add: Files downloaded from Azure

Author: dimikara

aml-pipelines-with-automated-machine-learning-step.ipynb

@@ -1,5 +1,5 @@
 {
-    "subscription_id": "2c48c51c-bd47-40d4-abbe-fb8eabd19c8c",
-    "resource_group": "aml-quickstarts-133946",
-    "workspace_name": "quick-starts-ws-133946"
+    "subscription_id": "9a7511b8-150f-4a58-8528-3e7d50216c31",
+    "resource_group": "aml-quickstarts-134056",
+    "workspace_name": "quick-starts-ws-134056"
 }

config.json

@@ -3,9 +3,9 @@
 
 # URL for the web service, should be similar to:
 # 'http://8530a665-66f3-49c8-a953-b82a2d312917.eastus.azurecontainer.io/score'
-scoring_uri = 'http://4703e826-117d-4dca-8e82-f444aeb9252f.southcentralus.azurecontainer.io/score'
+scoring_uri = 'http://9ffa1c83-50c1-4d07-9274-23e211036c7f.southcentralus.azurecontainer.io/score'
 # If the service is authenticated, set the key or token
-key = 'DwjqawPTPqlmc9SQZaMSOev647x6fgAn'
+key = 'ZKjuYKDmrm8BTXWqcGuJwx2Y1yYY1T6n'
 
 # Two sets of data to score, so we get two results back
 data = {"data":

endpoint.py

@@ -5,7 +5,7 @@
 ws = Workspace.from_config()
 
 # Set with the deployment name
-name = "best-model"
+name = "best-model2"
 
 # load existing web service
 service = Webservice(name=name, workspace=ws)

logs.py

@@ -1 +1 @@
-{"swagger": "2.0", "info": {"title": "best-model", "description": "API specification for the Azure Machine Learning service best-model", "version": "1.0"}, "schemes": ["https"], "consumes": ["application/json"], "produces": ["application/json"], "securityDefinitions": {"Bearer": {"type": "apiKey", "name": "Authorization", "in": "header", "description": "For example: Bearer abc123"}}, "paths": {"/": {"get": {"operationId": "ServiceHealthCheck", "description": "Simple health check endpoint to ensure the service is up at any given point.", "responses": {"200": {"description": "If service is up and running, this response will be returned with the content 'Healthy'", "schema": {"type": "string"}, "examples": {"application/json": "Healthy"}}, "default": {"description": "The service failed to execute due to an error.", "schema": {"$ref": "#/definitions/ErrorResponse"}}}}}, "/score": {"post": {"operationId": "RunMLService", "description": "Run web service's model and get the prediction output", "security": [{"Bearer": []}], "parameters": [{"name": "serviceInputPayload", "in": "body", "description": "The input payload for executing the real-time machine learning service.", "schema": {"$ref": "#/definitions/ServiceInput"}}], "responses": {"200": {"description": "The service processed the input correctly and provided a result prediction, if applicable.", "schema": {"$ref": "#/definitions/ServiceOutput"}}, "default": {"description": "The service failed to execute due to an error.", "schema": {"$ref": "#/definitions/ErrorResponse"}}}}}}, "definitions": {"ServiceInput": {"type": "object", "properties": {"data": {"type": "array", "items": {"type": "object", "properties": {"age": {"type": "integer", "format": "int64"}, "job": {"type": "string"}, "marital": {"type": "string"}, "education": {"type": "string"}, "default": {"type": "string"}, "housing": {"type": "string"}, "loan": {"type": "string"}, "contact": {"type": "string"}, "month": {"type": "string"}, "day_of_week": {"type": "string"}, "duration": {"type": "integer", "format": "int64"}, "campaign": {"type": "integer", "format": "int64"}, "pdays": {"type": "integer", "format": "int64"}, "previous": {"type": "integer", "format": "int64"}, "poutcome": {"type": "string"}, "emp.var.rate": {"type": "number", "format": "double"}, "cons.price.idx": {"type": "number", "format": "double"}, "cons.conf.idx": {"type": "number", "format": "double"}, "euribor3m": {"type": "number", "format": "double"}, "nr.employed": {"type": "number", "format": "double"}}}}}, "example": {"data": [{"age": 0, "job": "example_value", "marital": "example_value", "education": "example_value", "default": "example_value", "housing": "example_value", "loan": "example_value", "contact": "example_value", "month": "example_value", "day_of_week": "example_value", "duration": 0, "campaign": 0, "pdays": 0, "previous": 0, "poutcome": "example_value", "emp.var.rate": 0.0, "cons.price.idx": 0.0, "cons.conf.idx": 0.0, "euribor3m": 0.0, "nr.employed": 0.0}]}}, "ServiceOutput": {"type": "array", "items": {"type": "integer", "format": "int64"}, "example": [0]}, "ErrorResponse": {"type": "object", "properties": {"status_code": {"type": "integer", "format": "int32"}, "message": {"type": "string"}}}}}
+{"swagger": "2.0", "info": {"title": "best-model2", "description": "API specification for the Azure Machine Learning service best-model2", "version": "1.0"}, "schemes": ["https"], "consumes": ["application/json"], "produces": ["application/json"], "securityDefinitions": {"Bearer": {"type": "apiKey", "name": "Authorization", "in": "header", "description": "For example: Bearer abc123"}}, "paths": {"/": {"get": {"operationId": "ServiceHealthCheck", "description": "Simple health check endpoint to ensure the service is up at any given point.", "responses": {"200": {"description": "If service is up and running, this response will be returned with the content 'Healthy'", "schema": {"type": "string"}, "examples": {"application/json": "Healthy"}}, "default": {"description": "The service failed to execute due to an error.", "schema": {"$ref": "#/definitions/ErrorResponse"}}}}}, "/score": {"post": {"operationId": "RunMLService", "description": "Run web service's model and get the prediction output", "security": [{"Bearer": []}], "parameters": [{"name": "serviceInputPayload", "in": "body", "description": "The input payload for executing the real-time machine learning service.", "schema": {"$ref": "#/definitions/ServiceInput"}}], "responses": {"200": {"description": "The service processed the input correctly and provided a result prediction, if applicable.", "schema": {"$ref": "#/definitions/ServiceOutput"}}, "default": {"description": "The service failed to execute due to an error.", "schema": {"$ref": "#/definitions/ErrorResponse"}}}}}}, "definitions": {"ServiceInput": {"type": "object", "properties": {"data": {"type": "array", "items": {"type": "object", "properties": {"age": {"type": "integer", "format": "int64"}, "job": {"type": "string"}, "marital": {"type": "string"}, "education": {"type": "string"}, "default": {"type": "string"}, "housing": {"type": "string"}, "loan": {"type": "string"}, "contact": {"type": "string"}, "month": {"type": "string"}, "day_of_week": {"type": "string"}, "duration": {"type": "integer", "format": "int64"}, "campaign": {"type": "integer", "format": "int64"}, "pdays": {"type": "integer", "format": "int64"}, "previous": {"type": "integer", "format": "int64"}, "poutcome": {"type": "string"}, "emp.var.rate": {"type": "number", "format": "double"}, "cons.price.idx": {"type": "number", "format": "double"}, "cons.conf.idx": {"type": "number", "format": "double"}, "euribor3m": {"type": "number", "format": "double"}, "nr.employed": {"type": "number", "format": "double"}}}}}, "example": {"data": [{"age": 0, "job": "example_value", "marital": "example_value", "education": "example_value", "default": "example_value", "housing": "example_value", "loan": "example_value", "contact": "example_value", "month": "example_value", "day_of_week": "example_value", "duration": 0, "campaign": 0, "pdays": 0, "previous": 0, "poutcome": "example_value", "emp.var.rate": 0.0, "cons.price.idx": 0.0, "cons.conf.idx": 0.0, "euribor3m": 0.0, "nr.employed": 0.0}]}}, "ServiceOutput": {"type": "array", "items": {"type": "integer", "format": "int64"}, "example": [0]}, "ErrorResponse": {"type": "object", "properties": {"status_code": {"type": "integer", "format": "int32"}, "message": {"type": "string"}}}}}

metrics_data.txt

@@ -7,4 +7,4 @@
 # port to something above 8000 that is available.
 
 docker pull swaggerapi/swagger-ui
-docker run -p 8000:8080 swaggerapi/swagger-ui
+docker run -p 9000:8080 swaggerapi/swagger-ui