def __init__(
self,
*,
name: str,
modules: List[Module],
dependent_modules: List[Module],
dependent_modules_nonregular: List[Module],
replicas: int = 1,
state_type: StateType = True,
hooks: Optional[List[Callable]] = None,
options={},
):
self.name = name
self._modules = modules
self._state_type = state_type
self.dependencies = set([m.group for m in dependent_modules])
self.module_dependencies = set([m.name for m in dependent_modules])
self.dependencies_nonregular = set(
[m.group for m in dependent_modules_nonregular])
self.module_dependencies_nonregular = set(
[m.name for m in dependent_modules_nonregular])
self.pool = ParallelActorPool([
Actor.options(**options).remote(
name=name,
index=i if replicas > 1 else None,
state_type=state_type,
modules=modules,
) for i in range(replicas)
])
self._logger = None
self._hooks = hooks
async def test_should_run(self, runs, actors, ray_context):
pool = ParallelActorPool([
MultiplyActor.remote()
for _ in range(actors)
])
await asyncio.gather(*[
pool.run(data=0, delay=0.0001)
for _ in range(runs)
])
async def test_should_process_correctly(self, score, runs, factor, actors, ray_context):
pool = ParallelActorPool([
MultiplyActor.remote(factor)
for _ in range(actors)
])
results = await asyncio.gather(*[
pool.run(data=1, delay=0.0001)
for _ in range(runs)
])
assert sum(results) == score
class GroupRuntime:
def __init__(
self,
*,
name: str,
modules: List[Module],
dependent_modules: List[Module],
dependent_modules_nonregular: List[Module],
replicas: int = 1,
state_type: StateType = True,
options={},
):
self.name = name
self._modules = modules
self._state_type = state_type
self.dependencies = set([m.group for m in dependent_modules])
self.module_dependencies = set([m.name for m in dependent_modules])
self.dependencies_nonregular = set(
[m.group for m in dependent_modules_nonregular])
self.module_dependencies_nonregular = set(
[m.name for m in dependent_modules_nonregular])
self.pool = ParallelActorPool([
Actor.options(**options).remote(name, state_type, modules)
for _ in range(replicas)
])
@property
def modules(self) -> List[Module]:
return self._modules
@property
def is_replicated(self) -> bool:
return self.pool.replicas > 1
@property
def state_type(self) -> bool:
return self._state_type
def fulfills(self, names: Iterable[str]) -> bool:
return set(names).issuperset(self.module_dependencies)
async def bootstrap(self):
await self.pool.bootstrap()
async def run(self, job_id: UUID, request, results, is_regular_runtime):
return asyncio.ensure_future(
self.pool.run(job_id=job_id,
request=request,
results=results,
is_regular_runtime=is_regular_runtime))
async def teardown(self):
await self.pool.teardown()
def __init__(
self,
*,
name: str,
modules: List[Module],
dependent_modules: List[Module],
dependent_modules_nonregular: List[Module],
replicas: int = 1,
state_type: StateType = True,
options={},
):
self.name = name
self._modules = modules
self._state_type = state_type
self.dependencies = set([m.group for m in dependent_modules])
self.module_dependencies = set([m.name for m in dependent_modules])
self.dependencies_nonregular = set(
[m.group for m in dependent_modules_nonregular])
self.module_dependencies_nonregular = set(
[m.name for m in dependent_modules_nonregular])
self.pool = ParallelActorPool([
Actor.options(**options).remote(name, state_type, modules)
for _ in range(replicas)
])
async def test_can_create(self, actors, ray_context):
pool = ParallelActorPool([
MultiplyActor.remote()
for _ in range(actors)
])
assert pool.replicas == actors