Events
Module contains events processing mechanisms that are integrated with the standard python logging.
Example of usage:
from torch.distributed.elastic import events
event = events.Event(name="test_event", source=events.EventSource.WORKER, metadata={...})
events.get_logging_handler(destination="console").info(event)
API Methods
-
torch.distributed.elastic.events.record(event, destination='null')[source]
-
torch.distributed.elastic.events.get_logging_handler(destination='null')[source]
- Return type:
Handler
Event Objects
-
class torch.distributed.elastic.events.api.Event(name, source, timestamp=0, metadata=<factory>)[source]
The class represents the generic event that occurs during the torchelastic job execution.
The event can be any kind of meaningful action.
- Parameters:
name (str) – event name.
source (EventSource) – the event producer, e.g. agent or worker
timestamp (int) – timestamp in milliseconds when event occurred.
metadata (Dict[str, Optional[Union[str, int, float, bool]]]) – additional data that is associated with the event.
-
class torch.distributed.elastic.events.api.EventSource(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]
Known identifiers of the event producers.
-
torch.distributed.elastic.events.api.EventMetadataValue
alias of Optional[Union[str, int, float, bool]]
