def automl_fixture():
from zoo import init_spark_on_local
from zoo.ray.util.raycontext import RayContext
sc = init_spark_on_local(cores=4, spark_log_level="INFO")
ray_ctx = RayContext(sc=sc, object_store_memory="1g")
ray_ctx.init()
yield
ray_ctx.stop()
sc.stop()
def test_local(self):
node_num = 4
sc = init_spark_on_local(cores=node_num)
ray_ctx = RayContext(sc=sc)
ray_ctx.init()
actors = [TestRay.remote() for i in range(0, node_num)]
print([ray.get(actor.hostname.remote()) for actor in actors])
ray_ctx.stop()
sc.stop()
for process_info in ray_ctx.ray_processesMonitor.process_infos:
for pid in process_info.pids:
assert not psutil.pid_exists(pid)
def test_local(self):
node_num = 4
sc = init_spark_on_local(cores=node_num)
ray_ctx = RayContext(sc=sc, object_store_memory="1g")
ray_ctx.init()
actors = [TestRay.remote() for i in range(0, node_num)]
print(ray.get([actor.hostname.remote() for actor in actors]))
ray_ctx.stop()
time.sleep(3)
# repeat
print("-------------------first repeat begin!------------------")
ray_ctx = RayContext(sc=sc, object_store_memory="1g")
ray_ctx.init()
actors = [TestRay.remote() for i in range(0, node_num)]
print(ray.get([actor.hostname.remote() for actor in actors]))
ray_ctx.stop()
sc.stop()
time.sleep(3)
for process_info in ray_ctx.ray_processesMonitor.process_infos:
for pid in process_info.pids:
assert not psutil.pid_exists(pid)
# compute_gradient returns two variables, so action_id is a list
action_id = actors[i].compute_gradient.remote(model_id)
actions.append(action_id)
for i in range(batch_size):
# wait for one actor to finish its operation
# action_id is the ready object id
action_id, actions = ray.wait(actions)
grad, reward_sum = ray.get(action_id[0])
# Accumulate the gradient of each weight parameter over batch.
for k in model:
grad_buffer[k] += grad[k]
running_reward = (reward_sum if running_reward is None else
running_reward * 0.99 + reward_sum * 0.01)
end_time = time.time()
print("Batch {} computed {} rollouts in {} seconds, "
"running mean is {}".format(batch_num, batch_size,
end_time - start_time,
running_reward))
# update gradient after one iteration
for k, v in model.items():
g = grad_buffer[k]
rmsprop_cache[k] = (decay_rate * rmsprop_cache[k] +
(1 - decay_rate) * g**2)
model[k] += learning_rate * g / (np.sqrt(rmsprop_cache[k]) + 1e-5)
# Reset the batch gradient buffer.
grad_buffer[k] = np.zeros_like(v)
batch_num += 1
ray_ctx.stop()
sc.stop()
