ray.rllib.utils.metrics.metrics_logger.MetricsLogger.log_time#

MetricsLogger.log_time(key: str | Tuple[str, ...], *, reduce: str = 'mean', window: int | float | None = None, ema_coeff: float | None = None, clear_on_reduce: bool = False, with_throughput: bool = False, throughput_ema_coeff: float = 0.05, reduce_per_index_on_aggregate: bool = False) → Stats[source]#

Measures and logs a time delta value under key when used with a with-block.

import time
from ray.rllib.utils.metrics.metrics_logger import MetricsLogger

logger = MetricsLogger()

# First delta measurement:
with logger.log_time("my_block_to_be_timed", ema_coeff=0.1):
    time.sleep(1.0)

# EMA should be ~1sec.
assert 1.1 > logger.peek("my_block_to_be_timed") > 0.9

# Second delta measurement (note that we don't have to repeat the args
# again, as the stats under that name have already been created above with
# the correct args).
with logger.log_time("my_block_to_be_timed"):
    time.sleep(2.0)

# EMA should be ~1.1sec.
assert 1.15 > logger.peek("my_block_to_be_timed") > 1.05

# When calling `reduce()`, the latest, reduced value is returned.
results = logger.reduce()
# EMA should be ~1.1sec.
assert 1.15 > results["my_block_to_be_timed"] > 1.05
Parameters:

  • key – The key (or tuple of keys) to log the measured time delta under.

  • reduce – The reduction method to apply, once self.reduce() is called. If None, will collect all logged values under key in a list (and also return that list upon calling self.reduce()).

  • window – An optional window size to reduce over. If not None, then the reduction operation is only applied to the most recent window items, and - after reduction - the internal values list under key is shortened to hold at most window items (the most recent ones). Must be None if ema_coeff is provided. If None (and ema_coeff is None), reduction must not be “mean”.

  • ema_coeff – An optional EMA coefficient to use if reduce is “mean” and no window is provided. Note that if both window and ema_coeff are provided, an error is thrown. Also, if ema_coeff is provided, reduce must be “mean”. The reduction formula for EMA is: EMA(t1) = (1.0 - ema_coeff) * EMA(t0) + ema_coeff * new_value

  • clear_on_reduce – If True, all values under key will be emptied after self.reduce() is called. Setting this to True is useful for cases, in which the internal values list would otherwise grow indefinitely, for example if reduce is None and there is no window provided.

  • with_throughput – Whether to track a throughput estimate together with this metric. This is only supported for reduce=sum and clear_on_reduce=False metrics (aka. “lifetime counts”). The Stats object under the logged key then keeps track of the time passed between two consecutive calls to reduce() and update its throughput estimate. The current throughput estimate of a key can be obtained through: <MetricsLogger>.peek(key, throughput=True).

  • throughput_ema_coeff – The EMA coefficient to use for throughput tracking. Only used if with_throughput=True. Defaults to 0.05.

  • reduce_per_index_on_aggregate – If True, when merging Stats objects, we reduce incoming values per index such that the new value at index n will be the reduced value of all incoming values at index n. If False, when reducing n Stats, the first n merged values will be the reduced value of all incoming values at index 0, the next n merged values will be the reduced values of all incoming values at index 1, etc.