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https://github.com/wassname/ray.git
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[core worker] Python core worker object interface (#5272)
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Edward Oakes
@@ -1538,7 +1538,7 @@ def test_free_objects_multi_node(ray_start_cluster):
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class RawActor(object):
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def get(self):
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return ray.worker.global_worker.plasma_client.store_socket_name
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return ray.worker.global_worker.node.unique_id
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ActorOnNode0 = ray.remote(resources={"Custom0": 1})(RawActor)
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ActorOnNode1 = ray.remote(resources={"Custom1": 1})(RawActor)
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@@ -1585,7 +1585,7 @@ def test_free_objects_multi_node(ray_start_cluster):
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assert len(l1) == 2
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assert len(l2) == 1
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# The deleted object will have the same store with the driver.
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local_return = ray.worker.global_worker.plasma_client.store_socket_name
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local_return = ray.worker.global_worker.node.unique_id
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for object_id in l1:
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assert ray.get(object_id) != local_return
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@@ -1998,16 +1998,16 @@ def test_zero_cpus_actor(ray_start_cluster):
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cluster.add_node(num_cpus=2)
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ray.init(address=cluster.address)
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local_plasma = ray.worker.global_worker.plasma_client.store_socket_name
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node_id = ray.worker.global_worker.node.unique_id
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@ray.remote
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class Foo(object):
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def method(self):
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return ray.worker.global_worker.plasma_client.store_socket_name
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return ray.worker.global_worker.node.unique_id
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# Make sure tasks and actors run on the remote raylet.
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a = Foo.remote()
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assert ray.get(a.method.remote()) != local_plasma
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assert ray.get(a.method.remote()) != node_id
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def test_fractional_resources(shutdown_only):
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@@ -2080,32 +2080,32 @@ def test_multiple_raylets(ray_start_cluster):
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# This must be run on the zeroth raylet.
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@ray.remote(num_cpus=11)
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def run_on_0():
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return ray.worker.global_worker.plasma_client.store_socket_name
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return ray.worker.global_worker.node.plasma_store_socket_name
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# This must be run on the first raylet.
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@ray.remote(num_gpus=2)
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def run_on_1():
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return ray.worker.global_worker.plasma_client.store_socket_name
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return ray.worker.global_worker.node.plasma_store_socket_name
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# This must be run on the second raylet.
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@ray.remote(num_cpus=6, num_gpus=1)
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def run_on_2():
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return ray.worker.global_worker.plasma_client.store_socket_name
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return ray.worker.global_worker.node.plasma_store_socket_name
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# This can be run anywhere.
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@ray.remote(num_cpus=0, num_gpus=0)
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def run_on_0_1_2():
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return ray.worker.global_worker.plasma_client.store_socket_name
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return ray.worker.global_worker.node.plasma_store_socket_name
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# This must be run on the first or second raylet.
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@ray.remote(num_gpus=1)
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def run_on_1_2():
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return ray.worker.global_worker.plasma_client.store_socket_name
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return ray.worker.global_worker.node.plasma_store_socket_name
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# This must be run on the zeroth or second raylet.
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@ray.remote(num_cpus=8)
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def run_on_0_2():
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return ray.worker.global_worker.plasma_client.store_socket_name
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return ray.worker.global_worker.node.plasma_store_socket_name
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def run_lots_of_tasks():
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names = []
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@@ -2196,27 +2196,27 @@ def test_custom_resources(ray_start_cluster):
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@ray.remote
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def f():
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time.sleep(0.001)
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return ray.worker.global_worker.plasma_client.store_socket_name
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return ray.worker.global_worker.node.unique_id
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@ray.remote(resources={"CustomResource": 1})
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def g():
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time.sleep(0.001)
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return ray.worker.global_worker.plasma_client.store_socket_name
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return ray.worker.global_worker.node.unique_id
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@ray.remote(resources={"CustomResource": 1})
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def h():
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ray.get([f.remote() for _ in range(5)])
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return ray.worker.global_worker.plasma_client.store_socket_name
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return ray.worker.global_worker.node.unique_id
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# The f tasks should be scheduled on both raylets.
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assert len(set(ray.get([f.remote() for _ in range(50)]))) == 2
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local_plasma = ray.worker.global_worker.plasma_client.store_socket_name
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node_id = ray.worker.global_worker.node.unique_id
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# The g tasks should be scheduled only on the second raylet.
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raylet_ids = set(ray.get([g.remote() for _ in range(50)]))
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assert len(raylet_ids) == 1
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assert list(raylet_ids)[0] != local_plasma
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assert list(raylet_ids)[0] != node_id
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# Make sure that resource bookkeeping works when a task that uses a
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# custom resources gets blocked.
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@@ -2240,38 +2240,38 @@ def test_two_custom_resources(ray_start_cluster):
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@ray.remote(resources={"CustomResource1": 1})
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def f():
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time.sleep(0.001)
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return ray.worker.global_worker.plasma_client.store_socket_name
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return ray.worker.global_worker.node.unique_id
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@ray.remote(resources={"CustomResource2": 1})
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def g():
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time.sleep(0.001)
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return ray.worker.global_worker.plasma_client.store_socket_name
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return ray.worker.global_worker.node.unique_id
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@ray.remote(resources={"CustomResource1": 1, "CustomResource2": 3})
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def h():
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time.sleep(0.001)
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return ray.worker.global_worker.plasma_client.store_socket_name
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return ray.worker.global_worker.node.unique_id
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@ray.remote(resources={"CustomResource1": 4})
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def j():
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time.sleep(0.001)
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return ray.worker.global_worker.plasma_client.store_socket_name
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return ray.worker.global_worker.node.unique_id
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@ray.remote(resources={"CustomResource3": 1})
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def k():
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time.sleep(0.001)
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return ray.worker.global_worker.plasma_client.store_socket_name
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return ray.worker.global_worker.node.unique_id
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# The f and g tasks should be scheduled on both raylets.
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assert len(set(ray.get([f.remote() for _ in range(50)]))) == 2
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assert len(set(ray.get([g.remote() for _ in range(50)]))) == 2
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local_plasma = ray.worker.global_worker.plasma_client.store_socket_name
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node_id = ray.worker.global_worker.node.unique_id
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# The h tasks should be scheduled only on the second raylet.
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raylet_ids = set(ray.get([h.remote() for _ in range(50)]))
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assert len(raylet_ids) == 1
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assert list(raylet_ids)[0] != local_plasma
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assert list(raylet_ids)[0] != node_id
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# Make sure that tasks with unsatisfied custom resource requirements do
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# not get scheduled.
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@@ -2473,7 +2473,7 @@ def test_load_balancing(ray_start_cluster):
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@ray.remote
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def f():
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time.sleep(0.01)
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return ray.worker.global_worker.plasma_client.store_socket_name
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return ray.worker.global_worker.node.unique_id
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attempt_to_load_balance(f, [], 100, num_nodes, 10)
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attempt_to_load_balance(f, [], 1000, num_nodes, 100)
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@@ -2491,7 +2491,7 @@ def test_load_balancing_with_dependencies(ray_start_cluster):
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@ray.remote
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def f(x):
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time.sleep(0.010)
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return ray.worker.global_worker.plasma_client.store_socket_name
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return ray.worker.global_worker.node.unique_id
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# This object will be local to one of the raylets. Make sure
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# this doesn't prevent tasks from being scheduled on other raylets.
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@@ -2820,8 +2820,7 @@ def test_wait_reconstruction(shutdown_only):
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x_id = f.remote()
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ray.wait([x_id])
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ray.wait([f.remote()])
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assert not ray.worker.global_worker.plasma_client.contains(
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ray.pyarrow.plasma.ObjectID(x_id.binary()))
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assert not ray.worker.global_worker.core_worker.object_exists(x_id)
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ready_ids, _ = ray.wait([x_id])
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assert len(ready_ids) == 1
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