def get_run_cfg(ws, pip_packages, conda_packages, ext_wheels, gpu=True):
'''
get_run_cfg - Retrieves the AMLS run configuration.
:returns: AMLS run configuration
:rtype: RunConfiguration object
'''
conda_dep = CondaDependencies()
for pip_package in pip_packages:
conda_dep.add_pip_package(pip_package)
for conda_package in conda_packages:
conda_dep.add_conda_package(conda_package)
for whl_path in ext_wheels:
whl_url = Environment.add_private_pip_wheel(workspace=ws,
file_path=whl_path,
exist_ok=True)
conda_dep.add_pip_package(whl_url)
run_cfg = RunConfiguration(conda_dependencies=conda_dep)
run_cfg.environment.docker.enabled = True
run_cfg.environment.docker.gpu_support = gpu
if gpu:
run_cfg.environment.docker.base_image = DEFAULT_GPU_IMAGE
else:
run_cfg.environment.docker.base_image = DEFAULT_CPU_IMAGE
run_cfg.environment.spark.precache_packages = False
return run_cfg
def create_aml_environment(aml_interface):
aml_env = Environment(name=AML_ENVIRONMENT_NAME)
conda_dep = CondaDependencies()
conda_dep.add_pip_package("numpy==1.18.2")
conda_dep.add_pip_package("pandas==1.0.3")
conda_dep.add_pip_package("scikit-learn==0.22.2.post1")
conda_dep.add_pip_package("joblib==0.14.1")
conda_dep.add_pip_package("azure-storage-blob==12.3.0")
aml_env.environment_variables[AZURE_STORAGE_ACCOUNT_NAME] = os.getenv(
AZURE_STORAGE_ACCOUNT_NAME)
aml_env.environment_variables[AZURE_STORAGE_ACCOUNT_KEY] = os.getenv(
AZURE_STORAGE_ACCOUNT_KEY)
aml_env.environment_variables[MODEL_NAME_VARIABLE] = MODEL_NAME
logger.info(
f"set environment variables on compute environment: {aml_env.environment_variables}"
)
whl_filepath = retrieve_whl_filepath()
whl_url = Environment.add_private_pip_wheel(
workspace=aml_interface.workspace,
file_path=whl_filepath,
exist_ok=True)
conda_dep.add_pip_package(whl_url)
aml_env.python.conda_dependencies = conda_dep
aml_env.docker.enabled = True
return aml_env
def create_aml_environment(aml_interface):
aml_env = Environment(name=AML_ENV_NAME)
conda_dep = CondaDependencies()
conda_dep.add_pip_package("numpy==1.18.2")
conda_dep.add_pip_package("pandas==1.0.3")
conda_dep.add_pip_package("scikit-learn==0.22.2.post1")
conda_dep.add_pip_package("joblib==0.14.1")
whl_filepath = retrieve_whl_filepath()
whl_url = Environment.add_private_pip_wheel(
workspace=aml_interface.workspace,
file_path=whl_filepath,
exist_ok=True)
conda_dep.add_pip_package(whl_url)
aml_env.python.conda_dependencies = conda_dep
aml_env.docker.enabled = True
return aml_env
def _register_private_pip_wheel_to_blob(workspace, file_path, container_name=None, blob_name=None):
"""Register the private pip package wheel file on disk to the Azure storage blob attached to the workspace.
:param workspace: Workspace object to use to register the private pip package wheel.
:type workspace: azureml.core.workspace.Workspace
:param file_path: Path to the local pip wheel file on disk, including the file extension.
:type file_path: str
:param container_name: Container name to use to store the pip wheel. Defaults to private-packages.
:type container_name: str
:param blob_name: Full path to use to store the pip wheel on the blob container.
:type blob_name: str
:return: Returns the full URI to the uploaded pip wheel on Azure blob storage to use in conda dependencies.
:rtype: str
"""
import logging
logging.warning("_register_private_pip_wheel_to_blob() is going to be removed in the next SDK release."
"Please use Environment.add_private_pip_wheel() instead.")
from azureml.core.environment import Environment
return Environment.add_private_pip_wheel(workspace, file_path)