def register_sql_datastore(
workspace: Workspace,
sql_datastore_name: str,
sql_server_name: str,
sql_database_name: str,
sql_username: str,
sql_password: str,
) -> AzureSqlDatabaseDatastore:
"""
Register a Azure SQL DB with the Azure Machine Learning Workspace
:param workspace: Azure Machine Learning Workspace
:param sql_datastore_name: Name used to id the SQL Datastore
:param sql_server_name: Azure SQL Server Name
:param sql_database_name: Azure SQL Database Name
:param sql_username: Azure SQL Database Username
:param sql_password: Azure SQL Database Password
:return: Pointer to Azure Machine Learning SQL Datastore
"""
return Datastore.register_azure_sql_database(
workspace=workspace,
datastore_name=sql_datastore_name,
server_name=sql_server_name,
database_name=sql_database_name,
username=sql_username,
password=sql_password,
)
)
rc = RunConfiguration()
rc.framework = "R"
rc.environment.r = RSection()
# rc.environment.r.cran_packages = [aml]
rc.environment.docker.enabled = True
py_rc = RunConfiguration()
py_rc.framework = "Python"
py_rc.environment.python.conda_dependencies = cd
sql_datastore = Datastore.register_azure_sql_database(
workspace=ws,
datastore_name="modelling_db",
server_name="dbserver-mlops-demo",
database_name="asq-mlops-demo",
username=kv.get_secret("db-user"),
password=kv.get_secret("db-pass"),
)
traindata = Dataset.Tabular.from_sql_query(
(sql_datastore, "SELECT * FROM dbo.traindata"))
outdata = PipelineData("outdata", datastore=ws.get_default_datastore())
download_step = PythonScriptStep(
name="Load training data from database",
script_name="download_dataset.py",
arguments=["--dataset-name", "traindata", "--outpath", outdata],
inputs=[traindata.as_named_input("traindata")],
compute_target=compute_target,
source_directory=".",
