.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/examples_parallelism/plot_scaling_bo.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_scaling_bo.py: Scaling Bayesian Optimization with Heterogeneous Parallelism ============================================================ **Author(s)**: Romain Egele. This example demonstrates the advantages of mixing centralized parallelism (1 optimization process with N workers) with decentralized parallelism (N optimization processes) to scale Bayesian optimization. For this we will have a total of 1000 local workers simulated with threads and timeouts. In this example, we will start by demonstrating the behaviour of an efficient centralized bayesian optimization using 1000 workers. Then, we will run a mixed decentralized optimization with 10 replications of a centralized optimization each with 100 workers for a total of 1000 workers as well. Therefore, we start by defining a black-box ``run``-function that implements the Ackley function: .. image:: https://www.sfu.ca/~ssurjano/ackley.png :width: 400 :alt: Ackley Function in 2D .. GENERATED FROM PYTHON SOURCE LINES 22-41 .. dropdown:: Code (Import statements) .. code-block:: Python import multiprocessing import time import matplotlib.pyplot as plt import numpy as np import scipy.stats as ss from loky import get_reusable_executor from deephyper.analysis import figure_size from deephyper.analysis.hpo import plot_search_trajectory_single_objective_hpo from deephyper.analysis.hpo import plot_worker_utilization from deephyper.evaluator import Evaluator, profile from deephyper.evaluator.callback import TqdmCallback from deephyper.evaluator.storage import SharedMemoryStorage from deephyper.hpo import HpProblem, CBO .. GENERATED FROM PYTHON SOURCE LINES 42-43 We define the Ackley function: .. GENERATED FROM PYTHON SOURCE LINES 43-54 .. 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 55-61 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 61-74 .. 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 75-78 Then, we define the variable(s) we want to optimize. For this problem we optimize Ackley in a N-dimensional search space. Each dimension in the continuous range [-32.768, 32.768]. The true minimum is located at ``(0, ..., 0)``. .. GENERATED FROM PYTHON SOURCE LINES 78-85 .. code-block:: Python nb_dim = 10 problem = HpProblem() for i in range(nb_dim): problem.add_hyperparameter((-32.768, 32.768), f"x{i}") 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 x2, Type: UniformFloat, Range: [-32.768, 32.768], Default: 0.0 x3, Type: UniformFloat, Range: [-32.768, 32.768], Default: 0.0 x4, Type: UniformFloat, Range: [-32.768, 32.768], Default: 0.0 x5, Type: UniformFloat, Range: [-32.768, 32.768], Default: 0.0 x6, Type: UniformFloat, Range: [-32.768, 32.768], Default: 0.0 x7, Type: UniformFloat, Range: [-32.768, 32.768], Default: 0.0 x8, Type: UniformFloat, Range: [-32.768, 32.768], Default: 0.0 x9, Type: UniformFloat, Range: [-32.768, 32.768], Default: 0.0 .. GENERATED FROM PYTHON SOURCE LINES 86-90 Then, we define some default search parameters for the Bayesian Optimization algorithm. It is important to note the `"qUCBd"` parameter for the multi-point strategy. Using the classic constant-liar strategy (a.k.a, Krigging Believer) `"cl_min/max/mean` in our setting would totally freeze the execution (you can try!). .. GENERATED FROM PYTHON SOURCE LINES 90-102 .. code-block:: Python search_kwargs = { "acq_func_kwargs": { "kappa": 2.0, }, "acq_optimizer": "ga", # Use continuous Genetic Algorithm for the acquisition function optimizer "acq_optimizer_kwargs": { "filter_duplicated": False, # Deactivate filtration of duplicated new points }, "multi_point_strategy": "qUCBd", # Multi-point strategy for asynchronous batch generations (explained later) "random_state": 42, # Random seed } .. GENERATED FROM PYTHON SOURCE LINES 103-109 Then, we define the time budget for the optimization. The time budget is defined in seconds. The `sleep_loc` and `sleep_scale` parameters simulate the distribution of duration of evaluated black-box functions sampled from a normal law with mean `sleep_loc` and standard deviation `sleep_scale`. We also define here the total number of workers to 1000. Using so many workers for Bayesian optimization is quite rare. Usually it is limited to ~200 sequential iterations and a dozen of parallel workers. .. GENERATED FROM PYTHON SOURCE LINES 109-115 .. code-block:: Python timeout = 30 num_workers = 1_000 run_function_kwargs = dict(sleep_loc=1, sleep_scale=0.25) results = {} .. GENERATED FROM PYTHON SOURCE LINES 116-117 We define a utility function to preprocess our results before plotting. .. GENERATED FROM PYTHON SOURCE LINES 117-125 .. code-block:: Python def preprocess_results(results): results = results.dropna().copy() offset = results["m:timestamp_start"].min() results["m:timestamp_start"] -= offset results["m:timestamp_end"] -= offset return results .. GENERATED FROM PYTHON SOURCE LINES 126-127 Then, we can create a centralized parallel search with . .. GENERATED FROM PYTHON SOURCE LINES 127-151 .. code-block:: Python def execute_centralized_bo( problem, run_function, run_function_kwargs, num_workers, log_dir, search_kwargs, timeout ): evaluator = Evaluator.create( run_function, method="thread", method_kwargs={ "num_workers": num_workers, # For the parallel evaluations "callbacks": [TqdmCallback()], "run_function_kwargs": run_function_kwargs, }, ) search = CBO( problem, log_dir=log_dir, **search_kwargs, ) results = search.search(evaluator, timeout=timeout) results = preprocess_results(results) return results .. GENERATED FROM PYTHON SOURCE LINES 152-154 To execute the search, we use the ``if __name__ == "__main__":`` statement. It is important to avoid triggering searches recursively when launching child processes latter in the example. .. 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GENERATED FROM PYTHON SOURCE LINES 166-169 We can now plot the results of the centralized search. The first plot shows the evolution of the objective. The second plot shows the utilization of the worker over time. The drops of the function show the regular re-fit of the surrogate model and optimization of the acquisition function. .. GENERATED FROM PYTHON SOURCE LINES 169-189 .. dropdown:: Code (Make plot) .. code-block:: Python if __name__ == "__main__": fig, axes = plt.subplots( nrows=2, ncols=1, sharex=True, figsize=figure_size(width=600), tight_layout=True, ) fig, ax = plot_search_trajectory_single_objective_hpo( results["centralized"], mode="min", x_units="seconds", ax=axes[0] ) fig, ax = plot_worker_utilization( results["centralized"], num_workers=None, profile_type="start/end", ax=axes[1] ) .. image-sg:: /examples/examples_parallelism/images/sphx_glr_plot_scaling_bo_001.png :alt: plot scaling bo :srcset: /examples/examples_parallelism/images/sphx_glr_plot_scaling_bo_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 190-192 Then we move to the decentralized optimization. We defined it in a separate module for Pickling to work. This function will be launched in child processes to trigger each sub-instances of the decentralized search. .. GENERATED FROM PYTHON SOURCE LINES 192-238 .. code-block:: Python def execute_centralized_bo_with_share_memory( problem, run_function, run_function_kwargs, storage, search_id, search_random_state, log_dir, num_workers, is_master, kappa, search_kwargs, timeout, ): evaluator = Evaluator.create( run_function, method="thread", method_kwargs={ "num_workers": num_workers, "storage": storage, "search_id": search_id, "callbacks": [TqdmCallback()] if is_master else [], "run_function_kwargs": run_function_kwargs, }, ) search_kwargs["acq_func_kwargs"]["kappa"] = kappa search_kwargs["random_state"] = search_random_state search = CBO(problem, log_dir=log_dir, **search_kwargs) def dummy(*args, **kwargs): pass results = None if is_master: results = search.search(evaluator, timeout=timeout) else: # for concurrency reasons this is important to override these functions evaluator.dump_jobs_done_to_csv = dummy search.extend_results_with_pareto_efficient = dummy search.search(evaluator, timeout=timeout) return results .. GENERATED FROM PYTHON SOURCE LINES 239-247 This function is very similar to our previous centralized optimization. However, importantly you can see that we override two methods of the ``CBO`` with ``dummy`` function. Now we can define the decentralized search by launching centralized instances with an paralle executor. Importantly we use ``SharedMemoryStorage`` so that centralized sub-instances can communicate globally. We also create explicitely the ``search_id`` to make sure they communicate about the search Search instance. The ``kappa`` value is sampled from an Exponential distribution to enable a diverse set of exploration-exploitation trade-offs leading to better results. You can try to fix it to ``1.96`` instead (default parameter in DeepHyper). .. GENERATED FROM PYTHON SOURCE LINES 247-290 .. code-block:: Python def execute_decentralized_bo( problem, run_function, run_function_kwargs, num_workers, log_dir, search_kwargs, timeout, n_processes, ): storage = SharedMemoryStorage() search_id = storage.create_new_search() kappa = ss.expon.rvs( size=n_processes, scale=search_kwargs["acq_func_kwargs"]["kappa"], random_state=search_kwargs["random_state"], ) executor = get_reusable_executor(max_workers=n_processes, context="spawn") futures = [] for i in range(n_processes): future = executor.submit(execute_centralized_bo_with_share_memory, *( problem, run_function, run_function_kwargs, storage, search_id, i, log_dir, num_workers // n_processes, i == 0, kappa[i], search_kwargs, timeout ) ) futures.append(future) results = preprocess_results(futures[0].result()) return results .. GENERATED FROM PYTHON SOURCE LINES 291-292 We can now execute the decentralized optimization with 10 processes each using 100 workers: .. GENERATED FROM PYTHON SOURCE LINES 292-304 .. code-block:: Python if __name__ == "__main__": results["decentralized"] = execute_decentralized_bo( problem=problem, run_function=run_ackley, run_function_kwargs=run_function_kwargs, num_workers=num_workers, log_dir="search_decentralized", search_kwargs=search_kwargs, timeout=timeout, n_processes=multiprocessing.cpu_count(), ) .. rst-class:: sphx-glr-script-out .. code-block:: none 21881it [00:50, 286.56it/s, failures=0, objective=-13.6] .. GENERATED FROM PYTHON SOURCE LINES 305-306 Observing the results we can see a better objective and less intance drops in worker utilization: .. GENERATED FROM PYTHON SOURCE LINES 306-325 .. dropdown:: Code (Make plot) .. code-block:: Python if __name__ == "__main__": fig, axes = plt.subplots( nrows=2, ncols=1, sharex=True, figsize=figure_size(width=600), tight_layout=True, ) fig, ax = plot_search_trajectory_single_objective_hpo( results["decentralized"], mode="min", x_units="seconds", ax=axes[0] ) fig, ax = plot_worker_utilization( results["decentralized"], num_workers=None, profile_type="start/end", ax=axes[1] ) .. image-sg:: /examples/examples_parallelism/images/sphx_glr_plot_scaling_bo_002.png :alt: plot scaling bo :srcset: /examples/examples_parallelism/images/sphx_glr_plot_scaling_bo_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 326-330 If we compare the objective curves side by side we can see the improvement of decentralized optimization even better. Even if the total number of evaluations is less, the quality is much better as we infered more often the surrogate model (combining re-fitting and optimization of the acquisition function): .. GENERATED FROM PYTHON SOURCE LINES 330-364 .. dropdown:: Code (Make plot) .. code-block:: Python if __name__ == "__main__": fig, ax = plt.subplots(figsize=figure_size(width=600), tight_layout=True) labels = { "centralized": "Centralized Bayesian Optimization", "decentralized": "Decentralized Bayesian Optimization", } x_min = float("inf") x_max = -float("inf") for i, (key, label) in enumerate(labels.items()): df = results[key] plot_search_trajectory_single_objective_hpo( df, show_failures=False, mode="min", x_units="seconds", ax=ax, label=label, plot_kwargs={"color": f"C{i}"}, scatter_success_kwargs={"color": f"C{i}", "alpha": 0.5}, ) x_min = min(df["m:timestamp_start"].min(), x_min) x_max = max(df["m:timestamp_end"].max(), x_max) ax.set_xlim(x_min, x_max) ax.set_xlabel("Time (sec.)") ax.set_ylabel("Objective") ax.set_yscale("log") ax.grid(visible=True, which="minor", linestyle=":") ax.grid(visible=True, which="major", linestyle="-") ax.legend() .. image-sg:: /examples/examples_parallelism/images/sphx_glr_plot_scaling_bo_003.png :alt: plot scaling bo :srcset: /examples/examples_parallelism/images/sphx_glr_plot_scaling_bo_003.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (1 minutes 16.558 seconds) .. _sphx_glr_download_examples_examples_parallelism_plot_scaling_bo.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_scaling_bo.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_scaling_bo.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_scaling_bo.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_