.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/examples_parallelism/plot_profile_worker_utilization.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_examples_examples_parallelism_plot_profile_worker_utilization.py: Profile the Worker Utilization ============================== **Author(s)**: Romain Egele. In this example, you will learn how to profile the activity of workers during a search. We start by defining an artificial black-box ``run``-function by using the Ackley function: .. image:: https://www.sfu.ca/~ssurjano/ackley.png :width: 400 :alt: Ackley Function in 2D .. GENERATED FROM PYTHON SOURCE LINES 17-33 .. dropdown:: Code (Import statements) .. code-block:: Python import time import matplotlib.pyplot as plt import numpy as np from deephyper.analysis import figure_size from deephyper.analysis.hpo import ( plot_search_trajectory_single_objective_hpo, plot_worker_utilization, ) from deephyper.evaluator import Evaluator, profile from deephyper.evaluator.callback import TqdmCallback from deephyper.hpo import CBO, HpProblem .. GENERATED FROM PYTHON SOURCE LINES 34-35 We define the Ackley function: .. GENERATED FROM PYTHON SOURCE LINES 35-46 .. dropdown:: Code (Ackley function) .. code-block:: Python def ackley(x, a=20, b=0.2, c=2 * np.pi): d = len(x) s1 = np.sum(x**2) s2 = np.sum(np.cos(c * x)) term1 = -a * np.exp(-b * np.sqrt(s1 / d)) term2 = -np.exp(s2 / d) y = term1 + term2 + a + np.exp(1) return y .. GENERATED FROM PYTHON SOURCE LINES 47-53 We will use the ``time.sleep`` function to simulate a budget of 2 secondes of execution in average which helps illustrate the advantage of parallel evaluations. The ``@profile`` decorator is useful to collect starting/ending time of the ``run``-function execution which help us know exactly when we are inside the black-box. This decorator is necessary when profiling the worker utilization. When using this decorator, the ``run``-function will return a dictionnary with 2 new keys ``"timestamp_start"`` and ``"timestamp_end"``. .. GENERATED FROM PYTHON SOURCE LINES 53-66 .. code-block:: Python @profile def run_ackley(config, sleep_loc=2, sleep_scale=0.5): # to simulate the computation of an expensive black-box if sleep_loc > 0: t_sleep = np.random.normal(loc=sleep_loc, scale=sleep_scale) t_sleep = max(t_sleep, 0) time.sleep(t_sleep) x = np.array([config[k] for k in config if "x" in k]) x = np.asarray_chkfinite(x) # ValueError if any NaN or Inf return -ackley(x) # maximisation is performed .. GENERATED FROM PYTHON SOURCE LINES 67-70 Then we define the variable(s) we want to optimize. For this problem we optimize Ackley in a 2-dimensional search space, the true minimul is located at ``(0, 0)``. .. GENERATED FROM PYTHON SOURCE LINES 70-82 .. code-block:: Python def create_problem(nb_dim=2): nb_dim = 2 problem = HpProblem() for i in range(nb_dim): problem.add_hyperparameter((-32.768, 32.768), f"x{i}") return problem problem = create_problem() problem .. rst-class:: sphx-glr-script-out .. code-block:: none Configuration space object: Hyperparameters: x0, Type: UniformFloat, Range: [-32.768, 32.768], Default: 0.0 x1, Type: UniformFloat, Range: [-32.768, 32.768], Default: 0.0 .. GENERATED FROM PYTHON SOURCE LINES 83-86 Then we define a parallel search. As the ``run``-function is defined in the same module we use the "loky" backend that serialize by value. .. GENERATED FROM PYTHON SOURCE LINES 86-112 .. code-block:: Python def execute_search(timeout, num_workers): evaluator = Evaluator.create( run_ackley, method="loky", method_kwargs={ "num_workers": num_workers, "callbacks": [TqdmCallback()], }, ) search = CBO( problem, multi_point_strategy="qUCBd", random_state=42, ) results = search.search(evaluator, timeout=timeout) return results if __name__ == "__main__": timeout = 20 num_workers = 4 results = execute_search(timeout, num_workers) .. rst-class:: sphx-glr-script-out .. code-block:: none 0it [00:00, ?it/s] 1it [00:00, 7530.17it/s, failures=0, objective=-19.8] 2it [00:01, 2.00it/s, failures=0, objective=-19.8] 2it [00:01, 2.00it/s, failures=0, objective=-19.8] 3it [00:01, 2.00it/s, failures=0, objective=-19.8] 4it [00:01, 2.41it/s, failures=0, objective=-19.8] 4it [00:01, 2.41it/s, failures=0, objective=-19.8] 5it [00:01, 2.99it/s, failures=0, objective=-19.8] 5it [00:01, 2.99it/s, failures=0, objective=-19.8] 6it [00:02, 1.78it/s, failures=0, objective=-19.8] 6it [00:02, 1.78it/s, failures=0, objective=-19.8] 7it [00:03, 1.99it/s, failures=0, objective=-19.8] 7it [00:03, 1.99it/s, failures=0, objective=-15.4] 8it [00:03, 2.26it/s, failures=0, objective=-15.4] 8it [00:03, 2.26it/s, failures=0, objective=-15.4] 9it [00:04, 1.84it/s, failures=0, objective=-15.4] 9it [00:04, 1.84it/s, failures=0, objective=-15.4] 10it [00:05, 1.42it/s, failures=0, objective=-15.4] 10it [00:05, 1.42it/s, failures=0, objective=-15.4] 11it [00:05, 1.58it/s, failures=0, objective=-15.4] 11it [00:05, 1.58it/s, failures=0, objective=-15.4] 12it [00:06, 1.82it/s, failures=0, objective=-15.4] 12it [00:06, 1.82it/s, failures=0, objective=-14.2] 13it [00:07, 1.37it/s, failures=0, objective=-14.2] 13it [00:07, 1.37it/s, failures=0, objective=-14.2] 14it [00:07, 1.68it/s, failures=0, objective=-14.2] 14it [00:07, 1.68it/s, failures=0, objective=-14.2] 15it [00:08, 1.85it/s, failures=0, objective=-14.2] 15it [00:08, 1.85it/s, failures=0, objective=-14.2] 16it [00:08, 1.83it/s, failures=0, objective=-14.2] 16it [00:08, 1.83it/s, failures=0, objective=-10.8] 17it [00:09, 1.85it/s, failures=0, objective=-10.8] 17it [00:09, 1.85it/s, failures=0, objective=-10.8] 18it [00:09, 2.20it/s, failures=0, objective=-10.8] 18it [00:09, 2.20it/s, failures=0, objective=-10.8] 19it [00:10, 1.43it/s, failures=0, objective=-10.8] 19it [00:10, 1.43it/s, failures=0, objective=-10.8] 20it [00:10, 1.80it/s, failures=0, objective=-10.8] 20it [00:10, 1.80it/s, failures=0, objective=-6.95] 21it [00:12, 1.36it/s, failures=0, objective=-6.95] 21it [00:12, 1.36it/s, failures=0, objective=-5.75] 22it [00:12, 1.71it/s, failures=0, objective=-5.75] 22it [00:12, 1.71it/s, failures=0, objective=-5.75] 23it [00:12, 1.99it/s, failures=0, objective=-5.75] 23it [00:12, 1.99it/s, failures=0, objective=-5.75] 24it [00:13, 2.05it/s, failures=0, objective=-5.75] 24it [00:13, 2.05it/s, failures=0, objective=-5.75] 25it [00:14, 1.33it/s, failures=0, objective=-5.75] 25it [00:14, 1.33it/s, failures=0, objective=-5.75] 26it [00:15, 1.43it/s, failures=0, objective=-5.75] 26it [00:15, 1.43it/s, failures=0, objective=-5.75] 27it [00:15, 1.65it/s, failures=0, objective=-5.75] 27it [00:15, 1.65it/s, failures=0, objective=-3.36] 28it [00:15, 1.85it/s, failures=0, objective=-3.36] 28it [00:15, 1.85it/s, failures=0, objective=-3.36] 29it [00:17, 1.21it/s, failures=0, objective=-3.36] 29it [00:17, 1.21it/s, failures=0, objective=-3.36] 30it [00:17, 1.49it/s, failures=0, objective=-3.36] 30it [00:17, 1.49it/s, failures=0, objective=-3.36] 31it [00:17, 1.49it/s, failures=0, objective=-3.36] 32it [00:17, 1.49it/s, failures=0, objective=-3.36] 33it [00:18, 1.88it/s, failures=0, objective=-3.36] 33it [00:18, 1.88it/s, failures=0, objective=-3.36] 34it [00:20, 1.52it/s, failures=0, objective=-3.36] 34it [00:20, 1.52it/s, failures=0, objective=-2.07] 35it [00:20, 1.52it/s, failures=0, objective=-0.727] 36it [00:20, 1.52it/s, failures=0, objective=-0.727] .. GENERATED FROM PYTHON SOURCE LINES 113-114 Finally, we plot the results from the collected DataFrame. .. GENERATED FROM PYTHON SOURCE LINES 114-137 .. dropdown:: Code (Plot search trajectory an workers utilization) .. code-block:: Python if __name__ == "__main__": t0 = results["m:timestamp_start"].iloc[0] results["m:timestamp_start"] = results["m:timestamp_start"] - t0 results["m:timestamp_end"] = results["m:timestamp_end"] - t0 tmax = results["m:timestamp_end"].max() fig, axes = plt.subplots( nrows=2, ncols=1, sharex=True, figsize=figure_size(width=600), tight_layout=True, ) _ = plot_search_trajectory_single_objective_hpo( results, mode="min", x_units="seconds", ax=axes[0], ) _ = plot_worker_utilization( results, num_workers=num_workers, profile_type="start/end", ax=axes[1], ) .. image-sg:: /examples/examples_parallelism/images/sphx_glr_plot_profile_worker_utilization_001.png :alt: plot profile worker utilization :srcset: /examples/examples_parallelism/images/sphx_glr_plot_profile_worker_utilization_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 23.347 seconds) .. _sphx_glr_download_examples_examples_parallelism_plot_profile_worker_utilization.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_profile_worker_utilization.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_profile_worker_utilization.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_profile_worker_utilization.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_