def node_stats_to_dict(message):
decode_keys = {
"actorId",
"jobId",
"taskId",
"parentTaskId",
"sourceActorId",
"callerId",
"rayletId",
"workerId",
"placementGroupId",
}
core_workers_stats = message.core_workers_stats
message.ClearField("core_workers_stats")
try:
result = dashboard_utils.message_to_dict(message, decode_keys)
result["coreWorkersStats"] = [
dashboard_utils.message_to_dict(
m, decode_keys, including_default_value_fields=True
)
for m in core_workers_stats
]
return result
finally:
message.core_workers_stats.extend(core_workers_stats)
def actor_table_data_to_dict(message):
return dashboard_utils.message_to_dict(message, {
"actorId", "parentId", "jobId", "workerId", "rayletId",
"actorCreationDummyObjectId", "callerId", "taskId", "parentTaskId",
"sourceActorId", "placementGroupId"
},
including_default_value_fields=True)
def _message_to_dict(
self,
*,
message,
fields_to_decode: List[str],
preserving_proto_field_name: bool = True,
) -> dict:
return dashboard_utils.message_to_dict(
message,
fields_to_decode,
including_default_value_fields=True,
preserving_proto_field_name=preserving_proto_field_name,
)
def actor_table_data_to_dict(message):
orig_message = dashboard_utils.message_to_dict(
message,
{
"actorId",
"parentId",
"jobId",
"workerId",
"rayletId",
"actorCreationDummyObjectId",
"callerId",
"taskId",
"parentTaskId",
"sourceActorId",
"placementGroupId",
},
including_default_value_fields=True,
)
# The complete schema for actor table is here:
# src/ray/protobuf/gcs.proto
# It is super big and for dashboard, we don't need that much information.
# Only preserve the necessary ones here for memory usage.
fields = {
"actorId",
"jobId",
"pid",
"address",
"state",
"name",
"numRestarts",
"taskSpec",
"timestamp",
"numExecutedTasks",
}
light_message = {k: v for (k, v) in orig_message.items() if k in fields}
if "taskSpec" in light_message:
actor_class = actor_classname_from_task_spec(light_message["taskSpec"])
light_message["actorClass"] = actor_class
if "functionDescriptor" in light_message["taskSpec"]:
light_message["taskSpec"] = {
"functionDescriptor":
light_message["taskSpec"]["functionDescriptor"]
}
else:
light_message.pop("taskSpec")
return light_message
def gcs_node_info_to_dict(message):
return dashboard_utils.message_to_dict(message, {"nodeId"},
including_default_value_fields=True)
def job_table_data_to_dict(message):
decode_keys = {"jobId", "rayletId"}
return dashboard_utils.message_to_dict(
message, decode_keys, including_default_value_fields=True)
