mirror of
https://github.com/vale981/ray
synced 2025-03-06 10:31:39 -05:00
515da7721a
* Change ray.worker.cleanup -> ray.shutdown and improve API documentation. * Deprecate ray.worker.cleanup() gracefully. * Fix linting
548 lines
17 KiB
Python
548 lines
17 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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import os
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import pytest
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import time
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import ray
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import ray.ray_constants as ray_constants
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@pytest.fixture
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def ray_start_regular():
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# Start the Ray processes.
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ray.init(num_cpus=10)
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yield None
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# The code after the yield will run as teardown code.
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ray.shutdown()
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@pytest.fixture(params=[(1, 4), (4, 4)])
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def ray_start_combination(request):
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num_local_schedulers = request.param[0]
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num_workers_per_scheduler = request.param[1]
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# Start the Ray processes.
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ray.worker._init(
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start_ray_local=True,
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num_workers=num_workers_per_scheduler,
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num_local_schedulers=num_local_schedulers,
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num_cpus=10)
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yield num_local_schedulers, num_workers_per_scheduler
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# The code after the yield will run as teardown code.
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ray.shutdown()
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def test_submitting_tasks(ray_start_combination):
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@ray.remote
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def f(x):
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return x
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for _ in range(1):
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ray.get([f.remote(1) for _ in range(1000)])
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for _ in range(10):
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ray.get([f.remote(1) for _ in range(100)])
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for _ in range(100):
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ray.get([f.remote(1) for _ in range(10)])
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for _ in range(1000):
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ray.get([f.remote(1) for _ in range(1)])
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assert ray.services.all_processes_alive()
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@pytest.mark.skipif(
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os.environ.get("RAY_USE_XRAY") == "1",
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reason="This test does not work with xray yet.")
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def test_dependencies(ray_start_combination):
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@ray.remote
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def f(x):
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return x
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x = 1
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for _ in range(1000):
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x = f.remote(x)
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ray.get(x)
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@ray.remote
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def g(*xs):
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return 1
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xs = [g.remote(1)]
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for _ in range(100):
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xs.append(g.remote(*xs))
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xs.append(g.remote(1))
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ray.get(xs)
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assert ray.services.all_processes_alive()
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@pytest.mark.skipif(
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os.environ.get("RAY_USE_XRAY") == "1",
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reason="This test does not work with xray yet.")
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def test_submitting_many_tasks(ray_start_regular):
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@ray.remote
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def f(x):
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return 1
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def g(n):
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x = 1
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for i in range(n):
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x = f.remote(x)
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return x
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ray.get([g(1000) for _ in range(100)])
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assert ray.services.all_processes_alive()
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def test_getting_and_putting(ray_start_regular):
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for n in range(8):
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x = np.zeros(10**n)
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for _ in range(100):
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ray.put(x)
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x_id = ray.put(x)
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for _ in range(1000):
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ray.get(x_id)
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assert ray.services.all_processes_alive()
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def test_getting_many_objects(ray_start_regular):
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@ray.remote
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def f():
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return 1
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n = 10**4 # TODO(pcm): replace by 10 ** 5 once this is faster.
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lst = ray.get([f.remote() for _ in range(n)])
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assert lst == n * [1]
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assert ray.services.all_processes_alive()
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def test_wait(ray_start_combination):
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num_local_schedulers, num_workers_per_scheduler = ray_start_combination
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num_workers = num_local_schedulers * num_workers_per_scheduler
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@ray.remote
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def f(x):
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return x
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x_ids = [f.remote(i) for i in range(100)]
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for i in range(len(x_ids)):
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ray.wait([x_ids[i]])
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for i in range(len(x_ids) - 1):
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ray.wait(x_ids[i:])
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@ray.remote
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def g(x):
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time.sleep(x)
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for i in range(1, 5):
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x_ids = [
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g.remote(np.random.uniform(0, i)) for _ in range(2 * num_workers)
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]
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ray.wait(x_ids, num_returns=len(x_ids))
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assert ray.services.all_processes_alive()
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@pytest.fixture(params=[1, 4])
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def ray_start_reconstruction(request):
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num_local_schedulers = request.param
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# Start the Redis global state store.
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node_ip_address = "127.0.0.1"
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redis_address, redis_shards = ray.services.start_redis(node_ip_address)
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redis_ip_address = ray.services.get_ip_address(redis_address)
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redis_port = ray.services.get_port(redis_address)
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time.sleep(0.1)
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# Start the Plasma store instances with a total of 1GB memory.
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plasma_store_memory = 10**9
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plasma_addresses = []
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objstore_memory = plasma_store_memory // num_local_schedulers
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for i in range(num_local_schedulers):
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store_stdout_file, store_stderr_file = ray.services.new_log_files(
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"plasma_store_{}".format(i), True)
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manager_stdout_file, manager_stderr_file = (ray.services.new_log_files(
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"plasma_manager_{}".format(i), True))
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plasma_addresses.append(
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ray.services.start_objstore(
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node_ip_address,
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redis_address,
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objstore_memory=objstore_memory,
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store_stdout_file=store_stdout_file,
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store_stderr_file=store_stderr_file,
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manager_stdout_file=manager_stdout_file,
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manager_stderr_file=manager_stderr_file))
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# Start the rest of the services in the Ray cluster.
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address_info = {
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"redis_address": redis_address,
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"redis_shards": redis_shards,
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"object_store_addresses": plasma_addresses
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}
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ray.worker._init(
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address_info=address_info,
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start_ray_local=True,
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num_workers=1,
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num_local_schedulers=num_local_schedulers,
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num_cpus=[1] * num_local_schedulers,
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redirect_output=True)
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yield (redis_ip_address, redis_port, plasma_store_memory,
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num_local_schedulers)
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# The code after the yield will run as teardown code.
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assert ray.services.all_processes_alive()
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# Determine the IDs of all local schedulers that had a task scheduled
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# or submitted.
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state = ray.experimental.state.GlobalState()
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state._initialize_global_state(redis_ip_address, redis_port)
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if os.environ.get("RAY_USE_NEW_GCS") == "on":
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tasks = state.task_table()
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local_scheduler_ids = {
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task["LocalSchedulerID"]
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for task in tasks.values()
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}
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# Make sure that all nodes in the cluster were used by checking that
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# the set of local scheduler IDs that had a task scheduled or submitted
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# is equal to the total number of local schedulers started. We add one
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# to the total number of local schedulers to account for
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# NIL_LOCAL_SCHEDULER_ID. This is the local scheduler ID associated
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# with the driver task, since it is not scheduled by a particular local
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# scheduler.
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if os.environ.get("RAY_USE_NEW_GCS") == "on":
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assert len(local_scheduler_ids) == num_local_schedulers + 1
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# Clean up the Ray cluster.
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ray.shutdown()
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@pytest.mark.skipif(
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os.environ.get("RAY_USE_XRAY") == "1",
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reason="This test does not work with xray yet.")
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@pytest.mark.skipif(
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os.environ.get("RAY_USE_NEW_GCS") == "on",
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reason="Failing with new GCS API on Linux.")
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def test_simple(ray_start_reconstruction):
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_, _, plasma_store_memory, num_local_schedulers = ray_start_reconstruction
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# Define the size of one task's return argument so that the combined
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# sum of all objects' sizes is at least twice the plasma stores'
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# combined allotted memory.
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num_objects = 1000
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size = int(plasma_store_memory * 1.5 / (num_objects * 8))
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# Define a remote task with no dependencies, which returns a numpy
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# array of the given size.
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@ray.remote
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def foo(i, size):
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array = np.zeros(size)
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array[0] = i
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return array
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# Launch num_objects instances of the remote task.
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args = []
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for i in range(num_objects):
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args.append(foo.remote(i, size))
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# Get each value to force each task to finish. After some number of
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# gets, old values should be evicted.
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for i in range(num_objects):
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value = ray.get(args[i])
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assert value[0] == i
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# Get each value again to force reconstruction.
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for i in range(num_objects):
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value = ray.get(args[i])
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assert value[0] == i
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# Get values sequentially, in chunks.
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num_chunks = 4 * num_local_schedulers
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chunk = num_objects // num_chunks
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for i in range(num_chunks):
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values = ray.get(args[i * chunk:(i + 1) * chunk])
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del values
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@pytest.mark.skipif(
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os.environ.get("RAY_USE_XRAY") == "1",
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reason="This test does not work with xray yet.")
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@pytest.mark.skipif(
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os.environ.get("RAY_USE_NEW_GCS") == "on",
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reason="Failing with new GCS API on Linux.")
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def test_recursive(ray_start_reconstruction):
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_, _, plasma_store_memory, num_local_schedulers = ray_start_reconstruction
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# Define the size of one task's return argument so that the combined
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# sum of all objects' sizes is at least twice the plasma stores'
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# combined allotted memory.
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num_objects = 1000
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size = int(plasma_store_memory * 1.5 / (num_objects * 8))
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# Define a root task with no dependencies, which returns a numpy array
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# of the given size.
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@ray.remote
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def no_dependency_task(size):
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array = np.zeros(size)
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return array
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# Define a task with a single dependency, which returns its one
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# argument.
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@ray.remote
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def single_dependency(i, arg):
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arg = np.copy(arg)
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arg[0] = i
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return arg
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# Launch num_objects instances of the remote task, each dependent on
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# the one before it.
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arg = no_dependency_task.remote(size)
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args = []
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for i in range(num_objects):
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arg = single_dependency.remote(i, arg)
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args.append(arg)
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# Get each value to force each task to finish. After some number of
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# gets, old values should be evicted.
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for i in range(num_objects):
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value = ray.get(args[i])
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assert value[0] == i
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# Get each value again to force reconstruction.
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for i in range(num_objects):
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value = ray.get(args[i])
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assert value[0] == i
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# Get 10 values randomly.
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for _ in range(10):
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i = np.random.randint(num_objects)
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value = ray.get(args[i])
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assert value[0] == i
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# Get values sequentially, in chunks.
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num_chunks = 4 * num_local_schedulers
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chunk = num_objects // num_chunks
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for i in range(num_chunks):
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values = ray.get(args[i * chunk:(i + 1) * chunk])
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del values
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@pytest.mark.skipif(
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os.environ.get("RAY_USE_XRAY") == "1",
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reason="This test does not work with xray yet.")
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@pytest.mark.skipif(
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os.environ.get("RAY_USE_NEW_GCS") == "on",
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reason="Failing with new GCS API on Linux.")
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def test_multiple_recursive(ray_start_reconstruction):
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_, _, plasma_store_memory, _ = ray_start_reconstruction
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# Define the size of one task's return argument so that the combined
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# sum of all objects' sizes is at least twice the plasma stores'
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# combined allotted memory.
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num_objects = 1000
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size = plasma_store_memory * 2 // (num_objects * 8)
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# Define a root task with no dependencies, which returns a numpy array
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# of the given size.
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@ray.remote
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def no_dependency_task(size):
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array = np.zeros(size)
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return array
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# Define a task with multiple dependencies, which returns its first
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# argument.
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@ray.remote
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def multiple_dependency(i, arg1, arg2, arg3):
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arg1 = np.copy(arg1)
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arg1[0] = i
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return arg1
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# Launch num_args instances of the root task. Then launch num_objects
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# instances of the multi-dependency remote task, each dependent on the
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# num_args tasks before it.
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num_args = 3
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args = []
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for i in range(num_args):
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arg = no_dependency_task.remote(size)
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args.append(arg)
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for i in range(num_objects):
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args.append(multiple_dependency.remote(i, *args[i:i + num_args]))
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# Get each value to force each task to finish. After some number of
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# gets, old values should be evicted.
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args = args[num_args:]
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for i in range(num_objects):
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value = ray.get(args[i])
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assert value[0] == i
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# Get each value again to force reconstruction.
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for i in range(num_objects):
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value = ray.get(args[i])
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assert value[0] == i
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# Get 10 values randomly.
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for _ in range(10):
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i = np.random.randint(num_objects)
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value = ray.get(args[i])
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assert value[0] == i
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def wait_for_errors(error_check):
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# Wait for errors from all the nondeterministic tasks.
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errors = []
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time_left = 100
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while time_left > 0:
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errors = ray.error_info()
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if error_check(errors):
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break
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time_left -= 1
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time.sleep(1)
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# Make sure that enough errors came through.
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assert error_check(errors)
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return errors
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@pytest.mark.skipif(
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os.environ.get("RAY_USE_XRAY") == "1",
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reason="This test does not work with xray yet.")
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@pytest.mark.skipif(
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os.environ.get("RAY_USE_NEW_GCS") == "on",
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reason="Failing with new GCS API on Linux.")
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def test_nondeterministic_task(ray_start_reconstruction):
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_, _, plasma_store_memory, num_local_schedulers = ray_start_reconstruction
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# Define the size of one task's return argument so that the combined
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# sum of all objects' sizes is at least twice the plasma stores'
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# combined allotted memory.
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num_objects = 1000
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size = plasma_store_memory * 2 // (num_objects * 8)
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# Define a nondeterministic remote task with no dependencies, which
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# returns a random numpy array of the given size. This task should
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# produce an error on the driver if it is ever reexecuted.
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@ray.remote
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def foo(i, size):
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array = np.random.rand(size)
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array[0] = i
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return array
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# Define a deterministic remote task with no dependencies, which
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# returns a numpy array of zeros of the given size.
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@ray.remote
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def bar(i, size):
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array = np.zeros(size)
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array[0] = i
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return array
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# Launch num_objects instances, half deterministic and half
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# nondeterministic.
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args = []
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for i in range(num_objects):
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if i % 2 == 0:
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args.append(foo.remote(i, size))
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else:
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args.append(bar.remote(i, size))
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# Get each value to force each task to finish. After some number of
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# gets, old values should be evicted.
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for i in range(num_objects):
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value = ray.get(args[i])
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assert value[0] == i
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# Get each value again to force reconstruction.
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for i in range(num_objects):
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value = ray.get(args[i])
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assert value[0] == i
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def error_check(errors):
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if num_local_schedulers == 1:
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# In a single-node setting, each object is evicted and
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# reconstructed exactly once, so exactly half the objects will
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# produce an error during reconstruction.
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min_errors = num_objects // 2
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else:
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# In a multinode setting, each object is evicted zero or one
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# times, so some of the nondeterministic tasks may not be
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# reexecuted.
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min_errors = 1
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return len(errors) >= min_errors
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errors = wait_for_errors(error_check)
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# Make sure all the errors have the correct type.
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assert all(error["type"] == ray_constants.HASH_MISMATCH_PUSH_ERROR
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for error in errors)
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@pytest.mark.skipif(
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os.environ.get("RAY_USE_XRAY") == "1",
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reason="This test does not work with xray yet.")
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@pytest.mark.skipif(
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os.environ.get("RAY_USE_NEW_GCS") == "on",
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reason="Failing with new GCS API on Linux.")
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def test_driver_put_errors(ray_start_reconstruction):
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_, _, plasma_store_memory, _ = ray_start_reconstruction
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# Define the size of one task's return argument so that the combined
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# sum of all objects' sizes is at least twice the plasma stores'
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# combined allotted memory.
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num_objects = 1000
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size = plasma_store_memory * 2 // (num_objects * 8)
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# Define a task with a single dependency, a numpy array, that returns
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# another array.
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@ray.remote
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def single_dependency(i, arg):
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arg = np.copy(arg)
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arg[0] = i
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return arg
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# Launch num_objects instances of the remote task, each dependent on
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# the one before it. The first instance of the task takes a numpy array
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# as an argument, which is put into the object store.
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args = []
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arg = single_dependency.remote(0, np.zeros(size))
|
|
for i in range(num_objects):
|
|
arg = single_dependency.remote(i, arg)
|
|
args.append(arg)
|
|
# Get each value to force each task to finish. After some number of
|
|
# gets, old values should be evicted.
|
|
for i in range(num_objects):
|
|
value = ray.get(args[i])
|
|
assert value[0] == i
|
|
|
|
# Get each value starting from the beginning to force reconstruction.
|
|
# Currently, since we're not able to reconstruct `ray.put` objects that
|
|
# were evicted and whose originating tasks are still running, this
|
|
# for-loop should hang on its first iteration and push an error to the
|
|
# driver.
|
|
ray.worker.global_worker.local_scheduler_client.reconstruct_objects(
|
|
[args[0]], False)
|
|
|
|
def error_check(errors):
|
|
return len(errors) > 1
|
|
|
|
errors = wait_for_errors(error_check)
|
|
assert all(error["type"] == ray_constants.PUT_RECONSTRUCTION_PUSH_ERROR
|
|
for error in errors)
|
|
|
|
|
|
# NOTE(swang): This test tries to launch 1000 workers and breaks.
|
|
# TODO(rkn): This test needs to be updated to use pytest.
|
|
# class WorkerPoolTests(unittest.TestCase):
|
|
#
|
|
# def tearDown(self):
|
|
# ray.shutdown()
|
|
#
|
|
# def testBlockingTasks(self):
|
|
# @ray.remote
|
|
# def f(i, j):
|
|
# return (i, j)
|
|
#
|
|
# @ray.remote
|
|
# def g(i):
|
|
# # Each instance of g submits and blocks on the result of another remote
|
|
# # task.
|
|
# object_ids = [f.remote(i, j) for j in range(10)]
|
|
# return ray.get(object_ids)
|
|
#
|
|
# ray.init(num_workers=1)
|
|
# ray.get([g.remote(i) for i in range(1000)])
|
|
# ray.shutdown()
|