mirror of
https://github.com/vale981/ray
synced 2025-03-08 19:41:38 -05:00
dfb6107b22
* attribute-based heterogeneity-awareness in global scheduler and photon * minor post-rebase fix * photon: enforce dynamic capacity constraint on task dispatch * globalsched: cap the number of times we try to schedule a task in round robin * propagating ability to specify resource capacity to ray.init * adding resources to remote function export and fetch/register * globalsched: remove unused functions; update cached photon resource capacity (until next photon heartbeat) * Add some integration tests. * globalsched: cleanup + factor out constraint checking * lots of style * task_spec_required_resource: global refactor * clang format * clang format + comment update in photon * clang format photon comment * valgrind * reduce verbosity for Travis * Add test for scheduler load balancing. * addressing comments * refactoring global scheduler algorithm * Minor cleanups. * Linting. * Fix array_test.py and linting. * valgrind fix for photon tests * Attempt to fix stress tests. * fix hashmap free * fix hashmap free comment * memset photon resource vectors to 0 in case they get used before the first heartbeat * More whitespace changes. * Undo whitespace error I introduced.
302 lines
9.3 KiB
Python
302 lines
9.3 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 unittest
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import ray
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import numpy as np
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import time
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import redis
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class TaskTests(unittest.TestCase):
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def testSubmittingTasks(self):
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for num_local_schedulers in [1, 4]:
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for num_workers_per_scheduler in [4]:
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num_workers = num_local_schedulers * num_workers_per_scheduler
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ray.worker._init(start_ray_local=True, num_workers=num_workers,
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num_local_schedulers=num_local_schedulers,
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num_cpus=100)
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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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self.assertTrue(ray.services.all_processes_alive())
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ray.worker.cleanup()
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def testDependencies(self):
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for num_local_schedulers in [1, 4]:
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for num_workers_per_scheduler in [4]:
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num_workers = num_local_schedulers * num_workers_per_scheduler
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ray.worker._init(start_ray_local=True, num_workers=num_workers,
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num_local_schedulers=num_local_schedulers,
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num_cpus=100)
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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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self.assertTrue(ray.services.all_processes_alive())
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ray.worker.cleanup()
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def testGettingAndPutting(self):
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ray.init(num_workers=1)
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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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self.assertTrue(ray.services.all_processes_alive())
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ray.worker.cleanup()
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def testGettingManyObjects(self):
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ray.init()
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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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l = ray.get([f.remote() for _ in range(n)])
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self.assertEqual(l, n * [1])
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ray.worker.cleanup()
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def testWait(self):
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for num_local_schedulers in [1, 4]:
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for num_workers_per_scheduler in [4]:
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num_workers = num_local_schedulers * num_workers_per_scheduler
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ray.worker._init(start_ray_local=True, num_workers=num_workers,
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num_local_schedulers=num_local_schedulers,
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num_cpus=100)
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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 = [g.remote(np.random.uniform(0, i)) for _ in range(2 * num_workers)]
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ray.wait(x_ids, num_returns=len(x_ids))
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self.assertTrue(ray.services.all_processes_alive())
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ray.worker.cleanup()
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class ReconstructionTests(unittest.TestCase):
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num_local_schedulers = 1
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def setUp(self):
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# Start a Redis instance and Plasma store instances with a total of 1GB
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# memory.
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node_ip_address = "127.0.0.1"
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self.redis_port = ray.services.new_port()
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print(self.redis_port)
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redis_address = ray.services.address(node_ip_address, self.redis_port)
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self.plasma_store_memory = 10 ** 9
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plasma_addresses = []
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objstore_memory = (self.plasma_store_memory // self.num_local_schedulers)
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for i in range(self.num_local_schedulers):
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plasma_addresses.append(
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ray.services.start_objstore(node_ip_address, redis_address,
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objstore_memory=objstore_memory)
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)
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address_info = {
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"redis_address": redis_address,
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"object_store_addresses": plasma_addresses,
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}
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# Start the rest of the services in the Ray cluster.
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ray.worker._init(address_info=address_info, start_ray_local=True,
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num_workers=self.num_local_schedulers,
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num_local_schedulers=self.num_local_schedulers,
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num_cpus=100)
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def tearDown(self):
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self.assertTrue(ray.services.all_processes_alive())
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# Make sure that all nodes in the cluster were used by checking where tasks
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# were scheduled and/or submitted from.
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r = redis.StrictRedis(port=self.redis_port)
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task_ids = r.keys("TT:*")
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task_ids = [task_id[3:] for task_id in task_ids]
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node_ids = [r.execute_command("ray.task_table_get", task_id)[1] for task_id
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in task_ids]
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self.assertEqual(len(set(node_ids)), self.num_local_schedulers)
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# Clean up the Ray cluster.
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ray.worker.cleanup()
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def testSimple(self):
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# Define the size of one task's return argument so that the combined sum of
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# all objects' sizes is at least twice the plasma stores' combined allotted
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# memory.
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num_objects = 1000
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size = self.plasma_store_memory * 2 // (num_objects * 8)
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# Define a remote task with no dependencies, which returns a numpy array of
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# 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 gets,
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# 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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self.assertEqual(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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self.assertEqual(value[0], i)
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def testRecursive(self):
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# Define the size of one task's return argument so that the combined sum of
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# all objects' sizes is at least twice the plasma stores' combined allotted
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# memory.
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num_objects = 1000
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size = self.plasma_store_memory * 2 // (num_objects * 8)
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# Define a root task with no dependencies, which returns a numpy array of
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# 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 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 the
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# 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 gets,
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# 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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self.assertEqual(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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self.assertEqual(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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self.assertEqual(value[0], i)
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def testMultipleRecursive(self):
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# Define the size of one task's return argument so that the combined sum of
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# all objects' sizes is at least twice the plasma stores' combined allotted
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# memory.
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num_objects = 1000
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size = self.plasma_store_memory * 2 // (num_objects * 8)
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# Define a root task with no dependencies, which returns a numpy array of
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# 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 gets,
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# 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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self.assertEqual(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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self.assertEqual(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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self.assertEqual(value[0], i)
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class ReconstructionTestsMultinode(ReconstructionTests):
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# Run the same tests as the single-node suite, but with 4 local schedulers,
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# one worker each.
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num_local_schedulers = 4
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if __name__ == "__main__":
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unittest.main(verbosity=2)
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