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177 | class KubeflowPipelineWriter(BasePipelineWriter):
def _write_dag(self) -> None:
# Check if the given DAG flavor is a supported/valid one
try:
dag_flavor = KubeflowDagFlavor[
self.dag_config.get("dag_flavor", "ComponentPerArtifact")
]
except KeyError:
raise ValueError(
f'"{dag_flavor}" is an invalid kubeflow dag flavor.'
)
# Construct DAG text for the given flavor
full_code = self._write_operators(dag_flavor)
# Write out file
file = self.output_dir / f"{self.pipeline_name}_dag.py"
file.write_text(full_code)
logger.info(f"Generated DAG file: {file}")
def _write_operators(
self,
dag_flavor: KubeflowDagFlavor,
) -> str:
"""
Returns a code block containing all the operators for a Kubeflow DAG.
"""
DAG_TEMPLATE = load_plugin_template("kubeflow/kubeflow_dag.jinja")
if dag_flavor == KubeflowDagFlavor.ComponentPerSession:
task_breakdown = DagTaskBreakdown.TaskPerSession
elif dag_flavor == KubeflowDagFlavor.ComponentPerArtifact:
task_breakdown = DagTaskBreakdown.TaskPerArtifact
# Get task definitions based on dag_flavor
task_defs, task_graph = get_task_graph(
self.artifact_collection,
pipeline_name=self.pipeline_name,
task_breakdown=task_breakdown,
)
task_defs["setup"] = get_noop_setup_task_definition(self.pipeline_name)
task_defs["teardown"] = get_noop_teardown_task_definition(
self.pipeline_name
)
# insert in order to task_names so that setup runs first and teardown runs last
task_graph.insert_setup_task("setup")
task_graph.insert_teardown_task("teardown")
task_names = list(task_defs.keys())
task_defs = {tn: task_defs[tn] for tn in task_names}
rendered_task_defs = self.get_rendered_task_definitions(task_defs)
task_loading_blocks = self.get_task_input_loading_code_blocks(
task_defs
)
input_parameters_dict: Dict[str, Any] = {}
for parameter_name, input_spec in super().get_pipeline_args().items():
input_parameters_dict[parameter_name] = input_spec.value
task_dependencies = sorted(
[
f"task_{task1}.after(task_{task0})"
for task0, task1 in task_graph.graph.edges
]
)
full_code = DAG_TEMPLATE.render(
DAG_NAME=self.pipeline_name,
HOST_URL=self.dag_config.get("host_url", "http://localhost:3000"),
dag_params=input_parameters_dict,
task_definitions=rendered_task_defs,
tasks=task_defs,
task_loading_blocks=task_loading_blocks,
task_dependencies=task_dependencies,
)
return prettify(full_code)
@property
def docker_template_name(self) -> str:
return "kubeflow/kubeflow_dockerfile.jinja"
def get_task_input_loading_code_blocks(self, task_defs) -> Dict[str, str]:
"""
Returns a dictionary to lookup previous task outputs.
The returned dictionary is used by the DAG to connect the right input files to
output files for inter task communication.
"""
task_input_loading_code_blocks: Dict[str, str] = {}
for task_name, task_def in task_defs.items():
# this task will output variables to a file that other tasks can access
# through KFP's task.outputs attribute
for return_variable in task_def.return_vars:
task_input_loading_code_blocks[
return_variable
] = f'task_{task_name}.outputs["variable_{return_variable}"]'
return task_input_loading_code_blocks
def get_rendered_task_definitions(
self,
task_defs: Dict[str, TaskDefinition],
) -> List[str]:
"""
Returns rendered tasks for the pipeline tasks
"""
def user_input_variables_fn(task_def) -> str:
input_vars = task_def.user_input_variables
input_paths = [
f"variable_{loaded_input_variable}_path: kfp.components.InputPath(str)"
for loaded_input_variable in task_def.loaded_input_variables
]
output_paths = [
f"variable_{return_variable}_path: kfp.components.OutputPath(str)"
for return_variable in task_def.return_vars
]
return ", ".join(input_vars + input_paths + output_paths)
rendered_task_defs: List[str] = render_task_definitions(
task_defs,
self.pipeline_name,
task_serialization=TaskSerializer.ParametrizedPickle,
user_input_variables_fn=user_input_variables_fn,
include_imports_locally=True,
)
return rendered_task_defs
|