conda_dep.add_conda_package("numpy")
conda_dep.add_conda_package("pip")
conda_dep.add_conda_package("scikit-learn=0.20.3")
# You must list azureml-defaults as a pip dependency
conda_dep.add_pip_package("azureml-defaults==1.11.0")
conda_dep.add_pip_package("azureml-core")
conda_dep.add_pip_package("azureml-automl-runtime")
conda_dep.add_pip_package("packaging")
conda_dep.add_pip_package("azureml-explain-model==1.11.0")
conda_dep.add_pip_package("inference-schema")
conda_dep.add_conda_package("numpy")
# scikit-learn>=0.19.0,<=0.20.3
conda_dep.add_conda_package("pandas")
conda_dep.add_conda_package("py-xgboost")
# Save environment also locally to disk so we can test the score script directly by creating a local environment
conda_dep.save('temp/mortgage_score_env.yml')
myenv.python.conda_dependencies = conda_dep
# -
webservice_config = AciWebservice.deploy_configuration(cpu_cores=1,
memory_gb=1,
auth_enabled=True)
inference_config = InferenceConfig(entry_script='score.py', environment=myenv)
# ### Test in local docker container
print(
"\nTesting inference using local docker container before deploying it as web service\n"
)
# This is optional, if not provided Docker will choose a random unused port.
deployment_config = LocalWebservice.deploy_configuration(port=6789)
def build_pipeline_steps(automlconfig: AutoMLConfig,
data: Dataset,
target_column: str,
compute_target: ComputeTarget,
group_column_names: list,
time_column_name: str,
deploy: bool,
service_name: str = 'grouping-demo') -> StepSequence:
steps = []
metrics_output_name = 'metrics_{}'
best_model_output_name = 'best_model_{}'
count = 0
model_names = []
# get all automl configs by group
configs = _get_configs(automlconfig, data, target_column, compute_target, group_column_names)
# build a runconfig for register model
register_config = RunConfiguration()
cd = CondaDependencies()
cd.add_pip_package('azureml-pipeline')
register_config.environment.python.conda_dependencies = cd
# create each automl step end-to-end (train, register)
for group_name, conf in configs.items():
# create automl metrics output
metrics_data = PipelineData(
name='metrics_data_{}'.format(group_name),
pipeline_output_name=metrics_output_name.format(group_name),
training_output=TrainingOutput(type='Metrics'))
# create automl model output
model_data = PipelineData(
name='model_data_{}'.format(group_name),
pipeline_output_name=best_model_output_name.format(group_name),
training_output=TrainingOutput(type='Model', metric=conf.user_settings['primary_metric']))
automl_step = AutoMLStep(
name='automl_{}'.format(group_name),
automl_config=conf,
outputs=[metrics_data, model_data],
allow_reuse=True)
steps.append(automl_step)
# pass the group name as a parameter to the register step ->
# this will become the name of the model for this group.
group_name_param = PipelineParameter("group_name_{}".format(count), default_value=group_name)
count += 1
reg_model_step = PythonScriptStep(
'register.py',
name='register_{}'.format(group_name),
arguments=["--model_name", group_name_param, "--model_path", model_data],
inputs=[model_data],
compute_target=compute_target,
runconfig=register_config,
source_directory="register",
allow_reuse=True
)
steps.append(reg_model_step)
model_names.append(group_name)
final_steps = steps
if deploy:
# modify the conda dependencies to ensure we pick up correct
# versions of azureml-defaults and azureml-train-automl
cd = CondaDependencies.create(pip_packages=['azureml-defaults', 'azureml-train-automl'])
automl_deps = CondaDependencies(conda_dependencies_file_path='deploy/myenv.yml')
cd._merge_dependencies(automl_deps)
cd.save('deploy/myenv.yml')
# add deployment step
pp_group_column_names = PipelineParameter(
"group_column_names",
default_value="#####".join(list(reversed(group_column_names))))
pp_model_names = PipelineParameter(
"model_names",
default_value=json.dumps(model_names))
pp_service_name = PipelineParameter(
"service_name",
default_value=service_name)
deployment_step = PythonScriptStep(
'deploy.py',
name='service_deploy',
arguments=["--group_column_names", pp_group_column_names,
"--model_names", pp_model_names,
"--service_name", pp_service_name,
"--time_column_name", time_column_name],
compute_target=compute_target,
runconfig=RunConfiguration(),
source_directory="deploy"
)
final_steps = StepSequence(steps=[steps, deployment_step])
return final_steps
