async def get_actor_creation_tasks(cls):
infeasible_tasks = sum(
(list(node_stats.get("infeasibleTasks", []))
for node_stats in DataSource.node_stats.values()), [])
new_infeasible_tasks = []
for task in infeasible_tasks:
task = dict(task)
task["actorClass"] = actor_classname_from_task_spec(task)
task["state"] = "INFEASIBLE"
new_infeasible_tasks.append(task)
resource_pending_tasks = sum(
(list(data.get("readyTasks", []))
for data in DataSource.node_stats.values()), [])
new_resource_pending_tasks = []
for task in resource_pending_tasks:
task = dict(task)
task["actorClass"] = actor_classname_from_task_spec(task)
task["state"] = "PENDING_RESOURCES"
new_resource_pending_tasks.append(task)
results = {
task["actorCreationTaskSpec"]["actorId"]: task
for task in new_resource_pending_tasks + new_infeasible_tasks
}
return results
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 _process_actor_table_data(data):
actor_class = actor_classname_from_task_spec(
data.get("taskSpec", {}))
data["actorClass"] = actor_class