f9bfc733.0106a7ac.js

js ax.dev collected 2026-09-24 10:33:35 UTC 266,613 bytes, 1 lines download raw bytes

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1icial to allow Ax to orchestrate the entire\noptimization: deploying trials to external systems, polling their status, and reading\ntheir results. This can be common in a number of real world engineering tasks,\nincluding:"]}),"\n",(0,i.jsxs)(t.ul,{children:["\n",(0,i.jsxs)(t.li,{children:[(0,i.jsx)(t.strong,{children:"Large scale machine learning experiments"})," running workloads on high-performance\ncomputing clusters"]}),"\n",(0,i.jsxs)(t.li,{children:[(0,i.jsx)(t.strong,{children:"A/B tests"})," conducted using an external experimentation platform"]}),"\n",(0,i.jsxs)(t.li,{children:[(0,i.jsx)(t.strong,{children:"Materials science"})," optimizations utilizing a self-driving laboratory"]}),"\n"]}),"\n",(0,i.jsxs)(t.p,{children:["Ax's ",(0,i.jsx)(t.code,{children:"Client"})," can orchestrate automated adaptive experiments like this using its method\n",(0,i.jsx)(t.code,{children:"run_trials"}),". Users create custom classes which implement Ax's ",(0,i.jsx)(t.code,{children:"IMetric"})," and ",(0,i.jsx)(t.code,{children:"IRunner"}),"\nprotocols to handle data fetching and trial deployment respectively. Then, users simply\nconfigure their ",(0,i.jsx)(t.code,{children:"Client"})," as they would normally and call ",(0,i.jsx)(t.code,{children:"run_trials"}),"; Ax will deploy\ntrials, fetch data, generate candidates, and repeat as necessary. Ax can manage complex\norchestration tasks including launching multiple trials in parallel while still\nrespecting a user-defined concurrency limit, and gracefully handling trial failure by\nallowing the experiment to continue even if some trials do not complete successfully or\ndata fetching fails."]}),"\n",(0,i.jsx)(t.p,{children:"In this tutorial we will optimize the Hartmann6 function as before, but we will\nconfigure custom Runners and Metrics to mimic an external execution system. The Runner\nwill calculate Hartmann6 with the appropriate parameters, write the result to a file,\nand tell Ax the trial is ready after 5 seconds. The Metric will find the appropriate\nfile and report the results back to Ax."}),"\n",(0,i.jsx)(t.h3,{id:"learning-objectives",children:"Learning Objectives"}),"\n",(0,i.jsxs)(t.ul,{children:["\n",(0,i.jsx)(t.li,{children:"Learn when it can be appropriate and/or advantageous to run Ax in a closed-loop"}),"\n",(0,i.jsx)(t.li,{children:"Configure custom Runners and Metrics, allowing Ax to deploy trials and fetch data\nautomatically"}),"\n",(0,i.jsx)(t.li,{children:"Understand tradeoffs between parallelism and optimization performance"}),"\n"]}),"\n",(0,i.jsx)(t.h3,{id:"prerequisites",children:"Prerequisites"}),"\n",(0,i.jsxs)(t.ul,{children:["\n",(0,i.jsxs)(t.li,{children:["Understanding of ",(0,i.jsx)(t.a,{href:"/docs/intro-to-ae",children:"adaptive experimentation"})," and\n",(0,i.jsx)(t.a,{href:"/docs/intro-to-bo",children:"Bayesian optimization"})]}),"\n",(0,i.jsxs)(t.li,{children:["Familiarity with\n",(0,i.jsx)(t.a,{href:"/docs/tutorials/getting_started/",children:"configuring and conducting experiments in Ax"})]}),"\n"]}),"\n",(0,i.jsx)(t.h2,{id:"step-1-import-necessary-modules",children:"Step 1: Import Necessary Modules"}),"\n",(0,i.jsx)(t.p,{children:"First, ensure you have all the necessary imports:"}),"\n",(0,i.jsx)(t.pre,{children:(0,i.jsx)(t.code,{className:"language-python",children:"import os\nimport time\nfrom typing import Any, Mapping\n\nimport numpy as np\nfrom ax.api.client import Client\nfrom ax.api.configs import RangeParameterConfig\nfrom ax.api.protocols.metric import IMetric\nfrom ax.api.protocols.runner import IRunner, TrialStatus\nfrom ax.api.types import TParameterization\n"})}),"\n",(0,i.jsx)(n.A,{children:"[INFO 06-09 17:02:29] ax.storage.sqa_store.with_db_settings_base: Ax SQL storage initialized with SQLAlchemy 1.4.17"}),"\n",(0,i.jsx)(t.h1,{id:"step-2-defining-our-custom-runner-and-metric",children:"Step 2: Defining our custom Runner and Metric"}),"\n",(0,i.jsx)(t.p,{children:"As stated before, we will be creating custom Runner and Metric classes to mimic an\nexternal system. Let's start by defining our Hartmann6 function as before."}),"\n",(0,i.jsx)(t.pre,{children:(0,i.jsx)(t.code,{className:"language-python",children:"# Hartmann6 function\ndef hartmann6(x1, x2, x3, x4, x5, x6):\n    alph

1a = np.array([1.0, 1.2, 3.0, 3.2])\n    A = np.array([\n        [10, 3, 17, 3.5, 1.7, 8],\n        [0.05, 10, 17, 0.1, 8, 14],\n        [3, 3.5, 1.7, 10, 17, 8],\n        [17, 8, 0.05, 10, 0.1, 14]\n    ])\n    P = 10**-4 * np.array([\n        [1312, 1696, 5569, 124, 8283, 5886],\n        [2329, 4135, 8307, 3736, 1004, 9991],\n        [2348, 1451, 3522, 2883, 3047, 6650],\n        [4047, 8828, 8732, 5743, 1091, 381]\n    ])\n\n    outer = 0.0\n    for i in range(4):\n        inner = 0.0\n        for j, x in enumerate([x1, x2, x3, x4, x5, x6]):\n            inner += A[i, j] * (x - P[i, j])**2\n        outer += alpha[i] * np.exp(-inner)\n    return -outer\n\nhartmann6(0.1, 0.45, 0.8, 0.25, 0.552, 1.0)\n"})}),"\n",(0,i.jsx)(n.A,{children:"np.float64(-0.4878737485613134)"}),"\n",(0,i.jsxs)(t.p,{children:["Next, we will define the ",(0,i.jsx)(t.code,{children:"MockRunner"}),". The ",(0,i.jsx)(t.code,{children:"MockRunner"})," requires two methods:\n",(0,i.jsx)(t.code,{children:"run_trial"})," and ",(0,i.jsx)(t.code,{children:"poll_trial"}),"."]}),"\n",(0,i.jsxs)(t.p,{children:[(0,i.jsx)(t.code,{children:"run_trial"})," deploys a trial to the external system with the given parameters. In this\ncase, we will simply save a file containing the result of a call to the Hartmann6\nfunction."]}),"\n",(0,i.jsxs)(t.p,{children:[(0,i.jsx)(t.code,{children:"poll_trial"})," queries the external system to see if the trial has completed, failed, or\nif it's still running. In this mock example, we will check to see how many seconds have\nelapsed since the ",(0,i.jsx)(t.code,{children:"run_trial"})," was called and only report a trial as completed once 5\nseconds have elapsed."]}),"\n",(0,i.jsxs)(t.p,{children:["Runners may also optionally implement a ",(0,i.jsx)(t.code,{children:"stop_trial"})," method to terminate a trial's\nexecution before it has completed. This is necessary for using\n",(0,i.jsx)(t.a,{href:"../early_stopping",children:"early stopping"})," in closed-loop experimentation, but we will skip\nthis for now."]}),"\n",(0,i.jsx)(t.pre,{children:(0,i.jsx)(t.code,{className:"language-python",children:'class MockRunner(IRunner):\n    def run_trial(\n        self, trial_index: int, parameterization: TParameterization\n    ) -> dict[str, Any]:\n        file_name = f"{int(time.time())}.txt"\n\n        x1 = parameterization["x1"]\n        x2 = parameterization["x2"]\n        x3 = parameterization["x3"]\n        x4 = parameterization["x4"]\n        x5 = parameterization["x5"]\n        x6 = parameterization["x6"]\n\n        result = hartmann6(x1, x2, x3, x4, x5, x6)\n\n        with open(file_name, "w") as f:\n            f.write(f"{result}")\n\n        return {"file_name": file_name}\n\n    def poll_trial(\n        self, trial_index: int, trial_metadata: Mapping[str, Any]\n    ) -> TrialStatus:\n        file_name = trial_metadata["file_name"]\n        time_elapsed = time.time() - int(file_name[:4])\n\n        if time_elapsed < 5:\n            return TrialStatus.RUNNING\n\n        return TrialStatus.COMPLETED\n'})}),"\n",(0,i.jsxs)(t.p,{children:["It's worthwhile to instantiate your Runner and test it is behaving as expected. Let's\ndeploy a mock trial by manually calling ",(0,i.jsx)(t.code,{children:"run_trial"})," and ensuring it creates a file."]}),"\n",(0,i.jsx)(t.pre,{children:(0,i.jsx)(t.code,{className:"language-python",children:'runner = MockRunner()\n\ntrial_metadata = runner.run_trial(\n    trial_index=-1,\n    parameterization={\n        "x1": 0.1,\n        "x2": 0.45,\n        "x3": 0.8,\n        "x4": 0.25,\n        "x5": 0.552,\n        "x6": 1.0,\n    },\n)\n\nos.path.exists(trial_metadata["file_name"])\n'})}),"\n",(0,i.jsx)(n.A,{children:"True"}),"\n",(0,i.jsxs)(t.p,{children:["Now, we will implement the Metric. Metrics only need to implement a ",(0,i.jsx)(t.code,{children:"fetch"})," method,\nwhich returns a progression value (i.e. a step in a timeseries) and an observation\nvalue. Note that the observation can either be a simple float or a (mean, SEM) pair if\nthe external system can report observed noise."]}),"\n",(0,i.jsxs)(t.p,{children:["In this case, we have neither a relevant progression value nor observed noise so we will\nsimply read the file and report ",(0,i.jsx)(t.code,{children:"(0, value)"}),"."]}),"\n",(0,i.jsx)(t.pre,{children:(0,i.jsx)(t.code,{className:"language-python",children:"class MockMetric(IMetric):\n    def fetch(\n        self,\n        trial_index: int,\n        trial_metadata: Mapping[str, Any],\n    ) -> tuple[int, float | tuple[float, float]]:\n        file_name = trial_metadata[\"file_name\"]\n\n        with open(file_name, 'r') as file:\n            value = float(file.readline())\n            return (0, value)\n"})}),"\n",(0,i.jsx)(t.p,{children:"Again, let's validate the Metric created above by instantiating it and reporting the\nvalue from the file generated during testing of the Runner."}),"\n",(0,i.jsx)(t.pre,{children:(0,i.jsx)(t.code,{className:"language-python",children:'# Note: all Metrics must have a name. This will become relevant when attaching metrics to the Client\nhartmann6_metric = MockMetric(name="hartmann6")\n\nhartmann6_metric.fetch(trial_index=-1, trial_metadata=trial_metadata)\n'})}),"\n",(0,i.jsx)(n.A,{children:"(0, -0.4878737485613134)"}),"\n",(0,i.jsx)(t.h2,{id:"step-3-initialize-the-client-and-configure-the-experiment",children:"Step 3: Initialize the Client and Configure the Experiment"}),"\n",(0,i.jsxs)(t.p,{children:["Finally, we can initialize the ",(0,i.jsx)(t.code,{children:"Client"})," and configure the experiment as before. This\nwill be familiar to readers of the\n",(0,i.jsx)(t.a,{href:"../getting_started",children:"Getting Started with Ax tutorial"})," -- the only difference is we will\nattach the previously defined Runner and Metric by calling ",(0,i.jsx)(t.code,{children:"configure_runner"})," and\n",(0,i.jsx)(t.code,{children:"configure_metrics"})," respectively."]}),"\n",(0,i.jsxs)(t.p,{children:["Note that when initializing ",(0,i.jsx)(t.code,{children:"hartmann6_metric"})," we set ",(0,i.jsx)(t.code,{children:"name=hartmann6"}),", matching the\nobjective we now set in ",(0,i.jsx)(t.code,{children:"configure_optimization"}),". The ",(0,i.jsx)(t.code,{children:"configure_metrics"})," method uses\nthis name to ensure that data fetched by this Metric is used correctly during the\nexperiment. Be careful to correctly set the name of the Metric to reflect its use as an\nobjective or outcome constraint."]}),"\n",(0,i.jsx)(t.pre,{children:(0,i.jsx)(t.code,{className:"language-python",children:'client = Client()\n# Define six float parameters for the Hartmann6 function\nparameters = [\n    RangeParameterConfig(name=f"x{i + 1}", parameter_type="float", bounds=(0, 1))\n    for i in range(6)\n]\n\nclient.configure_experiment(\n    parameters=parameters,\n    # The following arguments are only necessary when saving to the DB\n    name="hartmann6_experiment",\n    description="Optimization of the Hartmann6 function",\n    owner="developer",\n)\nclient.configure_optimization(objective="-hartmann6")\n'})}),"\n",(0,i.jsx)(t.pre,{children:(0,i.jsx)(t.code,{className:"language-python",children:"client.configure_runner(runner=runner)\nclient.configure_metrics(metrics=[hartmann6_metric])\n"})}),"\n",(0,i.jsx)(t.h2,{id:"step-5-run-trials",children:"Step 5: Run trials"}),"\n",(0,i.jsxs)(t.p,{children:["Once the ",(0,i.jsx)(t.code,{children:"Client"})," has been configured, we can begin running trials."]}),"\n",(0,i.jsxs)(t.p,{children:["Internally, Ax uses a class named ",(0,i.jsx)(t.code,{children:"Scheduler"})," to orchestrate the trial deployment,\npolling, data fetching, and candidate generation."]}),"\n",(0,i.jsx)(t.p,{children:(0,i.jsx)(t.img,{alt:"Scheduler state machine",src:r(64068).A+"",width:"2138",height:"800"})}),"\n",(0,i.jsxs)(t.p,{children:["The ",(0,i.jsx)(t.code,{children:"run_trials"})," method provides users with control over various orchestration settings\nas well as the total maximum number of trials to evaluate:"]}),"\n",(0,i.jsxs)(t.ul,{children:["\n",(0,i.jsxs)(t.li,{children:[(0,i.jsx)(t.code,{children:"parallelism"})," defines the maximum number of trials that may be run at once. If your\nexternal system supports multiple evaluations in parallel, increasing this number can\nsignificantly decrease experimentation time. However, it is important to note that as\nparallelism increases, optimiztion performance often decreases. This is because\nadaptive experimentation methods rely on previously observed data for candidate\

1ngeneration -- the more tirals that have been observed prior to generation of a new\ncandidate, the more accurate Ax's model will be for generation of that candidate."]}),"\n",(0,i.jsxs)(t.li,{children:[(0,i.jsx)(t.code,{children:"tolerated_trial_failure_rate"})," sets the proportion of trials are allowed to fail\nbefore Ax raises an Exception. Depending on how expensive a single trial is to\nevaluate or how unreliable trials are expected to be, the experimenter may want to be\nnotified as soon as a single trial fails or they may not care until more than half the\ntrials are failing. Set this value as is appropriate for your context."]}),"\n",(0,i.jsxs)(t.li,{children:[(0,i.jsx)(t.code,{children:"initial_seconds_between_polls"})," sets the frequency at which the status of a trial is\nchecked and the results are attempted to be fetched. Set this to be low for trials\nthat are expected to complete quickly or high for trials the are expected to take a\nlong time."]}),"\n"]}),"\n",(0,i.jsx)(t.pre,{children:(0,i.jsx)(t.code,{className:"language-python",children:"client.run_trials(\n    max_trials=30,\n    parallelism=3,\n    tolerated_trial_failure_rate=0.1,\n    initial_seconds_between_polls=1,\n)\n"})}),"\n",(0,i.jsx)(n.A,{children:"[INFO 06-09 17:02:29] ax.api.client: GenerationStrategy(name='Center+Sobol+MBM:fast', nodes=[CenterGenerationNode(next_node_name='Sobol', use_existing_trials_for_initialization=True), GenerationNode(name='Sobol', generator_specs=[GeneratorSpec(generator_enum=Sobol, generator_key_override=None)], transition_criteria=[MinTrials(transition_to='MBM'), MinTrials(transition_to='MBM')], suggested_experiment_status=ExperimentStatus.INITIALIZATION, pausing_criteria=[MaxTrialsAwaitingData(threshold=5)]), GenerationNode(name='MBM', generator_specs=[GeneratorSpec(generator_enum=BoTorch, generator_key_override=None)], transition_criteria=None, suggested_experiment_status=ExperimentStatus.OPTIMIZATION, pausing_criteria=None)]) chosen based on user input and problem structure.\n[INFO 06-09 17:02:29] Orchestrator: Orchestrator requires experiment to have immutable search space and optimization config. Setting property immutable_search_space_and_opt_config to True on experiment.\n[INFO 06-09 17:02:29] Orchestrator: Running trials [0]...\n[INFO 06-09 17:02:30] Orchestrator: Running trials [1]...\n[INFO 06-09 17:02:31] Orchestrator: Running trials [2]...\n[INFO 06-09 17:02:32] Orchestrator: Retrieved COMPLETED trials: 0 - 2.\n[INFO 06-09 17:02:32] Orchestrator: Running trials [3]...\n[INFO 06-09 17:02:33] Orchestrator: Running trials [4]...\n[INFO 06-09 17:02:34] Orchestrator: Running trials [5]...\n[INFO 06-09 17:02:35] Orchestrator: Retrieved COMPLETED trials: 3 - 5.\n[INFO 06-09 17:02:36] Orchestrator: Running trials [6]...\n[INFO 06-09 17:02:38] Orchestrator: Running trials [7]...\n[INFO 06-09 17:02:39] Orchestrator: Running trials [8]...\n[INFO 06-09 17:02:40] Orchestrator: Retrieved COMPLETED trials: 6 - 8.\n[INFO 06-09 17:02:41] Orchestrator: Running trials [9]...\n[INFO 06-09 17:02:42] Orchestrator: Running trials [10]...\n[INFO 06-09 17:02:44] Orchestrator: Running trials [11]...\n[INFO 06-09 17:02:45] Orchestrator: Retrieved COMPLETED trials: 9 - 11.\n[INFO 06-09 17:02:45] Orchestrator: Running trials [12]...\n[INFO 06-09 17:02:47] Orchestrator: Running trials [13]...\n[INFO 06-09 17:02:48] Orchestrator: Running trials [14]...\n[INFO 06-09 17:02:49] Orchestrator: Retrieved COMPLETED trials: 12 - 14.\n[INFO 06-09 17:02:50] Orchestrator: Running trials [15]...\n[INFO 06-09 17:02:51] Orchestrator: Running trials [16]...\n[INFO 06-09 17:02:53] Orchestrator: Running trials [17]...\n[INFO 06-09 17:02:54] Orchestrator: Retrieved COMPLETED trials: 15 - 17.\n[INFO 06-09 17:02:54] Orchestrator: Running trials [18]...\n[INFO 06-09 17:02:56] Orchestrator: Running trials [19]...\n[INFO 06-09 17:02:57] Orchestrator: Running trials [20]...\n[INFO 06-09 17:02:58] Orchestrator: Retrieved COMPLETED trials: 18 - 20.\n[INFO 06-09 17:02:59] Orchestrator: Running trials [21]...\n[INFO 06-09 17:03:00] Orchestrator: Running trials [22]...\n[INFO 06-09 17:03:02] Orchestrator: Running trials [23]...\n[INFO 06-09 17:03:03] Orchestrator: Retrieved COMPLETED trials: 21 - 23.\n[INFO 06-09 17:03:03] Orchestrator: Running trials [24]...\n[INFO 06-09 17:03:05] Orchestrator: Running trials [25]...\n[INFO 06-09 17:03:06] Orchestrator: Running trials [26]...\n[INFO 06-09 17:03:07] Orchestrator: Retrieved COMPLETED trials: 24 - 26.\n[INFO 06-09 17:03:08] Orchestrator: Running trials [27]...\n[INFO 06-09 17:03:09] Orchestrator: Running trials [28]...\n[INFO 06-09 17:03:11] Orchestrator: Running trials [29]...\n[INFO 06-09 17:03:12] Orchestrator: Retrieved COMPLETED trials: 27 - 29."}),"\n",(0,i.jsx)(t.h2,{id:"step-6-analyze-results",children:"Step 6: Analyze Results"}),"\n",(0,i.jsx)(t.p,{children:"As before, Ax can compute the best parameterization observed and produce a number of\nanalyses to help interpret the results of the experiment."}),"\n",(0,i.jsxs)(t.p,{children:["It is also worth noting that the experiment can be resumed at any time using Ax's\nstorage functionality. When configured to use a SQL databse, the ",(0,i.jsx)(t.code,{children:"Client"}

1)," saves a\nsnapshot of itself at various points throughout the call to ",(0,i.jsx)(t.code,{children:"run_trials"}),", making it\nincredibly easy to continue optimization after an unexpected failure. You can learn more\nabout storage in Ax ",(0,i.jsx)(t.a,{href:"../../recipes/experiment-to-json",children:"here"}),"."]}),"\n",(0,i.jsx)(t.pre,{children:(0,i.jsx)(t.code,{className:"language-python",children:'best_parameters, prediction, index, name = client.get_best_parameterization()\nprint("Best Parameters:", best_parameters)\nprint("Prediction (mean, variance):", prediction)\n'})}),"\n",(0,i.jsx)(n.A,{children:"Best Parameters: {'x1': 0.4128790730429235, 'x2': 0.892736569243258, 'x3': 1.0, 'x4': 0.5884918287384889, 'x5': 0.0, 'x6': 0.0005565534350616575}\nPrediction (mean, variance): {'hartmann6': (np.float64(-3.1016881195927004), np.float64(0.0027606555547052595))}"}),"\n",(0,i.jsx)(t.pre,{children:(0,i.jsx)(t.code,{className:"language-python",children:"# display=True instructs Ax to sort then render the resulting analyses\ncards = client.compute_analyses(display=True)\n"})}),"\n",(0,i.jsx)(t.p,{children:(0,i.jsx)(t.strong,{children:"Modeled Arm Effects on hartmann6"})}),"\n",(0,i.jsx)(t.p,{children:"Modeled effects on hartmann6. This plot visualizes predictions of the true metric\nchanges for each arm based on Ax's model. This is the expected delta you would expect if\nyou (re-)ran that arm. This plot helps in anticipating the outcomes and performance of\narms based on the model's predictions. Note, flat predictions across arms indicate that\nthe model predicts that there is no effect, meaning if you were to re-run the\nexperiment, the delta you would see would be small and fall within the confidence\ninterval indicated in the plot."}),"\n",(0,i.jsx)(c.z,{data:r(82140)}),"\n",(0,i.jsx)(t.p,{children:(0,i.jsx)(t.strong,{children:"Observed Arm Effects on hartmann6"})}),"\n",(0,i.jsx)(t.p,{children:"Observed effects on hartmann6. This plot visualizes the effects from previously-run arms\non a specific metric, providing insights into their performance. This plot allows one to\ncompare and contrast the effectiveness of different arms, highlighting which\nconfigurations have yielded the most favorable outcomes."}),"\n",(0,i.jsx)(c.z,{data:r(59872)}),"\n",(0,i.jsx)(t.p,{children:(0,i.jsx)(t.strong,{children:"Utility Progression"})}),"\n",(0,i.jsx)(t.p,{children:"Shows the best hartmann6 value achieved so far across completed trials (objective is to\nminimize). The x-axis shows trial index. Only completed or early-stopped trials with\ncomplete metric data are included, so there may be gaps if some trials failed, were\nabandoned, or have incomplete data. The y-axis shows cumulative best utility. Only\nimprovements are plotted, so flat segments indicate trials that didn't surpass the\nprevious best. Infeasible trials (violating outcome constraints) don't contribute to the\nimprovements."}),"\n",(0,i.jsx)(c.z,{data:r(86797)}),"\n",(0,i.jsx)(t.p,{children:(0,i.jsx)(t.strong,{children:"hartmann6 by progression"})}),"\n",(0,i.jsx)(t.p,{children:"The progression plot tracks the evolution of each metric over the course of the\nexperiment. This visualization is typically used to monitor the improvement of metrics\nover Trial iterations, but can also be useful in informing decisions about early\nstopping for Trials."}),"\n",(0,i.jsx)(c.z,{data:r(9169)}),"\n",(0,i.jsx)(t.p,{children:(0,i.jsx)(t.strong,{children:"hartmann6 by wallclock time"})}),"\n",(0,i.jsx)(t.p,{children:"The progression plot tracks the evolution of each metric over the course of the\nexperiment. This visualization is typically used to monitor the improvement of metrics\nover Trial iterations, but can also be useful in informing decisions about early\nstopping for Trials."}),"\n",(0,i.jsx)(c.z,{data:r(62818)}),"\n",(0,i.jsx)(t.p,{children:(0,i.jsx)(t.strong,{children:"Best Trial for Experiment"})}),"\n",(0,i.jsx)(t.p,{children:"Displays the trial with the best objective value based on raw observations. This\nreflects actual measured performance during execution. This trial achieved the optimal\nobjective value and represents the recommended configuration for your optimization goal.\nOnly considering COMPLETED trials."}),"\n",(0,i.jsxs)(t.table,{children:[(0,i.jsx)(t.thead,{children:(0,i.jsxs)(t.tr,{children:[(0,i.jsx)(t.th,{style:{textAlign:"right"}}),(0,i.jsx)(t.th,{style:{textAlign:"right"},children:"trial_index"}),(0,i.jsx)(t.th,{style:{textAlign:"right"},children:"arm_name"}),(0,i.jsx)(t.th,{style:{textAlign:"left"},children:"trial_status"}),(0,i.jsx)(t.th,{style:{textAlign:"left"},children:"generation_node"}),(0,i.jsx)(t.th,{style:{textAlign:"right"},children:"hartmann6"}),(0,i.jsx)(t.th,{style:{textAlign:"right"},children:"x1"}),(0,i.jsx)(t.th,{style:{textAlign:"right"},children:"x2"}),(0,i.jsx)(t.th,{style:{textAlign:"right"},children:"x3"}),(0,i.jsx)(t.th,{style:{textAlign:"right"},children:"x4"}),(0,i.jsx)(t.th,{style:{textAlign:"right"},children:"x5"}),(0,i.jsx)(t.th,{style:{textAlign:"right"},children:"x6"})]})}),(0,i.jsx)(t.tbody,{children:(0,i.jsxs)(t.tr,{children:[(0,i.jsx)(t.td,{style:{textAlign:"right"},children:"0"}),(0,i.jsx)(t.td,{style:{textAlign:"right"},children:"29"}),(0,i.jsx)(t.td,{style:{textAlign:"right"},children:"29_0"}),(0,i.jsx)(t.td,{style:{textAlign:"left"},children:"COMPLETED"}),(0,i.jsx)(t.td,{style:{textAlign:"left"},children:"MBM"}),(0,i.jsx)(t.td,{style:{textAlign:"right"},children:"-3.14972"}),(0,i.jsx)(t.td,{style:{textAlign:"right"},children:"0.413992"}),(0,i.jsx)(t.td,{style:{textAlign:"right"},children:"0.906932"}),(0,i.jsx)(t.td,{style:{textAlign:"right"},children:"1"}),(0,i.jsx)(t.td,{style:{textAlign:"right"},children:"0.562515"}),(0,i.jsx)(t.td,{style:{textAlign:"right"},children:"0"}),(0,i.jsx)(t.td,{style:{textAlign:"right"},children:"0.059856"})]})})]}),"\n",(0,i.jsx)(t.p,{children:(0,i.jsx)(t.strong,{children:"Summary for hartmann6_experiment"})}),"\n",(0,i.jsxs)(t.p,{children:["High-level summary of the ",(0,i.jsx)(t.code,{children:"Trial"}),"-s in this 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