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# {% include 'templates/license_header' %}
import os
from typing import Optional
import click
from pipelines import (
feature_engineering,
inference,
breast_cancer_training,
breast_cancer_deployment_pipeline
)
from zenml.client import Client
from zenml.logger import get_logger
logger = get_logger(__name__)
@click.command(
help="""
ZenML Starter project CLI v0.0.1.
Run the ZenML starter project with basic options.
Examples:
\b
# Run the feature engineering pipeline
python run.py --feature-pipeline
\b
# Run the training pipeline
python run.py --training-pipeline
\b
# Run the training pipeline with versioned artifacts
python run.py --training-pipeline --train-dataset-version-name=1 --test-dataset-version-name=1
\b
# Run the inference pipeline
python run.py --inference-pipeline
"""
)
@click.option(
"--train-dataset-name",
default="dataset_trn",
type=click.STRING,
help="The name of the train dataset produced by feature engineering.",
)
@click.option(
"--train-dataset-version-name",
default=None,
type=click.STRING,
help="Version of the train dataset produced by feature engineering. "
"If not specified, a new version will be created.",
)
@click.option(
"--test-dataset-name",
default="dataset_tst",
type=click.STRING,
help="The name of the test dataset produced by feature engineering.",
)
@click.option(
"--test-dataset-version-name",
default=None,
type=click.STRING,
help="Version of the test dataset produced by feature engineering. "
"If not specified, a new version will be created.",
)
@click.option(
"--feature-pipeline",
is_flag=True,
default=False,
help="Whether to run the pipeline that creates the dataset.",
)
@click.option(
"--training-pipeline",
is_flag=True,
default=False,
help="Whether to run the pipeline that trains the model.",
)
@click.option(
"--inference-pipeline",
is_flag=True,
default=False,
help="Whether to run the pipeline that performs inference.",
)
@click.option(
"--deployment-pipeline",
is_flag=True,
default=False,
help="Whether to run the pipeline that deploys the model.",
)
def main(
train_dataset_name: str = "dataset_trn",
train_dataset_version_name: Optional[str] = None,
test_dataset_name: str = "dataset_tst",
test_dataset_version_name: Optional[str] = None,
feature_pipeline: bool = False,
training_pipeline: bool = False,
inference_pipeline: bool = False,
deployment_pipeline: bool = False,
):
"""Main entry point for the pipeline execution.
This entrypoint is where everything comes together:
* configuring pipeline with the required parameters
(some of which may come from command line arguments, but most
of which comes from the YAML config files)
* launching the pipeline
"""
config_folder = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
"configs",
)
# Execute Feature Engineering Pipeline
if feature_pipeline:
pipeline_args = {}
pipeline_args["config_path"] = os.path.join(
config_folder, "feature_engineering.yaml"
)
run_args_feature = {}
feature_engineering.with_options(**pipeline_args)(**run_args_feature)
logger.info("Feature Engineering pipeline finished successfully!")
# Execute Training Pipeline
if training_pipeline:
pipeline_args = {}
pipeline_args["config_path"] = os.path.join(config_folder, "training.yaml")
run_args_train = {}
# If train_dataset_version_name is specified, use versioned artifacts
if train_dataset_version_name or test_dataset_version_name:
# However, both train and test dataset versions must be specified
assert (
train_dataset_version_name is not None
and test_dataset_version_name is not None
)
client = Client()
train_dataset_artifact = client.get_artifact(
train_dataset_name, train_dataset_version_name
)
# If train dataset is specified, test dataset must be specified
test_dataset_artifact = client.get_artifact(
test_dataset_name, test_dataset_version_name
)
# Use versioned artifacts
run_args_train["train_dataset_id"] = train_dataset_artifact.id
run_args_train["test_dataset_id"] = test_dataset_artifact.id
breast_cancer_training.with_options(**pipeline_args)(**run_args_train)
logger.info("Training pipeline finished successfully!")
if inference_pipeline:
pipeline_args = {}
pipeline_args["config_path"] = os.path.join(config_folder, "inference.yaml")
run_args_inference = {}
inference.with_options(**pipeline_args)(**run_args_inference)
logger.info("Inference pipeline finished successfully!")
if deployment_pipeline:
pipeline_args = {}
pipeline_args["config_path"] = os.path.join(config_folder, "deployment.yaml")
run_args_inference = {}
breast_cancer_deployment_pipeline.with_options(**pipeline_args)(**run_args_inference)
logger.info("Deployment pipeline finished successfully!")
if __name__ == "__main__":
main()
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