also hpo controller:
` import os
from clearml import Task

os.environ['MPLBACKEND'] = "TkAg"

CLEARML_PROJECT = "Vodafone Sentiment full"
CLEARML_TASK = "HPO optimizer Controller"
os.environ["CLEARML_PROJECT"] = CLEARML_PROJECT
os.environ["CLEARML_TASK"] = CLEARML_TASK

Task.set_credentials(
api_host=" ",
web_host=" ",
files_host=" ",
key='88888888888',
secret='888888888888888'
)

from clearml.automation import UniformParameterRange, UniformIntegerParameterRange, DiscreteParameterRange
from clearml.automation import HyperParameterOptimizer
from clearml.automation import GridSearch

from clearml import Task

task = Task.init(project_name=CLEARML_PROJECT,
task_name=CLEARML_TASK,
task_type=Task.TaskTypes.optimizer,
reuse_last_task_id=False)

optimizer = HyperParameterOptimizer(

specifying the task to be optimized, task must be in system already so it can be cloned

base_task_id=base_task,

setting the hyper-parameters to optimize

hyper_parameters=[
UniformIntegerParameterRange('General/epochs', min_value=2, max_value=12, step_size=5),
UniformParameterRange('General/lr', min_value=0.000001, max_value=0.0001, step_size=0.002),
],

setting the objective metric we want to maximize/minimize

objective_metric_title='f1',
objective_metric_series='eval',
objective_metric_sign='max',

setting optimizer

optimizer_class=GridSearch,

configuring optimization parameters

execution_queue='default',
max_number_of_concurrent_tasks=4,
optimization_time_limit=60.,
compute_time_limit=120,
total_max_jobs=20,
min_iteration_per_job=0,
max_iteration_per_job=15,
)

optimizer.set_report_period(1)

start the optimization process

this function returns immediately

optimizer.start()

set the time limit for the optimization process (2 hours)

optimizer.set_time_limit(in_minutes=120.0)

wait until process is done (notice we are controlling the optimization process in the background)

optimizer.wait()

optimization is completed, print the top performing experiments id

top_exp = optimizer.get_top_experiments(top_k=3)
print([t.id for t in top_exp])

make sure background optimization stopped

optimizer.stop() `