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.
# !pip install ray[tune]
!pip install bayesian-optimization==1.2.0
Requirement already satisfied: bayesian-optimization==1.2.0 in /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages (1.2.0) Requirement already satisfied: scikit-learn>=0.18.0 in /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages (from bayesian-optimization==1.2.0) (0.24.2) Requirement already satisfied: numpy>=1.9.0 in /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages (from bayesian-optimization==1.2.0) (1.21.6) Requirement already satisfied: scipy>=0.14.0 in /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages (from bayesian-optimization==1.2.0) (1.4.1) Requirement already satisfied: joblib>=0.11 in /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages (from scikit-learn>=0.18.0->bayesian-optimization==1.2.0) (1.1.0) Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages (from scikit-learn>=0.18.0->bayesian-optimization==1.2.0) (3.0.0) WARNING: There was an error checking the latest version of pip.
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 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})
ray.init(configure_logging=False)
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
# If 1000 samples take too long, you can reduce this number.
# We override this number here for our smoke tests.
num_samples = 10
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()
| 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}
ray.shutdown()