nip.timing.timeables.Timeable

nip.timing.timeables.Timeable#

class nip.timing.timeables.Timeable(*, param_scale: float = 1.0)[source]#

Base class for an action that can be timed.

Parameters:

param_scale (float, default=1.0) – Key default parameters (if any) will be scaled by this factor.

Methods Summary

__init__(*[, param_scale])

_get_profiler_args(log_dir, record_shapes, ...)

Get the arguments for the PyTorch profiler.

run(profiler)

Run the action.

time([log_dir, record_shapes, ...])

Time the action.

Methods

__init__(*, param_scale: float = 1.0)[source]#
_get_profiler_args(log_dir: str | None, record_shapes: bool, profile_memory: bool, with_stack: bool) → dict[source]#

Get the arguments for the PyTorch profiler.

Parameters:

  • log_dir (str or None) – The directory to save the profiling results to, if any.

  • record_shapes (bool) – Whether to record tensor shapes. This introduces an additional overhead.

  • profile_memory (bool) – Whether to profile memory usage.

  • with_stack (bool) – Whether to record the stack trace. This introduces an additional overhead.

Returns:

profiler_args (dict) – The arguments for the PyTorch profiler.

abstract run(profiler: profile)[source]#

Run the action.

Parameters:

profiler (torch.profiler.profile) – The PyTorch profiler which is being used to time the action.

time(log_dir: str | None = None, record_shapes: bool = True, profile_memory: bool = True, with_stack: bool = False) → profile[source]#

Time the action.

Parameters:

  • log_dir (str, optional) – The directory to save the profiling results to, if any.

  • record_shapes (bool) – Whether to record tensor shapes. This introduces an additional overhead.

  • profile_memory (bool) – Whether to profile memory usage.

  • with_stack (bool) – Whether to record the stack trace. This introduces an additional overhead.

Returns:

profiler (torch.profiler.profile) – The PyTorch profiler containing the timing information.