deephyper.evaluator.LokyEvaluator

deephyper.evaluator.LokyEvaluator#

class deephyper.evaluator.LokyEvaluator(run_function: Callable, num_workers: int = 1, callbacks: list = None, run_function_kwargs: dict = None, storage: Storage = None, search_id: Hashable = None)[source]#

Bases: Evaluator

This evaluator uses the ProcessPoolExecutor from loky as backend.

The loky backend uses cloudpickle to serialize by value the called function and its arguments. This allows to pass lambda or local functions to the Evaluator as run_function.

Parameters:

  • run_function (callable) – Functions to be executed by the Evaluator.

  • num_workers (int, optional) – Number of parallel processes used to compute the run_function. Defaults to 1.

  • callbacks (list, optional) – A list of callbacks to trigger custom actions at the creation or completion of jobs. Defaults to None.

  • run_function_kwargs (dict, optional) – Static keyword arguments to pass to the run_function when executed.

  • storage (Storage, optional) – Storage used by the evaluator. Defaults to SharedMemoryStorage.

  • search_id (Hashable, optional) – The id of the search to use in the corresponding storage. If None it will create a new search identifier when initializing the search.

Methods

close

convert_for_csv

Convert an input value to an accepted format.

create

Create evaluator with a specific backend and configuration.

decode

Decode the key following a JSON format to return a dict.

dump_jobs_done_to_csv

Dump completed jobs to a CSV file.

execute

Execute the received job.

gather

Collect the completed tasks from the evaluator in batches of one or more.

gather_other_jobs_done

Access storage to return results from other processes.

process_local_tasks_done

set_event_loop

set_maximum_num_jobs_submitted

submit

Send configurations to be evaluated by available workers.

to_json

Returns a json version of the evaluator.

Attributes

FAIL_RETURN_VALUE

NEST_ASYNCIO_PATCHED

PYTHON_EXE

is_master

Boolean that indicates if the current Evaluator object is a "Master".

num_jobs_gathered

num_jobs_submitted

time_left

timeout

convert_for_csv(val)#

Convert an input value to an accepted format.

This is to be saved as a value of a CSV file (e.g., a list becomes it’s str representation).

Parameters:

val (Any) – The input value to convert.

Returns:

The converted value.

Return type:

Any

static create(run_function, method='serial', method_kwargs={})#

Create evaluator with a specific backend and configuration.

Parameters:

  • run_function (function) – The function to execute in parallel.

  • method (str, optional) – The backend to use in `` [“serial”, “thread”, “process”, “ray”, “mpicomm”]``. Defaults to "serial".

  • method_kwargs (dict, optional) – Configuration dictionnary of the corresponding backend. Keys corresponds to the keyword arguments of the corresponding implementation. Defaults to “{}”.

Raises:

ValueError – if the method is not acceptable.

Returns:

the Evaluator with the corresponding backend and configuration.

Return type:

Evaluator

decode(key)#

Decode the key following a JSON format to return a dict.

dump_jobs_done_to_csv(log_dir: str = '.', filename='results.csv', flush: bool = False)#

Dump completed jobs to a CSV file.

This will reset the Evaluator.jobs_done attribute to an empty list.

Parameters:

  • log_dir (str) – Directory where to dump the CSV file.

  • filename (str) – Name of the file where to write the data.

  • flush (bool) – A boolean indicating if the results should be flushed (i.e., forcing the dumping).

async execute(job: Job) → Job[source]#

Execute the received job. To be implemented with a specific backend.

Parameters:

job (Job) – the Job to be executed.

Returns:

the update Job.

Return type:

job

gather(type, size=1)#

Collect the completed tasks from the evaluator in batches of one or more.

Parameters:

  • type (str) –

    Options:
    "ALL"

    Block until all jobs submitted to the evaluator are completed.

    "BATCH"

    Specify a minimum batch size of jobs to collect from the evaluator. The method will block until at least size evaluations are completed.

  • size (int, optional) – The minimum batch size that we want to collect from the evaluator. Defaults to 1.

Raises:

Exception – Raised when a gather operation other than “ALL” or “BATCH” is provided.

Returns:

A batch of completed jobs that is at minimum the given size.

Return type:

List[Job]

gather_other_jobs_done()#

Access storage to return results from other processes.

property is_master#

Boolean that indicates if the current Evaluator object is a “Master”.

submit(args_list: List[Dict])#

Send configurations to be evaluated by available workers.

Parameters:

args_list (List[Dict]) – A list of dict which will be passed to the run function to be executed.

to_json()#

Returns a json version of the evaluator.

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