deephyper.evaluator.ProcessPoolEvaluator

class deephyper.evaluator.ProcessPoolEvaluator(run_function: Callable, num_workers: int = 1, callbacks: list = None, run_function_kwargs: dict = None, storage: Storage = None, search_id: Hashable = None)

Bases: Evaluator

This evaluator uses the ProcessPoolExecutor as backend.

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.

Methods

convert_for_csv	Convert an input value to an accepted format to be saved as a value of a CSV file (e.g., a list becomes it's str representation).
create	Create evaluator with a specific backend and configuration.
decode	Decode the key following a JSON format to return a dict.
dump_evals	Dump evaluations to a CSV file.
execute	Execute the received job.
gather	Collect the completed tasks from the evaluator in batches of one or more.
set_timeout	Set a timeout for the Evaluator.
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

convert_for_csv(val)

Convert an input value to an accepted format 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_evals(saved_keys=None, log_dir: str = '.', filename='results.csv')

Dump evaluations to a CSV file.

Parameters:

• saved_keys (list|callable) – If None the whole job.config will be added as row of the CSV file. If a list filtered keys will be added as a row of the CSV file. If a callable the output dictionnary will be added as a row of the CSV file.

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

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

async execute(job: Job) → Job

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

Parameters:

job (Job) – the Job to be executed.

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]

set_timeout(timeout)

Set a timeout for the Evaluator. It will create task with a “time budget” and will kill the the task if this budget is exhausted.

submit(configs: List[Dict])

Send configurations to be evaluated by available workers.

Parameters:

configs (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.