https://ax.dev/assets/js/161d53c9.c8278f7c.js

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1)," running workloads on high-performance\ncomputing clusters"]}),"\n",(0,l.jsxs)(e.li,{children:[(0,l.jsx)(e.strong,{children:"A/B tests"})," conducted using an external experimentation platform"]}),"\n",(0,l.jsxs)(e.li,{children:[(0,l.jsx)(e.strong,{children:"Materials science"})," optimizations utilizing a self-driving laboratory"]}),"\n"]}),"\n",(0,l.jsxs)(e.p,{children:["Ax's ",(0,l.jsx)(e.code,{children:"Client"})," can orchestrate automated adaptive experiments like this using its method\n",(0,l.jsx)(e.code,{children:"run_trials"}),". Users create custom classes which implement Ax's ",(0,l.jsx)(e.code,{children:"IMetric"})," and ",(0,l.jsx)(e.code,{children:"IRunner"}),"\nprotocols to handle data fetching and trial deployment respectively. Then, users simply\nconfigure their ",(0,l.jsx)(e.code,{children:"Client"})," as they would normally and call ",(0,l.jsx)(e.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,l.jsx)(e.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,l.jsx)(e.h3,{id:"learning-objectives",children:"Learning Objectives"}),"\n",(0,l.jsxs)(e.ul,{children:["\n",(0,l.jsx)(e.li,{children:"Learn when it can be appropriate and/or advantageous to run Ax in a closed-loop"}),"\n",(0,l.jsx)(e.li,{children:"Configure custom Runners and Metrics, allowing Ax to deploy trials and fetch data\nautomatically"}),"\n",(0,l.jsx)(e.li,{children:"Understand tradeoffs between parallelism and optimization performance"}),"\n"]}),"\n",(0,l.jsx)(e.h3,{id:"prerequisites",children:"Prerequisites"}),"\n",(0,l.jsxs)(e.ul,{children:["\n",(0,l.jsxs)(e.li,{children:["Understanding of ",(0,l.jsx)(e.a,{href:"/docs/1.0.0/intro-to-ae",children:"adaptive experimentation"})," and\n",(0,l.jsx)(e.a,{href:"/docs/1.0.0/intro-to-bo",children:"Bayesian optimization"})]}),"\n",(0,l.jsxs)(e.li,{children:["Familiarity with\n",(0,l.jsx)(e.a,{href:"/docs/1.0.0/tutorials/getting_started/",children:"configuring and conducting experiments in Ax"})]}),"\n"]}),"\n",(0,l.jsx)(e.h2,{id:"step-1-import-necessary-modules",children:"Step 1: Import Necessary Modules"}),"\n",(0,l.jsx)(e.p,{children:"First, ensure you have all the necessary imports:"}),"\n",(0,l.jsx)(e.pre,{children:(0,l.jsx)(e.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,l.jsx)(e.h1,{id:"step-2-defining-our-custom-runner-and-metric",children:"Step 2: Defining our custom Runner and Metric"}),"\n",(0,l.jsx)(e.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,l.jsx)(e.pre,{children:(0,l.jsx)(e.code,{className:"language-python",children:"# Hartmann6 function\ndef hartmann6(x1, x2, x3, x4, x5, x6):\n    alpha = 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,l.jsx)(a.A,{children:"np.float64(-0.4878737485613134)"}),"\n",(0,l.jsxs)(e.p,{children:["Next, we will define the ",(0,l.jsx)(e.code,{children:"MockRunner"}),". The ",(0,l.jsx)(e.code,{children:"MockRunner"})," requires two methods:\n",(0,l.jsx)(e.code,{children:"run_trial"})," and ",(0,l.jsx)(e.code,{children:"poll_trial"}),"."]}),"\n",(0,l.jsxs)(e.p,{children:[(0,l.jsx)(e.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,l.jsxs)(e.p,{children:[(0,l.jsx)(e.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 

1since the ",(0,l.jsx)(e.code,{children:"run_trial"})," was called and only report a trial as completed once 5\nseconds have elapsed."]}),"\n",(0,l.jsxs)(e.p,{children:["Runner's may also optionally implement a ",(0,l.jsx)(e.code,{children:"stop_trial"})," method to terminate a trial's\nexecution before it has completed. This is necessary for using\n",(0,l.jsx)(e.a,{href:"../early_stopping",children:"early stopping"})," in closed-loop experimentation, but we will skip\nthis for now."]}),"\n",(0,l.jsx)(e.pre,{children:(0,l.jsx)(e.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,l.jsxs)(e.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,l.jsx)(e.code,{children:"run_trial"})," and ensuring it creates a file."]}),"\n",(0,l.jsx)(e.pre,{children:(0,l.jsx)(e.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,l.jsx)(a.A,{children:"True"}),"\n",(0,l.jsxs)(e.p,{children:["Now, we will implement the Metric. Metrics only need to implement a ",(0,l.jsx)(e.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,l.jsxs)(e.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,l.jsx)(e.code,{children:"(0, value)"}),"."]}),"\n",(0,l.jsx)(e.pre,{children:(0,l.jsx)(e.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,l.jsx)(e.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,l.jsx)(e.pre,{children:(0,l.jsx)(e.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,l.jsx)(a.A,{children:"(0, -0.4878737485613134)"}),"\n",(0,l.jsx)(e.h2,{id:"step-3-initialize-the-client-and-configure-the-experiment",children:"Step 3: Initialize the Client and Configure the Experiment"}),"\n",(0,l.jsxs)(e.p,{children:["Finally, we can initialize the ",(0,l.jsx)(e.code,{children:"Client"})," and configure the experiment as before. This\nwill be familiar to readers of the\n",(0,l.jsx)(e.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,l.jsx)(e.code,{children:"configure_runner"})," and\n",(0,l.jsx)(e.code,{children:"configure_metrics"})," respectively."]}),"\n",(0,l.jsxs)(e.p,{children:["Note that when initializing ",(0,l.jsx)(e.code,{children:"hartmann6_metric"})," we set ",(0,l.jsx)(e.code,{children:"name=hartmann6"}),", matching the\nobjective we now set in ",(0,l.jsx)(e.code,{children:"configure_optimization"}),". The ",(0,l.jsx)(e.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,l.jsx)(e.pre,{children:(0,l.jsx)(e.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,l.jsx)(e.pre,{children:(0,l.jsx)(e.code,{className:"language-python",children:"client.configure_runner(runner=runner)\nclient.configure_metrics(metrics=[hartmann6_metric])\n"})}),"\n",(0,l.jsx)(e.h2,{id:"step-5-run-trials",children:"Step 5: Run trials"}),"\n",(0,l.jsxs)(e.p,{children:["Once the ",(0,l.jsx)(e.code,{children:"Client"})," has been configured, we can begin running trials."]}),"\n",(0,l.jsxs)(e.p,{children:["Internally, Ax uses a class named ",(0,l.jsx)(e.code,{children:"Scheduler"})," to orchestrate the trial deployment,\npolling, data fetching, and candidate generation."]}),"\n",(0,l.jsx)(e.p,{children:(0,l.jsx)(e.img,{alt:"Scheduler state machine",src:r(83618).A+"",width:"2138",height:"800"})}),"\n",(0,l.jsxs)(e.p,{children:["The ",(0,l.jsx)(e.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,l.jsxs)(e.ul,{children:["\n",(0,l.jsxs)(e.li,{children:[(0,l.jsx)(e.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,l.jsxs)(e.li,{children:[(0,l.jsx)(e.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,l.jsxs)(e.li,{children:[(0,l.jsx)(e.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,l.jsx)(e.pre,{children:(0,l.jsx)(e.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,l.jsx)(a.A,{children:"[INFO 05-08 22:38:15] Scheduler: Scheduler requires experiment to have immutable search space and optimization config. Setting property immutable_search_space_and_opt_config to True on experiment.\n[INFO 05-08 22:38:15] Scheduler: Running trials [0]...\n[INFO 05-08 22:38:16] Scheduler: Running trials [1]...\n[INFO 05-08 22:38:17] Scheduler: Running trials [2]...\n[INFO 05-08 22:38:18] Scheduler: Retrieved COMPLETED trials: 0 - 2.\n[INFO 05-08 22:38:18] Scheduler: Running trials [3]...\n[INFO 05-08 22:38:19] Scheduler: Running trials [4]...\n[INFO 05-08 22:38:20] Scheduler: Running trials [5]...\n[INFO 05-08 22:38:21] Scheduler: Retrieved COMPLETED trials: 3 - 5.\n[INFO 05-08 22:38:22] Scheduler: Running trials [6]...\n[INFO 05-08 22:38:25] Scheduler: Running trials [7]...\n[INFO 05-08 22:38:27] Scheduler: Running trials [8]...\n[INFO 05-08 22:38:28] Scheduler: Retrieved COMPLETED trials: 6 - 8.\n[INFO 05-08 22:38:31] Scheduler: Running trials [9]...\n[INFO 05-08 22:38:34] Scheduler: Running trials [10]...\n[INFO 05-08 22:38:40] Scheduler: Running trials [11]...\n[INFO 05-08 22:38:41] Scheduler: Retrieved COMPLETED trials: 9 - 11.\n[INFO 05-08 22:38:44] Scheduler: Running trials [12]...\n[INFO 05-08 22:38:47] Scheduler: Running trials [13]...\n[INFO 05-08 22:38:51] Scheduler: Running trials [14]...\n[INFO 05-08 22:38:52] Scheduler: Retrieved COMPLETED trials: 12 - 14.\n[INFO 05-08 22:38:54] Scheduler: Running trials [15]...\n[INFO 05-08 22:38:56] Scheduler: Running trials [16]...\n[INFO 05-08 22:38:58] Scheduler: Running trials [17]...\n[INFO 05-08 22:38:59] Scheduler: Retrieved COMPLETED trials: 15 - 17.\n[INFO 05-08 22:39:03] Scheduler: Running trials [18]...\n[INFO 05-08 22:39:07] Scheduler: Running trials [19]...\n[INFO 05-08 22:39:11] Scheduler: Running trials [20]...\n[INFO 05-08 22:39:12] Scheduler: Retrieved COMPLETED trials: 18 - 20.\n[INFO 05-08 22:39:15] Scheduler: Running trials [21]...\n[INFO 05-08 22:39:19] Scheduler: Running trials [22]...\n[INFO 05-08 22:39:22] Scheduler: Running trials [23]...\n[INFO 05-08 22:39:23] Scheduler: Retrieved COMPLETED trials: 21 - 23.\n[INFO 05-08 22:39:25] Scheduler: Running trials [24]...\n[INFO 05-08 22:39:29] Scheduler: Running trials [25]...\n[INFO 05-08 22:39:30] Scheduler: Running trials [26]...\n[INFO 05-08 22:39:31] Scheduler: Retrieved COMPLETED trials: 24 - 26.\n[INFO 05-08 22:39:34] Scheduler: Running trials [27]...\n[INFO 05-08 22:39:39] Scheduler: Running trials [28]...\n[INFO 05-08 22:39:42] Scheduler: Running trials [29]...\n[INFO 05-08 22:39:43] Scheduler: Retrieved COMPLETED trials: 27 - 29."}),"\n",(0,l.jsx)(e.h2,{id:"step-6-analyze-results",children:"Step 6: Analyze Results"}),"\n",(0,l.jsx)(e.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,l.jsxs)(e.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,l.jsx)(e.code,{children:"Client"}

1)," saves a\nsnapshot of itself at various points throughout the call to ",(0,l.jsx)(e.code,{children:"run_trials"}),", making it\nincredibly easy to continue optimization after an unexpected failure. You can learn more\nabout storage in Ax ",(0,l.jsx)(e.a,{href:"../../recipes/experiment-to-json",children:"here"}),"."]}),"\n",(0,l.jsx)(e.pre,{children:(0,l.jsx)(e.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,l.jsx)(a.A,{children:"Best Parameters: {'x1': 0.40969082369062376, 'x2': 0.9578901325810083, 'x3': 0.7999735876213786, 'x4': 0.5847710063741918, 'x5': 0.46918227302071247, 'x6': 0.04104230959179908}\nPrediction (mean, variance): {'hartmann6': (np.float64(-2.9916773867281474), np.float64(0.0024136261442267437))}"}),"\n",(0,l.jsx)(e.pre,{children:(0,l.jsx)(e.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,l.jsx)(e.p,{children:(0,l.jsx)(e.strong,{children:"Parallel Coordinates for hartmann6"})}),"\n",(0,l.jsx)(e.p,{children:"The parallel coordinates plot displays multi-dimensional data by representing each\nparameter as a parallel axis. This plot helps in asse

1ssing how thoroughly the search\nspace has been explored and in identifying patterns or clusterings associated with\nhigh-performing (good) or low-performing (bad) arms. By tracing lines across the axes,\none can observe correlations and interactions between parameters, gaining insights into\nthe relationships that contribute to the success or failure of different configurations\nwithin the experiment."}),"\n",(0,l.jsx)(s.z,{data:r(98858)}),"\n",(0,l.jsx)(e.p,{children:(0,l.jsx)(e.strong,{children:"Summary for hartmann6_experiment"})}),"\n",(0,l.jsxs)(e.p,{children:["High-level summary of the ",(0,l.jsx)(e.code,{children:"Trial"}),"-s in this 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Analysis for hartmann6"})}),"\n",(0,l.jsx)(e.p,{children:"Understand how each parameter affects hartmann6 according to a second-order sensitivity\nanalysis."}),"\n",(0,l.jsx)(s.z,{data:r(14178)}),"\n",(0,l.jsx)(e.p,{children:(0,l.jsx)(e.strong,{children:"x2, x6 vs. hartmann6"})}),"\n",(0,l.jsx)(e.p,{children:"The contour plot visualizes the predicted outcomes for hartmann6 across a\ntwo-dimensional parameter space, with other parameters held fixed at their status_quo\nvalue (or mean value if status_quo is unavailable). This plot helps in identifying\nregions of optimal performance and understanding how changes in the selected parameters\ninfluence the predicted outcomes. Contour lines represent levels of constant predicted\nvalues, providing insights into the gradient and potential optima within the parameter\nspace."}),"\n",(0,l.jsx)(s.z,{data:r(66255)}),"\n",(0,l.jsx)(e.p,{children:(0,l.jsx)(e.strong,{children:"x1, x6 vs. hartmann6"})}),"\n",(0,l.jsx)(e.p,{children:"The contour plot visualizes the predicted outcomes for hartmann6 across a\ntwo-dimensional parameter space, with other parameters held fixed at their status_quo\nvalue (or mean value if status_quo is unavailable). This plot helps in identifying\nregions of optimal performance and understanding how changes in the selected parameters\ninfluence the predicted outcomes. Contour lines represent levels of constant predicted\nvalues, providing insights into the gradient and potential optima within the parameter\nspace."}),"\n",(0,l.jsx)(s.z,{data:r(2947)}),"\n",(0,l.jsx)(e.p,{children:(0,l.jsx)(e.strong,{children:"x6 vs. hartmann6"})}),"\n",(0,l.jsx)(e.p,{children:"The slice plot provides a one-dimensional view of predicted outcomes for hartmann6 as a\nfunction of a single parameter, while keeping all other parameters fixed at their\nstatus_quo value (or mean value if status_quo is unavailable). This visualization helps\nin understanding the sensitivity and impact of changes in the selected parameter on the\npredicted metric outcomes."}),"\n",(0,l.jsx)(s.z,{data:r(62077)}),"\n",(0,l.jsx)(e.p,{children:(0,l.jsx)(e.strong,{children:"x4 vs. hartmann6"})}),"\n",(0,l.jsx)(e.p,{children:"The slice plot provides a one-dimensional view of predicted outcomes for hartmann6 as a\nfunction of a single parameter, while keeping all other parameters fixed at their\nstatus_quo value (or mean value if status_quo is unavailable). This visualization helps\nin understanding the sensitivity and impact of changes in the selected parameter on the\npredicted metric outcomes."}),"\n",(0,l.jsx)(s.z,{data:r(22620)}),"\n",(0,l.jsx)(e.p,{children:(0,l.jsx)(e.strong,{children:"x1 vs. hartmann6"})}),"\n",(0,l.jsx)(e.p,{children:"The slice plot provides a one-dimensional view of predicted outcomes for hartmann6 as a\nfunction of a single parameter, while keeping all other parameters fixed at their\nstatus_quo value (or mean value if status_quo is unavailable). This visualization helps\nin understanding the sensitivity and impact of changes in the selected parameter on the\npredicted metric outcomes."}),"\n",(0,l.jsx)(s.z,{data:r(61039)}),"\n",(0,l.jsx)(e.p,{children:(0,l.jsx)(e.strong,{children:"Cross Validation for hartmann6"})}),"\n",(0,l.jsx)(e.p,{children:"The cross-validation plot displays the model fit for each metric in the experiment. It\nemploys a leave-one-out approach, where the model is trained on all data except one\nsample, which is used for validation. The plot shows the predicted outcome for the\nvalidation set on the y-axis against its actual value on the x-axis. Points that align\nclosely with the dotted diagonal line indicate a strong model fit, signifying accurate\npredictions. Additionally, the plot includes 95% confidence intervals that provide\ninsight into the noise in observations and the uncertainty in model predictions. A\nhorizontal, flat line of predictions indicates that the model has not picked up on\nsufficient signal in the data, and instead is just predicting the mean."}),"\n",(0,l.jsx)(s.z,{data:r(23223)}),"\n",(0,l.jsx)(e.h2,{id:"conclusion",children:"Conclusion"}),"\n",(0,l.jsxs)(e.p,{children:["This tutorial demonstrates how to use Ax's ",(0,l.jsx)(e.code,{children:"Client"})," for closed-loop optimization using\nthe Hartmann6 function as an example. This style of optimization is useful in scenarios\nwhere trials are evaluated on some external system or when experimenters wish to take\nadvantage of parallel evaluation, trial failure handling, or simply to manage\

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