def deleteWebService(ws, args):
services = Webservice.list(workspace=ws, model_name=args.modelName)
if (len(services) == 0):
print("Webservice is not deployed.")
else:
print("Webservice is deployed")
services[0].delete()
print("Deleted webservice")
def getWebservice(workspace, webserviceName):
webservice = None
services = Webservice.list(workspace)
for svc in services:
if svc.name == webserviceName:
webservice = svc
break
return webservice
def createWebservice(workspace, container_image, service_name, replica_count,
cores_count, compute_target):
'''
TODO: Should allow for the overwrite flag.
Attach a azureml.core.webservice.Webservice for a given container on an AKS cluster.
If a WebService already exists (by name) on the given workspace, return it instead.
PARAMS:
workspace : azureml.core.Workspace : Existing AMLS Workspace
container_image : azureml.core.image.ContainerImage : Name of an existing AKS cluster
service_name : String : Name of the webservice (deployment) in the AMLS workpsace.
replica_count : int : Number of requested instances of container on cluster.
cores_count : int : Number of cores to allocate to each container
compute_target : azureml.core.compute.AksCompute : AKS cluster to create the service on
RETURNS:
azureml.core.webservice.Webservice
'''
web_service = None
services = Webservice.list(workspace=workspace,
image_name=container_image.name)
if len(services) > 0:
for svc in services:
if svc.name == service_name:
print("Returning existing deployed web service ....",
service_name)
web_service = svc
break
if web_service == None:
print("Creating new web service.....", service_name)
aks_config = AksWebservice.deploy_configuration(
num_replicas=replica_count, cpu_cores=cores_count)
web_service = Webservice.deploy_from_image(
workspace=workspace,
name=service_name,
image=container_image,
deployment_config=aks_config,
deployment_target=compute_target,
)
web_service.wait_for_deployment(show_output=True)
return web_service
_workspace=ws)
print('Latest model id: ', latest_model_id)
print('Latest model name: ', latest_model_name)
print('Latest model version: ', latest_model_version)
print('Latest model path: ', latest_model_path)
latest_model_run_id = latest_model.tags.get("run_id")
print('Latest model run id: ', latest_model_run_id)
latest_model_run = Run(run.experiment, run_id=latest_model_run_id)
latest_model_accuracy = latest_model_run.get_metrics().get("acc")
print('Latest model accuracy: ', latest_model_accuracy)
ws_list = Webservice.list(ws, model_name=latest_model_name)
print('webservice list')
print(ws_list)
deploy_model = False
current_model = None
if (len(ws_list) > 0):
webservice = ws_list[0]
try:
image_id = webservice.tags['image_id']
image = Image(ws, id=image_id)
current_model = image.models[0]
print('Found current deployed model!')
except:
deploy_model = True
ws = getOrCreateWorkspace(subscription_id, resource_group, workspace_name,
workspace_region)
# In[ ]:
from azureml.core.webservice import Webservice
webservice_name = "complianceservice-zst"
# It is important to change the current working directory so that your generated scoring-service.py is at the root
# This is required by the Azure Machine Learning SDK
os.chdir(deployment_folder)
webservice = None
for service in Webservice.list(ws):
if (service.name == webservice_name):
webservice = service
print(webservice.name, webservice)
if webservice == None:
print(os.getcwd())
webservice = deployModelAsWebService(ws,
model_folder_path=onnx_export_folder,
model_name="component_compliance",
service_name=webservice_name)
# Change the directory back...
os.chdir('../')
# Test your deployed web service.
train_filepath = os.path.join(args.input, 'train_info.json')
with open(train_filepath) as f:
train_info = json.load(f)
print("train_info.json")
print(train_info)
#pipeline_run = PipelineRun(run.experiment, run_id = run.properties['azureml.pipelinerunid'])
#latest_model_run = Run(run.experiment, run_id = pipeline_run.find_step_run('train')[0].id)
latest_model_run_id = train_info['train_run_id']
#latest_model_run = Run(run.experiment, run_id=latest_model_run_id)
print('Latest model run id: ', latest_model_run_id)
#latest_model_accuracy = latest_model_run.get_metrics().get("acc")
latest_model_accuracy = float(train_info['acc'])
print('Latest model accuracy: ', latest_model_accuracy)
ws_list = Webservice.list(ws, model_name = args.model_name)
print('webservice list')
print(ws_list)
deploy_model = False
current_model_run_id = None
current_model_accuracy = -1 # undefined
if(len(ws_list) > 0):
webservice = None
for i in range(len(ws_list)):
if ws_list[i].compute_type != 'ACI':
webservice = ws_list[i]
if webservice != None:
#current_model_run_id = webservice.tags.get("run_id")
current_model_run_id = webservice.models[0].tags['run_id']
containerizeStepLogInfo["deploy_model_bool"] = deployModelBool
containerizeStepLogInfo["image_name"] = args.image_name
containerizeStepLogInfo["image_id"] = ""
else:
print('..No freshly trained model found! This should not have happened!')
print('..3. completed')
print('')
print('')
print(
'4. Determine if we have a REST service deployed for the experiment previously'
)
print('.............................................')
restServiceList = Webservice.list(amlWs, model_name=currentlyTrainedModelName)
if (len(restServiceList) > 0):
previouslyDeployedRestServiceFound = True
previouslyDeployedRestService = restServiceList[0]
print('List of existing REST services for the experiment-')
print(restServiceList)
else:
print('No existing REST service instances found for the experiment')
previouslyDeployedRestServiceFound = False
print('..4. completed')
print('')
print('')
print(
'5. Get the previously deployed model from previously deployed REST service, if any'
def get_service():
ws = get_workspace()
services = Webservice.list(workspace=ws, compute_type='AKS')
return services[0]
from azureml.core import Workspace as ws
#from azureml.core.model import Model
#from azureml.core.webservice import AciWebservice
import os
#import matplotlib
import matplotlib.pyplot as plt
# find 30 random samples from test set
n = 30
myws = ws.get(
name='mnist1',
subscription_id='a383ccde-05bc-45bf-b3ae-f51bd72644df',
resource_group='mnist4',
)
#model = Model(myws, 'sklearn-mnist')
#web = 'https://mlworkspace.azure.ai/portal/subscriptions/bcbc4e01-e5d6-42b0-95af-06286341e6ca/resourceGroups/mnist3/providers/Microsoft.MachineLearningServices/workspaces/mnist1/deployments/mnist'
print(Webservice.list(myws)[0].name)
service = Webservice(myws, 'sklearn-mnist-image')
#print(type(model))
data_folder = os.path.join(os.getcwd(), 'data')
X_test = load_data(os.path.join(data_folder, 'test-images.gz'), False) / 255.0
y_test = load_data(os.path.join(data_folder, 'test-labels.gz'),
True).reshape(-1)
sample_indices = np.random.permutation(X_test.shape[0])[0:n]
test_samples = json.dumps({"data": X_test[sample_indices].tolist()})
test_samples = bytes(test_samples, encoding='utf8')
#result = service.run(input_data=test_samples)
# predict using the deployed model
result = service.run(input_data=test_samples)
# compare actual value vs. the predicted values:
print("Argument 1: %s" % args.service_name)
print("Argument 2: %s" % args.aci_name)
print("Argument 3: %s" % args.description)
print('creating AzureCliAuthentication...')
cli_auth = AzureCliAuthentication()
print('done creating AzureCliAuthentication!')
print('get workspace...')
ws = Workspace.from_config(auth=cli_auth)
print('done getting workspace!')
image = Image(ws, image_name)
print(image)
ws_list = Webservice.list(ws, image_name=image_name)
print(ws_list)
if len(ws_list) > 0:
if ws_list[0].name == args.service_name:
print('Deleting: ', ws_list[0].name)
ws_list[0].delete()
print('Done')
aci_config = AciWebservice.deploy_configuration(cpu_cores=1,
memory_gb=1,
tags={'name': args.aci_name},
description=args.description)
aci_service = Webservice.deploy_from_image(deployment_config=aci_config,
image=image,
json_direct = aks_service.run(sc_validate.to_json()) fcdf_direct = ForecastDataFrame.construct_from_json(json_direct) fcdf_direct.head() # ### Delete AKS service and resiurce group # # This part of a notebook is oprtional and intended to clean up after work is complete. First delete the service. # In[ ]: aks_service.delete() # Check if services are present in the workspace. # In[ ]: [svc.name for svc in Webservice.list(ws)] # Delete the resource group.<br/> # **Note** This operation is danger and will delete all the content of the resource group. # To delete group the azure sdk package needs to be installed: # ``` # pip install https://azuremlftkrelease.blob.core.windows.net/azpkgdaily/azpkgamlsdk-1.0.18309.1b1-py3-none-any.whl # ``` # In[ ]: from azpkgamlsdk.deployment.utils_environment import delete_resource_group delete_resource_group(ws.resource_group, ws.subscription_id)
def getOperationOutput(self,
operationNoun,
operationId,
userId,
subscriptionId,
downloadFiles=True):
operationName = self.GetOperationNameByNoun(operationNoun)
if operationName == 'train':
tags = [['userId', userId], ['modelId', operationId],
['subscriptionId', subscriptionId]]
models = Model.list(self._workspace, tags=tags)
if len(models) == 0:
return None
model = models[0]
result = {
'id': operationId,
'description': model.description,
'created_time': model.created_time
}
return result, "model"
if operationName == 'deploy':
tags = [['userId', userId], ['endpointId', operationId],
['subscriptionId', subscriptionId]]
endpoints = Webservice.list(self._workspace, tags=tags)
if len(endpoints) == 0:
return None, None
endpoint = endpoints[0]
primaryKey, secondaryKey = endpoint.get_keys()
result = {
'id': operationId,
'description': endpoint.description,
'created_time': endpoint.created_time,
'scoring_uri': endpoint.scoring_uri,
'primary_key': primaryKey,
'secondary_key': secondaryKey
}
return result, "endpoint"
tags = {
'userId': userId,
'operationId': operationId,
'operationName': operationName,
'subscriptionId': subscriptionId
}
experimentName = subscriptionId
exp = Experiment(self._workspace, experimentName)
runs = exp.get_runs(type='azureml.PipelineRun', tags=tags)
try:
run = next(runs)
child_runs = run.get_children()
child_run = next(child_runs)
outputType = self._utils.GetOutputType(operationName)
if outputType == 'json':
with tempfile.TemporaryDirectory() as tmp:
path = os.path.join(tmp, 'output.json')
files = child_run.download_file('/outputs/output.json',
path)
with open(path) as file:
return json.load(file), "json"
elif outputType == 'file':
if downloadFiles:
tmp = tempfile.TemporaryDirectory().name
path = os.path.join(tmp, "outputs")
zip_file_path = os.path.join(
tmp, "output_{}.zip".format(operationId))
files = child_run.download_files("/outputs",
path,
append_prefix=False)
zipf = zipfile.ZipFile(zip_file_path, "w",
zipfile.ZIP_DEFLATED)
self.zipdir(path, zipf, "outputs")
zipf.close()
return zip_file_path, "file"
else:
return "file", "file"
except StopIteration:
return None
import azureml
from azureml.core import Workspace
# display the core SDK version number
print("Azure ML SDK Version: ", azureml.core.VERSION)
ws = Workspace.from_config()
print("Resource group: ", ws.resource_group)
print("Location: ", ws.location)
print("Workspace name: ", ws.name)
from azureml.core.webservice import Webservice
for web_svc in Webservice.list(ws):
print("Deleting web service", web_svc.name, "...")
web_svc.delete()
from azureml.core import ComputeTarget
for target in ComputeTarget.list(ws):
print("Deleting compute target", target.name, "...")
target.delete()
from azureml.core import Image
for img in Image.list(ws):
print("Deleting image", img.id, "...")
img.delete()
from azureml.core.model import Model
import requests
import json
from azureml.core.webservice import Webservice
from azureml.core.workspace import Workspace
# URL for the web service, should be similar to:
# 'http://8530a665-66f3-49c8-a953-b82a2d312917.eastus.azurecontainer.io/score'
ws = Workspace.from_config()
deployed_webservice = Webservice.list(ws)[0]
scoring_uri = deployed_webservice.scoring_uri
# If the service is authenticated, set the key or token
key = deployed_webservice.get_keys()[0]
# Two sets of data to score, so we get two results back
data = {
"data": [
{
"age": 17,
"campaign": 1,
"cons.conf.idx": -46.2,
"cons.price.idx": 92.893,
"contact": "cellular",
"day_of_week": "mon",
"default": "no",
"duration": 971,
"education": "university.degree",
"emp.var.rate": -1.8,
"euribor3m": 1.299,
"housing": "yes",
