def bayesian(matrix, configs, metrics):
"""Create suggestions based on bayesian optimization."""
from polyaxon.polyflow import V1Bayes
from polyaxon.polytune.search_managers.bayesian_optimization.manager import (
BayesSearchManager, )
matrix = V1Bayes.read(matrix)
suggestions = BayesSearchManager(config=matrix).get_suggestions(
configs=configs, metrics=metrics)
log_suggestions(suggestions)
Printer.print_success("Suggestions generated with bayesian optimization")
def bayes(matrix, join, iteration):
"""Create suggestions based on bayesian optimization."""
from polyaxon.client import RunClient
from polyaxon.polyflow import V1Bayes, V1Join
from polyaxon.polytune.iteration_lineage import (
get_iteration_definition,
handle_iteration,
handle_iteration_failure,
)
from polyaxon.polytune.search_managers.bayesian_optimization.manager import (
BayesSearchManager, )
matrix = V1Bayes.read(matrix)
join = V1Join.read(join)
client = RunClient()
values = get_iteration_definition(
client=client,
iteration=iteration,
join=join,
optimization_metric=matrix.metric.name,
)
if not values:
return
run_uuids, configs, metrics = values
retry = 1
exp = None
suggestions = None
while retry < 3:
try:
suggestions = BayesSearchManager(config=matrix, ).get_suggestions(
configs=configs, metrics=metrics)
exp = None
break
except Exception as exp:
retry += 1
logger.warning(exp)
if exp:
handle_iteration_failure(client=client, exp=exp)
return
handle_iteration(
client=client,
iteration=iteration,
suggestions=suggestions,
)
def get_bo_tuner(
matrix: V1Bayes,
join: V1Join,
iteration: int,
) -> V1Operation:
iteration = matrix.create_iteration(iteration)
return get_tuner(
name="bayesian-tuner",
container=get_container(
tuner_container=get_default_tuner_container(
["polyaxon", "tuner", "bayes"]),
container=matrix.container,
),
matrix=matrix,
join=join,
iteration=iteration,
)
def bayes(matrix, configs, metrics, iteration):
"""Create suggestions based on bayesian optimization."""
from polyaxon.client import RunClient
from polyaxon.polyflow import V1Bayes
from polyaxon.polytune.iteration_lineage import (
handle_iteration,
handle_iteration_failure,
)
from polyaxon.polytune.search_managers.bayesian_optimization.manager import (
BayesSearchManager, )
matrix = V1Bayes.read(matrix)
if configs:
configs = ujson.loads(configs)
if metrics:
metrics = ujson.loads(metrics)
client = RunClient()
retry = 1
exp = None
suggestions = None
while retry < 3:
try:
suggestions = BayesSearchManager(config=matrix, ).get_suggestions(
configs=configs, metrics=metrics)
exp = None
break
except Exception as e:
retry += 1
logger.warning(e)
exp = e
if exp:
handle_iteration_failure(client=client, exp=exp)
return
handle_iteration(
client=client,
suggestions=suggestions,
)