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Hi I Was Running An Hyperparameter Optimization Task Using The Optuna Optimizer And Even Though The Hyperparameteroptimizer’S Argument Is Set To

Hi I was running an Hyperparameter Optimization task using the Optuna Optimizer and even though the HyperParameterOptimizer’s argument is set to max_number_of_concurrent_tasks=100 it only seems to spawn 20 tasks. Is there something I am doing incorrectly?
` import argparse

from clearml import Task
from clearml.automation import DiscreteParameterRange, HyperParameterOptimizer
from clearml.automation.optuna import OptimizerOptuna

from utils.hpo import job_complete_callback

task = Task.init(
project_name='***', task_name='***', task_type=Task.TaskTypes.optimizer,
reuse_last_task_id=False
)

parser = argparse.ArgumentParser()

parser.add_argument('--project_name', type=str, default='***')
parser.add_argument('--task_name', type=str, default='***')
parser.add_argument('--execution_queue', type=str, default='***')

args = parser.parse_args()
args = task.connect(args)

task.execute_remotely()

optimizer = HyperParameterOptimizer(
base_task_id=Task.get_task(project_name=args.project_name, task_name=args.task_name).id,
hyper_parameters=[
DiscreteParameterRange(
'Args/***',
values=['***', '***', '***', '...'],
),
],
objective_metric_title='metrics',
objective_metric_series='***',
objective_metric_sign='max_global',
max_number_of_concurrent_tasks=100,
optimizer_class=OptimizerOptuna,
execution_queue=args.execution_queue,
spawn_project=None,
save_top_k_tasks_only=5,
time_limit_per_job=None,
pool_period_min=5,
total_max_jobs=None,
min_iteration_per_job=10,
max_iteration_per_job=100,
)

optimizer.set_report_period(5)
optimizer.start(job_complete_callback=job_complete_callback)
optimizer.wait()
optimizer.stop() `

  
  
Posted 2 years ago
Votes Newest

Answers 2


Thx so much đź‘Ť

  
  
Posted 2 years ago

Hi UpsetBlackbird87
This is an Optuna decision on how many concurrent tests to run simultaneously.
You limited it to 100, but remember Optuna does a Bayesian optimization process, where it decides on the best set of arguments based on the performance of the previous set, this means it will first try X trials, then decide on the next batch.
That said you can a pruner to Optuna specifying how it should start
https://optuna.readthedocs.io/en/v1.4.0/reference/pruners.html#optuna.pruners.MedianPruner
HyperParameterOptimizer(..., optimizer_kwargs=dict( optuna_pruner=optuna.pruners.MedianPruner(n_startup_trials=50, n_warmup_steps=30, interval_steps=10)))Make sense ?

  
  
Posted 2 years ago
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2 Answers
2 years ago
one year ago
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