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)