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)