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RandomSearch

class automation.RandomSearch()​

Random search strategy controller. Random uniform sampling of hyper-parameters.

Initialize a random search optimizer.

  • Parameters

    • base_task_id (str ) – The Task ID.

    • hyper_parameters (list ) – The list of Parameter objects to optimize over.

    • objective_metric (Objective ) – The Objective metric to maximize / minimize.

    • execution_queue (str ) – The execution queue to use for launching Tasks (experiments).

    • num_concurrent_workers (int ) – The maximum umber of concurrent running machines.

    • pool_period_min (float ) – The time between two consecutive pools (minutes).

    • time_limit_per_job (float ) – The maximum execution time per single job in minutes, when time limit is exceeded job is aborted. (Optional)

    • compute_time_limit (float ) – The maximum compute time in minutes. When time limit is exceeded, all jobs aborted. (Optional)

    • max_iteration_per_job (int ) – The maximum iterations (of the Objective metric) per single job. When exceeded, the job is aborted.

    • total_max_jobs (int ) – The total maximum jobs for the optimization process. The default is None, for unlimited.

create_job​

create_job()

Create a new job if needed. Return the newly created job. If no job needs to be created, return None.

  • Return type

    Optional[ClearmlJob]

  • Returns

    A newly created ClearmlJob object, or None if no ClearmlJob created

get_created_jobs_ids​

get_created_jobs_ids()

Return a Task IDs dict created by this optimizer until now, including completed and running jobs. The values of the returned dict are the parameters used in the specific job

  • Return type

    Mapping[str, dict]

  • Returns

    dict of task IDs (str) as keys, and their parameters dict as values.

get_created_jobs_tasks​

get_created_jobs_tasks()

Return a Task IDs dict created by this optimizer until now. The values of the returned dict are the ClearmlJob.

  • Return type

    Mapping[str, dict]

  • Returns

    dict of task IDs (str) as keys, and their ClearmlJob as values.

get_objective_metric​

get_objective_metric()

Return the metric title, series pair of the objective.

  • Return type

    (str, str)

  • Returns

    (title, series)

get_running_jobs​

get_running_jobs()

Return the current running ClearmlJob.

  • Return type

    Sequence[ClearmlJob]

  • Returns

    List of ClearmlJob objects.

get_top_experiments​

get_top_experiments(top_k)

Return a list of Tasks of the top performing experiments, based on the controller Objective object.

  • Parameters

    top_k (int ) – The number of Tasks (experiments) to return.

  • Return type

    Sequence[Task]

  • Returns

    A list of Task objects, ordered by performance, where index 0 is the best performing Task.

get_top_experiments_details​

get_top_experiments_details(top_k, all_metrics=False, all_hyper_parameters=False, only_completed=False)

Return a list of dictionaries of the top performing experiments. Example: [{'task_id': Task-ID, 'metrics': scalar-metric-dict, 'hyper_parameters': Hyper-Parameters},]

Order is based on the controller Objective object.

  • Parameters

    • top_k (int ) – The number of Tasks (experiments) to return.

    • all_metrics (bool ) – Default False, only return the objective metric on the metrics dictionary. If True, return all scalar metrics of the experiment

    • all_hyper_parameters (bool ) – Default False. If True, return all the hyper-parameters from all the sections.

    • only_completed (bool ) – return only completed Tasks. Default False.

  • Return type

    Sequence[(str, dict)]

  • Returns

    A list of dictionaries ({task_id: ‘’, hyper_parameters: {}, metrics: {}}), ordered by performance, where index 0 is the best performing Task. Example w/ all_metrics=False:

    [
        {
            task_id: '0593b76dc7234c65a13a301f731958fa',
            hyper_parameters: {'General/lr': '0.03', 'General/batch_size': '32'},
            metrics: {
                'accuracy per class/cat': {
                    'metric': 'accuracy per class',
                    'variant': 'cat',
                    'value': 0.119,
                    'min_value': 0.119,
                    'max_value': 0.782
                },
            }
        },
    ]

    Example w/ all_metrics=True:

    [
        {
            task_id: '0593b76dc7234c65a13a301f731958fa',
            hyper_parameters: {'General/lr': '0.03', 'General/batch_size': '32'},
            metrics: {
                'accuracy per class/cat': {
                    'metric': 'accuracy per class',
                    'variant': 'cat',
                    'value': 0.119,
                    'min_value': 0.119,
                    'max_value': 0.782
                },
                'accuracy per class/deer': {
                    'metric': 'accuracy per class',
                    'variant': 'deer',
                    'value': 0.219,
                    'min_value': 0.219,
                    'max_value': 0.282
                },
            }
        },
    ]

get_top_experiments_id_metrics_pair​

get_top_experiments_id_metrics_pair(top_k, all_metrics=False, only_completed=False)

Return a list of pairs (Task ID, scalar metric dict) of the top performing experiments. Order is based on the controller Objective object.

  • Parameters

    • top_k (int ) – The number of Tasks (experiments) to return.

    • all_metrics (bool ) – Default False, only return the objective metric on the metrics dictionary. If True, return all scalar metrics of the experiment

    • only_completed (bool ) – return only completed Tasks. Default False.

  • Return type

    Sequence[(str, dict)]

  • Returns

    A list of pairs (Task ID, metric values dict), ordered by performance,

where index 0 is the best performing Task. Example w/ all_metrics=False:

[
    ('0593b76dc7234c65a13a301f731958fa',
        {
            'accuracy per class/cat': {
                'metric': 'accuracy per class',
                'variant': 'cat',
                'value': 0.119,
                'min_value': 0.119,
                'max_value': 0.782
            },
        }
    ),
]

Example w/ all_metrics=True:

[
    ('0593b76dc7234c65a13a301f731958fa',
        {
            'accuracy per class/cat': {
                'metric': 'accuracy per class',
                'variant': 'cat',
                'value': 0.119,
                'min_value': 0.119,
                'max_value': 0.782
            },
            'accuracy per class/deer': {
                'metric': 'accuracy per class',
                'variant': 'deer',
                'value': 0.219,
                'min_value': 0.219,
                'max_value': 0.282
            },
        }
    ),
]

helper_create_job​

helper_create_job(base_task_id, parameter_override=None, task_overrides=None, tags=None, parent=None, kwargs)**

Create a Job using the specified arguments, ClearmlJob for details.

  • Return type

    ClearmlJob

  • Returns

    A newly created Job instance.

  • Parameters

    • base_task_id (str ) –

    • parameter_override (Optional [ Mapping [ str , str ] ] ) –

    • task_overrides (Optional [ Mapping [ str , str ] ] ) –

    • tags (Optional [ Sequence [ str ] ] ) –

    • parent (Optional [ str ] ) –

    • kwargs (Any ) –

monitor_job​

monitor_job(job)

Helper function, Implementation is not required. Default use in process_step default implementation. Check if the job needs to be aborted or already completed.

If returns False, the job was aborted / completed, and should be taken off the current job list.

If there is a budget limitation, this call should update self.budget.compute_time.update / self.budget.iterations.update

  • Parameters

    job (ClearmlJob ) – A ClearmlJob object to monitor.

  • Return type

    bool

  • Returns

    False, if the job is no longer relevant.

process_step​

process_step()

Abstract helper function. Implementation is not required. Default use in start default implementation Main optimization loop, called from the daemon thread created by start.

  • Call monitor job on every ClearmlJob in jobs:

    • Check the performance or elapsed time, and then decide whether to kill the jobs.

  • Call create_job:

    • Check if spare job slots exist, and if they do call create a new job based on previous tested experiments.

  • Return type

    bool

  • Returns

    True, if continue the optimization. False, if immediately stop.

set_job_class​

set_job_class(job_class)

Set the class to use for the helper_create_job function.

  • Parameters

    job_class (ClearmlJob ) – The Job Class type.

  • Return type

    ()

set_job_default_parent​

set_job_default_parent(job_parent_task_id, project_name=None)

Set the default parent for all Jobs created by the helper_create_job method.

  • Parameters

    • job_parent_task_id (str ) – The parent Task ID.

    • project_name (str ) – If specified, create the jobs in the specified project

  • Return type

    ()

set_job_naming_scheme​

set_job_naming_scheme(naming_function)

Set the function used to name a newly created job.

  • Parameters

    naming_function (callable ) – Callable function for naming a newly created job. Use the following format:

    naming_functor(base_task_name, argument_dict) -> str

  • Return type

    ()

set_optimizer_task​

set_optimizer_task(task)

Set the optimizer task object to be used to store/generate reports on the optimization process. Usually this is the current task of this process.

  • Parameters

    task (Task ) – The optimizer`s current Task.

  • Return type

    ()

start​

start()

Start the Optimizer controller function loop(). If the calling process is stopped, the controller will stop as well.

info

This function returns only after the optimization is completed or stop was called.

  • Return type

    ()

stop​

stop()

Stop the current running optimization loop. Called from a different thread than the start.

  • Return type

    ()