使用 AxSearch 运行 Tune 实验

在本教程中，我们将介绍 Ax，并运行一个简单的 Ray Tune 实验。Tune 的搜索算法与 Ax 集成，因此您可以无缝地扩展 Ax 优化过程 - 而不牺牲性能。

Ax 是一个用于优化任何类型实验的平台，包括机器学习实验、A/B 测试和模拟。Ax 可以使用多臂老虎机优化离散配置（例如，A/B 测试的变体），并使用贝叶斯优化连续/有序配置（例如浮点/整数参数）。使用强化学习智能体进行的 A/B 测试和模拟结果通常表现出较高的噪声。Ax 支持最先进的算法，这些算法在高噪声环境下比传统的贝叶斯优化效果更好。Ax 还支持多目标和约束优化，这在实际问题中很常见（例如，在不增加数据使用的情况下改善加载时间）。Ax 属于“无导数”和“黑盒”优化的范畴。

在此示例中，我们将最小化一个简单目标，简要演示通过 AxSearch 在 Ray Tune 中使用 AxSearch。请记住，尽管侧重于机器学习实验，Ray Tune 优化任何隐式或显式目标。这里我们假设已安装 ax-platform==0.2.4 库，且 Python 版本 >= 3.7。要了解更多信息，请参阅 Ax 网站。

点击下方查看此示例所需的所有导入。

显示代码单元格源码 隐藏代码单元格源码

import numpy as np
import time

import ray
from ray import tune
from ray.tune.search.ax import AxSearch

我们首先定义一个经典的全局优化基准。这里的形式是显式的，用于演示，但它通常是一个黑盒。我们人为地休眠一小段时间 (0.02 秒)，以模拟长时间运行的 ML 实验。此设置假设我们正在运行实验的多个 step，并尝试调整 x 超参数的 6 个维度。

def landscape(x):
    """
    Hartmann 6D function containing 6 local minima.
    It is a classic benchmark for developing global optimization algorithms.
    """
    alpha = np.array([1.0, 1.2, 3.0, 3.2])
    A = np.array(
        [
            [10, 3, 17, 3.5, 1.7, 8],
            [0.05, 10, 17, 0.1, 8, 14],
            [3, 3.5, 1.7, 10, 17, 8],
            [17, 8, 0.05, 10, 0.1, 14],
        ]
    )
    P = 10 ** (-4) * np.array(
        [
            [1312, 1696, 5569, 124, 8283, 5886],
            [2329, 4135, 8307, 3736, 1004, 9991],
            [2348, 1451, 3522, 2883, 3047, 6650],
            [4047, 8828, 8732, 5743, 1091, 381],
        ]
    )
    y = 0.0
    for j, alpha_j in enumerate(alpha):
        t = 0
        for k in range(6):
            t += A[j, k] * ((x[k] - P[j, k]) ** 2)
        y -= alpha_j * np.exp(-t)
    return y

接下来，我们的 objective 函数接收一个 Tune config，在训练循环中评估我们实验的 landscape，并使用 tune.report 将 landscape 报告回 Tune。

def objective(config):
    for i in range(config["iterations"]):
        x = np.array([config.get("x{}".format(i + 1)) for i in range(6)])
        tune.report(
            {"timesteps_total": i, "landscape": landscape(x), "l2norm": np.sqrt((x ** 2).sum())}
        )
        time.sleep(0.02)

接下来我们定义一个搜索空间。关键假设是最佳超参数存在于此空间内。然而，如果空间非常大，那么在短时间内可能难以找到这些超参数。

search_space = {
    "iterations":100,
    "x1": tune.uniform(0.0, 1.0),
    "x2": tune.uniform(0.0, 1.0),
    "x3": tune.uniform(0.0, 1.0),
    "x4": tune.uniform(0.0, 1.0),
    "x5": tune.uniform(0.0, 1.0),
    "x6": tune.uniform(0.0, 1.0)
}

现在我们定义 AxSearch 的搜索算法。如果您想约束您的参数甚至结果空间，可以通过如下传递参数轻松完成。

algo = AxSearch(
    parameter_constraints=["x1 + x2 <= 2.0"],
    outcome_constraints=["l2norm <= 1.25"],
)

我们还使用 ConcurrencyLimiter 将并发 trials 限制为 4 个。

algo = tune.search.ConcurrencyLimiter(algo, max_concurrent=4)

样本数量是要尝试的超参数组合的数量。此 Tune 运行设置为 1000 个样本。如果您的机器运行时间过长，可以减少此数量，或者如我们将在此处所示，通过 RunConfig() 中的 stop 参数轻松设置时间限制。

num_samples = 100
stop_timesteps = 200

最后，我们运行实验以找到提供的 landscape（包含 5 个假最小值）的全局最小值。指标参数 "landscape" 通过 objective 函数的 session.report 提供。实验通过 algo 在 search_space 内搜索 num_samples 次或当 "timesteps_total": stop_timesteps 时，最小化 "min" landscape 的“mean_loss”。前一句话完全描述了我们旨在解决的搜索问题。考虑到这一点，请注意执行 tuner.fit() 的效率有多高。

tuner = tune.Tuner(
    objective,
    tune_config=tune.TuneConfig(
        metric="landscape",
        mode="min",
        search_alg=algo,
        num_samples=num_samples,
    ),
    run_config=tune.RunConfig(
        name="ax",
        stop={"timesteps_total": stop_timesteps}
    ),
    param_space=search_space,
)
results = tuner.fit()

[INFO 07-22 15:04:18] ax.service.ax_client: Starting optimization with verbose logging. To disable logging, set the `verbose_logging` argument to `False`. Note that float values in the logs are rounded to 6 decimal points.
[INFO 07-22 15:04:18] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[FixedParameter(name='iterations', parameter_type=INT, value=100), RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[ParameterConstraint(1.0*x1 + 1.0*x2 <= 2.0)]).
[INFO 07-22 15:04:18] ax.modelbridge.dispatch_utils: Using Bayesian optimization since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 07-22 15:04:18] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to  model-fitting.
Detected sequential enforcement. Be sure to use a ConcurrencyLimiter.

== 状态 ==
当前时间: 2022-07-22 15:04:35 (已运行 00:00:16.56)
此节点内存使用: 9.9/16.0 GiB
使用 FIFO 调度算法。
请求资源: 0/16 CPU, 0/0 GPU, 0.0/5.13 GiB 堆内存, 0.0/2.0 GiB 对象内存
当前最佳 trial: 34b7abda, landscape=-1.6624439263544026, 参数={'iterations': 100, 'x1': 0.26526361983269453, 'x2': 0.9248840995132923, 'x3': 0.15171580761671066, 'x4': 0.43602637108415365, 'x5': 0.8573104059323668, 'x6': 0.08981018699705601}
结果日志目录: /Users/kai/ray_results/ax
Trial 数量: 10/10 (10 个已终止)

Trial 名称	状态	位置	迭代次数	x1	x2	x3	x4	x5	x6	迭代	总时间 (秒)	时间戳	landscape	l2 范数
objective_2dfbe86a	已终止	127.0.0.1:44721	100	0.0558336	0.0896192	0.958956	0.234474	0.174516	0.970311	100	2.57372	99	-0.805233	1.39917
objective_2fa776c0	已终止	127.0.0.1:44726	100	0.744772	0.754537	0.0950125	0.273877	0.0966829	0.368943	100	2.6361	99	-0.11286	1.16341
objective_2fabaa1a	已终止	127.0.0.1:44727	100	0.405704	0.374626	0.935628	0.222185	0.787212	0.00812439	100	2.62393	99	-0.11348	1.35995
objective_2faee7c0	已终止	127.0.0.1:44728	100	0.664728	0.207519	0.359514	0.704578	0.755882	0.812402	100	2.62069	99	-0.0119837	1.53035
objective_313d3d3a	已终止	127.0.0.1:44747	100	0.0418746	0.992783	0.906027	0.594429	0.825393	0.646362	100	3.16233	99	-0.00677976	1.80573
objective_32c9acd8	已终止	127.0.0.1:44726	100	0.126064	0.703408	0.344681	0.337363	0.401396	0.679202	100	3.12119	99	-0.904622	1.16864
objective_32cf8ca2	已终止	127.0.0.1:44756	100	0.0910936	0.304138	0.869848	0.405435	0.567922	0.228608	100	2.70791	99	-0.146532	1.18178
objective_32d8dd20	已终止	127.0.0.1:44758	100	0.603178	0.409057	0.729056	0.0825984	0.572948	0.508304	100	2.64158	99	-0.247223	1.28691
objective_34adf04a	已终止	127.0.0.1:44768	100	0.454189	0.271772	0.530871	0.991841	0.691843	0.472366	100	2.70327	99	-0.0132915	1.49917
objective_34b7abda	已终止	127.0.0.1:44771	100	0.265264	0.924884	0.151716	0.436026	0.85731	0.0898102	100	2.68521	99	-1.66244	1.37185

[INFO 07-22 15:04:19] ax.service.ax_client: Generated new trial 0 with parameters {'x1': 0.055834, 'x2': 0.089619, 'x3': 0.958956, 'x4': 0.234474, 'x5': 0.174516, 'x6': 0.970311, 'iterations': 100}.
[INFO 07-22 15:04:22] ax.service.ax_client: Generated new trial 1 with parameters {'x1': 0.744772, 'x2': 0.754537, 'x3': 0.095012, 'x4': 0.273877, 'x5': 0.096683, 'x6': 0.368943, 'iterations': 100}.
[INFO 07-22 15:04:22] ax.service.ax_client: Generated new trial 2 with parameters {'x1': 0.405704, 'x2': 0.374626, 'x3': 0.935628, 'x4': 0.222185, 'x5': 0.787212, 'x6': 0.008124, 'iterations': 100}.
[INFO 07-22 15:04:22] ax.service.ax_client: Generated new trial 3 with parameters {'x1': 0.664728, 'x2': 0.207519, 'x3': 0.359514, 'x4': 0.704578, 'x5': 0.755882, 'x6': 0.812402, 'iterations': 100}.

Result for objective_2dfbe86a:
  date: 2022-07-22_15-04-22
  done: false
  experiment_id: 4ef8a12ac94a4f4fa483ec18e347967f
  hostname: Kais-MacBook-Pro.local
  iterations_since_restore: 1
  l2norm: 1.3991721132671366
  landscape: -0.8052333562869153
  node_ip: 127.0.0.1
  pid: 44721
  time_since_restore: 0.00022912025451660156
  time_this_iter_s: 0.00022912025451660156
  time_total_s: 0.00022912025451660156
  timestamp: 1658498662
  timesteps_since_restore: 0
  timesteps_total: 0
  training_iteration: 1
  trial_id: 2dfbe86a
  warmup_time: 0.0035619735717773438
  

[INFO 07-22 15:04:24] ax.service.ax_client: Completed trial 0 with data: {'landscape': (-0.805233, None), 'l2norm': (1.399172, None)}.
[INFO 07-22 15:04:24] ax.service.ax_client: Generated new trial 4 with parameters {'x1': 0.041875, 'x2': 0.992783, 'x3': 0.906027, 'x4': 0.594429, 'x5': 0.825393, 'x6': 0.646362, 'iterations': 100}.

Result for objective_2faee7c0:
  date: 2022-07-22_15-04-24
  done: false
  experiment_id: 3699644e85ac439cb7c1a36ed0976307
  hostname: Kais-MacBook-Pro.local
  iterations_since_restore: 1
  l2norm: 1.530347488145437
  landscape: -0.011983676977099367
  node_ip: 127.0.0.1
  pid: 44728
  time_since_restore: 0.00022292137145996094
  time_this_iter_s: 0.00022292137145996094
  time_total_s: 0.00022292137145996094
  timestamp: 1658498664
  timesteps_since_restore: 0
  timesteps_total: 0
  training_iteration: 1
  trial_id: 2faee7c0
  warmup_time: 0.0027179718017578125
  
Result for objective_2fa776c0:
  date: 2022-07-22_15-04-24
  done: false
  experiment_id: c555bfed13ac43e5b8c8e9f6d4b9b2f7
  hostname: Kais-MacBook-Pro.local
  iterations_since_restore: 1
  l2norm: 1.1634068454629019
  landscape: -0.11285961764770336
  node_ip: 127.0.0.1
  pid: 44726
  time_since_restore: 0.000225067138671875
  time_this_iter_s: 0.000225067138671875
  time_total_s: 0.000225067138671875
  timestamp: 1658498664
  timesteps_since_restore: 0
  timesteps_total: 0
  training_iteration: 1
  trial_id: 2fa776c0
  warmup_time: 0.0026290416717529297
  
Result for objective_2dfbe86a:
  date: 2022-07-22_15-04-24
  done: true
  experiment_id: 4ef8a12ac94a4f4fa483ec18e347967f
  experiment_tag: 1_iterations=100,x1=0.0558,x2=0.0896,x3=0.9590,x4=0.2345,x5=0.1745,x6=0.9703
  hostname: Kais-MacBook-Pro.local
  iterations_since_restore: 100
  l2norm: 1.3991721132671366
  landscape: -0.8052333562869153
  node_ip: 127.0.0.1
  pid: 44721
  time_since_restore: 2.573719024658203
  time_this_iter_s: 0.0251619815826416
  time_total_s: 2.573719024658203
  timestamp: 1658498664
  timesteps_since_restore: 0
  timesteps_total: 99
  training_iteration: 100
  trial_id: 2dfbe86a
  warmup_time: 0.0035619735717773438
  
Result for objective_2fabaa1a:
  date: 2022-07-22_15-04-24
  done: false
  experiment_id: eb9287e4fe5f44c7868dc943e2642312
  hostname: Kais-MacBook-Pro.local
  iterations_since_restore: 1
  l2norm: 1.3599537840291782
  landscape: -0.11348012497414121
  node_ip: 127.0.0.1
  pid: 44727
  time_since_restore: 0.00022077560424804688
  time_this_iter_s: 0.00022077560424804688
  time_total_s: 0.00022077560424804688
  timestamp: 1658498664
  timesteps_since_restore: 0
  timesteps_total: 0
  training_iteration: 1
  trial_id: 2fabaa1a
  warmup_time: 0.0025510787963867188
  

[INFO 07-22 15:04:27] ax.service.ax_client: Completed trial 3 with data: {'landscape': (-0.011984, None), 'l2norm': (1.530347, None)}.
[INFO 07-22 15:04:27] ax.service.ax_client: Generated new trial 5 with parameters {'x1': 0.126064, 'x2': 0.703408, 'x3': 0.344681, 'x4': 0.337363, 'x5': 0.401396, 'x6': 0.679202, 'iterations': 100}.
[INFO 07-22 15:04:27] ax.service.ax_client: Completed trial 1 with data: {'landscape': (-0.11286, None), 'l2norm': (1.163407, None)}.
[INFO 07-22 15:04:27] ax.service.ax_client: Generated new trial 6 with parameters {'x1': 0.091094, 'x2': 0.304138, 'x3': 0.869848, 'x4': 0.405435, 'x5': 0.567922, 'x6': 0.228608, 'iterations': 100}.
[INFO 07-22 15:04:27] ax.service.ax_client: Completed trial 2 with data: {'landscape': (-0.11348, None), 'l2norm': (1.359954, None)}.
[INFO 07-22 15:04:27] ax.service.ax_client: Generated new trial 7 with parameters {'x1': 0.603178, 'x2': 0.409057, 'x3': 0.729056, 'x4': 0.082598, 'x5': 0.572948, 'x6': 0.508304, 'iterations': 100}.

Result for objective_313d3d3a:
  date: 2022-07-22_15-04-27
  done: false
  experiment_id: fa7afd557e154fbebe4f54d8eedb3573
  hostname: Kais-MacBook-Pro.local
  iterations_since_restore: 1
  l2norm: 1.805729990121368
  landscape: -0.006779757704679272
  node_ip: 127.0.0.1
  pid: 44747
  time_since_restore: 0.00021076202392578125
  time_this_iter_s: 0.00021076202392578125
  time_total_s: 0.00021076202392578125
  timestamp: 1658498667
  timesteps_since_restore: 0
  timesteps_total: 0
  training_iteration: 1
  trial_id: 313d3d3a
  warmup_time: 0.0029790401458740234
  
Result for objective_2faee7c0:
  date: 2022-07-22_15-04-27
  done: true
  experiment_id: 3699644e85ac439cb7c1a36ed0976307
  experiment_tag: 4_iterations=100,x1=0.6647,x2=0.2075,x3=0.3595,x4=0.7046,x5=0.7559,x6=0.8124
  hostname: Kais-MacBook-Pro.local
  iterations_since_restore: 100
  l2norm: 1.530347488145437
  landscape: -0.011983676977099367
  node_ip: 127.0.0.1
  pid: 44728
  time_since_restore: 2.6206929683685303
  time_this_iter_s: 0.027359962463378906
  time_total_s: 2.6206929683685303
  timestamp: 1658498667
  timesteps_since_restore: 0
  timesteps_total: 99
  training_iteration: 100
  trial_id: 2faee7c0
  warmup_time: 0.0027179718017578125
  
Result for objective_2fa776c0:
  date: 2022-07-22_15-04-27
  done: true
  experiment_id: c555bfed13ac43e5b8c8e9f6d4b9b2f7
  experiment_tag: 2_iterations=100,x1=0.7448,x2=0.7545,x3=0.0950,x4=0.2739,x5=0.0967,x6=0.3689
  hostname: Kais-MacBook-Pro.local
  iterations_since_restore: 100
  l2norm: 1.1634068454629019
  landscape: -0.11285961764770336
  node_ip: 127.0.0.1
  pid: 44726
  time_since_restore: 2.6361019611358643
  time_this_iter_s: 0.0264589786529541
  time_total_s: 2.6361019611358643
  timestamp: 1658498667
  timesteps_since_restore: 0
  timesteps_total: 99
  training_iteration: 100
  trial_id: 2fa776c0
  warmup_time: 0.0026290416717529297
  
Result for objective_32c9acd8:
  date: 2022-07-22_15-04-27
  done: false
  experiment_id: c555bfed13ac43e5b8c8e9f6d4b9b2f7
  hostname: Kais-MacBook-Pro.local
  iterations_since_restore: 1
  l2norm: 1.1686440476629836
  landscape: -0.9046216637367911
  node_ip: 127.0.0.1
  pid: 44726
  time_since_restore: 0.00020194053649902344
  time_this_iter_s: 0.00020194053649902344
  time_total_s: 0.00020194053649902344
  timestamp: 1658498667
  timesteps_since_restore: 0
  timesteps_total: 0
  training_iteration: 1
  trial_id: 32c9acd8
  warmup_time: 0.0026290416717529297
  
Result for objective_2fabaa1a:
  date: 2022-07-22_15-04-27
  done: true
  experiment_id: eb9287e4fe5f44c7868dc943e2642312
  experiment_tag: 3_iterations=100,x1=0.4057,x2=0.3746,x3=0.9356,x4=0.2222,x5=0.7872,x6=0.0081
  hostname: Kais-MacBook-Pro.local
  iterations_since_restore: 100
  l2norm: 1.3599537840291782
  landscape: -0.11348012497414121
  node_ip: 127.0.0.1
  pid: 44727
  time_since_restore: 2.623929977416992
  time_this_iter_s: 0.032716989517211914
  time_total_s: 2.623929977416992
  timestamp: 1658498667
  timesteps_since_restore: 0
  timesteps_total: 99
  training_iteration: 100
  trial_id: 2fabaa1a
  warmup_time: 0.0025510787963867188
  
Result for objective_32d8dd20:
  date: 2022-07-22_15-04-30
  done: false
  experiment_id: 171527593b0f4cbf941c0a03faaf0953
  hostname: Kais-MacBook-Pro.local
  iterations_since_restore: 1
  l2norm: 1.2869105702896437
  landscape: -0.24722262157458608
  node_ip: 127.0.0.1
  pid: 44758
  time_since_restore: 0.00021886825561523438
  time_this_iter_s: 0.00021886825561523438
  time_total_s: 0.00021886825561523438
  timestamp: 1658498670
  timesteps_since_restore: 0
  timesteps_total: 0
  training_iteration: 1
  trial_id: 32d8dd20
  warmup_time: 0.002732992172241211
  
Result for objective_32cf8ca2:
  date: 2022-07-22_15-04-29
  done: false
  experiment_id: 37610500f6df493aae4e7e46bb21bf09
  hostname: Kais-MacBook-Pro.local
  iterations_since_restore: 1
  l2norm: 1.1817810425508524
  landscape: -0.14653248187442922
  node_ip: 127.0.0.1
  pid: 44756
  time_since_restore: 0.00025081634521484375
  time_this_iter_s: 0.00025081634521484375
  time_total_s: 0.00025081634521484375
  timestamp: 1658498669
  timesteps_since_restore: 0
  timesteps_total: 0
  training_iteration: 1
  trial_id: 32cf8ca2
  warmup_time: 0.0032138824462890625
  

[INFO 07-22 15:04:30] ax.service.ax_client: Completed trial 4 with data: {'landscape': (-0.00678, None), 'l2norm': (1.80573, None)}.
[INFO 07-22 15:04:30] ax.service.ax_client: Generated new trial 8 with parameters {'x1': 0.454189, 'x2': 0.271772, 'x3': 0.530871, 'x4': 0.991841, 'x5': 0.691843, 'x6': 0.472366, 'iterations': 100}.
[INFO 07-22 15:04:30] ax.service.ax_client: Completed trial 5 with data: {'landscape': (-0.904622, None), 'l2norm': (1.168644, None)}.
[INFO 07-22 15:04:30] ax.service.ax_client: Generated new trial 9 with parameters {'x1': 0.265264, 'x2': 0.924884, 'x3': 0.151716, 'x4': 0.436026, 'x5': 0.85731, 'x6': 0.08981, 'iterations': 100}.

Result for objective_313d3d3a:
  date: 2022-07-22_15-04-30
  done: true
  experiment_id: fa7afd557e154fbebe4f54d8eedb3573
  experiment_tag: 5_iterations=100,x1=0.0419,x2=0.9928,x3=0.9060,x4=0.5944,x5=0.8254,x6=0.6464
  hostname: Kais-MacBook-Pro.local
  iterations_since_restore: 100
  l2norm: 1.805729990121368
  landscape: -0.006779757704679272
  node_ip: 127.0.0.1
  pid: 44747
  time_since_restore: 3.1623308658599854
  time_this_iter_s: 0.02911996841430664
  time_total_s: 3.1623308658599854
  timestamp: 1658498670
  timesteps_since_restore: 0
  timesteps_total: 99
  training_iteration: 100
  trial_id: 313d3d3a
  warmup_time: 0.0029790401458740234
  
Result for objective_32c9acd8:
  date: 2022-07-22_15-04-30
  done: true
  experiment_id: c555bfed13ac43e5b8c8e9f6d4b9b2f7
  experiment_tag: 6_iterations=100,x1=0.1261,x2=0.7034,x3=0.3447,x4=0.3374,x5=0.4014,x6=0.6792
  hostname: Kais-MacBook-Pro.local
  iterations_since_restore: 100
  l2norm: 1.1686440476629836
  landscape: -0.9046216637367911
  node_ip: 127.0.0.1
  pid: 44726
  time_since_restore: 3.1211891174316406
  time_this_iter_s: 0.02954697608947754
  time_total_s: 3.1211891174316406
  timestamp: 1658498670
  timesteps_since_restore: 0
  timesteps_total: 99
  training_iteration: 100
  trial_id: 32c9acd8
  warmup_time: 0.0026290416717529297
  

[INFO 07-22 15:04:32] ax.service.ax_client: Completed trial 7 with data: {'landscape': (-0.247223, None), 'l2norm': (1.286911, None)}.
[INFO 07-22 15:04:32] ax.service.ax_client: Completed trial 6 with data: {'landscape': (-0.146532, None), 'l2norm': (1.181781, None)}.

Result for objective_32d8dd20:
  date: 2022-07-22_15-04-32
  done: true
  experiment_id: 171527593b0f4cbf941c0a03faaf0953
  experiment_tag: 8_iterations=100,x1=0.6032,x2=0.4091,x3=0.7291,x4=0.0826,x5=0.5729,x6=0.5083
  hostname: Kais-MacBook-Pro.local
  iterations_since_restore: 100
  l2norm: 1.2869105702896437
  landscape: -0.24722262157458608
  node_ip: 127.0.0.1
  pid: 44758
  time_since_restore: 2.6415798664093018
  time_this_iter_s: 0.026781082153320312
  time_total_s: 2.6415798664093018
  timestamp: 1658498672
  timesteps_since_restore: 0
  timesteps_total: 99
  training_iteration: 100
  trial_id: 32d8dd20
  warmup_time: 0.002732992172241211
  
Result for objective_32cf8ca2:
  date: 2022-07-22_15-04-32
  done: true
  experiment_id: 37610500f6df493aae4e7e46bb21bf09
  experiment_tag: 7_iterations=100,x1=0.0911,x2=0.3041,x3=0.8698,x4=0.4054,x5=0.5679,x6=0.2286
  hostname: Kais-MacBook-Pro.local
  iterations_since_restore: 100
  l2norm: 1.1817810425508524
  landscape: -0.14653248187442922
  node_ip: 127.0.0.1
  pid: 44756
  time_since_restore: 2.707913875579834
  time_this_iter_s: 0.027456998825073242
  time_total_s: 2.707913875579834
  timestamp: 1658498672
  timesteps_since_restore: 0
  timesteps_total: 99
  training_iteration: 100
  trial_id: 32cf8ca2
  warmup_time: 0.0032138824462890625
  
Result for objective_34adf04a:
  date: 2022-07-22_15-04-33
  done: false
  experiment_id: 4f65c5b68f5c49d98fda388e37c83deb
  hostname: Kais-MacBook-Pro.local
  iterations_since_restore: 1
  l2norm: 1.4991655675380078
  landscape: -0.01329150870283869
  node_ip: 127.0.0.1
  pid: 44768
  time_since_restore: 0.00021600723266601562
  time_this_iter_s: 0.00021600723266601562
  time_total_s: 0.00021600723266601562
  timestamp: 1658498673
  timesteps_since_restore: 0
  timesteps_total: 0
  training_iteration: 1
  trial_id: 34adf04a
  warmup_time: 0.0027239322662353516
  
Result for objective_34b7abda:
  date: 2022-07-22_15-04-33
  done: false
  experiment_id: f135a2c40f5644ba9d2ae096a9dd10e0
  hostname: Kais-MacBook-Pro.local
  iterations_since_restore: 1
  l2norm: 1.3718451333547932
  landscape: -1.6624439263544026
  node_ip: 127.0.0.1
  pid: 44771
  time_since_restore: 0.0002338886260986328
  time_this_iter_s: 0.0002338886260986328
  time_total_s: 0.0002338886260986328
  timestamp: 1658498673
  timesteps_since_restore: 0
  timesteps_total: 0
  training_iteration: 1
  trial_id: 34b7abda
  warmup_time: 0.002721071243286133
  

[INFO 07-22 15:04:35] ax.service.ax_client: Completed trial 8 with data: {'landscape': (-0.013292, None), 'l2norm': (1.499166, None)}.
[INFO 07-22 15:04:35] ax.service.ax_client: Completed trial 9 with data: {'landscape': (-1.662444, None), 'l2norm': (1.371845, None)}.

Result for objective_34adf04a:
  date: 2022-07-22_15-04-35
  done: true
  experiment_id: 4f65c5b68f5c49d98fda388e37c83deb
  experiment_tag: 9_iterations=100,x1=0.4542,x2=0.2718,x3=0.5309,x4=0.9918,x5=0.6918,x6=0.4724
  hostname: Kais-MacBook-Pro.local
  iterations_since_restore: 100
  l2norm: 1.4991655675380078
  landscape: -0.01329150870283869
  node_ip: 127.0.0.1
  pid: 44768
  time_since_restore: 2.7032668590545654
  time_this_iter_s: 0.029300928115844727
  time_total_s: 2.7032668590545654
  timestamp: 1658498675
  timesteps_since_restore: 0
  timesteps_total: 99
  training_iteration: 100
  trial_id: 34adf04a
  warmup_time: 0.0027239322662353516
  
Result for objective_34b7abda:
  date: 2022-07-22_15-04-35
  done: true
  experiment_id: f135a2c40f5644ba9d2ae096a9dd10e0
  experiment_tag: 10_iterations=100,x1=0.2653,x2=0.9249,x3=0.1517,x4=0.4360,x5=0.8573,x6=0.0898
  hostname: Kais-MacBook-Pro.local
  iterations_since_restore: 100
  l2norm: 1.3718451333547932
  landscape: -1.6624439263544026
  node_ip: 127.0.0.1
  pid: 44771
  time_since_restore: 2.6852078437805176
  time_this_iter_s: 0.029579877853393555
  time_total_s: 2.6852078437805176
  timestamp: 1658498675
  timesteps_since_restore: 0
  timesteps_total: 99
  training_iteration: 100
  trial_id: 34b7abda
  warmup_time: 0.002721071243286133
  

现在我们得到了找到的用于最小化平均损失的超参数。

print("Best hyperparameters found were: ", results.get_best_result().config)

Best hyperparameters found were:  {'iterations': 100, 'x1': 0.26526361983269453, 'x2': 0.9248840995132923, 'x3': 0.15171580761671066, 'x4': 0.43602637108415365, 'x5': 0.8573104059323668, 'x6': 0.08981018699705601}