neurtu.Benchmark

class neurtu.Benchmark(wall_time=None, cpu_time=False, peak_memory=False, repeat=1, aggregate=('mean', 'max', 'std'), to_dataframe=None, progress_bar=5.0, **kwargs)[source]

Benchmark calculations

Parameters:
  • wall_time ({bool, dict}, default=None) – measure wall time. When a dictionary, it is passed as parameters to the func:measure_wall_time function. Will default to True, unless some other metric is enabled.
  • cpu_time ({bool, dict}, default=False) – measure CPU time. When a dictionary, it is passed as parameters to the measure_cpu_time() function.
  • peak_memory ({bool, dict}, default=False) – measure peak memory usage. When a dictionary, it is passed as parameters to the measure_peak_memory() function.
  • repeat (int, default=1) – number of repeated measurements
  • aggregate ({collection, False}, default=('mean', 'max', 'std')) – when repeat > 1, different runs are indexed by the runid key. If pandas is installed and aggregate is a collection, aggregate repeated runs with the provided methods.
  • to_dataframe (bool, default=None) – whether to convert parametric results to a daframe. By default convert to dataframe is pandas is installed.
  • progress_bar ({bool, float}, default=5.0) – if a number, and tqdm is installed, display the progress bar when the estimated benchmark time is larger than the given number of seconds. If False, the progress bar will not be displayed.
  • **kwargs (dict) – custom evaluation metrics of the form key=func, where key is the metric name, and the func is the evaluation metric that accepts a Delayed object: func(obj).
__init__(wall_time=None, cpu_time=False, peak_memory=False, repeat=1, aggregate=('mean', 'max', 'std'), to_dataframe=None, progress_bar=5.0, **kwargs)[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__([wall_time, cpu_time, peak_memory, …]) Initialize self.