Running Tune experiments with BayesOpt

In this tutorial we introduce BayesOpt, while running a simple Ray Tune experiment. Tune’s Search Algorithms integrate with BayesOpt and, as a result, allow you to seamlessly scale up a BayesOpt optimization process - without sacrificing performance.

BayesOpt is a constrained global optimization package utilizing Bayesian inference on gaussian processes, where the emphasis is on finding the maximum value of an unknown function in as few iterations as possible. BayesOpt’s techniques are particularly suited for optimization of high cost functions, situations where the balance between exploration and exploitation is important. Therefore BayesOpt falls in the domain of “derivative-free” and “black-box” optimization. In this example we minimize a simple objective to briefly demonstrate the usage of BayesOpt with Ray Tune via BayesOptSearch, including conditional search spaces. It’s useful to keep in mind that despite the emphasis on machine learning experiments, Ray Tune optimizes any implicit or explicit objective. Here we assume bayesian-optimization==1.2.0 library is installed. To learn more, please refer to BayesOpt website.

Click below to see all the imports we need for this example. You can also launch directly into a Binder instance to run this notebook yourself. Just click on the rocket symbol at the top of the navigation.

import time

import ray
from ray import tune
from ray.air import session
from ray.tune.search import ConcurrencyLimiter
from ray.tune.search.bayesopt import BayesOptSearch

Let’s start by defining a simple evaluation function. We artificially sleep for a bit (0.1 seconds) to simulate a long-running ML experiment. This setup assumes that we’re running multiple steps of an experiment and try to tune two hyperparameters, namely width and height.

def evaluate(step, width, height):
    time.sleep(0.1)
    return (0.1 + width * step / 100) ** (-1) + height * 0.1

Next, our objective function takes a Tune config, evaluates the score of your experiment in a training loop, and uses session.report to report the score back to Tune.

def objective(config):
    for step in range(config["steps"]):
        score = evaluate(step, config["width"], config["height"])
        session.report({"iterations": step, "mean_loss": score})

Now we define the search algorithm built from BayesOptSearch, constrained to a maximum of 4 concurrent trials with a ConcurrencyLimiter.

algo = BayesOptSearch(utility_kwargs={"kind": "ucb", "kappa": 2.5, "xi": 0.0})
algo = ConcurrencyLimiter(algo, max_concurrent=4)

The number of samples is the number of hyperparameter combinations that will be tried out. This Tune run is set to 1000 samples. (you can decrease this if it takes too long on your machine).

num_samples = 1000

Next we define a search space. The critical assumption is that the optimal hyperparameters live within this space. Yet, if the space is very large, then those hyperparameters may be difficult to find in a short amount of time.

search_space = {
    "steps": 100,
    "width": tune.uniform(0, 20),
    "height": tune.uniform(-100, 100),
}

Finally, we run the experiment to "min"imize the “mean_loss” of the objective by searching search_config via algo, num_samples times. This previous sentence is fully characterizes the search problem we aim to solve. With this in mind, notice how efficient it is to execute tuner.fit().

tuner = tune.Tuner(
    objective,
    tune_config=tune.TuneConfig(
        metric="mean_loss",
        mode="min",
        search_alg=algo,
        num_samples=num_samples,
    ),
    param_space=search_space,
)
results = tuner.fit()
Function checkpointing is disabled. This may result in unexpected behavior when using checkpointing features or certain schedulers. To enable, set the train function arguments to be `func(config, checkpoint_dir=None)`.
BayesOpt does not support specific sampling methods. The Uniform sampler will be dropped.
BayesOpt does not support specific sampling methods. The Uniform sampler will be dropped.
== Status ==
Current time: 2022-07-22 15:30:53 (running for 00:00:43.91)
Memory usage on this node: 10.4/16.0 GiB
Using FIFO scheduling algorithm.
Resources requested: 0/16 CPUs, 0/0 GPUs, 0.0/4.47 GiB heap, 0.0/2.0 GiB objects
Current best trial: d42ac71c with mean_loss=-9.536507956046009 and parameters={'steps': 100, 'width': 19.398197043239886, 'height': -95.88310114083951}
Result logdir: /Users/kai/ray_results/objective_2022-07-22_15-30-08
Number of trials: 10/10 (10 TERMINATED)
Trial name status loc height width loss iter total time (s) iterations neg_mean_loss
objective_c9daa5d4TERMINATED127.0.0.1:46960-25.092 19.0143 -2.45636 100 10.9865 99 2.45636
objective_cb9bc830TERMINATED127.0.0.1:46968 46.398811.9732 4.72354 100 11.5661 99 -4.72354
objective_cb9d338cTERMINATED127.0.0.1:46969-68.7963 3.11989-6.56602 100 11.648 99 6.56602
objective_cb9e97e0TERMINATED127.0.0.1:46970-88.383317.3235 -8.78036 100 11.6948 99 8.78036
objective_d229961eTERMINATED127.0.0.1:47009 20.223 14.1615 2.09312 100 10.8549 99 -2.09312
objective_d42ac71cTERMINATED127.0.0.1:47036-95.883119.3982 -9.53651 100 10.7931 99 9.53651
objective_d43ca61cTERMINATED127.0.0.1:47039 66.4885 4.24678 6.88118 100 10.7606 99 -6.88118
objective_d43fb190TERMINATED127.0.0.1:47040-63.635 3.66809-6.09551 100 10.7997 99 6.09551
objective_da1ff46cTERMINATED127.0.0.1:47057-39.151610.4951 -3.81983 100 10.7762 99 3.81983
objective_dc25c796TERMINATED127.0.0.1:47062-13.611 5.82458-1.19064 100 10.7213 99 1.19064


Result for objective_c9daa5d4:
  date: 2022-07-22_15-30-12
  done: false
  experiment_id: 422a6d2a512a470480e33913d7825a7a
  hostname: Kais-MacBook-Pro.local
  iterations: 0
  iterations_since_restore: 1
  mean_loss: 7.490802376947249
  neg_mean_loss: -7.490802376947249
  node_ip: 127.0.0.1
  pid: 46960
  time_since_restore: 0.1042318344116211
  time_this_iter_s: 0.1042318344116211
  time_total_s: 0.1042318344116211
  timestamp: 1658500212
  timesteps_since_restore: 0
  training_iteration: 1
  trial_id: c9daa5d4
  warmup_time: 0.0032601356506347656
  
Result for objective_cb9bc830:
  date: 2022-07-22_15-30-15
  done: false
  experiment_id: 3a9a6bef89ec4b57bd0fa24dd3b407e6
  hostname: Kais-MacBook-Pro.local
  iterations: 0
  iterations_since_restore: 1
  mean_loss: 14.639878836228101
  neg_mean_loss: -14.639878836228101
  node_ip: 127.0.0.1
  pid: 46968
  time_since_restore: 0.10442280769348145
  time_this_iter_s: 0.10442280769348145
  time_total_s: 0.10442280769348145
  timestamp: 1658500215
  timesteps_since_restore: 0
  training_iteration: 1
  trial_id: cb9bc830
  warmup_time: 0.0038840770721435547
  
Result for objective_cb9e97e0:
  date: 2022-07-22_15-30-15
  done: false
  experiment_id: b0266e323ced4991b155344b34c25c59
  hostname: Kais-MacBook-Pro.local
  iterations: 0
  iterations_since_restore: 1
  mean_loss: 1.1616722433639897
  neg_mean_loss: -1.1616722433639897
  node_ip: 127.0.0.1
  pid: 46970
  time_since_restore: 0.10328483581542969
  time_this_iter_s: 0.10328483581542969
  time_total_s: 0.10328483581542969
  timestamp: 1658500215
  timesteps_since_restore: 0
  training_iteration: 1
  trial_id: cb9e97e0
  warmup_time: 0.004090070724487305
  
Result for objective_cb9d338c:
  date: 2022-07-22_15-30-15
  done: false
  experiment_id: 2731a83e40eb468fb79e19f872b8f597
  hostname: Kais-MacBook-Pro.local
  iterations: 0
  iterations_since_restore: 1
  mean_loss: 3.120372808848731
  neg_mean_loss: -3.120372808848731
  node_ip: 127.0.0.1
  pid: 46969
  time_since_restore: 0.1042470932006836
  time_this_iter_s: 0.1042470932006836
  time_total_s: 0.1042470932006836
  timestamp: 1658500215
  timesteps_since_restore: 0
  training_iteration: 1
  trial_id: cb9d338c
  warmup_time: 0.003387928009033203
  
Result for objective_c9daa5d4:
  date: 2022-07-22_15-30-17
  done: false
  experiment_id: 422a6d2a512a470480e33913d7825a7a
  hostname: Kais-MacBook-Pro.local
  iterations: 45
  iterations_since_restore: 46
  mean_loss: -2.393676542940848
  neg_mean_loss: 2.393676542940848
  node_ip: 127.0.0.1
  pid: 46960
  time_since_restore: 5.1730430126190186
  time_this_iter_s: 0.10674905776977539
  time_total_s: 5.1730430126190186
  timestamp: 1658500217
  timesteps_since_restore: 0
  training_iteration: 46
  trial_id: c9daa5d4
  warmup_time: 0.0032601356506347656
  
Result for objective_cb9bc830:
  date: 2022-07-22_15-30-20
  done: false
  experiment_id: 3a9a6bef89ec4b57bd0fa24dd3b407e6
  hostname: Kais-MacBook-Pro.local
  iterations: 47
  iterations_since_restore: 48
  mean_loss: 4.8144784432736065
  neg_mean_loss: -4.8144784432736065
  node_ip: 127.0.0.1
  pid: 46968
  time_since_restore: 5.1083409786224365
  time_this_iter_s: 0.10834097862243652
  time_total_s: 5.1083409786224365
  timestamp: 1658500220
  timesteps_since_restore: 0
  training_iteration: 48
  trial_id: cb9bc830
  warmup_time: 0.0038840770721435547
  
Result for objective_cb9e97e0:
  date: 2022-07-22_15-30-20
  done: false
  experiment_id: b0266e323ced4991b155344b34c25c59
  hostname: Kais-MacBook-Pro.local
  iterations: 47
  iterations_since_restore: 48
  mean_loss: -8.716998803293404
  neg_mean_loss: 8.716998803293404
  node_ip: 127.0.0.1
  pid: 46970
  time_since_restore: 5.117117881774902
  time_this_iter_s: 0.10473918914794922
  time_total_s: 5.117117881774902
  timestamp: 1658500220
  timesteps_since_restore: 0
  training_iteration: 48
  trial_id: cb9e97e0
  warmup_time: 0.004090070724487305
  
Result for objective_cb9d338c:
  date: 2022-07-22_15-30-20
  done: false
  experiment_id: 2731a83e40eb468fb79e19f872b8f597
  hostname: Kais-MacBook-Pro.local
  iterations: 47
  iterations_since_restore: 48
  mean_loss: -6.241199660085543
  neg_mean_loss: 6.241199660085543
  node_ip: 127.0.0.1
  pid: 46969
  time_since_restore: 5.1075780391693115
  time_this_iter_s: 0.1051321029663086
  time_total_s: 5.1075780391693115
  timestamp: 1658500220
  timesteps_since_restore: 0
  training_iteration: 48
  trial_id: cb9d338c
  warmup_time: 0.003387928009033203
  
Result for objective_c9daa5d4:
  date: 2022-07-22_15-30-22
  done: false
  experiment_id: 422a6d2a512a470480e33913d7825a7a
  hostname: Kais-MacBook-Pro.local
  iterations: 92
  iterations_since_restore: 93
  mean_loss: -2.452357296882761
  neg_mean_loss: 2.452357296882761
  node_ip: 127.0.0.1
  pid: 46960
  time_since_restore: 10.23116397857666
  time_this_iter_s: 0.10653018951416016
  time_total_s: 10.23116397857666
  timestamp: 1658500222
  timesteps_since_restore: 0
  training_iteration: 93
  trial_id: c9daa5d4
  warmup_time: 0.0032601356506347656
  
Result for objective_c9daa5d4:
  date: 2022-07-22_15-30-23
  done: true
  experiment_id: 422a6d2a512a470480e33913d7825a7a
  experiment_tag: 1_height=-25.0920,steps=100,width=19.0143
  hostname: Kais-MacBook-Pro.local
  iterations: 99
  iterations_since_restore: 100
  mean_loss: -2.456355072354658
  neg_mean_loss: 2.456355072354658
  node_ip: 127.0.0.1
  pid: 46960
  time_since_restore: 10.986503839492798
  time_this_iter_s: 0.10757803916931152
  time_total_s: 10.986503839492798
  timestamp: 1658500223
  timesteps_since_restore: 0
  training_iteration: 100
  trial_id: c9daa5d4
  warmup_time: 0.0032601356506347656
  
Result for objective_cb9bc830:
  date: 2022-07-22_15-30-24
  done: false
  experiment_id: 3a9a6bef89ec4b57bd0fa24dd3b407e6
  hostname: Kais-MacBook-Pro.local
  iterations: 91
  iterations_since_restore: 92
  mean_loss: 4.73082443425139
  neg_mean_loss: -4.73082443425139
  node_ip: 127.0.0.1
  pid: 46968
  time_since_restore: 9.829612970352173
  time_this_iter_s: 0.10725593566894531
  time_total_s: 9.829612970352173
  timestamp: 1658500224
  timesteps_since_restore: 0
  training_iteration: 92
  trial_id: cb9bc830
  warmup_time: 0.0038840770721435547
  
Result for objective_cb9e97e0:
  date: 2022-07-22_15-30-24
  done: false
  experiment_id: b0266e323ced4991b155344b34c25c59
  hostname: Kais-MacBook-Pro.local
  iterations: 90
  iterations_since_restore: 91
  mean_loss: -8.774597648541096
  neg_mean_loss: 8.774597648541096
  node_ip: 127.0.0.1
  pid: 46970
  time_since_restore: 9.72621202468872
  time_this_iter_s: 0.10692906379699707
  time_total_s: 9.72621202468872
  timestamp: 1658500224
  timesteps_since_restore: 0
  training_iteration: 91
  trial_id: cb9e97e0
  warmup_time: 0.004090070724487305
  
Result for objective_cb9d338c:
  date: 2022-07-22_15-30-24
  done: false
  experiment_id: 2731a83e40eb468fb79e19f872b8f597
  hostname: Kais-MacBook-Pro.local
  iterations: 90
  iterations_since_restore: 91
  mean_loss: -6.535736572413468
  neg_mean_loss: 6.535736572413468
  node_ip: 127.0.0.1
  pid: 46969
  time_since_restore: 9.71235203742981
  time_this_iter_s: 0.10665416717529297
  time_total_s: 9.71235203742981
  timestamp: 1658500224
  timesteps_since_restore: 0
  training_iteration: 91
  trial_id: cb9d338c
  warmup_time: 0.003387928009033203
  
Result for objective_d229961e:
  date: 2022-07-22_15-30-25
  done: false
  experiment_id: d8bb04569c644d6fabad5064c1828ba3
  hostname: Kais-MacBook-Pro.local
  iterations: 0
  iterations_since_restore: 1
  mean_loss: 12.022300234864176
  neg_mean_loss: -12.022300234864176
  node_ip: 127.0.0.1
  pid: 47009
  time_since_restore: 0.1041719913482666
  time_this_iter_s: 0.1041719913482666
  time_total_s: 0.1041719913482666
  timestamp: 1658500225
  timesteps_since_restore: 0
  training_iteration: 1
  trial_id: d229961e
  warmup_time: 0.003198862075805664
  
Result for objective_cb9bc830:
  date: 2022-07-22_15-30-26
  done: true
  experiment_id: 3a9a6bef89ec4b57bd0fa24dd3b407e6
  experiment_tag: 2_height=46.3988,steps=100,width=11.9732
  hostname: Kais-MacBook-Pro.local
  iterations: 99
  iterations_since_restore: 100
  mean_loss: 4.723536776402224
  neg_mean_loss: -4.723536776402224
  node_ip: 127.0.0.1
  pid: 46968
  time_since_restore: 11.566141843795776
  time_this_iter_s: 0.10738396644592285
  time_total_s: 11.566141843795776
  timestamp: 1658500226
  timesteps_since_restore: 0
  training_iteration: 100
  trial_id: cb9bc830
  warmup_time: 0.0038840770721435547
  
Result for objective_cb9d338c:
  date: 2022-07-22_15-30-26
  done: true
  experiment_id: 2731a83e40eb468fb79e19f872b8f597
  experiment_tag: 3_height=-68.7963,steps=100,width=3.1199
  hostname: Kais-MacBook-Pro.local
  iterations: 99
  iterations_since_restore: 100
  mean_loss: -6.566018929214734
  neg_mean_loss: 6.566018929214734
  node_ip: 127.0.0.1
  pid: 46969
  time_since_restore: 11.647998809814453
  time_this_iter_s: 0.1123647689819336
  time_total_s: 11.647998809814453
  timestamp: 1658500226
  timesteps_since_restore: 0
  training_iteration: 100
  trial_id: cb9d338c
  warmup_time: 0.003387928009033203
  
Result for objective_cb9e97e0:
  date: 2022-07-22_15-30-26
  done: true
  experiment_id: b0266e323ced4991b155344b34c25c59
  experiment_tag: 4_height=-88.3833,steps=100,width=17.3235
  hostname: Kais-MacBook-Pro.local
  iterations: 99
  iterations_since_restore: 100
  mean_loss: -8.780357708936942
  neg_mean_loss: 8.780357708936942
  node_ip: 127.0.0.1
  pid: 46970
  time_since_restore: 11.694752931594849
  time_this_iter_s: 0.12678027153015137
  time_total_s: 11.694752931594849
  timestamp: 1658500226
  timesteps_since_restore: 0
  training_iteration: 100
  trial_id: cb9e97e0
  warmup_time: 0.004090070724487305
  
Result for objective_d42ac71c:
  date: 2022-07-22_15-30-29
  done: false
  experiment_id: 3fdfaecb7adc4c5cb54c0aa76849d532
  hostname: Kais-MacBook-Pro.local
  iterations: 0
  iterations_since_restore: 1
  mean_loss: 0.41168988591604894
  neg_mean_loss: -0.41168988591604894
  node_ip: 127.0.0.1
  pid: 47036
  time_since_restore: 0.10324597358703613
  time_this_iter_s: 0.10324597358703613
  time_total_s: 0.10324597358703613
  timestamp: 1658500229
  timesteps_since_restore: 0
  training_iteration: 1
  trial_id: d42ac71c
  warmup_time: 0.0028409957885742188
  
Result for objective_d43ca61c:
  date: 2022-07-22_15-30-29
  done: false
  experiment_id: 8f92f519ea5443be9efd6f4a8937b8ee
  hostname: Kais-MacBook-Pro.local
  iterations: 0
  iterations_since_restore: 1
  mean_loss: 16.648852816008436
  neg_mean_loss: -16.648852816008436
  node_ip: 127.0.0.1
  pid: 47039
  time_since_restore: 0.10412001609802246
  time_this_iter_s: 0.10412001609802246
  time_total_s: 0.10412001609802246
  timestamp: 1658500229
  timesteps_since_restore: 0
  training_iteration: 1
  trial_id: d43ca61c
  warmup_time: 0.002924203872680664
  
Result for objective_d43fb190:
  date: 2022-07-22_15-30-29
  done: false
  experiment_id: 18283da742c74042ad3db1846fa7b460
  hostname: Kais-MacBook-Pro.local
  iterations: 0
  iterations_since_restore: 1
  mean_loss: 3.6364993441420124
  neg_mean_loss: -3.6364993441420124
  node_ip: 127.0.0.1
  pid: 47040
  time_since_restore: 0.10391902923583984
  time_this_iter_s: 0.10391902923583984
  time_total_s: 0.10391902923583984
  timestamp: 1658500229
  timesteps_since_restore: 0
  training_iteration: 1
  trial_id: d43fb190
  warmup_time: 0.0027680397033691406
  
Result for objective_d229961e:
  date: 2022-07-22_15-30-30
  done: false
  experiment_id: d8bb04569c644d6fabad5064c1828ba3
  hostname: Kais-MacBook-Pro.local
  iterations: 46
  iterations_since_restore: 47
  mean_loss: 2.1734885512401174
  neg_mean_loss: -2.1734885512401174
  node_ip: 127.0.0.1
  pid: 47009
  time_since_restore: 5.153247117996216
  time_this_iter_s: 0.10638809204101562
  time_total_s: 5.153247117996216
  timestamp: 1658500230
  timesteps_since_restore: 0
  training_iteration: 47
  trial_id: d229961e
  warmup_time: 0.003198862075805664
  
Result for objective_d42ac71c:
  date: 2022-07-22_15-30-34
  done: false
  experiment_id: 3fdfaecb7adc4c5cb54c0aa76849d532
  hostname: Kais-MacBook-Pro.local
  iterations: 46
  iterations_since_restore: 47
  mean_loss: -9.477484325687673
  neg_mean_loss: 9.477484325687673
  node_ip: 127.0.0.1
  pid: 47036
  time_since_restore: 5.123893976211548
  time_this_iter_s: 0.10898423194885254
  time_total_s: 5.123893976211548
  timestamp: 1658500234
  timesteps_since_restore: 0
  training_iteration: 47
  trial_id: d42ac71c
  warmup_time: 0.0028409957885742188
  
Result for objective_d43ca61c:
  date: 2022-07-22_15-30-34
  done: false
  experiment_id: 8f92f519ea5443be9efd6f4a8937b8ee
  hostname: Kais-MacBook-Pro.local
  iterations: 47
  iterations_since_restore: 48
  mean_loss: 7.12595486600941
  neg_mean_loss: -7.12595486600941
  node_ip: 127.0.0.1
  pid: 47039
  time_since_restore: 5.194939136505127
  time_this_iter_s: 0.10889291763305664
  time_total_s: 5.194939136505127
  timestamp: 1658500234
  timesteps_since_restore: 0
  training_iteration: 48
  trial_id: d43ca61c
  warmup_time: 0.002924203872680664
  
Result for objective_d43fb190:
  date: 2022-07-22_15-30-34
  done: false
  experiment_id: 18283da742c74042ad3db1846fa7b460
  hostname: Kais-MacBook-Pro.local
  iterations: 47
  iterations_since_restore: 48
  mean_loss: -5.815255760980219
  neg_mean_loss: 5.815255760980219
  node_ip: 127.0.0.1
  pid: 47040
  time_since_restore: 5.2366979122161865
  time_this_iter_s: 0.10901784896850586
  time_total_s: 5.2366979122161865
  timestamp: 1658500234
  timesteps_since_restore: 0
  training_iteration: 48
  trial_id: d43fb190
  warmup_time: 0.0027680397033691406
  
Result for objective_d229961e:
  date: 2022-07-22_15-30-35
  done: false
  experiment_id: d8bb04569c644d6fabad5064c1828ba3
  hostname: Kais-MacBook-Pro.local
  iterations: 93
  iterations_since_restore: 94
  mean_loss: 2.097657333615391
  neg_mean_loss: -2.097657333615391
  node_ip: 127.0.0.1
  pid: 47009
  time_since_restore: 10.209784984588623
  time_this_iter_s: 0.10757803916931152
  time_total_s: 10.209784984588623
  timestamp: 1658500235
  timesteps_since_restore: 0
  training_iteration: 94
  trial_id: d229961e
  warmup_time: 0.003198862075805664
  
Result for objective_d229961e:
  date: 2022-07-22_15-30-36
  done: true
  experiment_id: d8bb04569c644d6fabad5064c1828ba3
  experiment_tag: 5_height=20.2230,steps=100,width=14.1615
  hostname: Kais-MacBook-Pro.local
  iterations: 99
  iterations_since_restore: 100
  mean_loss: 2.093122581973529
  neg_mean_loss: -2.093122581973529
  node_ip: 127.0.0.1
  pid: 47009
  time_since_restore: 10.854872226715088
  time_this_iter_s: 0.10703516006469727
  time_total_s: 10.854872226715088
  timestamp: 1658500236
  timesteps_since_restore: 0
  training_iteration: 100
  trial_id: d229961e
  warmup_time: 0.003198862075805664
  
Result for objective_da1ff46c:
  date: 2022-07-22_15-30-39
  done: false
  experiment_id: 9163132451a14ace8ddf394aeaae9018
  hostname: Kais-MacBook-Pro.local
  iterations: 0
  iterations_since_restore: 1
  mean_loss: 6.0848448591907545
  neg_mean_loss: -6.0848448591907545
  node_ip: 127.0.0.1
  pid: 47057
  time_since_restore: 0.10405993461608887
  time_this_iter_s: 0.10405993461608887
  time_total_s: 0.10405993461608887
  timestamp: 1658500239
  timesteps_since_restore: 0
  training_iteration: 1
  trial_id: da1ff46c
  warmup_time: 0.0030031204223632812
  
Result for objective_d42ac71c:
  date: 2022-07-22_15-30-39
  done: false
  experiment_id: 3fdfaecb7adc4c5cb54c0aa76849d532
  hostname: Kais-MacBook-Pro.local
  iterations: 93
  iterations_since_restore: 94
  mean_loss: -9.533184304791206
  neg_mean_loss: 9.533184304791206
  node_ip: 127.0.0.1
  pid: 47036
  time_since_restore: 10.145818948745728
  time_this_iter_s: 0.10763311386108398
  time_total_s: 10.145818948745728
  timestamp: 1658500239
  timesteps_since_restore: 0
  training_iteration: 94
  trial_id: d42ac71c
  warmup_time: 0.0028409957885742188
  
Result for objective_d43ca61c:
  date: 2022-07-22_15-30-39
  done: false
  experiment_id: 8f92f519ea5443be9efd6f4a8937b8ee
  hostname: Kais-MacBook-Pro.local
  iterations: 94
  iterations_since_restore: 95
  mean_loss: 6.893233568918634
  neg_mean_loss: -6.893233568918634
  node_ip: 127.0.0.1
  pid: 47039
  time_since_restore: 10.217039108276367
  time_this_iter_s: 0.10719418525695801
  time_total_s: 10.217039108276367
  timestamp: 1658500239
  timesteps_since_restore: 0
  training_iteration: 95
  trial_id: d43ca61c
  warmup_time: 0.002924203872680664
  
Result for objective_d43fb190:
  date: 2022-07-22_15-30-39
  done: false
  experiment_id: 18283da742c74042ad3db1846fa7b460
  hostname: Kais-MacBook-Pro.local
  iterations: 94
  iterations_since_restore: 95
  mean_loss: -6.08165210701758
  neg_mean_loss: 6.08165210701758
  node_ip: 127.0.0.1
  pid: 47040
  time_since_restore: 10.262099027633667
  time_this_iter_s: 0.10874485969543457
  time_total_s: 10.262099027633667
  timestamp: 1658500239
  timesteps_since_restore: 0
  training_iteration: 95
  trial_id: d43fb190
  warmup_time: 0.0027680397033691406
  
Result for objective_d42ac71c:
  date: 2022-07-22_15-30-39
  done: true
  experiment_id: 3fdfaecb7adc4c5cb54c0aa76849d532
  experiment_tag: 6_height=-95.8831,steps=100,width=19.3982
  hostname: Kais-MacBook-Pro.local
  iterations: 99
  iterations_since_restore: 100
  mean_loss: -9.536507956046009
  neg_mean_loss: 9.536507956046009
  node_ip: 127.0.0.1
  pid: 47036
  time_since_restore: 10.793061017990112
  time_this_iter_s: 0.10741710662841797
  time_total_s: 10.793061017990112
  timestamp: 1658500239
  timesteps_since_restore: 0
  training_iteration: 100
  trial_id: d42ac71c
  warmup_time: 0.0028409957885742188
  
Result for objective_d43ca61c:
  date: 2022-07-22_15-30-40
  done: true
  experiment_id: 8f92f519ea5443be9efd6f4a8937b8ee
  experiment_tag: 7_height=66.4885,steps=100,width=4.2468
  hostname: Kais-MacBook-Pro.local
  iterations: 99
  iterations_since_restore: 100
  mean_loss: 6.881177852950684
  neg_mean_loss: -6.881177852950684
  node_ip: 127.0.0.1
  pid: 47039
  time_since_restore: 10.760617017745972
  time_this_iter_s: 0.10911297798156738
  time_total_s: 10.760617017745972
  timestamp: 1658500240
  timesteps_since_restore: 0
  training_iteration: 100
  trial_id: d43ca61c
  warmup_time: 0.002924203872680664
  
Result for objective_d43fb190:
  date: 2022-07-22_15-30-40
  done: true
  experiment_id: 18283da742c74042ad3db1846fa7b460
  experiment_tag: 8_height=-63.6350,steps=100,width=3.6681
  hostname: Kais-MacBook-Pro.local
  iterations: 99
  iterations_since_restore: 100
  mean_loss: -6.09550539698523
  neg_mean_loss: 6.09550539698523
  node_ip: 127.0.0.1
  pid: 47040
  time_since_restore: 10.799743175506592
  time_this_iter_s: 0.1067342758178711
  time_total_s: 10.799743175506592
  timestamp: 1658500240
  timesteps_since_restore: 0
  training_iteration: 100
  trial_id: d43fb190
  warmup_time: 0.0027680397033691406
  
Result for objective_dc25c796:
  date: 2022-07-22_15-30-42
  done: false
  experiment_id: c0f302c32b284f8e99dbdfa90657ee7d
  hostname: Kais-MacBook-Pro.local
  iterations: 0
  iterations_since_restore: 1
  mean_loss: 8.638900372842315
  neg_mean_loss: -8.638900372842315
  node_ip: 127.0.0.1
  pid: 47062
  time_since_restore: 0.10459494590759277
  time_this_iter_s: 0.10459494590759277
  time_total_s: 0.10459494590759277
  timestamp: 1658500242
  timesteps_since_restore: 0
  training_iteration: 1
  trial_id: dc25c796
  warmup_time: 0.002794981002807617
  
Result for objective_da1ff46c:
  date: 2022-07-22_15-30-44
  done: false
  experiment_id: 9163132451a14ace8ddf394aeaae9018
  hostname: Kais-MacBook-Pro.local
  iterations: 47
  iterations_since_restore: 48
  mean_loss: -3.7164550549457847
  neg_mean_loss: 3.7164550549457847
  node_ip: 127.0.0.1
  pid: 47057
  time_since_restore: 5.180424928665161
  time_this_iter_s: 0.10843396186828613
  time_total_s: 5.180424928665161
  timestamp: 1658500244
  timesteps_since_restore: 0
  training_iteration: 48
  trial_id: da1ff46c
  warmup_time: 0.0030031204223632812
  
Result for objective_dc25c796:
  date: 2022-07-22_15-30-47
  done: false
  experiment_id: c0f302c32b284f8e99dbdfa90657ee7d
  hostname: Kais-MacBook-Pro.local
  iterations: 47
  iterations_since_restore: 48
  mean_loss: -1.0086834162426133
  neg_mean_loss: 1.0086834162426133
  node_ip: 127.0.0.1
  pid: 47062
  time_since_restore: 5.151978015899658
  time_this_iter_s: 0.10736894607543945
  time_total_s: 5.151978015899658
  timestamp: 1658500247
  timesteps_since_restore: 0
  training_iteration: 48
  trial_id: dc25c796
  warmup_time: 0.002794981002807617
  
Result for objective_da1ff46c:
  date: 2022-07-22_15-30-49
  done: false
  experiment_id: 9163132451a14ace8ddf394aeaae9018
  hostname: Kais-MacBook-Pro.local
  iterations: 94
  iterations_since_restore: 95
  mean_loss: -3.814808150093952
  neg_mean_loss: 3.814808150093952
  node_ip: 127.0.0.1
  pid: 47057
  time_since_restore: 10.23661208152771
  time_this_iter_s: 0.1076211929321289
  time_total_s: 10.23661208152771
  timestamp: 1658500249
  timesteps_since_restore: 0
  training_iteration: 95
  trial_id: da1ff46c
  warmup_time: 0.0030031204223632812
  
Result for objective_da1ff46c:
  date: 2022-07-22_15-30-49
  done: true
  experiment_id: 9163132451a14ace8ddf394aeaae9018
  experiment_tag: 9_height=-39.1516,steps=100,width=10.4951
  hostname: Kais-MacBook-Pro.local
  iterations: 99
  iterations_since_restore: 100
  mean_loss: -3.819827867781687
  neg_mean_loss: 3.819827867781687
  node_ip: 127.0.0.1
  pid: 47057
  time_since_restore: 10.77621078491211
  time_this_iter_s: 0.10817480087280273
  time_total_s: 10.77621078491211
  timestamp: 1658500249
  timesteps_since_restore: 0
  training_iteration: 100
  trial_id: da1ff46c
  warmup_time: 0.0030031204223632812
  
Result for objective_dc25c796:
  date: 2022-07-22_15-30-52
  done: false
  experiment_id: c0f302c32b284f8e99dbdfa90657ee7d
  hostname: Kais-MacBook-Pro.local
  iterations: 94
  iterations_since_restore: 95
  mean_loss: -1.1817308993292515
  neg_mean_loss: 1.1817308993292515
  node_ip: 127.0.0.1
  pid: 47062
  time_since_restore: 10.179337978363037
  time_this_iter_s: 0.1043100357055664
  time_total_s: 10.179337978363037
  timestamp: 1658500252
  timesteps_since_restore: 0
  training_iteration: 95
  trial_id: dc25c796
  warmup_time: 0.002794981002807617
  
Result for objective_dc25c796:
  date: 2022-07-22_15-30-53
  done: true
  experiment_id: c0f302c32b284f8e99dbdfa90657ee7d
  experiment_tag: 10_height=-13.6110,steps=100,width=5.8246
  hostname: Kais-MacBook-Pro.local
  iterations: 99
  iterations_since_restore: 100
  mean_loss: -1.190635502081924
  neg_mean_loss: 1.190635502081924
  node_ip: 127.0.0.1
  pid: 47062
  time_since_restore: 10.721266031265259
  time_this_iter_s: 0.10741806030273438
  time_total_s: 10.721266031265259
  timestamp: 1658500253
  timesteps_since_restore: 0
  training_iteration: 100
  trial_id: dc25c796
  warmup_time: 0.002794981002807617
  

Here are the hyperparamters found to minimize the mean loss of the defined objective.

print("Best hyperparameters found were: ", results.get_best_result().config)
Best hyperparameters found were:  {'steps': 100, 'width': 19.398197043239886, 'height': -95.88310114083951}