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173 | class RayPipelineWriter(BasePipelineWriter):
def _write_dag(self) -> None:
# Check if the given DAG flavor is a supported/valid one
try:
dag_flavor = RayDagFlavor[
self.dag_config.get("dag_flavor", "TaskPerArtifact")
]
except KeyError:
raise ValueError(f'"{dag_flavor}" is an invalid ray 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: RayDagFlavor,
) -> str:
"""
Returns a code block containing all the operators for a Ray DAG.
"""
if self.dag_config.get("use_workflows", True):
DAG_TEMPLATE = load_plugin_template("ray/ray_dag_workflow.jinja")
else:
DAG_TEMPLATE = load_plugin_template("ray/ray_dag_remote.jinja")
if dag_flavor == RayDagFlavor.TaskPerSession:
task_breakdown = DagTaskBreakdown.TaskPerSession
elif dag_flavor == RayDagFlavor.TaskPerArtifact:
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,
)
if (
self.dag_config.get("use_workflows", True)
and len(task_graph.sink_nodes) > 1
):
raise RuntimeError(
"Ray workflows do not currently support multiple artifacts being returned as sink nodes.\n\
Consider use use_workflows=False to disable using Ray Workflows API."
)
rendered_task_defs = self.get_rendered_task_definitions(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
# set ray working dir to local directory so that module file can be picked up
# if this config is not already set
ray_runtime_env = self.dag_config.get("runtime_env", {})
if "working_dir" not in ray_runtime_env:
ray_runtime_env["working_dir"] = "."
full_code = DAG_TEMPLATE.render(
DAG_NAME=self.pipeline_name,
MODULE_NAME=self.pipeline_name + "_module",
RAY_RUNTIME_ENV=ray_runtime_env,
RAY_STORAGE=self.dag_config.get("storage", "/tmp"),
task_definitions=rendered_task_defs,
tasks=task_defs,
dag_params=input_parameters_dict,
# sink tasks needed for ray since DAG needs to specify them
sink_tasks=task_graph.sink_nodes,
)
return prettify(full_code)
@property
def docker_template_name(self) -> str:
return "ray/ray_dockerfile.jinja"
def _get_requirements(self):
libraries = super()._get_requirements()
# add packaging library which is required for the DAG to check ray version
# this library is not used by any task and must be added in post
if "packaging" not in libraries:
libraries["packaging"] = "21.3"
return libraries
def get_rendered_task_definitions(
self,
task_defs: Dict[str, TaskDefinition],
) -> List[str]:
"""
Returns rendered tasks for the pipeline tasks along with 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.
"""
def user_input_variables_fn(task_def) -> str:
input_vars = (
task_def.user_input_variables + task_def.loaded_input_variables
)
return ", ".join(input_vars)
def function_decorator_fn(task_def) -> str:
# only specify num returns in function decorator for worflow
function_decorator = "@ray.remote"
if not self.dag_config.get("use_workflows", True):
function_decorator += (
f"(num_returns={len(task_def.return_vars)})"
)
elif len(task_def.return_vars) > 1:
raise RuntimeError(
f"Ray workflows do not currently support tasks with multiple returns. Task {task_def.function_name} has {len(task_def.return_vars)} returns.\n\
Consider use use_workflows=False to disable using Ray Workflows API."
)
return function_decorator
def return_block_fn(task_def) -> str:
return f"return {', '.join(task_def.return_vars)}"
rendered_task_defs: List[str] = render_task_definitions(
task_defs,
self.pipeline_name,
task_serialization=None,
function_decorator_fn=function_decorator_fn,
user_input_variables_fn=user_input_variables_fn,
return_block_fn=return_block_fn,
)
return rendered_task_defs
|