def run_merge_job(
vector,
target,
entity_rows=None,
timestamp_column=None,
run_config=None,
drop_columns=None,
):
name = vector.metadata.name
if not name:
raise mlrun.errors.MLRunInvalidArgumentError(
"feature vector name must be specified")
if not target or not hasattr(target, "to_dict"):
raise mlrun.errors.MLRunInvalidArgumentError(
"target object must be specified")
name = f"{name}_merger"
run_config = run_config or RunConfig()
if not run_config.function:
function_ref = vector.spec.function.copy()
if function_ref.is_empty():
function_ref = FunctionReference(name=name, kind="job")
if not function_ref.url:
function_ref.code = _default_merger_handler
run_config.function = function_ref
function = run_config.to_function(
"job", mlrun.mlconf.feature_store.default_job_image)
function.metadata.project = vector.metadata.project
function.metadata.name = function.metadata.name or name
task = new_task(
name=name,
params={
"vector_uri": vector.uri,
"target": target.to_dict(),
"timestamp_column": timestamp_column,
"drop_columns": drop_columns,
},
inputs={"entity_rows": entity_rows},
)
task.spec.secret_sources = run_config.secret_sources
task.set_label("job-type",
"feature-merge").set_label("feature-vector", vector.uri)
task.metadata.uid = uuid.uuid4().hex
vector.status.run_uri = task.metadata.uid
vector.save()
run = function.run(
task,
handler=run_config.handler or "merge_handler",
local=run_config.local,
watch=run_config.watch,
)
logger.info(f"feature vector merge job started, run id = {run.uid()}")
return RemoteVectorResponse(vector, run)
def run_merge_job(
vector,
target,
entity_rows=None,
timestamp_column=None,
local=None,
watch=None,
drop_columns=None,
function=None,
secrets=None,
auto_mount=None,
):
name = vector.metadata.name
if not name:
raise mlrun.errors.MLRunInvalidArgumentError(
"feature vector name must be specified")
if not target or not hasattr(target, "to_dict"):
raise mlrun.errors.MLRunInvalidArgumentError(
"target object must be specified")
name = f"{name}_merger"
if not function:
function_ref = vector.spec.function
if not function_ref.to_dict():
function_ref = FunctionReference(name=name, kind="job")
function_ref.image = (function_ref.image
or mlrun.mlconf.feature_store.default_job_image)
if not function_ref.url:
function_ref.code = _default_merger_handler
function = function_ref.to_function()
if auto_mount:
function.apply(mlrun.platforms.auto_mount())
function.metadata.project = vector.metadata.project
task = new_task(
name=name,
params={
"vector_uri": vector.uri,
"target": target.to_dict(),
"timestamp_column": timestamp_column,
"drop_columns": drop_columns,
},
inputs={"entity_rows": entity_rows},
)
if secrets:
task.with_secrets("inline", secrets)
task.metadata.uid = uuid.uuid4().hex
vector.status.run_uri = task.metadata.uid
vector.save()
run = function.run(task, handler="merge_handler", local=local, watch=watch)
logger.info(f"feature vector merge job started, run id = {run.uid()}")
return RemoteVectorResponse(vector, run)
def _generate_task(self):
return new_task(name=self.name,
project=self.project,
artifact_path=self.artifact_path)