def main():
try:
global workspace
global datastore
# Parse command line arguments
args = parse_args()
# Retreive workspace
workspace = Workspace.get(
subscription_id=args.subscription_id,
resource_group=args.resource_group,
name=args.workspace_name,
)
# Retreive default datastore for testing
datastore = workspace.get_default_datastore()
# Define directories for input and output test data on datastore
input_file_path = f"tests/inputs/{args.build_id}"
output_file_path = f"tests/outputs/{args.build_id}"
print("Variable [input_file_path]:", input_file_path)
print("Variable [output_file_path]:", output_file_path)
# Copy data to input directory on datastore for testing
copy_data_for_tests(args.dataset_name, input_file_path)
# Define pipeline parameters
pipeline_parameters = {
"build_id":
args.build_id,
"input_datapath":
DataPath(datastore=datastore, path_on_datastore=input_file_path),
"output_datapath":
DataPath(datastore=datastore, path_on_datastore=output_file_path),
}
print("Variable [pipeline_parameters]:", pipeline_parameters)
# Run pipeline
run_pipeline(workspace, args.pipeline_name, pipeline_parameters)
# List all files in input and output datasets
input_dataset_files = get_dataset_file(input_file_path)
output_dataset_files = get_dataset_file(output_file_path)
print("Variable [input_dataset_files]:", input_dataset_files)
print("Variable [output_dataset_files]:", output_dataset_files)
# Should have scored all input files and saved result to output datastore
assert len(input_dataset_files) == len(output_dataset_files)
except Exception:
print(f"Exception: run_pipeline.py\n{traceback.format_exc()}")
exit(1)
def main():
try:
global args
# Parse command line arguments
args = parse_args(sys.argv[1:])
# Retreive workspace
workspace = Workspace.get(
subscription_id=args.subscription_id,
resource_group=args.resource_group,
name=args.workspace_name,
)
if args.pipeline_action == "draft":
pipeline = create_pipeline(workspace)
draft_pipeline(
workspace,
pipeline,
args.pipeline_name,
args.experiment_name,
args.build_id,
args.pipeline_metadata_file,
)
elif args.pipeline_action == "run":
# Define pipeline parameters
pipeline_parameters = {
"build_id": args.build_id,
"input_datapath": DataPath(
datastore=args.input_datastore_name,
path_on_datastore=args.input_datastore_path,
),
"output_datapath": DataPath(
datastore=args.output_datastore_name,
path_on_datastore=args.output_datastore_path,
),
}
run_pipeline(workspace, args.pipeline_name, pipeline_parameters)
elif args.pipeline_action == "publish":
publish_pipeline(
workspace, args.pipeline_name, args.disable_published_pipelines
)
else:
raise Exception("Invalid pipeline action:", args.pipeline_action)
except Exception:
exception = f"Exception: train_pipeline.py\n{traceback.format_exc()}"
print(exception)
exit(1)
def get_input_dataset(ws: Workspace, ds: Datastore, env: Env) -> Dataset:
"""
Gets an input dataset wrapped around an input data file. The input
data file is assumed to exist in the supplied datastore.
:param ws: AML Workspace
:param ds: Datastore containing the data file
:param env: Environment variables
:returns: Input Dataset
"""
scoringinputds = Dataset.Tabular.from_delimited_files(
path=DataPath(ds, env.scoring_datastore_input_filename)
)
scoringinputds = scoringinputds.register(
ws,
name=env.scoring_dataset_name,
tags={"purpose": "scoring input", "format": "csv"},
create_new_version=True,
).as_named_input(env.scoring_dataset_name)
return scoringinputds
def process_step(datastore: Datastore, compute: ComputeTarget,
path_on_datastore: str) -> (PipelineData, EstimatorStep):
datapath = DataPath(datastore=datastore,
path_on_datastore=path_on_datastore)
data_path_pipeline_param = (PipelineParameter(name="data",
default_value=datapath),
DataPathComputeBinding(mode='mount'))
seer_tfrecords = PipelineData("tfrecords_set",
datastore=datastore,
is_directory=True)
prep = Estimator(source_directory='.',
compute_target=compute,
entry_script='prep.py',
pip_requirements_file='requirements.txt')
prepStep = EstimatorStep(name='Data Preparation',
estimator=prep,
estimator_entry_script_arguments=[
"--source_path", data_path_pipeline_param,
"--target_path", seer_tfrecords
],
inputs=[data_path_pipeline_param],
outputs=[seer_tfrecords],
compute_target=compute)
return seer_tfrecords, prepStep
def load_tabular_partition(self,
partition_name: str,
datastore_name: str = None,
columns: np.array = None,
first_row_header: bool = False,
cloud_storage: bool = True) -> pd.DataFrame:
'''
Loads a partition from a tabular dataset.
The implementation will connect to the DataStore and get all delimited files matching the partition_name
When configured locally, the implementation will append all files in the datastore_path with name {partition_name}.csv
Args:
partition_name (str): The name of the partition as a wildcard filter. Example: B* will take all files starting with B, ending with csv
columns: (np.array): The column names to assign to the dataframe
datastore_path (str): The name of a DataStore that contains Datasets
cloud_storage (bool): When changed to False, the dataset will be loaded from the local folder
Returns:
pd.DataFrame: The dataset, loaded as a DataFrame
'''
if not datastore_name:
# No datastore name is given, so we'll take the default one
datastore_name = self.__datastore_path
if cloud_storage:
# Connecting data store
datastore = Datastore(self.__workspace, name=datastore_name)
try:
_header = PromoteHeadersBehavior.ALL_FILES_HAVE_SAME_HEADERS if first_row_header else False
_aml_dataset = Dataset.Tabular.from_delimited_files(
header=_header,
path=DataPath(datastore, '/' + partition_name +
'.csv')) #, set_column_types=columns
_df = _aml_dataset.to_pandas_dataframe()
except DatasetValidationError as dsvalex:
if 'provided path is not valid' in str(dsvalex):
return None
else:
raise
else:
# Reading data from sub files in a folder
_folder_path = datastore_name
_partition_files = glob.glob(_folder_path + '/' + partition_name +
'.csv')
_record_found = False
_df = None
for filename in _partition_files:
_header = 0 if first_row_header else None
df = pd.read_csv(filename, index_col=None, header=_header)
if not _record_found:
_df = df
_record_found = True
else:
_df = _df.append(df)
if not _record_found:
return None
if columns != None:
_df.columns = columns
return _df
def get_input_dataset(workspace, datastore, env):
scoring_input_ds = Dataset.Tabular.from_delimited_files(
path=DataPath(datastore, env.scoring_datastore_input_filename))
scoring_input_ds = scoring_input_ds.register(
workspace=workspace,
name=env.scoring_dataset_name,
tag={
'purpose': 'for scoring',
'format': 'csv'
},
create_new_version=True).as_named_input(env.scoring_dataset_name)
return scoring_input_ds
def submit_pipeline(
workspace=None, # Auto populated args + object
pipeline_id=None,
experiment_name=None,
pipeline_yaml=None,
pipeline_params=None,
datapath_params=None,
output_file=None,
# We enforce a logger
logger=None):
"""
Submit a pipeline run based on a published pipeline ID
"""
if pipeline_id is None and pipeline_yaml is None:
raise UserErrorException("Please specify a pipeline ID or a pipeline YAML file")
published_pipeline = None
pipeline = None
if pipeline_id is not None:
from azureml.pipeline.core import PublishedPipeline
published_pipeline = PublishedPipeline.get(workspace, pipeline_id)
if experiment_name is None or experiment_name == '':
# Use the pipeline name as the experiment name
experiment_name = published_pipeline._sanitize_name()
else:
from azureml.pipeline.core import Pipeline
pipeline = Pipeline.load_yaml(workspace, pipeline_yaml)
if experiment_name is None:
raise UserErrorException("Please specify an experiment name")
assigned_params = _parse_key_values(pipeline_params, 'Parameter assignment')
datapaths = _parse_key_values(datapath_params, 'Datapath assignment')
for datapath_param_name in datapaths:
datastore_with_path = datapaths[datapath_param_name]
if '/' not in datastore_with_path:
raise UserErrorException("Datapath value %s should have format datastore/path" % datastore_with_path)
path_tokens = datastore_with_path.split('/', 1)
from azureml.core import Datastore
from azureml.data.datapath import DataPath
datastore = Datastore(workspace, path_tokens[0])
assigned_params[datapath_param_name] = DataPath(datastore=datastore, path_on_datastore=path_tokens[1])
dict_output = _pipeline_run_submit(experiment_name, assigned_params, published_pipeline, pipeline,
workspace, output_file, logger)
return dict_output
def datastore_upload_files(args):
"""
Get the default datastore and upload files into it
"""
workspace = package_utils.get_workspace()
datastore = package_utils.get_default_datastore(workspace)
directory = pathlib.Path(args.dataset_path, args.dataset_name)
if not os.path.exists(directory):
msg = f"The dataset directory {directory} does not exist"
logger.exception(msg)
raise RuntimeError(msg)
files = [
os.path.abspath(file)
for file in sorted(glob.glob(f"{directory}/*.csv"))
]
target_path = f"{args.dataset_name}_{args.dataset_version}"
kwargs = {
"files": files,
"target_path": target_path,
"overwrite": args.dataset_overwrite,
}
logger.info(msg="datastore.upload_files", extra={"kwargs": kwargs})
if not args.dry_run:
try:
_ = upload_files(datastore, **kwargs)
except:
msg = f"Upload to target_path {target_path} failed"
logger.exception(msg)
raise RuntimeError(msg)
datastore_path = [
DataPath(datastore,
str(pathlib.Path(target_path, os.path.basename(file))))
for file in files
]
return datastore_path, target_path
def upload(self, folder_to_upload, path_datastore, dataset_name=None):
"""
Upload files to Azure Blob Storage attached to AzureML Workspace.
Args:
folder_to_upload: Local folder to be uploaded to the DataStore.
path_datastore: Path in the Datastore where files in
folder_to_upload will be stored.
dataset_name: Name of the Dataset created as a result ot the
upload.
Returns:
Returns a Filedataset of the uploaded folder in Datastore.
"""
targetPath = DataPath(self.datastore, path_datastore)
fileDataset = Dataset.File.upload_directory(
folder_to_upload,
targetPath)
if dataset_name is not None:
fileDataset.register(self.workspace, dataset_name)
return fileDataset
#### Define Pipeline! ####
##########################
# The following will be created and then run:
# 1. Pipeline Parameters
# 2. Data Process Step
# 3. Training Step
# 4. Model Registration Step
# 5. Pipeline registration
# 6. Submit the pipeline for execution
## Pipeline Parameters ##
# We need to tell the Pipeline what it needs to learn to see!
datapath = DataPath(datastore=datastore, path_on_datastore=datastorepath)
data_path_pipeline_param = (PipelineParameter(name="data",
default_value=datapath),
DataPathComputeBinding(mode='mount'))
# Configuration for data prep and training steps #
dataprepEnvironment = Environment.from_pip_requirements(
'dataprepenv', 'requirements-dataprepandtraining.txt')
dataprepRunConfig = RunConfiguration()
dataprepRunConfig.environment = dataprepEnvironment
## Data Process Step ##
# parse.py file parses the images in our data source #
seer_tfrecords = PipelineData("tfrecords_set",
from azureml.data.datapath import DataPath, DataPathComputeBinding
from azureml.train.dnn import TensorFlow
clusterName = "NV6AICluster"
# Load workspace
ws = Workspace.from_config()
# Connect to Compute Target
computeCluster = ComputeTarget(workspace=ws, name=clusterName)
# connect to datastores
source_ds = Datastore.get(ws, 'SimpsonDataStore')
training_ds = Datastore.get(ws, 'SimpsonTrainingDataStore')
source_dataset = DataPath(datastore=source_ds, path_on_datastore="trainingdata")
# Parameters make it easy for us to re-run this training pipeline, including for retraining.
source_dataset_param = (PipelineParameter(name="source_dataset",default_value=source_dataset),
DataPathComputeBinding())
script_folder = "./steps"
# == Step 1 ==
cd = CondaDependencies.create(pip_packages=["azureml-sdk","opencv-python"])
amlcompute_run_config = RunConfiguration(conda_dependencies=cd)
training_data_location = PipelineData(name="trainingdata", datastore=training_ds)
preProcessDataStep = PythonScriptStep(name="Pre-process data",
script_name="prep.py",
TRAIN_DATA_SPLIT = 0.8
NUMBER_ESTIMATORS = 10
TRAIN_FOLDER_NAME = "src/train"
TRAIN_FILE_NAME = "train.py"
MODELNAME = "script-classifier"
SERVICENAME = "script-deployment"
MODELFILENAME = "model.pkl"
ws = Workspace.from_config()
exp = Experiment(ws, "MaxFreezerTemperatureExceeded", _create_in_cloud=True)
logger.info("Experiment created")
# ACCESS DATA
datastore = Datastore.get(ws, "sensordata")
datapath = DataPath(datastore=datastore,
path_on_datastore="/processed/json/**")
dataset = Dataset.Tabular.from_json_lines_files(
path=datapath,
validate=True,
include_path=False,
set_column_types={
"allevents": DataType.to_string(),
"ConnectionDeviceID": DataType.to_string(),
},
partition_format="/{PartitionDate:yyyy/MM/dd}/",
)
dataset.register(
workspace=ws,
name="processed_json",
description="Output from Stream Analytics",
create_new_version=True,
#########################MODIFY###########################
#Get Azure SQL Datastore - CHANGE AZURE SQL DATASTORE NAME
azsql_ds = Datastore.get(ws, 'azsql_ds')
#UPDATE QUERY STRING HERE
query_string = 'SELECT * FROM Filter WHERE D={}'.format(str(query_param))
##########################################################
#Query Azure SQL Datastore
filter_sql_query = DataPath(azsql_ds, query_string)
filter_sql_ds = Dataset.Tabular.from_sql_query(filter_sql_query, query_timeout=10)
#Convert dataset to pandas dataframe
filter_df = filter_sql_ds.to_pandas_dataframe()
#Write dataframe to output dataset path
os.makedirs(filter_dataset, exist_ok=True)
filter_df.to_csv(os.path.join(filter_dataset, 'filter_data.csv'), index=False)
# Storage account access key
account_key = env.storage_account_key
# Verify that the blob store does not exist already
try:
blob_datastore = Datastore.get(aml_workspace, blob_datastore_name)
print('Found existing datastore, use it.')
except HttpOperationError:
blob_datastore = Datastore.register_azure_blob_container(
workspace=aml_workspace,
datastore_name=blob_datastore_name,
container_name=container_name,
account_name=account_name,
account_key=account_key)
print("Registered blob datastore with name: %s" % blob_datastore_name)
# Register dataset without creating new version
input_datastore_paths = [DataPath(blob_datastore, env.input_dataset_name)]
input_dataset = Dataset.File.from_files(path=input_datastore_paths)
input_dataset = input_dataset.register(workspace=aml_workspace,
name=env.input_dataset_name,
description=env.input_dataset_name)
print("Registered dataset: %s" % input_dataset.name)
waves_datastore_paths = [DataPath(blob_datastore, env.waves_dataset_name)]
waves_dataset = Dataset.File.from_files(path=waves_datastore_paths)
waves_dataset = waves_dataset.register(workspace=aml_workspace,
name=env.waves_dataset_name,
description=env.waves_dataset_name)
print("Registered dataset: %s" % waves_dataset.name)
from azureml.core import Dataset, Datastore
from azureml.data.datapath import DataPath
from azureml.data.dataset_factory import TabularDatasetFactory
# Connect to the Azure Machine Learning Workspace
azureml_workspace = Workspace.from_config(auth=sp_auth)
# Like the DBFS Mount, the Azure ML Datastore references the same `processed` container on Azure Storage
processed_ds = Datastore.get(azureml_workspace, 'datastoreprocessed')
# Dataset A: a subset of comments in the gaming category.
# We will use it to run a quick feasiblity analysis experiment. As well to have a cost-effective way to experiment with changes while we iterate on model versions.
comments_subset_gaming_dataset = TabularDatasetFactory.from_parquet_files([
DataPath(processed_ds, path) for path in match_pattern_on_storage(
"redditcomments/subreddit=gaming/*.parquet")
])
# Dataset: the full set of comments for scale model training
comments_full_dataset = TabularDatasetFactory.from_parquet_files([
DataPath(processed_ds, path)
for path in match_pattern_on_storage("redditcomments/*/*.parquet")
])
# Register the data set versions in Azure ML for reference during training
comments_full_dataset.register(azureml_workspace,
name="redditcomments",
create_new_version=True,
description="The full dataset of comments")
#### Define Pipeline! ####
##########################
# The following will be created and then run:
# 1. Pipeline Parameters
# 2. Data Process Step
# 3. Training Step
# 4. Model Registration Step
# 5. Pipeline registration
# 6. Submit the pipeline for execution
## Pipeline Parameters ##
# We need to tell the Pipeline what it needs to learn to see!
source_dataset = DataPath(datastore=ds, path_on_datastore="seer")
source_dataset_param = (PipelineParameter(name="source_dataset",
default_value=source_dataset),
DataPathComputeBinding())
# Configuration for data prep and training steps #
## Data Process Step ##
# prep.py file versions our data in our data source #
# Output location for the pre-proccessed trainings images
training_data_location = PipelineData(name="seertrainingdata", datastore=ds)
# Create the pre-process step
preProcessDataStep = PythonScriptStep(name="Pre-process data",
from azureml.data.datapath import DataPath
# Get workspace
ws = Workspace(
subscription_id=args.subscription_id,
resource_group=args.resource_group,
workspace_name=args.workspace_name
)
files = [
h5_path
]
datastore = ws.get_default_datastore()
datastore.upload_files(
files=files,
relative_root=args.outputs_path,
target_path=args.outputs_path
)
logger.success("Files uploaded to '{}' in the datastore".format(args.outputs_path))
# Create dataset and register it
paths = [
DataPath(datastore=datastore, path_on_datastore=h5_path),
]
dataset = Dataset.File.from_files(path=paths)
ds_name = "voc-classification"
dataset.register(
workspace=ws,
name=ds_name,
description="Preprocessed features and labels of Pascal VOC 2012: 0-padding, resizing and features normalization on classification task"
)
print("File dataset {} registered".format(ds_name))
##########################
# The following will be created and then run:
# 1. Pipeline Parameters
# 2. Data Process Step
# 3. Training Step
# 4. Model Registration Step
# 5. Pipeline registration
# 6. Submit the pipeline for execution
## Pipeline Parameters ##
# We need to tell the Pipeline what it needs to learn to see!
source_dataset = DataPath(
datastore=ds,
path_on_datastore="simpsonslego-v3")
source_dataset_param = (PipelineParameter(name="source_dataset",default_value=source_dataset),
DataPathComputeBinding())
# Configuration for data prep and training steps #
## Data Process Step ##
# prep.py file versions our data in our data source #
# Output location for the pre-proccessed trainings images
training_data_location = PipelineData(name="simpsons_training_data", datastore=ds)
# Create the pre-process step
preProcessDataStep = PythonScriptStep(
from azureml.core import Environment
from azureml.core.conda_dependencies import CondaDependencies
from azureml.core import Dataset
subscription_id = 'bd04922c-a444-43dc-892f-74d5090f8a9a'
resource_group = 'mlplayarearg'
workspace_name = 'testdeployment'
workspace = Workspace(subscription_id, resource_group, workspace_name)
mydatastore = Datastore.get(workspace, 'billingdatablobstorage')
from azureml.data.datapath import DataPath, DataPathComputeBinding
from azureml.pipeline.core.graph import PipelineParameter
data_path = DataPath(datastore=mydatastore, path_on_datastore='rawdata')
datapath1_pipeline_param = PipelineParameter(name="input_datapath",
default_value=data_path)
datapath_input = (datapath1_pipeline_param,
DataPathComputeBinding(mode='mount'))
string_pipeline_param = PipelineParameter(name="input_string",
default_value='sample_string1')
compute_config = RunConfiguration()
compute_config.target = "cpu-cluster"
dependencies = CondaDependencies()
dependencies.add_pip_package("adal==0.4.7")
compute_config.environment.python.conda_dependencies = dependencies
from azureml.pipeline.core import Pipeline, PipelineData, PipelineParameter
from azureml.pipeline.steps import PythonScriptStep
workspace = Workspace.from_config()
blobstore = workspace.get_default_datastore()
environment = Environment.get(workspace, name="AzureML-Scikit-learn-0.20.3")
environment.docker.enabled = True
run_config = RunConfiguration()
run_config.environment = environment
compute_target = workspace.compute_targets["cpu"]
run_config.target = compute_target
train_features_datapath = DataPath(
datastore=blobstore, path_on_datastore="training_set_features.csv")
train_features_path_parameter = PipelineParameter(
name="train_features", default_value=train_features_datapath)
train_features_path = (train_features_path_parameter,
DataPathComputeBinding(mode="mount"))
train_labels_datapath = DataPath(datastore=blobstore,
path_on_datastore="training_set_labels.csv")
train_labels_path_parameter = PipelineParameter(
name="train_labels", default_value=train_labels_datapath)
train_labels_path = (train_labels_path_parameter,
DataPathComputeBinding(mode="mount"))
test_features_datapath = DataPath(datastore=blobstore,
path_on_datastore="test_set_features.csv")
test_features_path_parameter = PipelineParameter(
#########################MODIFY########################### #Get Azure SQL Datastore - CHANGE AZURE SQL DATASTORE NAME azsql_ds = Datastore.get(ws, 'azsql_ds') #UPDATE QUERY STRING HERE query_string = 'SELECT * FROM Profile' ########################################################## #Query Azure SQL Datastore profile_sql_query = DataPath(azsql_ds, query_string) profile_sql_ds = Dataset.Tabular.from_sql_query(profile_sql_query, query_timeout=10) #Convert dataset to pandas dataframe and return profile_df = profile_sql_ds.to_pandas_dataframe() #Write dataframe to output dataset path os.makedirs(profile_dataset, exist_ok=True) profile_df.to_csv(os.path.join(profile_dataset, 'profile_data.csv'), index=False)
pip_packages=["azureml-defaults", 'tensorflow==1.8.0'])
amlcompute_run_config = RunConfiguration(conda_dependencies=cd)
# Define our computes
data_factory_compute = DataFactoryCompute(ws, data_factory_name)
aml_compute = AmlCompute(ws, aml_compute_target)
# We explicitly declare the data we're using in this training pipeline
source_images = DataReference(datastore=source_ds,
data_reference_name="original_images",
path_on_datastore=default_dataset)
dest_images = DataReference(datastore=ds,
data_reference_name="transferred_images",
path_on_datastore='training_images')
training_dataset = DataPath(datastore=source_ds,
path_on_datastore=default_dataset)
# Parameters make it easy for us to re-run this training pipeline, including for retraining.
model_variant = PipelineParameter(name="model_variant",
default_value='sodacans')
training_dataset_param = (PipelineParameter(name="training_dataset",
default_value=training_dataset),
DataPathComputeBinding())
# Copying data into a datastore we manage ensures we can reproduce the model later on.
datatransfer = DataTransferStep(
name=
"Copy training data for improved performance and model reproducibility",
source_data_reference=source_images,
destination_data_reference=dest_images,
compute_target=data_factory_compute)
def create_pipeline(workspace):
# Retreive compute cluster
compute_target = workspace.compute_targets[args.compute_target]
# Setup batch scoring environment from conda dependencies
environment = Environment.from_conda_specification(
name=args.environment_name, file_path=args.environment_specification
)
# Add environment variables
environment.environment_variables = {
"APPLICATIONINSIGHTS_CONNECTION_STRING": args.ai_connection_string
}
# Enable docker run
environment.docker.enabled = True
# Create run config
run_config = RunConfiguration()
run_config.environment = environment
# Retreive input and output datastores
input_datastore = Datastore(workspace, args.input_datastore_name)
output_datastore = Datastore(workspace, args.output_datastore_name)
# Define build id parameter
build_id_param = PipelineParameter("build_id", default_value=args.build_id)
# Define input datapath parameter
input_datapath = DataPath(datastore=input_datastore, path_on_datastore="")
input_datapath_param = (
PipelineParameter(name="input_datapath", default_value=input_datapath),
DataPathComputeBinding(mode="mount"),
)
# Define output datapath parameter
output_datapath = DataPath(datastore=output_datastore, path_on_datastore="")
output_datapath_param = (
PipelineParameter(name="output_datapath", default_value=output_datapath),
DataPathComputeBinding(mode="mount"),
)
# Define score step for pipeline
score_step = PythonScriptStep(
name="score_data",
compute_target=compute_target,
source_directory="src/score",
script_name="score.py",
inputs=[input_datapath_param, output_datapath_param],
runconfig=run_config,
allow_reuse=False,
arguments=[
"--build_id",
build_id_param,
"--input_datapath",
input_datapath_param,
"--output_datapath",
output_datapath_param,
],
)
# Define pipeline for batch scoring
pipeline = Pipeline(workspace=workspace, steps=[score_step])
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