This module is a prototype release, and its interfaces and functionality may change without warning in future PyTorch releases.

torch.monitor provides an interface for logging events and counters from PyTorch.

The stat interfaces are designed to be used for tracking high level metrics that are periodically logged out to be used for monitoring system performance. Since the stats aggregate with a specific window size you can log to them from critical loops with minimal performance impact.

For more infrequent events or values such as loss, accuracy, usage tracking the event interface can be directly used.

Event handlers can be registered to handle the events and pass them to an external event sink.

API Reference

class torch.monitor.Aggregation

These are types of aggregations that can be used to accumulate stats.



VALUE returns the last value to be added.


MEAN computes the arithmetic mean of all the added values.


COUNT returns the total number of added values.


SUM returns the sum of the added values.


MAX returns the max of the added values.


MIN returns the min of the added values.

property name
class torch.monitor.Stat

Stat is used to compute summary statistics in a performant way over fixed intervals. Stat logs the statistics as an Event once every window_size duration. When the window closes the stats are logged via the event handlers as a torch.monitor.Stat event.

window_size should be set to something relatively high to avoid a huge number of events being logged. Ex: 60s. Stat uses millisecond precision.

If max_samples is set, the stat will cap the number of samples per window by discarding add calls once max_samples adds have occurred. If it’s not set, all add calls during the window will be included. This is an optional field to make aggregations more directly comparable across windows when the number of samples might vary.

When the Stat is destructed it will log any remaining data even if the window hasn’t elapsed.

__init__(self: torch._C._monitor.Stat, name: str, aggregations: List[torch._C._monitor.Aggregation], window_size: datetime.timedelta, max_samples: int = 9223372036854775807) None

Constructs the Stat.

add(self: torch._C._monitor.Stat, v: float) None

Adds a value to the stat to be aggregated according to the configured stat type and aggregations.

property count

Number of data points that have currently been collected. Resets once the event has been logged.

get(self: torch._C._monitor.Stat) Dict[torch._C._monitor.Aggregation, float]

Returns the current value of the stat, primarily for testing purposes. If the stat has logged and no additional values have been added this will be zero.

property name

The name of the stat that was set during creation.

class torch.monitor.data_value_t

data_value_t is one of str, float, int, bool.

class torch.monitor.Event

Event represents a specific typed event to be logged. This can represent high-level data points such as loss or accuracy per epoch or more low-level aggregations such as through the Stats provided through this library.

All Events of the same type should have the same name so downstream handlers can correctly process them.

__init__(self: torch._C._monitor.Event, name: str, timestamp: datetime.datetime, data: Dict[str, data_value_t]) None

Constructs the Event.

property data

The structured data contained within the Event.

property name

The name of the Event.

property timestamp

The timestamp when the Event happened.

class torch.monitor.EventHandlerHandle

EventHandlerHandle is a wrapper type returned by register_event_handler used to unregister the handler via unregister_event_handler. This cannot be directly initialized.

torch.monitor.log_event(event: torch._C._monitor.Event) None

log_event logs the specified event to all of the registered event handlers. It’s up to the event handlers to log the event out to the corresponding event sink.

If there are no event handlers registered this method is a no-op.

torch.monitor.register_event_handler(callback: Callable[[torch._C._monitor.Event], None]) torch._C._monitor.EventHandlerHandle

register_event_handler registers a callback to be called whenever an event is logged via log_event. These handlers should avoid blocking the main thread since that may interfere with training as they run during the log_event call.

torch.monitor.unregister_event_handler(handler: torch._C._monitor.EventHandlerHandle) None

unregister_event_handler unregisters the EventHandlerHandle returned after calling register_event_handler. After this returns the event handler will no longer receive events.

class torch.monitor.TensorboardEventHandler(writer)[source]

TensorboardEventHandler is an event handler that will write known events to the provided SummaryWriter.

This currently only supports torch.monitor.Stat events which are logged as scalars.

>>> from torch.utils.tensorboard import SummaryWriter
>>> from torch.monitor import TensorboardEventHandler, register_event_handler
>>> writer = SummaryWriter("log_dir")
>>> register_event_handler(TensorboardEventHandler(writer))

Constructs the TensorboardEventHandler.


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