRT
 
+
 - '@type': /modelzoo.Model
-  model_name: Text Dumy
+  model_name: Resnet101-PyTorch
   metadata:
   - key: category
-    value: text generation
+    value: image classification
   - key: service_type
     value: clipper
   - key: clipper_url
-    value: http://localhost:10000/text_generation/predict
+    value: http://34.218.240.189:3000/pytorch/predict
   - key: input_type
-    value: text
+    value: image
   - key: output_type
-    value: text
+    value: table  
+  - key: framework
+    value: PyTorch

go/tests/seed_generated.json

@@ -1 +1 @@
-[{"@type":"/modelzoo.User","email":"[email protected]","password":"password"},{"@type":"/modelzoo.User","email":"[email protected]","password":"password"},{"@type":"/modelzoo.Model","metadata":[{"key":"category","value":"vision classification"},{"key":"author","value":"Microsoft Research"},{"key":"service_type","value":"clipper"},{"key":"clipper_url","value":"http://localhost:10000/vision_classification/predict"},{"key":"input_type","value":"image"},{"key":"output_type","value":"table"},{"key":"framework","value":"PyTorch"},{"key":"paper_link","value":"https://arxiv.org/pdf/1512.03385.pdf"},{"key":"metric_ImageNet_top_5_error_%","value":"5.25"}],"model_name":"ImageNet Classification ResNet50"},{"@type":"/modelzoo.Model","metadata":[{"key":"category","value":"text generation"},{"key":"service_type","value":"clipper"},{"key":"clipper_url","value":"http://localhost:10000/text_generation/predict"},{"key":"input_type","value":"text"},{"key":"output_type","value":"text"}],"model_name":"Text Dumy"}]
+[{"@type":"/modelzoo.User","email":"[email protected]","password":"password"},{"@type":"/modelzoo.User","email":"[email protected]","password":"password"},{"@type":"/modelzoo.Model","metadata":[{"key":"category","value":"image classification"},{"key":"service_type","value":"clipper"},{"key":"clipper_url","value":"http://34.218.240.189:3000/onnxrtresnet/predict"},{"key":"input_type","value":"image"},{"key":"output_type","value":"table"},{"key":"framework","value":"ONNXRuntime + TensorRT"}],"model_name":"Resnet101-ONNX"},{"@type":"/modelzoo.Model","metadata":[{"key":"category","value":"image classification"},{"key":"service_type","value":"clipper"},{"key":"clipper_url","value":"http://34.218.240.189:3000/pytorch/predict"},{"key":"input_type","value":"image"},{"key":"output_type","value":"table"},{"key":"framework","value":"PyTorch"}],"model_name":"Resnet101-PyTorch"}]

js/src/Components/Compare.tsx

@@ -47,8 +47,8 @@ function createPlot(runs: InferenceRun[]): JSX.Element {
         null])
 
     return <Chart
-        width={'500px'}
-        height={'300px'}
+        width={'1000px'}
+        height={'400px'}
         chartType="BarChart"
         loader={<div>Loading Chart</div>}
         data={[
@@ -77,6 +77,7 @@ function createPlot(runs: InferenceRun[]): JSX.Element {
             legend: { position: 'none' },
             hAxis: {
                 title: "Latency(ms)",
+                viewWindow: { min: 0, max: 25 }
             },
             yAxis: {
                 title: "Query"