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174 | class Pipeline:
def __init__(
self,
artifacts: List[str],
name: Optional[str] = None,
dependencies: TaskGraphEdge = {},
):
if len(artifacts) == 0:
raise ValueError(
"Pipelines must contain at least one artifact\nEmpty Pipelines are invalid"
)
self.dependencies = dependencies
self.artifact_names: List[str] = artifacts
artifact_safe_names = []
for artifact_name in artifacts:
artifact_var = slugify(artifact_name)
if len(artifact_var) == 0:
raise ValueError(f"Invalid slice name {artifact_name}.")
artifact_safe_names.append(artifact_var)
self.name = name or "_".join(artifact_safe_names)
self.id = get_new_id()
def export(
self,
framework: str = "SCRIPT",
output_dir: str = ".",
input_parameters: List[str] = [],
reuse_pre_computed_artifacts: List[str] = [],
generate_test: bool = False,
pipeline_dag_config: Optional[Dict] = {},
include_non_slice_as_comment=False,
) -> Path:
# Check if the specified framework is a supported/valid one
if framework not in PipelineType.__members__:
raise Exception(f"No PipelineType for {framework}")
# get artifact_collection for use in pipeline writers
artifact_collection = self._get_artifact_collection(
input_parameters=input_parameters,
reuse_pre_computed_artifacts=reuse_pre_computed_artifacts,
include_non_slice_as_comment=include_non_slice_as_comment,
dependencies=self.dependencies,
)
# Construct pipeline writer. Check out class:PipelineType for supported frameworks
# If you want to add a new framework, please read the "adding a new pipeline writer" tutorial
pipeline_writer = PipelineWriterFactory.get(
pipeline_type=PipelineType[framework],
artifact_collection=artifact_collection,
pipeline_name=self.name,
output_dir=output_dir,
generate_test=generate_test,
dag_config=pipeline_dag_config,
include_non_slice_as_comment=include_non_slice_as_comment,
)
# Write out pipeline files
pipeline_writer.write_pipeline_files()
# Provide user warning about currently unsupported functionality
if len(reuse_pre_computed_artifacts) > 0 and framework == "AIRFLOW":
warnings.warn(
"Reuse of pre-computed artifacts is currently NOT supported "
"for Airflow DAGs. Hence, the generated Airflow DAG file would "
"recompute all artifacts in the pipeline."
)
# Track the event
track(
ToPipelineEvent(
framework,
len(self.artifact_names),
len(self.dependencies) > 0,
pipeline_dag_config is not None,
)
)
return pipeline_writer.output_dir
def _get_artifact_collection(
self,
input_parameters,
reuse_pre_computed_artifacts,
dependencies,
include_non_slice_as_comment,
):
# Create artifact collection
execution_context = get_context()
artifact_defs = [
get_lineaartifactdef(art_entry=art_entry)
for art_entry in self.artifact_names
]
reuse_pre_computed_artifact_defs = [
get_lineaartifactdef(art_entry=art_entry)
for art_entry in reuse_pre_computed_artifacts
]
artifact_collection = ArtifactCollection(
db=execution_context.executor.db,
target_artifacts=artifact_defs,
input_parameters=input_parameters,
reuse_pre_computed_artifacts=reuse_pre_computed_artifact_defs,
dependencies=dependencies,
)
return artifact_collection
def save(self):
"""
Save this pipeline to the db using PipelineORM.
"""
execution_context = get_context()
db = execution_context.executor.db
session_orm = (
db.session.query(SessionContextORM)
.order_by(SessionContextORM.creation_time.desc())
.all()
)
if len(session_orm) == 0:
track(ExceptionEvent(ErrorType.PIPELINE, "No session found in DB"))
raise Exception("No sessions found in the database.")
artifacts_to_save = {
artifact_name: db.get_artifactorm_by_name(artifact_name)
for artifact_name in self.artifact_names
}
art_deps_to_save = []
for post_artifact, pre_artifacts in self.dependencies.items():
post_to_save = artifacts_to_save[post_artifact]
pre_to_save = [artifacts_to_save[a] for a in pre_artifacts]
art_dep_to_save = ArtifactDependencyORM(
post_artifact=post_to_save,
pre_artifacts=set(pre_to_save),
)
art_deps_to_save.append(art_dep_to_save)
pipeline_to_write = PipelineORM(
name=self.name,
artifacts=set(artifacts_to_save.values()),
dependencies=art_deps_to_save,
)
db.write_pipeline(art_deps_to_save, pipeline_to_write)
|