def create_experiment_config(workspace):
########################################
### Creating data prep Pipeline Step ###
########################################
# Load settings
print("Loading settings")
data_prep_step_path = os.path.join("steps", "data_prep")
with open(os.path.join(data_prep_step_path, "step.json")) as f:
data_prep_settings = json.load(f)
# Setup datasets of first step
print("Setting up datasets")
data_prep_input = Dataset.get_by_name(workspace=workspace,
name=data_prep_settings.get(
"dataset_input_name",
None)).as_named_input(
data_prep_settings.get(
"dataset_input_name",
None)).as_mount()
data_prep_output = PipelineData(
name=data_prep_settings.get("dataset_output_name", None),
datastore=Datastore(workspace=workspace,
name=data_prep_settings.get(
"datastore_output_name",
"workspaceblobstore")),
output_mode="mount").as_dataset()
# Uncomment next lines, if you want to register intermediate dataset
#data_prep_output.register(
# name=data_prep_settings.get("dataset_output_name", None),
# create_new_version=True
#)
# Create conda dependencies
print("Creating conda dependencies")
data_prep_dependencies = CondaDependencies.create(
pip_packages=data_prep_settings.get("pip_packages", []),
conda_packages=data_prep_settings.get("conda_packages", []),
python_version=data_prep_settings.get("python_version", "3.6.2"))
# Create run configuration
print("Creating RunConfiguration")
data_prep_run_config = RunConfiguration(
conda_dependencies=data_prep_dependencies,
framework=data_prep_settings.get("framework", "Python"))
# Loading compute target
print("Loading ComputeTarget")
data_prep_compute_target = ComputeTarget(workspace=workspace,
name=data_prep_settings.get(
"compute_target_name", None))
# Create python step
print("Creating Step")
data_prep = PythonScriptStep(
name=data_prep_settings.get("step_name", None),
script_name=data_prep_settings.get("script_name", None),
arguments=data_prep_settings.get("arguments", []),
compute_target=data_prep_compute_target,
runconfig=data_prep_run_config,
inputs=[data_prep_input],
outputs=[data_prep_output],
params=data_prep_settings.get("parameters", []),
source_directory=data_prep_step_path,
allow_reuse=data_prep_settings.get("allow_reuse", True),
version=data_prep_settings.get("version", None),
)
###############################################
### Creating data model train Pipeline Step ###
###############################################
# Load settings
print("Loading settings")
model_train_step_path = os.path.join("steps", "model_train")
with open(os.path.join(model_train_step_path, "step.json")) as f:
model_train_settings = json.load(f)
hyperparameter_sampling_settings = model_train_settings.get(
"hyperparameter_sampling", {})
# Setup datasets of first step
print("Setting up datasets")
model_train_input = data_prep_output.as_named_input(
name=model_train_settings.get("dataset_input_name", None))
model_train_output = PipelineData(
name=model_train_settings.get("dataset_output_name", None),
datastore=Datastore(workspace=workspace,
name=model_train_settings.get(
"datastore_output_name", None)),
output_mode="mount",
).as_dataset()
# Uncomment next lines, if you want to register intermediate dataset
#model_train_output.register(
# name=model_train_settings.get("dataset_output_name", None),
# create_new_version=True
#)
# Create conda dependencies
print("Creating conda dependencies")
model_train_dependencies = CondaDependencies.create(
pip_packages=model_train_settings.get("pip_packages", []),
conda_packages=model_train_settings.get("conda_packages", []),
python_version=model_train_settings.get("python_version", "3.6.2"))
# Create run configuration
print("Creating RunConfiguration")
model_train_run_config = RunConfiguration(
conda_dependencies=model_train_dependencies,
framework=model_train_settings.get("framework", "Python"))
# Loading compute target
print("Loading ComputeTarget")
model_train_compute_target = ComputeTarget(workspace=workspace,
name=model_train_settings.get(
"compute_target_name",
None))
# Create distributed training backend
print("Creating distributed training backend")
distributed_training_backend = get_distributed_backend(
backend_name=model_train_settings.get("distributed_backend", None))
# Create Estimator for Training
print("Creating Estimator for training")
model_train_estimator = Estimator(
source_directory=model_train_step_path,
entry_script=model_train_settings.get("script_name", None),
environment_variables=model_train_settings.get("parameters", None),
compute_target=model_train_compute_target,
node_count=model_train_settings.get("node_count", None),
distributed_training=distributed_training_backend,
conda_packages=model_train_settings.get("conda_packages", None),
pip_packages=model_train_settings.get("pip_packages", None),
)
try:
# Create parameter sampling
print("Creating Parameter Sampling")
parameter_dict = {}
parameters = hyperparameter_sampling_settings.get(
"parameters",
{}) if "parameters" in hyperparameter_sampling_settings else {}
for parameter_name, parameter_details in parameters.items():
parameter_distr = get_parameter_distribution(
distribution=parameter_details.get("distribution", None),
**parameter_details.get("settings", {}))
parameter_dict[f"--{parameter_name}"] = parameter_distr
model_train_ps = get_parameter_sampling(
sampling_method=hyperparameter_sampling_settings.get(
"method", None),
parameter_dict=parameter_dict)
# Get Policy definition
policy_settings = hyperparameter_sampling_settings.get("policy", {})
kwargs = {
key: value
for key, value in policy_settings.items() if key not in
["policy_method", "evaluation_interval", "delay_evaluation"]
}
# Create termination policy
print("Creating early termination policy")
model_train_policy = get_policy(
policy_method=policy_settings.get("method", ""),
evaluation_interval=policy_settings.get("evaluation_interval",
None),
delay_evaluation=policy_settings.get("delay_evaluation", None),
**kwargs)
# Create HyperDriveConfig
print("Creating HyperDriveConfig")
model_train_hyperdrive_config = HyperDriveConfig(
estimator=model_train_estimator,
hyperparameter_sampling=model_train_ps,
policy=model_train_policy,
primary_metric_name=hyperparameter_sampling_settings.get(
"primary_metric", None),
primary_metric_goal=PrimaryMetricGoal.MINIMIZE
if "min" in hyperparameter_sampling_settings.get(
"primary_metric_goal", None) else PrimaryMetricGoal.MAXIMIZE,
max_total_runs=hyperparameter_sampling_settings.get(
"max_total_runs", 1),
max_concurrent_runs=hyperparameter_sampling_settings.get(
"max_concurrent_runs", 1),
max_duration_minutes=hyperparameter_sampling_settings.get(
"max_duration_minutes", None))
# Create HyperDriveStep
print("Creating HyperDriveStep")
model_train = HyperDriveStep(
name=model_train_settings.get("step_name", None),
hyperdrive_config=model_train_hyperdrive_config,
estimator_entry_script_arguments=model_train_settings.get(
"arguments", None),
inputs=[model_train_input],
outputs=[model_train_output],
allow_reuse=model_train_settings.get("allow_reuse", True),
version=model_train_settings.get("version", True))
except:
print("Not all required parameters specified for HyperDrive step")
# Create EstimatorStep
print("Creating EstimatorStep")
model_train = EstimatorStep(
name=model_train_settings.get("step_name", None),
estimator=model_train_estimator,
estimator_entry_script_arguments=model_train_settings.get(
"arguments", None),
inputs=[model_train_input],
outputs=[model_train_output],
compute_target=model_train_compute_target,
allow_reuse=model_train_settings.get("allow_reuse", True),
version=model_train_settings.get("version", True))
#########################
### Creating Pipeline ###
#########################
# Create Pipeline
print("Creating Pipeline")
pipeline = Pipeline(
workspace=workspace,
steps=[model_train],
description="Training Pipeline",
)
# Validate pipeline
print("Validating pipeline")
pipeline.validate()
return pipeline
def create_experiment_config(workspace):
########################################
### Creating data prep Pipeline Step ###
########################################
# Load settings
print("Loading settings")
data_prep_step_path = os.path.join("steps", "data_prep")
with open(os.path.join(data_prep_step_path, "step.json")) as f:
data_prep_settings = json.load(f)
# Setup datasets - Create PipelineParameter for dynamic pipeline input
print("Setting up datasets with dynamic input")
data_prep_input_path = DataPath(
datastore=Datastore(workspace=workspace,
name=data_prep_settings.get(
"datastore_input_name", "workspaceblobstore")),
path_on_datastore=
"golden/Atlantis/PAX1/15-Mar-2020-23-37-50-279971/PAX1.parquet/")
data_prep_input_path_pipeline_parameter = PipelineParameter(
name="input_path", default_value=data_prep_input_path)
data_prep_input = (data_prep_input_path_pipeline_parameter,
DataPathComputeBinding(mode="mount"))
data_prep_output = PipelineData(
name=data_prep_settings.get("dataset_output_name", None),
datastore=Datastore(workspace=workspace,
name=data_prep_settings.get(
"datastore_output_name",
"workspaceblobstore")),
output_mode="mount").as_dataset()
# Uncomment next lines, if you want to register intermediate dataset
#data_prep_output.register(
# name=data_prep_settings.get("dataset_output_name", None),
# create_new_version=True
#)
# Create conda dependencies
print("Creating conda dependencies")
data_prep_dependencies = CondaDependencies.create(
pip_packages=data_prep_settings.get("pip_packages", []),
conda_packages=data_prep_settings.get("conda_packages", []),
python_version=data_prep_settings.get("python_version", "3.6.2"))
# Create run configuration
print("Creating RunConfiguration")
data_prep_run_config = RunConfiguration(
conda_dependencies=data_prep_dependencies,
framework=data_prep_settings.get("framework", "Python"))
# Loading compute target
print("Loading ComputeTarget")
data_prep_compute_target = ComputeTarget(workspace=workspace,
name=data_prep_settings.get(
"compute_target_name", None))
# Create python step
print("Creating Step")
data_prep = PythonScriptStep(
name=data_prep_settings.get("step_name", None),
script_name=data_prep_settings.get("script_name", None),
arguments=data_prep_settings.get("arguments", []) +
["--input-datapath", data_prep_input],
compute_target=data_prep_compute_target,
runconfig=data_prep_run_config,
inputs=[data_prep_input],
outputs=[data_prep_output],
params=data_prep_settings.get("parameters", []),
source_directory=data_prep_step_path,
allow_reuse=data_prep_settings.get("allow_reuse", True),
version=data_prep_settings.get("version", None),
)
############################################
### Creating inference Parallel Run Step ###
############################################
# Load settings
print("Loading settings")
batch_inference_step_path = os.path.join("steps", "batch_inference")
with open(os.path.join(batch_inference_step_path, "step.json")) as f:
batch_inference_settings = json.load(f)
# Setup datasets of first step
print("Setting up datasets")
batch_inference_input = data_prep_output.as_named_input(
name=batch_inference_settings.get("dataset_input_name", None))
batch_inference_output = PipelineData(
name=batch_inference_settings.get("dataset_output_name", None),
datastore=Datastore(workspace=workspace,
name=batch_inference_settings.get(
"datastore_output_name", None)),
output_mode="mount",
).as_dataset()
# Uncomment next lines, if you want to register intermediate dataset
#batch_inference_output.register(
# name=batch_inference_settings.get("dataset_output_name", None),
# create_new_version=True
#)
# Create conda dependencies
print("Creating conda dependencies")
batch_inference_dependencies = CondaDependencies.create(
pip_packages=batch_inference_settings.get("pip_packages", []),
conda_packages=batch_inference_settings.get("conda_packages", []),
python_version=batch_inference_settings.get("python_version", "3.6.2"))
# Create run configuration
print("Creating RunConfiguration")
data_prep_run_config = RunConfiguration(
conda_dependencies=batch_inference_dependencies,
framework=batch_inference_settings.get("framework", "Python"))
# Loading compute target
print("Loading ComputeTarget")
batch_inference_compute_target = ComputeTarget(
workspace=workspace,
name=batch_inference_settings.get("compute_target_name", None))
# Create python step
print("Creating Step")
batch_inference = PythonScriptStep(
name=batch_inference_settings.get("step_name", None),
script_name=batch_inference_settings.get("script_name", None),
arguments=batch_inference_settings.get("arguments", []),
compute_target=batch_inference_compute_target,
runconfig=data_prep_run_config,
inputs=[batch_inference_input],
outputs=[batch_inference_output],
params=batch_inference_settings.get("parameters", []),
source_directory=batch_inference_step_path,
allow_reuse=batch_inference_settings.get("allow_reuse", True),
version=batch_inference_settings.get("version", None),
)
#########################
### Creating Pipeline ###
#########################
# Create Pipeline
print("Creating Pipeline")
pipeline = Pipeline(
workspace=workspace,
steps=[batch_inference],
description="Batch Inference Pipeline",
)
return pipeline