deephyper.evaluator.RayEvaluator#

class deephyper.evaluator.RayEvaluator(run_function: Callable, callbacks: list | None = None, run_function_kwargs: dict | None = None, storage: Storage | None = None, search_id: Hashable | None = None, address: str | None = None, password: str | None = None, num_cpus: int | None = None, num_gpus: int | None = None, include_dashboard: bool = False, num_cpus_per_task: float = 1, num_gpus_per_task: float | None = None, ray_kwargs: dict | None = None, num_workers: int | None = None)[source]#

Bases: Evaluator

This evaluator uses the ray library as backend.

Parameters:

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

  • 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 MemoryStorage.

  • 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.

  • address (str, optional) – address of the Ray-head. Defaults to None, if no Ray-head was started.

  • password (str, optional) – password to connect ot the Ray-head. Defaults to None, if the default Ray-password is used.

  • num_cpus (int, optional) – number of CPUs available in the Ray-cluster. Defaults to None, if the Ray-cluster was already started it will be automatically computed.

  • num_gpus (int, optional) – number of GPUs available in the Ray-cluster. Defaults to None, if the Ray-cluster was already started it will be automatically computed.

  • num_cpus_per_task (float, optional) – number of CPUs used per remote task. Defaults to 1.

  • num_gpus_per_task (float, optional) – number of GPUs used per remote task. Defaults to None.

  • ray_kwargs (dict, optional) – other ray keyword arguments passed to ray.init(...). Defaults to {}.

  • num_workers (int, optional) – number of workers available to compute remote-tasks in parallel. Defaults to None, or if it is -1 it is automatically computed based with num_workers = int(num_cpus // num_cpus_per_task).

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[source]#

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.