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"This tutorial illustrates use of a Global Stopping Strategy (GSS) in combination with the Service API. For background on the Service API, see the Service API Tutorial: https://ax.dev/tutorials/gpei_hartmann_service.html GSS is also supported in the Scheduler API, where it can be provided as part of `SchedulerOptions`. For more on `Scheduler`, see the Scheduler tutorial: https://ax.dev/tutorials/scheduler.html\n",
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"[INFO 01-31 07:10:03] ax.utils.notebook.plotting: Injecting Plotly library into cell. Do not overwrite or delete cell.\n"
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"[INFO 01-31 07:10:03] ax.utils.notebook.plotting: Please see\n",
" (https://ax.dev/tutorials/visualizations.html#Fix-for-plots-that-are-not-rendering)\n",
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"import numpy as np\n",
"\n",
"from ax.service.ax_client import AxClient, ObjectiveProperties\n",
"from ax.utils.measurement.synthetic_functions import Branin, branin\n",
"from ax.utils.notebook.plotting import init_notebook_plotting, render\n",
"\n",
"init_notebook_plotting()"
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"source": [
"# 1. What happens without global stopping? Optimization can run for too long.\n",
"This example uses the Branin test problem. We run 25 trials, which turns out to be far more than needed, because we get close to the optimum quite quickly."
]
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"def evaluate(parameters):\n",
" x = np.array([parameters.get(f\"x{i+1}\") for i in range(2)])\n",
" return {\"branin\": (branin(x), 0.0)}"
]
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"params = [\n",
" {\n",
" \"name\": f\"x{i + 1}\",\n",
" \"type\": \"range\",\n",
" \"bounds\": [*Branin._domain[i]],\n",
" \"value_type\": \"float\",\n",
" \"log_scale\": False,\n",
" }\n",
"\n",
" for i in range(2)\n",
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"[WARNING 01-31 07:10:03] ax.service.ax_client: Random seed set to 0. Note that this setting only affects the Sobol quasi-random generator and BoTorch-powered Bayesian optimization models. For the latter models, setting random seed to the same number for two optimizations will make the generated trials similar, but not exactly the same, and over time the trials will diverge more.\n"
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"[INFO 01-31 07:10:03] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-5.0, 10.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 15.0])], parameter_constraints=[]).\n"
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"[INFO 01-31 07:10:03] ax.core.experiment: The is_test flag has been set to True. This flag is meant purely for development and integration testing purposes. If you are running a live experiment, please set this flag to False\n"
]
},
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"[INFO 01-31 07:10:03] ax.modelbridge.dispatch_utils: Using Models.BOTORCH_MODULAR since there is at least one ordered parameter and there are no unordered categorical parameters.\n"
]
},
{
"name": "stderr",
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"text": [
"[INFO 01-31 07:10:03] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False\n"
]
},
{
"name": "stderr",
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"text": [
"[INFO 01-31 07:10:03] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 07:10:03] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5\n"
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},
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"name": "stderr",
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"text": [
"[INFO 01-31 07:10:03] ax.modelbridge.dispatch_utils: `verbose`, `disable_progbar`, and `jit_compile` are not yet supported when using `choose_generation_strategy` with ModularBoTorchModel, dropping these arguments.\n"
]
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"[INFO 01-31 07:10:03] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+BoTorch', steps=[Sobol for 5 trials, BoTorch for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.\n"
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