我是 ray 新手，我正在尝试并行化我开发的模拟器。这是我的模拟器的一个例子，显然它更复杂。
import some_library
import sim_library_with_global_object
class Model(object):
def __init__(self,init_vals):
#initialize object using some of the global_object from sim_library.
#the Model object have it's own variables not global
def do_step(self,time):
#calculate Model step using the global_object from sim_library
#edit the Model variables with respect to the step
class ManyModel(object):
def init(self):
self.models=[]
def add_model(self,init_vals):
model = Model(init_vals)
self.model.append(model)
def step(self,time):
for model in self.models:
model.do_step(time)
def get_data_step(self):
data=[]
for model in self.models:
data.append(model.myvalues)
return data
sim=ManyModel()
inits=[] #####list of init_vals
times=[] ####list of times to simulate
for init in intis:
sim.add_model(init)
for time in times:
sim.step(time)
step_data=sim.get_data_step()
到目前为止，我已经尝试通过以下两种方式@ray.remote在 Model类 (1) 和类 (2) 上使用带有装饰器的 ray：ManyModel
(1)
############################## (1) ###############
import some_library
import sim_library_with_global_object
@ray.remote
class Model(object):
def __init__(self,init_vals):
#initialize object using some of the global_object from sim_library.
#the Model object have it's own variables not global
def do_step(self,time):
#calculate Model step using the global_object from sim_library
#edit the Model variables with respect to the step
class ManyModel(object):
def init(self):
self.models=[]
def add_model(self,init_vals):
model = Model.remote(init_vals)
self.model.append(model)
def step(self,time):
futures=[]
for model in self.models:
futures.append(model.do_step.remote(time))
return futures
def get_data_step(self,futures):
data=[]
while len(futures)>0:
ready, not_ready = ray.wait(ids)
results=ray.get(ready)
data.append(results)
return data
ray.init()
sim=ManyModel()
inits=[] #####list of init_vals
times=[] ####list of times to simulate
for init in intis:
sim.add_model(init)
for time in times:
sim.step(time)
step_data=sim.get_data_step()
( 2)
########################## (2) #################
import some_library
import sim_library_with_global_object
class Model(object):
def __init__(self,init_vals):
#initialize object using some of the global_object from sim_library.
#the Model object have it's own variables not global
def do_step(self,time):
#calculate Model step using the global_object from sim_library
#edit the Model variables with respect to the step
@ray.remote
class ManyModel(object):
def init(self):
self.models=[]
self.data=[]
def add_model(self,init_vals):
model = Model(init_vals)
self.model.append(model)
def step(self,time):
for model in self.models:
model.do_step(time)
def get_data_step(self):
self.data=[]
for model in self.models:
self.data.append(model.myvalues)
return self.data
ray.init()
sim=ManyModel.remote()
inits=[] #####list of init_vals
times=[] ####list of times to simulate
for init in intis:
sim.add_model.remote(init)
for time in times:
sim.step.remote(time)
future=sim.get_data_step.remote()
step_data=ray.get(future)
在这两种方式中，我都没有从使用 ray 库中获得任何好处。你能帮我使用吗？
方法（1） 的更新第一种方法的问题是我收到了这个警告信息
2020-11-09 11:33:20,517 WARNING worker.py:1779 -- WARNING: 12 PYTHON workers have been started. This could be a result of using a large number of actors, or it could be a consequence of using nested tasks (see https://github.com/ray-project/ray/issues/3644) for some a discussion of workarounds.
使用 10 xModel 这是性能结果： 不使用射线：10 x Model -> do_step 0.11 [s] 使用射线 (1)：10 x Model -> do_step 0.22 [s]
此外，每次我使用方法 (1) 创建一个 Actor 时，它都会复制导入库的所有 global_objects 并且 ram 消耗变得疯狂。我需要用超过 10 万个Model 对象进行午餐模拟。
总的来说，我不明白在 ray 中创建多个演员是否是个好主意。