def knative_executor(config=None, runtime=None, runtime_memory=None, workers=None, region=None, storage_backend=None, storage_backend_region=None, rabbitmq_monitor=None, remote_invoker=None, log_level=None): """ Function executor for Knative """ compute_backend = 'knative' return FunctionExecutor(config=config, runtime=runtime, runtime_memory=runtime_memory, workers=workers, compute_backend=compute_backend, compute_backend_region=region, storage_backend=storage_backend, storage_backend_region=storage_backend_region, rabbitmq_monitor=rabbitmq_monitor, remote_invoker=remote_invoker, log_level=log_level)
def function_executor(config=None, runtime=None, runtime_memory=None, workers=None, backend=None, region=None, storage_backend=None, storage_backend_region=None, rabbitmq_monitor=None, remote_invoker=None, log_level=None): """ Generic function executor """ return FunctionExecutor(config=config, runtime=runtime, runtime_memory=runtime_memory, workers=workers, compute_backend=backend, compute_backend_region=region, storage_backend=storage_backend, storage_backend_region=storage_backend_region, rabbitmq_monitor=rabbitmq_monitor, remote_invoker=remote_invoker, log_level=log_level)
def _direct_array_copy( source: ReadableArray, target: WriteableArray, chunks: Tuple[int, ...], pywren_function_executor: FunctionExecutor, ) -> None: """Direct copy between arrays using Pywren for parallelism""" iterdata = [(source, target, key) for key in chunk_keys(source.shape, chunks)] def direct_copy(iterdata): source, target, key = iterdata target[key] = source[key] futures = pywren_function_executor.map(direct_copy, iterdata) pywren_function_executor.get_result(futures)
def local_executor(config=None, storage_backend=None, storage_backend_region=None, rabbitmq_monitor=None, log_level=None): """ Localhost function executor """ compute_backend = 'localhost' if storage_backend is None: storage_backend = 'localhost' return FunctionExecutor(config=config, compute_backend=compute_backend, storage_backend=storage_backend, storage_backend_region=storage_backend_region, rabbitmq_monitor=rabbitmq_monitor, log_level=log_level)
def ibm_cf_executor(config=None, runtime=None, runtime_memory=None, compute_backend_region=None, storage_backend=None, storage_backend_region=None, rabbitmq_monitor=None, log_level=None): """ Function executor for IBM Cloud Functions """ compute_backend = 'ibm_cf' return FunctionExecutor(config=config, runtime=runtime, runtime_memory=runtime_memory, compute_backend=compute_backend, compute_backend_region=compute_backend_region, storage_backend=storage_backend, storage_backend_region=storage_backend_region, rabbitmq_monitor=rabbitmq_monitor, log_level=log_level)
def docker_executor(config=None, runtime=None, workers=None, storage_backend=None, storage_backend_region=None, rabbitmq_monitor=None, log_level=None): """ Localhost function executor """ compute_backend = 'docker' if storage_backend is None: storage_backend = 'localhost' return FunctionExecutor(config=config, runtime=runtime, workers=workers, compute_backend=compute_backend, storage_backend=storage_backend, storage_backend_region=storage_backend_region, rabbitmq_monitor=rabbitmq_monitor, log_level=log_level)