netpyne.batchtools.search module

netpyne.batchtools.search.ray_optuna_search(...)

Parameters:

• dispatcher_constructor (Callable, # constructor for the dispatcher (e.g. INETDispatcher))

• submit_constructor (Callable, # constructor for the submit (e.g. SHubmitSOCK))

• run_config (Dict, # batch configuration, (keyword: string pairs to customize the submit template))

• params (Dict, # search space (dictionary of parameter keys: tune search spaces))

• label (Optional[str] = 'optuna_search', # label for the search)

• output_path (Optional[str] = '../batch', # directory for storing generated files)

• checkpoint_path (Optional[str] = '../ray', # directory for storing checkpoint files)

• max_concurrent (Optional[int] = 1, # number of concurrent trials to run at one time)

• batch (Optional[bool] = True, # whether concurrent trials should run synchronously or asynchronously)

• num_samples (Optional[int] = 1, # number of trials to run)

• metric (Optional[str] = "loss", # metric to optimize (this should match some key: value pair in the returned data)

• mode (Optional[str] = "min", # either 'min' or 'max' (whether to minimize or maximize the metric)

• optuna_config (Optional[dict] = None, # additional configuration for the optuna search algorithm (incl. sampler, seed, etc.))

• Creates

• -------

• <label>.csv (file containing the results of the search)

Returns:

Study

Return type:

namedtuple(‘Study’, [‘algo’, ‘results’])(algo, results), # named tuple containing the created algorithm and the results of the search

netpyne.batchtools.search.ray_search(dispatcher_constructor: Callable, submit_constructor: Callable, run_config: Dict, params: Dict, algorithm: str | None = 'variant_generator', label: str | None = 'search', output_path: str | None = '../batch', checkpoint_path: str | None = '../ray', max_concurrent: int | None = 1, batch: bool | None = True, num_samples: int | None = 1, metric: str | None = 'loss', mode: str | None = 'min', algorithm_config: dict | None = None) → ResultGrid

class netpyne.batchtools.search.constructors(dispatcher, submit)

Bases: tuple

dispatcher

Alias for field number 0

submit

Alias for field number 1

netpyne.batchtools.search.constructor_tuples = {('sge', 'sfs'): (<class 'batchtk.runtk.dispatchers.SFSDispatcher'>, <class 'batchtk.runtk.submits.SGESubmitSFS'>), ('sge', 'socket'): (<class 'batchtk.runtk.dispatchers.INETDispatcher'>, <class 'batchtk.runtk.submits.SGESubmitSOCK'>), ('sh', 'sfs'): (<class 'batchtk.runtk.dispatchers.SFSDispatcher'>, <class 'batchtk.runtk.submits.SHSubmitSFS'>), ('sh', 'socket'): (<class 'batchtk.runtk.dispatchers.UNIXDispatcher'>, <class 'batchtk.runtk.submits.SHSubmitSOCK'>)}

some shim functions before ray_search

netpyne.batchtools.search.generate_constructors(job_type, comm_type='socket', **kwargs)

” returns the dispatcher, submit constructor pair for ray_search based on the job_type and comm_type inputs

netpyne.batchtools.search.generate_parameters(params, algorithm, **kwargs)

Parameters:

{'synMechTau2' (params =) – #’synMechTau2’: [3.0, 7.0], # assumes lower/upper bounds by default if evol-like algo ‘connWeight’ : paramtypes.sample_from(lambda _: numpy.random.uniform(0.005, 0.15))} # can optionally pass any of the paramtypes (= ray.tune data types)

Return type:

a dictionary of parameters for ray_search based on the input dictionary

netpyne.batchtools.search.shim(job_type: str, comm_type: str, run_config: Dict, params: Dict, algorithm: str | None = 'variant_generator', label: str | None = 'search', output_path: str | None = '../batch', checkpoint_path: str | None = '../ray', max_concurrent: int | None = 1, batch: bool | None = True, num_samples: int | None = 1, metric: str | None = 'loss', mode: str | None = 'min', algorithm_config: dict | None = None)

netpyne.batchtools.search.search(...)

Parameters:

• job_type (str, # the submission engine to run a single simulation (e.g. 'sge', 'sh'))

• comm_type (str, # the method of communication between host dispatcher and the simulation (e.g. 'socket', 'filesystem'))

• run_config (Dict, # batch configuration, (keyword: string pairs to customize the submit template))

• params (Dict, # search space (dictionary of parameter keys: tune search spaces))

• algorithm (Optional[str] = "variant_generator", # search algorithm to use, see SEARCH_ALG_IMPORT for available options)

• label (Optional[str] = 'search', # label for the search)

• output_path (Optional[str] = '../batch', # directory for storing generated files)

• checkpoint_path (Optional[str] = '../ray', # directory for storing checkpoint files)

• max_concurrent (Optional[int] = 1, # number of concurrent trials to run at one time)

• batch (Optional[bool] = True, # whether concurrent trials should run synchronously or asynchronously)

• num_samples (Optional[int] = 1, # number of trials to run)

• metric (Optional[str] = "loss", # metric to optimize (this should match some key: value pair in the returned data)

• mode (Optional[str] = "min", # either 'min' or 'max' (whether to minimize or maximize the metric)

• algorithm_config (Optional[dict] = None, # additional configuration for the search algorithm)

• Creates

• -------

• <label>.csv (file containing the results of the search)

Returns:

ResultGrid

Return type:

tune.ResultGrid # results of the search