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.
Show code cell source
import time
import ray
from ray import train, tune
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 train.report to report the score back to Tune.
def objective(config):
for step in range(config["steps"]):
score = evaluate(step, config["width"], config["height"])
train.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()
== 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)
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_c9daa5d4 | TERMINATED | 127.0.0.1:46960 | -25.092 | 19.0143 | -2.45636 | 100 | 10.9865 | 99 | 2.45636 |
| objective_cb9bc830 | TERMINATED | 127.0.0.1:46968 | 46.3988 | 11.9732 | 4.72354 | 100 | 11.5661 | 99 | -4.72354 |
| objective_cb9d338c | TERMINATED | 127.0.0.1:46969 | -68.7963 | 3.11989 | -6.56602 | 100 | 11.648 | 99 | 6.56602 |
| objective_cb9e97e0 | TERMINATED | 127.0.0.1:46970 | -88.3833 | 17.3235 | -8.78036 | 100 | 11.6948 | 99 | 8.78036 |
| objective_d229961e | TERMINATED | 127.0.0.1:47009 | 20.223 | 14.1615 | 2.09312 | 100 | 10.8549 | 99 | -2.09312 |
| objective_d42ac71c | TERMINATED | 127.0.0.1:47036 | -95.8831 | 19.3982 | -9.53651 | 100 | 10.7931 | 99 | 9.53651 |
| objective_d43ca61c | TERMINATED | 127.0.0.1:47039 | 66.4885 | 4.24678 | 6.88118 | 100 | 10.7606 | 99 | -6.88118 |
| objective_d43fb190 | TERMINATED | 127.0.0.1:47040 | -63.635 | 3.66809 | -6.09551 | 100 | 10.7997 | 99 | 6.09551 |
| objective_da1ff46c | TERMINATED | 127.0.0.1:47057 | -39.1516 | 10.4951 | -3.81983 | 100 | 10.7762 | 99 | 3.81983 |
| objective_dc25c796 | TERMINATED | 127.0.0.1:47062 | -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}