def deploy_local(self):
try:
LocalWebservice(self.ws, "test").delete()
print("webservice deleted")
except WebserviceException:
pass
shoes_designer_env = Environment('shoes_designer_env')
shoes_designer_env.python.conda_dependencies.add_pip_package("joblib")
shoes_designer_env.python.conda_dependencies.add_pip_package("torch")
shoes_designer_env.python.conda_dependencies.add_pip_package("torchvision")
shoes_designer_env.python.conda_dependencies.add_pip_package("azure-storage-blob")
shoes_designer_env.python.conda_dependencies.add_pip_package("azureml-sdk")
shoes_designer_env.python.conda_dependencies.add_pip_package("PyYAML")
shoes_designer_env.python.conda_dependencies.add_pip_package("scikit-learn")
shoes_designer_env.python.conda_dependencies.add_pip_package("matplotlib")
conda_dep.add_pip_package("opencensus-ext-azure")
# explicitly set base_image to None when setting base_dockerfile
shoes_designer_env.docker.base_image = None
shoes_designer_env.docker.base_dockerfile = "FROM mcr.microsoft.com/azureml/base:intelmpi2018.3-ubuntu16.04\nRUN echo \"this is test\""
shoes_designer_env.inferencing_stack_version = "latest"
inference_config = InferenceConfig(entry_script="score.py",environment=shoes_designer_env)
model = self.ws.models[self.MODEL_NAME]
# This is optional, if not provided Docker will choose a random unused port.
deployment_config = LocalWebservice.deploy_configuration(port=6789)
local_service = Model.deploy(self.ws, "test", [model], inference_config, deployment_config)
local_service.wait_for_deployment()
print("success deployement")
return local_service
def get_environment(workspace,
name,
pip_requirements=None,
conda_specification=None,
conda_env=None,
docker_image=None,
docker_file=None,
override=False,
inference_stack=None):
"""
Get an Azure ML environment from PIP or Conda. From:
- pip_requirements
- conda_specification
- conda_env
at most one can be provided. If none is provided, it is assumed that the
requirements are taken care of by the user.
From:
- docker_image
- docker_file
at most one can be provided. If none is provided, the base Azure image is
used.
:params workspace: The Azure ML workspace to look for existing
environments.
:params name: Name for this environment
:params pip_requirements: Path to the pip requirements file
:params conda_specifidation: Path to the conda specification file
:params conda_env: Name of the conda environment to use
:params docker_image: Base the image off an existing docker image
:params docker_file: Base the image off a Dockerfile.
:params override: Create a new environment with this name,
regardless of if one already exists.
:params inference_stack: Add a stack that enables this environment
for inference. "latest" is a valid option.
Set to None to not add this.
:returns: Azure ML environment or None in case of
failure
"""
if not override:
try:
env = Environment.get(workspace, name)
print("Existing environment found, using that")
return env
except:
print("No environment with that name found, creating new one")
# Validate at most one of pip_requirements, conda_specification, conda_env
# is provided
if sum([
1 for x in [pip_requirements, conda_specification, conda_env]
if x is not None
]) > 1:
print("Provide at most 1 of pip_requirements, conda_specification, "
"conda_env")
return None
# Validate that at most one of docker_image, docker_file is
# provided
if sum([1 for x in [docker_image, docker_file] if x is not None]) > 1:
print("Provide at most 1 of docker_image, docker_file")
return None
if pip_requirements is not None:
env = Environment.from_pip_requirements(name, pip_requirements)
elif conda_specification is not None:
env = Environment.from_conda_specification(name, conda_specification)
elif conda_env is not None:
env = Environment.from_existing_conda_environment(name, conda_env)
else:
env = Environment(name)
env.python.user_managed_dependencies = True
if docker_file is not None:
env.docker.enabled = True
env.docker.base_image = None
env.docker.base_dockerfile = docker_file
elif docker_image is not None:
env.docker.enabled = True
env.docker.base_image = docker_image
if inference_stack is not None:
env.inferencing_stack_version = inference_stack
# Register environment
env.register(workspace=workspace)
return env
