def test_load_pipeline_yaml_invalid_inputs():
"""
Unit test to check the load_pipeline_yaml function with invalid inputs
"""
workspace = object()
pipeline_yaml_file = "invalid.yml"
run_config = load_pipeline_yaml(workspace=workspace,
pipeline_yaml_file=pipeline_yaml_file)
assert run_config is None
def main():
# Loading azure credentials
print("::debug::Loading azure credentials")
azure_credentials = os.environ.get("INPUT_AZURE_CREDENTIALS", default="{}")
try:
azure_credentials = json.loads(azure_credentials)
except JSONDecodeError:
print(
"::error::Please paste output of `az ad sp create-for-rbac --name <your-sp-name> --role contributor --scopes /subscriptions/<your-subscriptionId>/resourceGroups/<your-rg> --sdk-auth` as value of secret variable: AZURE_CREDENTIALS"
)
raise AMLConfigurationException(
"Incorrect or poorly formed output from azure credentials saved in AZURE_CREDENTIALS secret. See setup in https://github.com/Azure/aml-workspace/blob/master/README.md"
)
# Checking provided parameters
print("::debug::Checking provided parameters")
validate_json(data=azure_credentials,
schema=azure_credentials_schema,
input_name="AZURE_CREDENTIALS")
# Mask values
print("::debug::Masking parameters")
mask_parameter(parameter=azure_credentials.get("tenantId", ""))
mask_parameter(parameter=azure_credentials.get("clientId", ""))
mask_parameter(parameter=azure_credentials.get("clientSecret", ""))
mask_parameter(parameter=azure_credentials.get("subscriptionId", ""))
# Loading parameters file
print("::debug::Loading parameters file")
parameters_file = os.environ.get("INPUT_PARAMETERS_FILE",
default="run.json")
parameters_file_path = os.path.join(".cloud", ".azure", parameters_file)
try:
with open(parameters_file_path) as f:
parameters = json.load(f)
except FileNotFoundError:
print(
f"::debug::Could not find parameter file in {parameters_file_path}. Please provide a parameter file in your repository if you do not want to use default settings (e.g. .cloud/.azure/run.json)."
)
parameters = {}
# Checking provided parameters
print("::debug::Checking provided parameters")
validate_json(data=parameters,
schema=parameters_schema,
input_name="PARAMETERS_FILE")
# Define target cloud
if azure_credentials.get(
"resourceManagerEndpointUrl",
"").startswith("https://management.usgovcloudapi.net"):
cloud = "AzureUSGovernment"
elif azure_credentials.get(
"resourceManagerEndpointUrl",
"").startswith("https://management.chinacloudapi.cn"):
cloud = "AzureChinaCloud"
else:
cloud = "AzureCloud"
# Loading Workspace
print("::debug::Loading AML Workspace")
sp_auth = ServicePrincipalAuthentication(
tenant_id=azure_credentials.get("tenantId", ""),
service_principal_id=azure_credentials.get("clientId", ""),
service_principal_password=azure_credentials.get("clientSecret", ""),
cloud=cloud)
config_file_path = os.environ.get("GITHUB_WORKSPACE",
default=".cloud/.azure")
config_file_name = "aml_arm_config.json"
try:
ws = Workspace.from_config(path=config_file_path,
_file_name=config_file_name,
auth=sp_auth)
except AuthenticationException as exception:
print(
f"::error::Could not retrieve user token. Please paste output of `az ad sp create-for-rbac --name <your-sp-name> --role contributor --scopes /subscriptions/<your-subscriptionId>/resourceGroups/<your-rg> --sdk-auth` as value of secret variable: AZURE_CREDENTIALS: {exception}"
)
raise AuthenticationException
except AuthenticationError as exception:
print(f"::error::Microsoft REST Authentication Error: {exception}")
raise AuthenticationError
except AdalError as exception:
print(
f"::error::Active Directory Authentication Library Error: {exception}"
)
raise AdalError
except ProjectSystemException as exception:
print(f"::error::Workspace authorizationfailed: {exception}")
raise ProjectSystemException
# Create experiment
print("::debug::Creating experiment")
try:
# Default experiment name
repository_name = os.environ.get("GITHUB_REPOSITORY").split("/")[-1]
branch_name = os.environ.get("GITHUB_REF").split("/")[-1]
default_experiment_name = f"{repository_name}-{branch_name}"
experiment = Experiment(
workspace=ws,
name=parameters.get("experiment_name",
default_experiment_name)[:36])
except TypeError as exception:
experiment_name = parameters.get("experiment", None)
print(
f"::error::Could not create an experiment with the specified name {experiment_name}: {exception}"
)
raise AMLExperimentConfigurationException(
f"Could not create an experiment with the specified name {experiment_name}: {exception}"
)
except UserErrorException as exception:
experiment_name = parameters.get("experiment", None)
print(
f"::error::Could not create an experiment with the specified name {experiment_name}: {exception}"
)
raise AMLExperimentConfigurationException(
f"Could not create an experiment with the specified name {experiment_name}: {exception}"
)
# Loading run config
print("::debug::Loading run config")
run_config = None
if run_config is None:
# Loading run config from runconfig yaml file
print("::debug::Loading run config from runconfig yaml file")
run_config = load_runconfig_yaml(runconfig_yaml_file=parameters.get(
"runconfig_yaml_file", "code/train/run_config.yml"))
if run_config is None:
# Loading run config from pipeline yaml file
print("::debug::Loading run config from pipeline yaml file")
run_config = load_pipeline_yaml(workspace=ws,
pipeline_yaml_file=parameters.get(
"pipeline_yaml_file",
"code/train/pipeline.yml"))
if run_config is None:
# Loading run config from python runconfig file
print("::debug::Loading run config from python runconfig file")
run_config = load_runconfig_python(
workspace=ws,
runconfig_python_file=parameters.get("runconfig_python_file",
"code/train/run_config.py"),
runconfig_python_function_name=parameters.get(
"runconfig_python_function_name", "main"))
if run_config is None:
# Loading values for errors
pipeline_yaml_file = parameters.get("pipeline_yaml_file",
"code/train/pipeline.yml")
runconfig_yaml_file = parameters.get("runconfig_yaml_file",
"code/train/run_config.yml")
runconfig_python_file = parameters.get("runconfig_python_file",
"code/train/run_config.py")
runconfig_python_function_name = parameters.get(
"runconfig_python_function_name", "main")
print(
f"::error::Error when loading runconfig yaml definition your repository (Path: /{runconfig_yaml_file})."
)
print(
f"::error::Error when loading pipeline yaml definition your repository (Path: /{pipeline_yaml_file})."
)
print(
f"::error::Error when loading python script or function in your repository which defines the experiment config (Script path: '/{runconfig_python_file}', Function: '{runconfig_python_function_name}()')."
)
print(
"::error::You have to provide either a yaml definition for your run, a yaml definition of your pipeline or a python script, which returns a runconfig (Pipeline, ScriptRunConfig, AutoMlConfig, Estimator, etc.). Please read the documentation for more details."
)
raise AMLExperimentConfigurationException(
"You have to provide a yaml definition for your run, a yaml definition of your pipeline or a python script, which returns a runconfig. Please read the documentation for more details."
)
# Submit run config
print("::debug::Submitting experiment config")
try:
# Defining default tags
print("::debug::Defining default tags")
default_tags = {
"GITHUB_ACTOR": os.environ.get("GITHUB_ACTOR"),
"GITHUB_REPOSITORY": os.environ.get("GITHUB_REPOSITORY"),
"GITHUB_SHA": os.environ.get("GITHUB_SHA"),
"GITHUB_REF": os.environ.get("GITHUB_REF")
}
run = experiment.submit(config=run_config,
tags=dict(parameters.get("tags", {}),
**default_tags))
except AzureMLException as exception:
print(
f"::error::Could not submit experiment config. Your script passed object of type {type(run_config)}. Object must be correctly configured and of type e.g. estimator, pipeline, etc.: {exception}"
)
raise AMLExperimentConfigurationException(
f"Could not submit experiment config. Your script passed object of type {type(run_config)}. Object must be correctly configured and of type e.g. estimator, pipeline, etc.: {exception}"
)
except TypeError as exception:
print(
f"::error::Could not submit experiment config. Your script passed object of type {type(run_config)}. Object must be correctly configured and of type e.g. estimator, pipeline, etc.: {exception}"
)
raise AMLExperimentConfigurationException(
f"Could not submit experiment config. Your script passed object of type {type(run_config)}. Object must be correctly configured and of type e.g. estimator, pipeline, etc.: {exception}"
)
# Create outputs
print("::debug::Creating outputs")
print(f"::set-output name=experiment_name::{run.experiment.name}")
print(f"::set-output name=run_id::{run.id}")
print(f"::set-output name=run_url::{run.get_portal_url()}")
# Waiting for run to complete
print("::debug::Waiting for run to complete")
if parameters.get("wait_for_completion", True):
run.wait_for_completion(show_output=True)
# Creating additional outputs of finished run
run_metrics = run.get_metrics(recursive=True)
print(f"::set-output name=run_metrics::{run_metrics}")
run_metrics_markdown = convert_to_markdown(run_metrics)
print(
f"::set-output name=run_metrics_markdown::{run_metrics_markdown}")
# Download artifacts if enabled
if parameters.get("download_artifacts", False):
# Defining artifacts folder
print("::debug::Defining artifacts folder")
root_path = os.environ.get("GITHUB_WORKSPACE", default=None)
folder_name = f"aml_artifacts_{run.id}"
artifact_path = os.path.join(root_path, folder_name)
# Downloading artifacts
print("::debug::Downloading artifacts")
run.download_files(
output_directory=os.path.join(artifact_path, "parent"))
children = run.get_children(recursive=True)
for i, child in enumerate(children):
child.download_files(
output_directory=os.path.join(artifact_path, f"child_{i}"))
# Creating additional outputs
print(f"::set-output name=artifact_path::{artifact_path}")
# Publishing pipeline
print("::debug::Publishing pipeline")
if type(run) is PipelineRun and parameters.get("publish_pipeline", False):
# Default pipeline name
repository_name = os.environ.get("GITHUB_REPOSITORY").split("/")[-1]
branch_name = os.environ.get("GITHUB_REF").split("/")[-1]
default_pipeline_name = f"{repository_name}-{branch_name}"
published_pipeline = run.publish_pipeline(
name=parameters.get("pipeline_name", default_pipeline_name),
description="Pipeline registered by GitHub Run Action",
version=parameters.get("pipeline_version", None),
continue_on_step_failure=parameters.get(
"pipeline_continue_on_step_failure", False))
# Creating additional outputs
print(
f"::set-output name=published_pipeline_id::{published_pipeline.id}"
)
print(
f"::set-output name=published_pipeline_status::{published_pipeline.status}"
)
print(
f"::set-output name=published_pipeline_endpoint::{published_pipeline.endpoint}"
)
elif parameters.get("publish_pipeline", False):
print(
"::error::Could not register pipeline because you did not pass a pipeline to the action"
)
print("::debug::Successfully finished Azure Machine Learning Train Action")
def submitRun(ws, parameters):
# Create experiment
print("::debug::Creating experiment")
try:
# Default experiment name
repository_name = os.environ.get("GITHUB_REPOSITORY").split("/")[-1]
branch_name = os.environ.get("GITHUB_REF").split("/")[-1]
default_experiment_name = f"{repository_name}-{branch_name}"
experiment = Experiment(
workspace=ws,
name=parameters.get("experiment_name", default_experiment_name)[:36]
)
except TypeError as exception:
experiment_name = parameters.get("experiment", None)
print(f"::error::Could not create an experiment with the specified name {experiment_name}: {exception}")
raise AMLExperimentConfigurationException(f"Could not create an experiment with the specified name {experiment_name}: {exception}")
except UserErrorException as exception:
experiment_name = parameters.get("experiment", None)
print(f"::error::Could not create an experiment with the specified name {experiment_name}: {exception}")
raise AMLExperimentConfigurationException(f"Could not create an experiment with the specified name {experiment_name}: {exception}")
# Loading run config
print("::debug::Loading run config")
run_config = None
if run_config is None:
# Loading run config from runconfig yaml file
print("::debug::Loading run config from runconfig yaml file")
run_config = load_runconfig_yaml(
runconfig_yaml_file=parameters.get("runconfig_yaml_file", "code/train/run_config.yml")
)
if run_config is None:
# Loading run config from pipeline yaml file
print("::debug::Loading run config from pipeline yaml file")
run_config = load_pipeline_yaml(
workspace=ws,
pipeline_yaml_file=parameters.get("pipeline_yaml_file", "code/train/pipeline.yml")
)
if run_config is None:
# Loading run config from python runconfig file
print("::debug::Loading run config from python runconfig file")
run_config = load_runconfig_python(
workspace=ws,
runconfig_python_file=parameters.get("runconfig_python_file", "code/train/run_config.py"),
runconfig_python_function_name=parameters.get("runconfig_python_function_name", "main")
)
if run_config is None:
# Loading values for errors
pipeline_yaml_file = parameters.get("pipeline_yaml_file", "code/train/pipeline.yml")
runconfig_yaml_file = parameters.get("runconfig_yaml_file", "code/train/run_config.yml")
runconfig_python_file = parameters.get("runconfig_python_file", "code/train/run_config.py")
runconfig_python_function_name = parameters.get("runconfig_python_function_name", "main")
print(f"::error::Error when loading runconfig yaml definition your repository (Path: /{runconfig_yaml_file}).")
print(f"::error::Error when loading pipeline yaml definition your repository (Path: /{pipeline_yaml_file}).")
print(f"::error::Error when loading python script or function in your repository which defines the experiment config (Script path: '/{runconfig_python_file}', Function: '{runconfig_python_function_name}()').")
print("::error::You have to provide a yaml definition for your run, a yaml definition of your pipeline or a python script, which returns a runconfig. Please read the documentation for more details.")
raise AMLExperimentConfigurationException("You have to provide a yaml definition for your run, a yaml definition of your pipeline or a python script, which returns a runconfig. Please read the documentation for more details.")
# Submit run config
print("::debug::Submitting experiment config")
try:
run = experiment.submit(
config=run_config,
tags=parameters.get("tags", {})
)
except AzureMLException as exception:
print(f"::error::Could not submit experiment config. Your script passed object of type {type(run_config)}. Object must be correctly configured and of type e.g. estimator, pipeline, etc.: {exception}")
raise AMLExperimentConfigurationException(f"Could not submit experiment config. Your script passed object of type {type(run_config)}. Object must be correctly configured and of type e.g. estimator, pipeline, etc.: {exception}")
except TypeError as exception:
print(f"::error::Could not submit experiment config. Your script passed object of type {type(run_config)}. Object must be correctly configured and of type e.g. estimator, pipeline, etc.: {exception}")
raise AMLExperimentConfigurationException(f"Could not submit experiment config. Your script passed object of type {type(run_config)}. Object must be correctly configured and of type e.g. estimator, pipeline, etc.: {exception}")
# Create outputs
print("::debug::Creating outputs")
print(f"::set-output name=experiment_name::{run.experiment.name}")
print(f"::set-output name=run_id::{run.id}")
print(f"::set-output name=run_url::{run.get_portal_url()}")
# we can publish the pipeline without waiting for run to be finished. need to verify it
# Publishing pipeline
print("::debug::Publishing pipeline")
if type(run) is PipelineRun and parameters.get("publish_pipeline", False):
# Default pipeline name
repository_name = os.environ.get("GITHUB_REPOSITORY").split("/")[-1]
branch_name = os.environ.get("GITHUB_REF").split("/")[-1]
default_pipeline_name = f"{repository_name}-{branch_name}"
published_pipeline = run.publish_pipeline(
name=parameters.get("pipeline_name", default_pipeline_name),
description="Pipeline registered by GitHub Run Action",
version=parameters.get("pipeline_version", None),
continue_on_step_failure=parameters.get("pipeline_continue_on_step_failure", False)
)
# Creating additional outputs
print(f"::set-output name=published_pipeline_id::{published_pipeline.id}")
print(f"::set-output name=published_pipeline_status::{published_pipeline.status}")
print(f"::set-output name=published_pipeline_endpoint::{published_pipeline.endpoint}")
elif parameters.get("publish_pipeline", False):
print(f"::error::Could not register pipeline because you did not pass a pipeline to the action")
print("::debug::Successfully finished Azure Machine Learning Train Action")
wait_for_completion = False
# as we don't want to wait here, we just return the run object from here.
if parameters.get("wait_for_completion", True):
wait_for_completion = True
return (run, wait_for_completion)