def lightgbm_test_image_locally(image: Image, directory: str):
"""
Test LightGBM image Locally.
:param image: Machine Learning Image to test.
:param directory: root directory that contains data directory.
"""
dupes_test = get_dupes_test(directory)
text_to_score = dupes_test.iloc[0, 4]
json_text = text_to_json(text_to_score)
image.run(input_data=json_text)
def create_service(self):
iothub_compute = IotHubCompute(self.ws, self.iot_device_id)
print('IotHubCompute:\n{0}'.format(iothub_compute))
routes = {"route": "FROM /messages/* INTO "}
# Here, we define the Azure ML module with the container_config options above
aml_module = IotBaseModuleSettings(name=self.module_name,
create_option=self.container_config)
# This time, we will leave off the external module from the deployment manifest
deploy_config = IotWebservice.deploy_configuration(
device_id=self.iot_device_id, routes=routes, aml_module=aml_module)
# Deploy from latest version of image, using module_name as your IotWebservice name
iot_service_name = self.module_name
# Can specify version=x, otherwise will grab latest
image = Image(self.ws, self.image_name)
print('Deploying image: {0}'.format(image))
iot_service = IotWebservice.deploy_from_image(self.ws,
iot_service_name, image,
deploy_config,
iothub_compute)
# MAGIC %md
# MAGIC
# MAGIC ### Option 2: Connected to a previously created image
# COMMAND ----------
import mlflow.azureml
from azureml.core import Image
from azureml.core.image import ContainerImage
#enter the image name from the Azure ML workspace
image_name = "model"
#retrieve the image configuration
model_image = Image(workspace, name=image_name)
# COMMAND ----------
# #for DEV deployments, Azure Container Instances
from azureml.core.webservice import AciWebservice, Webservice
dev_webservice_name = "skurecs-dev"
dev_webservice_deployment_config = AciWebservice.deploy_configuration()
dev_webservice = Webservice.deploy_from_image(
name=dev_webservice_name,
image=model_image,
deployment_config=dev_webservice_deployment_config,
workspace=workspace)
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
print('Image id tag not found!')
else:
deploy_model = True
print('No deployed webservice for model: ', latest_model_name)
current_model_accuracy = -1 # undefined
if current_model != None:
current_model_run = Run(run.experiment,
run_id=current_model.tags.get("run_id"))
current_model_accuracy = current_model_run.get_metrics().get("acc")
print('accuracies')
args = parser.parse_args()
print("Argument 1: %s" % args.service_name)
print("Argument 2: %s" % args.aks_name)
print("Argument 3: %s" % args.aks_region)
print("Argument 4: %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, id=image_id)
print(image)
aks_name = args.aks_name
aks_region = args.aks_region
aks_service_name = args.service_name
try:
service = Webservice(name=aks_service_name, workspace=ws)
print("Deleting AKS service {}".format(aks_service_name))
service.delete()
except:
print("No existing webservice found: ", aks_service_name)
compute_list = ws.compute_targets
aks_target = None
with open(os.path.join("aml_service", "profiling_result.json")) as f:
profiling_result = json.load(f)
# Get workspace
print("Loading Workspace")
cli_auth = AzureCliAuthentication()
config_file_path = os.environ.get("GITHUB_WORKSPACE", default="aml_service")
config_file_name = "aml_arm_config.json"
ws = Workspace.from_config(path=config_file_path,
auth=cli_auth,
_file_name=config_file_name)
print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\n')
# Loading Image
image_details = profiling_result["image_id"].split(":")
image = Image(workspace=ws, name=image_details[0], version=image_details[1])
# Deploying model on ACI
print("Deploying model on ACI")
try:
print("Trying to update existing ACI service")
dev_service = AciWebservice(workspace=ws, name=aci_settings["name"])
dev_service.update(image=image,
tags=deployment_settings["image"]["tags"],
properties=deployment_settings["image"]["properties"],
description=deployment_settings["image"]["description"],
auth_enabled=aci_settings["auth_enabled"],
ssl_enabled=aci_settings["ssl_enabled"],
ssl_cert_pem_file=aci_settings["ssl_cert_pem_file"],
ssl_key_pem_file=aci_settings["ssl_key_pem_file"],
ssl_cname=aci_settings["ssl_cname"],
previouslyDeployedRestServiceFound = False
print('..4. completed')
print('')
print('')
print(
'5. Get the previously deployed model from previously deployed REST service, if any'
)
print('.............................................')
previouslyDeployedModel = None
if previouslyDeployedRestService != None:
try:
previouslyDeployedContainerImageId = previouslyDeployedRestService.tags[
'image_id']
previouslyDeployedContainerImage = Image(amlWs, id=image_id)
previouslyDeployedModel = previouslyDeployedContainerImage.models[0]
print(
'Found the model of the previously deployed REST service, for the experiment!'
)
except:
print(
'No previously deployed container image not found for the experiment!'
)
else:
deployModelBool = True
print('No deployed Rest service for model: ', currentlyTrainedModelName)
print('..5. completed')
print('')
print('')
ws = Workspace(auth=spAuth,
subscription_id=subscription_id,
resource_group=resource_group,
workspace_name=workspace_name)
print("Loaded workspace: " + ws.name)
build_version = os.environ["BUILD_BUILDNUMBER"]
aciconfig = AciWebservice.deploy_configuration(
cpu_cores=2,
memory_gb=4,
tags={'BuildVersion': build_version},
description=
'Container instance hosting web service to consume ABC Bricks routing solution'
)
image = Image(ws,
name="breast-cancer-image",
tags=[['BuildVersion', build_version]])
print("Picked image with version: " + str(image.version) +
" which was built on : " + str(image.created_time))
aci_service = Webservice.deploy_from_image(deployment_config=aciconfig,
image=image,
name='breast-cancer-instance',
workspace=ws)
aci_service.wait_for_deployment(True)
print(aci_service.state)
print(aci_service.scoring_uri)
required=True)
args = parser.parse_args()
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)
runtime = 'python'
# create container image configuration
print("Creating container image configuration...")
from azureml.core.image import ContainerImage
image_config = ContainerImage.image_configuration(execution_script='score.py',
docker_file='dockerfile',
runtime=runtime,
conda_file=conda_file)
# create the image
image_name = 'nyc-taxi-fare-image'
from azureml.core import Image
image = Image.create(name=image_name,
models=[registered_model, registered_scoring_explainer],
image_config=image_config,
workspace=ws)
# wait for image creation to finish
image.wait_for_creation(show_output=True)
# ### Deploy Model to Azure Container Instance (ACI) as a Web Service
# In[ ]:
from azureml.core.webservice import AciWebservice, Webservice
aci_name = 'sklearn-aci-cluster01'
aci_config = AciWebservice.deploy_configuration(
cpu_cores=1,
def create_service(self):
self.iothub_compute = IotHubCompute(self.ws, self.iot_device_id)
compute_targets = ComputeTarget.list(self.ws)
for t in compute_targets:
if t.type == "IotHub":
print("IotHub '{}' has provisioning state '{}'.".format(t.name, t.provisioning_state))
self.container_config = """{
"ExposedPorts": {
"50051/tcp": {}
},
"HostConfig": {
"Binds": [
"/etc/hosts:/etc/hosts"
],
"Privileged": true,
"Devices": [
{
"PathOnHost": "/dev/catapult0",
"PathInContainer": "/dev/catapult0"
},
{
"PathOnHost": "/dev/catapult1",
"PathInContainer": "/dev/catapult1"
}
],
"PortBindings": {
"50051/tcp": [
{
"HostPort": "50051"
}
]
}
}
}"""
self.routes = {
"route": "FROM /messages/* INTO "
}
# Here, we define the Azure ML module with the container_config options above
self.aml_module = IotBaseModuleSettings(
name = self.module_name,
create_option = self.container_config
)
# This time, we will leave off the external module from the deployment manifest
self.deploy_config = IotWebservice.deploy_configuration(
device_id = self.iot_device_id,
routes = self.routes,
aml_module = self.aml_module
)
# Deploy from latest version of image, using module_name as your IotWebservice name
iot_service_name = self.module_name
# Can specify version=x, otherwise will grab latest
self.image = Image(self.ws, self.image_name)
self.iot_service = IotWebservice.deploy_from_image(
self.ws,
iot_service_name,
self.image,
self.deploy_config,
self.iothub_compute
)
data = f.read()
with open('score_fixed.py', "w") as f:
f.write(data.replace('MODEL-NAME',
args.model_name)) #replace the placeholder MODEL-NAME
print('score_fixed.py saved')
# create a Conda dependencies environment file
print("Creating conda dependencies file locally...")
conda_packages = ['numpy', 'pandas', 'scikit-learn==0.20.3']
pip_packages = ['azureml-sdk', 'sklearn_pandas']
mycondaenv = CondaDependencies.create(conda_packages=conda_packages,
pip_packages=pip_packages)
conda_file = 'scoring_dependencies.yml'
with open(conda_file, 'w') as f:
f.write(mycondaenv.serialize_to_string())
# create container image configuration
print("Creating container image configuration...")
image_config = ContainerImage.image_configuration(
execution_script='score_fixed.py', runtime='python', conda_file=conda_file)
print("Creating image...")
image = Image.create(name=args.image_name,
models=[latest_model],
image_config=image_config,
workspace=ws)
# wait for image creation to finish
image.wait_for_creation(show_output=True)
print('')
print('5. Authenticating with AzureCliAuthentication...')
clientAuthn = AzureCliAuthentication()
print('..5.completed')
print('')
print('')
print('6. Instantiate AML workspace')
amlWs = Workspace.from_config(auth=clientAuthn)
print('..6.completed')
print('')
print('')
print('7. Instantiate image')
containerImage = Image(amlWs, id=image_id)
print(containerImage)
print('..7.completed')
print('')
print('')
print('8. Check for and delete any existing web service instance')
aksName = args.aks_name
aksRegion = args.aks_region
aksServiceName = args.service_name
print('aksName=', aksName)
print('aksRegion=', aksRegion)
print('aksServiceName=', aksServiceName)
from azureml.core import Image
import azure
import azureml.core
from azureml.core import Workspace
from azureml.core import ScriptRunConfig
import configparser
# Read config file
config = configparser.ConfigParser()
config.read('ml-config.ini')
# Initialize workspace from config
ws = Workspace.from_config()
for i in Image.list(workspace=ws,
image_name=config['docker']['docker_image_name']):
if i.version == int(config['deploy']['docker_image_version']):
image = i
from azureml.core.webservice import AciWebservice
aciconfig = AciWebservice.deploy_configuration(
cpu_cores=int(config['deploy']['cpu_cores']),
memory_gb=int(config['deploy']['memory']),
tags={
'area': "meter_classification",
'type': "meter_classification"
},
description="Image with re-trained vgg model")
from azureml.core.webservice import Webservice
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
for model in Model.list(ws):
print("Deleting model", model.id, "...")
model.delete()