def mi_run_config(ws, compute):
whl_url = Environment.add_private_pip_wheel(workspace=ws,
file_path=d.WHL_VINX_AZURE_ML,
exist_ok=True)
run_config = RunConfiguration()
run_config.target = compute
run_config.environment.docker.enabled = True
run_config.environment.docker.base_image = None
run_config.environment.docker.base_dockerfile = 'FROM mcr.microsoft.com/azureml/base:latest\nRUN apt-get update && apt-get -y install freetds-dev freetds-bin vim gcc'
run_config.environment.python.user_managed_dependencies = False
run_config.environment.python.conda_dependencies = CondaDependencies.create(
conda_packages=[
'tqdm', 'cython', 'matplotlib', 'scikit-learn', 'fbprophet'
],
pip_packages=[
'azureml-sdk', 'pandas', 'lightgbm', 'scipy==1.4.1', 'statsmodels',
'mlxtend', 'optuna', 'xgboost', 'CatBoost', 'tensorflow', 'keras',
'jpholiday', 'joblib', 'pymssql==2.1.1'
],
pin_sdk_version=False)
run_config.environment.python.conda_dependencies.add_pip_package(whl_url)
return run_config
def create_or_update_mlapp_env(workspace, requirements_path, wheel_path,
env_name):
"""
Usage:
ws = init_workspace()
create_mlapp_environment(
workspace=ws,
requirements_path='../../../requirements.txt',
wheel_path='./../../dist/mlapp-2.0.0-py3-none-any.whl',
env_name='mlapp')
"""
# get or create environment and add requirements.txt file
try:
restored_env = Environment.get(workspace=workspace, name=env_name)
new_env = restored_env.from_pip_requirements(
name=env_name, file_path=requirements_path)
except Exception as e:
new_env = Environment.from_pip_requirements(
name=env_name, file_path=requirements_path)
# settings for environment
new_env.docker.enabled = True
new_env.python.user_managed_dependencies = False
# add private package
whl_url = Environment.add_private_pip_wheel(workspace,
wheel_path,
exist_ok=False)
new_env.python.conda_dependencies.add_pip_package(whl_url)
# build and register environment
new_env = new_env.register(workspace)
build_env_run = new_env.build(workspace)
build_env_run.wait_for_completion(show_output=False)
print(build_env_run.log_url)
print(build_env_run.status)
def deploy(model_uri, workspace, deployment_config=None, service_name=None, model_name=None,
tags=None, mlflow_home=None, synchronous=True):
"""
Register an MLflow model with Azure ML and deploy a websevice to Azure Container Instances (ACI)
or Azure Kubernetes Service (AKS).
The deployed service will contain a webserver that processes model queries.
For information about the input data formats accepted by this webserver, see the
:ref:`MLflow deployment tools documentation <azureml_deployment>`.
:param model_uri: The location, in URI format, of the MLflow model used to build the Azure
ML deployment image. For example:
- ``/Users/me/path/to/local/model``
- ``relative/path/to/local/model``
- ``s3://my_bucket/path/to/model``
- ``runs:/<mlflow_run_id>/run-relative/path/to/model``
- ``models:/<model_name>/<model_version>``
- ``models:/<model_name>/<stage>``
For more information about supported URI schemes, see
`Referencing Artifacts <https://www.mlflow.org/docs/latest/concepts.html#
artifact-locations>`_.
:param workspace: The AzureML workspace in which to deploy the service. This is a
`azureml.core.Workspace` object.
:param deployment_config: The configuration for the Azure web service. This configuration
allows you to specify the resources the webservice will use and
the compute cluster it will be deployed in. If unspecified, the web
service will be deployed into a Azure Container Instance. This is a
`azureml.core.DeploymentConfig` object. For more information, see
`<https://docs.microsoft.com/python/api/azureml-core/
azureml.core.webservice.aks.aksservicedeploymentconfiguration>`_ and
`<https://docs.microsoft.com/en-us/python/api/azureml-core/azureml
.core.webservice.aci.aciservicedeploymentconfiguration>`_
:param service_name: The name to assign the Azure Machine learning webservice that will be
created. If unspecified, a unique name will be generated.
:param model_name: The name to assign the Azure Model will be created. If unspecified,
a unique model name will be generated.
:param tags: A collection of tags, represented as a dictionary of string key-value pairs, to
associate with the Azure Model and Deployment that will be created.
These tags are added to a set of default tags that include the model uri,
and more. For more information, see
`<https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model(class)?view=azure-ml-py>`_.
:param mlflow_home: Path to a local copy of the MLflow GitHub repository. If specified, the
image will install MLflow from this directory. Otherwise, it will install
MLflow from pip.
:param synchronous: If ``True``, this method blocks until the image creation procedure
terminates before returning. If ``False``, the method returns immediately,
but the returned image will not be available until the asynchronous
creation process completes. Use the
``azureml.core.Webservice.wait_for_deployment()`` function to wait
for the deployment process to complete.
:return: A tuple containing the following elements in order:
- An ``azureml.core.webservice.Webservice`` object containing metadata for the
new service.
- An ``azureml.core.model.Model`` object containing metadata for the new model.
.. code-block:: python
:caption: Example
import mlflow.azureml
from azureml.core import Workspace
from azureml.core.webservice import AciWebservice, Webservice
# Load or create an Azure ML Workspace
workspace_name = "<Name of your Azure ML workspace>"
subscription_id = "<Your Azure subscription ID>"
resource_group = "<Name of the Azure resource group in which to create Azure ML resources>"
location = "<Name of the Azure location (region) in which to create Azure ML resources>"
azure_workspace = Workspace.create(name=workspace_name,
subscription_id=subscription_id,
resource_group=resource_group,
location=location,
create_resource_group=True,
exist_ok=True)
# Create an Azure Container Instance webservice for an MLflow model
azure_service, azure_model = mlflow.azureml.deploy(model_uri="<model_uri>",
service_name="<deployment-name>",
workspace=azure_workspace,
synchronous=True)
"""
# The Azure ML SDK is only compatible with Python 3. However, the `mlflow.azureml` module should
# still be accessible for import from Python 2. Therefore, we will only import from the SDK
# upon method invocation.
# pylint: disable=import-error
from azureml.core.model import Model as AzureModel, InferenceConfig
from azureml.core import Environment as AzureEnvironment
from azureml.core import VERSION as AZUREML_VERSION
from azureml.core.webservice import AciWebservice
absolute_model_path = _download_artifact_from_uri(model_uri)
model_pyfunc_conf, model = _load_pyfunc_conf_with_model(model_path=absolute_model_path)
model_python_version = model_pyfunc_conf.get(pyfunc.PY_VERSION, None)
run_id = None
run_id_tag = None
try:
run_id = model.run_id
run_id_tag = run_id
except AttributeError:
run_id = str(uuid.uuid4())
if model_python_version is not None and\
StrictVersion(model_python_version) < StrictVersion("3.0.0"):
raise MlflowException(
message=("Azure ML can only deploy models trained in Python 3 and above. See"
" the following MLflow GitHub issue for a thorough explanation of this"
" limitation and a workaround to enable support for deploying models"
" trained in Python 2: https://github.com/mlflow/mlflow/issues/668"),
error_code=INVALID_PARAMETER_VALUE)
tags = _build_tags(model_uri=model_uri, model_python_version=model_python_version,
user_tags=tags, run_id=run_id_tag)
if service_name is None:
service_name = _get_mlflow_azure_name(run_id)
if model_name is None:
model_name = _get_mlflow_azure_name(run_id)
with TempDir(chdr=True) as tmp:
model_directory_path = tmp.path("model")
tmp_model_path = os.path.join(
model_directory_path,
_copy_file_or_tree(src=absolute_model_path, dst=model_directory_path))
registered_model = AzureModel.register(workspace=workspace, model_path=tmp_model_path,
model_name=model_name, tags=tags)
_logger.info("Registered an Azure Model with name: `%s` and version: `%s`",
registered_model.name, registered_model.version)
# Create an execution script (entry point) for the image's model server. Azure ML requires
# the container's execution script to be located in the current working directory during
# image creation, so we create the execution script as a temporary file in the current
# working directory.
execution_script_path = tmp.path("execution_script.py")
_create_execution_script(output_path=execution_script_path, azure_model=registered_model)
environment = None
if pyfunc.ENV in model_pyfunc_conf:
environment = AzureEnvironment.from_conda_specification(
_get_mlflow_azure_name(run_id),
os.path.join(tmp_model_path, model_pyfunc_conf[pyfunc.ENV]))
else:
environment = AzureEnvironment(_get_mlflow_azure_name(run_id))
if mlflow_home is not None:
path = tmp.path("dist")
_logger.info("Bulding temporary MLFlow wheel in %s", path)
wheel = _create_mlflow_wheel(mlflow_home, path)
whl_url = AzureEnvironment.add_private_pip_wheel(
workspace=workspace,
file_path=wheel,
exist_ok=True)
environment.python.conda_dependencies.add_pip_package(whl_url)
else:
environment.python.conda_dependencies.add_pip_package(
"mlflow=={}".format(mlflow_version))
# AzureML requires azureml-defaults to be installed to include
# flask for the inference server.
environment.python.conda_dependencies.add_pip_package(
"azureml-defaults=={}".format(AZUREML_VERSION))
inference_config = InferenceConfig(entry_script=execution_script_path,
environment=environment)
if deployment_config is not None:
if deployment_config.tags is not None:
# We want more narrowly-scoped tags to win on merge
tags.update(deployment_config.tags)
deployment_config.tags = tags
else:
deployment_config = AciWebservice.deploy_configuration(tags=tags)
webservice = AzureModel.deploy(
workspace=workspace,
name=service_name,
models=[registered_model],
inference_config=inference_config,
deployment_config=deployment_config
)
_logger.info("Deploying an Azure Webservice with name: `%s`",
webservice.name)
if synchronous:
webservice.wait_for_deployment(show_output=True)
return webservice, registered_model
