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Make object_manager_test::test_object_transfer_retry less flaky. (#4057)
* Make object_manager_test::test_object_transfer_retry less flaky. * Make the test pass.
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1 changed files with 32 additions and 17 deletions
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@ -213,63 +213,78 @@ def test_object_transfer_retry(ray_start_empty_cluster):
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repeated_push_delay = 10
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# Force the sending object manager to allow duplicate pushes again sooner.
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# Also, force the receiving object manager to retry the Pull sooner.
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# Also, force the receiving object manager to retry the Pull sooner. We
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# make the chunk size smaller in order to make it easier to test objects
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# with multiple chunks.
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config = json.dumps({
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"object_manager_repeated_push_delay_ms": repeated_push_delay * 1000,
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"object_manager_pull_timeout_ms": repeated_push_delay * 1000 / 4,
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"object_manager_default_chunk_size": 1000
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})
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cluster.add_node(_internal_config=config)
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cluster.add_node(num_gpus=1, _internal_config=config)
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object_store_memory = 10**8
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cluster.add_node(
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object_store_memory=object_store_memory, _internal_config=config)
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cluster.add_node(
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num_gpus=1,
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object_store_memory=object_store_memory,
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_internal_config=config)
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ray.init(redis_address=cluster.redis_address)
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@ray.remote(num_gpus=1)
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def f(size):
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return np.zeros(size, dtype=np.uint8)
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x_ids = [f.remote(10**i) for i in [1, 2, 3, 4, 5, 6, 7]]
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# Transfer an object to warm up the object manager.
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ray.get(f.remote(10**6))
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x_ids = [f.remote(10**i) for i in [1, 2, 3, 4]]
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assert not any(
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ray.worker.global_worker.plasma_client.contains(
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ray.pyarrow.plasma.ObjectID(x_id.binary())) for x_id in x_ids)
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# Get the objects locally to cause them to be transferred. This is the
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# first time the objects are getting transferred, so it should happen
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# quickly.
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start_time = time.time()
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# Get the objects locally to cause them to be transferred.
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xs = ray.get(x_ids)
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end_time = time.time()
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if end_time - start_time > repeated_push_delay:
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warnings.warn("The initial transfer took longer than the repeated "
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"push delay, so this test may not be testing the thing "
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"it's supposed to test.")
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# Cause all objects to be flushed.
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del xs
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x = np.zeros(10**7, dtype=np.uint8)
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for _ in range(10):
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x = np.zeros(object_store_memory // 10, dtype=np.uint8)
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for _ in range(15):
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ray.put(x)
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assert not any(
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ray.worker.global_worker.plasma_client.contains(
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ray.pyarrow.plasma.ObjectID(x_id.binary())) for x_id in x_ids)
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end_time = time.time()
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print(end_time - start_time)
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# Make sure that the first time the objects get transferred, it happens
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# quickly.
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assert end_time - start_time < repeated_push_delay
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# Get the objects again and make sure they get transferred.
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xs = ray.get(x_ids)
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end_transfer_time = time.time()
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# We should have had to wait for the repeated push delay.
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assert end_transfer_time - start_time >= repeated_push_delay
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# Flush the objects again and wait longer than the repeated push delay and
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# make sure that the objects are transferred again.
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del xs
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for _ in range(10):
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for _ in range(15):
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ray.put(x)
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assert not any(
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ray.worker.global_worker.plasma_client.contains(
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ray.pyarrow.plasma.ObjectID(x_id.binary())) for x_id in x_ids)
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time.sleep(repeated_push_delay)
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# Get the objects locally to cause them to be transferred. This should
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# happen quickly.
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start_time = time.time()
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ray.get(x_ids)
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end_time = time.time()
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assert end_time - start_time < repeated_push_delay
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# The purpose of this test is to make sure we can transfer many objects. In the
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