mirror of
https://github.com/vale981/ray
synced 2025-03-09 12:56:46 -04:00
2069 lines
70 KiB
Python
2069 lines
70 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 collections
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import random
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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 sys
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import time
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import unittest
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import ray
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import ray.ray_constants as ray_constants
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import ray.test.test_utils
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class ActorAPI(unittest.TestCase):
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def tearDown(self):
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ray.shutdown()
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def testKeywordArgs(self):
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ray.init(num_workers=0, driver_mode=ray.SILENT_MODE)
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@ray.remote
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class Actor(object):
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def __init__(self, arg0, arg1=1, arg2="a"):
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self.arg0 = arg0
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self.arg1 = arg1
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self.arg2 = arg2
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def get_values(self, arg0, arg1=2, arg2="b"):
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return self.arg0 + arg0, self.arg1 + arg1, self.arg2 + arg2
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actor = Actor.remote(0)
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assert ray.get(actor.get_values.remote(1)) == (1, 3, "ab")
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actor = Actor.remote(1, 2)
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assert ray.get(actor.get_values.remote(2, 3)) == (3, 5, "ab")
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actor = Actor.remote(1, 2, "c")
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assert ray.get(actor.get_values.remote(2, 3, "d")) == (3, 5, "cd")
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actor = Actor.remote(1, arg2="c")
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assert ray.get(actor.get_values.remote(0, arg2="d")) == (1, 3, "cd")
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assert ray.get(actor.get_values.remote(0, arg2="d", arg1=0)) == (1, 1,
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"cd")
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actor = Actor.remote(1, arg2="c", arg1=2)
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assert ray.get(actor.get_values.remote(0, arg2="d")) == (1, 4, "cd")
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assert ray.get(actor.get_values.remote(0, arg2="d", arg1=0)) == (1, 2,
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"cd")
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assert ray.get(actor.get_values.remote(arg2="d", arg1=0,
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arg0=2)) == (3, 2, "cd")
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# Make sure we get an exception if the constructor is called
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# incorrectly.
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with pytest.raises(Exception):
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actor = Actor.remote()
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with pytest.raises(Exception):
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actor = Actor.remote(0, 1, 2, arg3=3)
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with pytest.raises(Exception):
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actor = Actor.remote(0, arg0=1)
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# Make sure we get an exception if the method is called incorrectly.
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actor = Actor.remote(1)
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with pytest.raises(Exception):
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ray.get(actor.get_values.remote())
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def testVariableNumberOfArgs(self):
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ray.init(num_workers=0)
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@ray.remote
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class Actor(object):
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def __init__(self, arg0, arg1=1, *args):
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self.arg0 = arg0
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self.arg1 = arg1
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self.args = args
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def get_values(self, arg0, arg1=2, *args):
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return self.arg0 + arg0, self.arg1 + arg1, self.args, args
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actor = Actor.remote(0)
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assert ray.get(actor.get_values.remote(1)) == (1, 3, (), ())
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actor = Actor.remote(1, 2)
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assert ray.get(actor.get_values.remote(2, 3)) == (3, 5, (), ())
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actor = Actor.remote(1, 2, "c")
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assert ray.get(actor.get_values.remote(2, 3, "d")) == (3, 5, ("c", ),
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("d", ))
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actor = Actor.remote(1, 2, "a", "b", "c", "d")
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assert ray.get(actor.get_values.remote(
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2, 3, 1, 2, 3, 4)) == (3, 5, ("a", "b", "c", "d"), (1, 2, 3, 4))
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@ray.remote
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class Actor(object):
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def __init__(self, *args):
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self.args = args
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def get_values(self, *args):
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return self.args, args
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a = Actor.remote()
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assert ray.get(a.get_values.remote()) == ((), ())
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a = Actor.remote(1)
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assert ray.get(a.get_values.remote(2)) == ((1, ), (2, ))
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a = Actor.remote(1, 2)
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assert ray.get(a.get_values.remote(3, 4)) == ((1, 2), (3, 4))
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def testNoArgs(self):
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ray.init(num_workers=0)
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@ray.remote
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class Actor(object):
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def __init__(self):
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pass
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def get_values(self):
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pass
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actor = Actor.remote()
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assert ray.get(actor.get_values.remote()) is None
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def testNoConstructor(self):
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# If no __init__ method is provided, that should not be a problem.
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ray.init(num_workers=0)
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@ray.remote
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class Actor(object):
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def get_values(self):
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pass
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actor = Actor.remote()
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assert ray.get(actor.get_values.remote()) is None
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def testCustomClasses(self):
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ray.init(num_workers=0)
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class Foo(object):
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def __init__(self, x):
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self.x = x
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@ray.remote
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class Actor(object):
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def __init__(self, f2):
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self.f1 = Foo(1)
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self.f2 = f2
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def get_values1(self):
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return self.f1, self.f2
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def get_values2(self, f3):
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return self.f1, self.f2, f3
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actor = Actor.remote(Foo(2))
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results1 = ray.get(actor.get_values1.remote())
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assert results1[0].x == 1
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assert results1[1].x == 2
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results2 = ray.get(actor.get_values2.remote(Foo(3)))
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assert results2[0].x == 1
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assert results2[1].x == 2
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assert results2[2].x == 3
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def testCachingActors(self):
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# Test defining actors before ray.init() has been called.
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@ray.remote
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class Foo(object):
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def __init__(self):
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pass
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def get_val(self):
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return 3
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# Check that we can't actually create actors before ray.init() has been
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# called.
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with pytest.raises(Exception):
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f = Foo.remote()
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ray.init(num_workers=0)
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f = Foo.remote()
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assert ray.get(f.get_val.remote()) == 3
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def testDecoratorArgs(self):
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ray.init(num_workers=0, driver_mode=ray.SILENT_MODE)
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# This is an invalid way of using the actor decorator.
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with pytest.raises(Exception):
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@ray.remote()
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class Actor(object):
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def __init__(self):
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pass
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# This is an invalid way of using the actor decorator.
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with pytest.raises(Exception):
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@ray.remote(invalid_kwarg=0) # noqa: F811
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class Actor(object):
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def __init__(self):
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pass
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# This is an invalid way of using the actor decorator.
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with pytest.raises(Exception):
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@ray.remote(num_cpus=0, invalid_kwarg=0) # noqa: F811
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class Actor(object):
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def __init__(self):
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pass
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# This is a valid way of using the decorator.
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@ray.remote(num_cpus=1) # noqa: F811
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class Actor(object):
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def __init__(self):
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pass
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# This is a valid way of using the decorator.
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@ray.remote(num_gpus=1) # noqa: F811
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class Actor(object):
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def __init__(self):
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pass
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# This is a valid way of using the decorator.
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@ray.remote(num_cpus=1, num_gpus=1) # noqa: F811
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class Actor(object):
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def __init__(self):
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pass
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def testRandomIDGeneration(self):
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ray.init(num_workers=0)
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@ray.remote
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class Foo(object):
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def __init__(self):
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pass
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# Make sure that seeding numpy does not interfere with the generation
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# of actor IDs.
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np.random.seed(1234)
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random.seed(1234)
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f1 = Foo.remote()
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np.random.seed(1234)
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random.seed(1234)
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f2 = Foo.remote()
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assert f1._ray_actor_id.id() != f2._ray_actor_id.id()
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def testActorClassName(self):
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ray.init(num_workers=0)
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@ray.remote
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class Foo(object):
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def __init__(self):
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pass
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Foo.remote()
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r = ray.worker.global_worker.redis_client
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actor_keys = r.keys("ActorClass*")
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assert len(actor_keys) == 1
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actor_class_info = r.hgetall(actor_keys[0])
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assert actor_class_info[b"class_name"] == b"Foo"
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assert actor_class_info[b"module"] == b"actor_test"
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def testMultipleReturnValues(self):
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ray.init(num_workers=0)
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@ray.remote
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class Foo(object):
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def method0(self):
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return 1
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@ray.method(num_return_vals=1)
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def method1(self):
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return 1
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@ray.method(num_return_vals=2)
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def method2(self):
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return 1, 2
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@ray.method(num_return_vals=3)
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def method3(self):
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return 1, 2, 3
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f = Foo.remote()
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id0 = f.method0.remote()
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assert ray.get(id0) == 1
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id1 = f.method1.remote()
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assert ray.get(id1) == 1
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id2a, id2b = f.method2.remote()
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assert ray.get([id2a, id2b]) == [1, 2]
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id3a, id3b, id3c = f.method3.remote()
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assert ray.get([id3a, id3b, id3c]) == [1, 2, 3]
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class ActorMethods(unittest.TestCase):
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def tearDown(self):
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ray.shutdown()
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def testDefineActor(self):
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ray.init()
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@ray.remote
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class Test(object):
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def __init__(self, x):
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self.x = x
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def f(self, y):
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return self.x + y
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t = Test.remote(2)
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assert ray.get(t.f.remote(1)) == 3
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# Make sure that calling an actor method directly raises an exception.
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with pytest.raises(Exception):
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t.f(1)
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def testActorDeletion(self):
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ray.init(num_workers=0)
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# Make sure that when an actor handles goes out of scope, the actor
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# destructor is called.
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@ray.remote
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class Actor(object):
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def getpid(self):
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return os.getpid()
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a = Actor.remote()
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pid = ray.get(a.getpid.remote())
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a = None
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ray.test.test_utils.wait_for_pid_to_exit(pid)
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actors = [Actor.remote() for _ in range(10)]
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pids = ray.get([a.getpid.remote() for a in actors])
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a = None
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actors = None
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[ray.test.test_utils.wait_for_pid_to_exit(pid) for pid in pids]
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@ray.remote
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class Actor(object):
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def method(self):
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return 1
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# Make sure that if we create an actor and call a method on it
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# immediately, the actor doesn't get killed before the method is
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# called.
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assert ray.get(Actor.remote().method.remote()) == 1
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def testActorDeletionWithGPUs(self):
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ray.init(num_workers=0, num_gpus=1)
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# When an actor that uses a GPU exits, make sure that the GPU resources
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# are released.
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@ray.remote(num_gpus=1)
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class Actor(object):
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def getpid(self):
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return os.getpid()
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for _ in range(5):
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# If we can successfully create an actor, that means that enough
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# GPU resources are available.
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a = Actor.remote()
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ray.get(a.getpid.remote())
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def testActorState(self):
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ray.init()
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@ray.remote
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class Counter(object):
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def __init__(self):
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self.value = 0
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def increase(self):
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self.value += 1
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def value(self):
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return self.value
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c1 = Counter.remote()
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c1.increase.remote()
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assert ray.get(c1.value.remote()) == 1
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c2 = Counter.remote()
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c2.increase.remote()
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c2.increase.remote()
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assert ray.get(c2.value.remote()) == 2
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def testActorClassMethods(self):
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ray.init()
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class Foo(object):
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x = 2
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@classmethod
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def as_remote(cls):
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return ray.remote(cls)
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@classmethod
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def f(cls):
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return cls.x
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@classmethod
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def g(cls, y):
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return cls.x + y
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def echo(self, value):
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return value
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a = Foo.as_remote().remote()
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assert ray.get(a.echo.remote(2)) == 2
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assert ray.get(a.f.remote()) == 2
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assert ray.get(a.g.remote(2)) == 4
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def testMultipleActors(self):
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# Create a bunch of actors and call a bunch of methods on all of them.
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ray.init(num_workers=0)
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@ray.remote
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class Counter(object):
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def __init__(self, value):
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self.value = value
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def increase(self):
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self.value += 1
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return self.value
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def reset(self):
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self.value = 0
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num_actors = 20
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num_increases = 50
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# Create multiple actors.
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actors = [Counter.remote(i) for i in range(num_actors)]
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results = []
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# Call each actor's method a bunch of times.
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for i in range(num_actors):
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results += [
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actors[i].increase.remote() for _ in range(num_increases)
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]
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result_values = ray.get(results)
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for i in range(num_actors):
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v = result_values[(num_increases * i):(num_increases * (i + 1))]
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assert v == list(range(i + 1, num_increases + i + 1))
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# Reset the actor values.
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[actor.reset.remote() for actor in actors]
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# Interweave the method calls on the different actors.
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results = []
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for j in range(num_increases):
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results += [actor.increase.remote() for actor in actors]
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result_values = ray.get(results)
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for j in range(num_increases):
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v = result_values[(num_actors * j):(num_actors * (j + 1))]
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assert v == num_actors * [j + 1]
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class ActorNesting(unittest.TestCase):
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def tearDown(self):
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ray.shutdown()
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def testRemoteFunctionWithinActor(self):
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# Make sure we can use remote funtions within actors.
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ray.init(num_cpus=10)
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# Create some values to close over.
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val1 = 1
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val2 = 2
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@ray.remote
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def f(x):
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return val1 + x
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@ray.remote
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def g(x):
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return ray.get(f.remote(x))
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@ray.remote
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class Actor(object):
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def __init__(self, x):
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self.x = x
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self.y = val2
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self.object_ids = [f.remote(i) for i in range(5)]
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self.values2 = ray.get([f.remote(i) for i in range(5)])
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def get_values(self):
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return self.x, self.y, self.object_ids, self.values2
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def f(self):
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return [f.remote(i) for i in range(5)]
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def g(self):
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return ray.get([g.remote(i) for i in range(5)])
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def h(self, object_ids):
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return ray.get(object_ids)
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actor = Actor.remote(1)
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values = ray.get(actor.get_values.remote())
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assert values[0] == 1
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assert values[1] == val2
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assert ray.get(values[2]) == list(range(1, 6))
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assert values[3] == list(range(1, 6))
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assert ray.get(ray.get(actor.f.remote())) == list(range(1, 6))
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assert ray.get(actor.g.remote()) == list(range(1, 6))
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assert ray.get(actor.h.remote(
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[f.remote(i) for i in range(5)])) == list(range(1, 6))
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def testDefineActorWithinActor(self):
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# Make sure we can use remote funtions within actors.
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ray.init(num_cpus=10)
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@ray.remote
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class Actor1(object):
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def __init__(self, x):
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self.x = x
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def new_actor(self, z):
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@ray.remote
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class Actor2(object):
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def __init__(self, x):
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self.x = x
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def get_value(self):
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return self.x
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self.actor2 = Actor2.remote(z)
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def get_values(self, z):
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self.new_actor(z)
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return self.x, ray.get(self.actor2.get_value.remote())
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actor1 = Actor1.remote(3)
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assert ray.get(actor1.get_values.remote(5)) == (3, 5)
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def testUseActorWithinActor(self):
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# Make sure we can use actors within actors.
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|
ray.init(num_cpus=10)
|
|
|
|
@ray.remote
|
|
class Actor1(object):
|
|
def __init__(self, x):
|
|
self.x = x
|
|
|
|
def get_val(self):
|
|
return self.x
|
|
|
|
@ray.remote
|
|
class Actor2(object):
|
|
def __init__(self, x, y):
|
|
self.x = x
|
|
self.actor1 = Actor1.remote(y)
|
|
|
|
def get_values(self, z):
|
|
return self.x, ray.get(self.actor1.get_val.remote())
|
|
|
|
actor2 = Actor2.remote(3, 4)
|
|
assert ray.get(actor2.get_values.remote(5)) == (3, 4)
|
|
|
|
def testDefineActorWithinRemoteFunction(self):
|
|
# Make sure we can define and actors within remote funtions.
|
|
ray.init(num_cpus=10)
|
|
|
|
@ray.remote
|
|
def f(x, n):
|
|
@ray.remote
|
|
class Actor1(object):
|
|
def __init__(self, x):
|
|
self.x = x
|
|
|
|
def get_value(self):
|
|
return self.x
|
|
|
|
actor = Actor1.remote(x)
|
|
return ray.get([actor.get_value.remote() for _ in range(n)])
|
|
|
|
assert ray.get(f.remote(3, 1)) == [3]
|
|
assert ray.get([f.remote(i, 20)
|
|
for i in range(10)]) == [20 * [i] for i in range(10)]
|
|
|
|
def testUseActorWithinRemoteFunction(self):
|
|
# Make sure we can create and use actors within remote funtions.
|
|
ray.init(num_cpus=10)
|
|
|
|
@ray.remote
|
|
class Actor1(object):
|
|
def __init__(self, x):
|
|
self.x = x
|
|
|
|
def get_values(self):
|
|
return self.x
|
|
|
|
@ray.remote
|
|
def f(x):
|
|
actor = Actor1.remote(x)
|
|
return ray.get(actor.get_values.remote())
|
|
|
|
assert ray.get(f.remote(3)) == 3
|
|
|
|
def testActorImportCounter(self):
|
|
# This is mostly a test of the export counters to make sure that when
|
|
# an actor is imported, all of the necessary remote functions have been
|
|
# imported.
|
|
ray.init(num_cpus=10)
|
|
|
|
# Export a bunch of remote functions.
|
|
num_remote_functions = 50
|
|
for i in range(num_remote_functions):
|
|
|
|
@ray.remote
|
|
def f():
|
|
return i
|
|
|
|
@ray.remote
|
|
def g():
|
|
@ray.remote
|
|
class Actor(object):
|
|
def __init__(self):
|
|
# This should use the last version of f.
|
|
self.x = ray.get(f.remote())
|
|
|
|
def get_val(self):
|
|
return self.x
|
|
|
|
actor = Actor.remote()
|
|
return ray.get(actor.get_val.remote())
|
|
|
|
assert ray.get(g.remote()) == num_remote_functions - 1
|
|
|
|
|
|
class ActorInheritance(unittest.TestCase):
|
|
def tearDown(self):
|
|
ray.shutdown()
|
|
|
|
def testInheritActorFromClass(self):
|
|
# Make sure we can define an actor by inheriting from a regular class.
|
|
# Note that actors cannot inherit from other actors.
|
|
ray.init()
|
|
|
|
class Foo(object):
|
|
def __init__(self, x):
|
|
self.x = x
|
|
|
|
def f(self):
|
|
return self.x
|
|
|
|
def g(self, y):
|
|
return self.x + y
|
|
|
|
@ray.remote
|
|
class Actor(Foo):
|
|
def __init__(self, x):
|
|
Foo.__init__(self, x)
|
|
|
|
def get_value(self):
|
|
return self.f()
|
|
|
|
actor = Actor.remote(1)
|
|
assert ray.get(actor.get_value.remote()) == 1
|
|
assert ray.get(actor.g.remote(5)) == 6
|
|
|
|
|
|
class ActorSchedulingProperties(unittest.TestCase):
|
|
def tearDown(self):
|
|
ray.shutdown()
|
|
|
|
def testRemoteFunctionsNotScheduledOnActors(self):
|
|
# Make sure that regular remote functions are not scheduled on actors.
|
|
ray.init(num_workers=0)
|
|
|
|
@ray.remote
|
|
class Actor(object):
|
|
def __init__(self):
|
|
pass
|
|
|
|
def get_id(self):
|
|
return ray.worker.global_worker.worker_id
|
|
|
|
a = Actor.remote()
|
|
actor_id = ray.get(a.get_id.remote())
|
|
|
|
@ray.remote
|
|
def f():
|
|
return ray.worker.global_worker.worker_id
|
|
|
|
resulting_ids = ray.get([f.remote() for _ in range(100)])
|
|
assert actor_id not in resulting_ids
|
|
|
|
|
|
class ActorsOnMultipleNodes(unittest.TestCase):
|
|
def tearDown(self):
|
|
ray.shutdown()
|
|
|
|
def testActorsOnNodesWithNoCPUs(self):
|
|
ray.init(num_cpus=0)
|
|
|
|
@ray.remote
|
|
class Foo(object):
|
|
def method(self):
|
|
pass
|
|
|
|
f = Foo.remote()
|
|
ready_ids, _ = ray.wait([f.method.remote()], timeout=100)
|
|
assert ready_ids == []
|
|
|
|
def testActorLoadBalancing(self):
|
|
num_local_schedulers = 3
|
|
ray.worker._init(
|
|
start_ray_local=True,
|
|
num_workers=0,
|
|
num_local_schedulers=num_local_schedulers)
|
|
|
|
@ray.remote
|
|
class Actor1(object):
|
|
def __init__(self):
|
|
pass
|
|
|
|
def get_location(self):
|
|
return ray.worker.global_worker.plasma_client.store_socket_name
|
|
|
|
# Create a bunch of actors.
|
|
num_actors = 30
|
|
num_attempts = 20
|
|
minimum_count = 5
|
|
|
|
# Make sure that actors are spread between the local schedulers.
|
|
attempts = 0
|
|
while attempts < num_attempts:
|
|
actors = [Actor1.remote() for _ in range(num_actors)]
|
|
locations = ray.get(
|
|
[actor.get_location.remote() for actor in actors])
|
|
names = set(locations)
|
|
counts = [locations.count(name) for name in names]
|
|
print("Counts are {}.".format(counts))
|
|
if (len(names) == num_local_schedulers
|
|
and all(count >= minimum_count for count in counts)):
|
|
break
|
|
attempts += 1
|
|
assert attempts < num_attempts
|
|
|
|
# Make sure we can get the results of a bunch of tasks.
|
|
results = []
|
|
for _ in range(1000):
|
|
index = np.random.randint(num_actors)
|
|
results.append(actors[index].get_location.remote())
|
|
ray.get(results)
|
|
|
|
|
|
class ActorsWithGPUs(unittest.TestCase):
|
|
def tearDown(self):
|
|
ray.shutdown()
|
|
|
|
@unittest.skipIf(
|
|
os.environ.get('RAY_USE_NEW_GCS', False), "Crashing with new GCS API.")
|
|
@unittest.skipIf(
|
|
os.environ.get("RAY_USE_XRAY") == "1",
|
|
"This test does not work with xray yet.")
|
|
def testActorGPUs(self):
|
|
num_local_schedulers = 3
|
|
num_gpus_per_scheduler = 4
|
|
ray.worker._init(
|
|
start_ray_local=True,
|
|
num_workers=0,
|
|
num_local_schedulers=num_local_schedulers,
|
|
num_cpus=(num_local_schedulers * [10 * num_gpus_per_scheduler]),
|
|
num_gpus=(num_local_schedulers * [num_gpus_per_scheduler]))
|
|
|
|
@ray.remote(num_gpus=1)
|
|
class Actor1(object):
|
|
def __init__(self):
|
|
self.gpu_ids = ray.get_gpu_ids()
|
|
|
|
def get_location_and_ids(self):
|
|
assert ray.get_gpu_ids() == self.gpu_ids
|
|
return (
|
|
ray.worker.global_worker.plasma_client.store_socket_name,
|
|
tuple(self.gpu_ids))
|
|
|
|
# Create one actor per GPU.
|
|
actors = [
|
|
Actor1.remote()
|
|
for _ in range(num_local_schedulers * num_gpus_per_scheduler)
|
|
]
|
|
# Make sure that no two actors are assigned to the same GPU.
|
|
locations_and_ids = ray.get(
|
|
[actor.get_location_and_ids.remote() for actor in actors])
|
|
node_names = {location for location, gpu_id in locations_and_ids}
|
|
assert len(node_names) == num_local_schedulers
|
|
location_actor_combinations = []
|
|
for node_name in node_names:
|
|
for gpu_id in range(num_gpus_per_scheduler):
|
|
location_actor_combinations.append((node_name, (gpu_id, )))
|
|
assert set(locations_and_ids) == set(location_actor_combinations)
|
|
|
|
# Creating a new actor should fail because all of the GPUs are being
|
|
# used.
|
|
a = Actor1.remote()
|
|
ready_ids, _ = ray.wait([a.get_location_and_ids.remote()], timeout=10)
|
|
assert ready_ids == []
|
|
|
|
@unittest.skipIf(
|
|
os.environ.get("RAY_USE_XRAY") == "1",
|
|
"This test does not work with xray yet.")
|
|
def testActorMultipleGPUs(self):
|
|
num_local_schedulers = 3
|
|
num_gpus_per_scheduler = 5
|
|
ray.worker._init(
|
|
start_ray_local=True,
|
|
num_workers=0,
|
|
num_local_schedulers=num_local_schedulers,
|
|
num_cpus=(num_local_schedulers * [10 * num_gpus_per_scheduler]),
|
|
num_gpus=(num_local_schedulers * [num_gpus_per_scheduler]))
|
|
|
|
@ray.remote(num_gpus=2)
|
|
class Actor1(object):
|
|
def __init__(self):
|
|
self.gpu_ids = ray.get_gpu_ids()
|
|
|
|
def get_location_and_ids(self):
|
|
assert ray.get_gpu_ids() == self.gpu_ids
|
|
return (
|
|
ray.worker.global_worker.plasma_client.store_socket_name,
|
|
tuple(self.gpu_ids))
|
|
|
|
# Create some actors.
|
|
actors1 = [Actor1.remote() for _ in range(num_local_schedulers * 2)]
|
|
# Make sure that no two actors are assigned to the same GPU.
|
|
locations_and_ids = ray.get(
|
|
[actor.get_location_and_ids.remote() for actor in actors1])
|
|
node_names = {location for location, gpu_id in locations_and_ids}
|
|
assert len(node_names) == num_local_schedulers
|
|
|
|
# Keep track of which GPU IDs are being used for each location.
|
|
gpus_in_use = {node_name: [] for node_name in node_names}
|
|
for location, gpu_ids in locations_and_ids:
|
|
gpus_in_use[location].extend(gpu_ids)
|
|
for node_name in node_names:
|
|
assert len(set(gpus_in_use[node_name])) == 4
|
|
|
|
# Creating a new actor should fail because all of the GPUs are being
|
|
# used.
|
|
a = Actor1.remote()
|
|
ready_ids, _ = ray.wait([a.get_location_and_ids.remote()], timeout=10)
|
|
assert ready_ids == []
|
|
|
|
# We should be able to create more actors that use only a single GPU.
|
|
@ray.remote(num_gpus=1)
|
|
class Actor2(object):
|
|
def __init__(self):
|
|
self.gpu_ids = ray.get_gpu_ids()
|
|
|
|
def get_location_and_ids(self):
|
|
return (
|
|
ray.worker.global_worker.plasma_client.store_socket_name,
|
|
tuple(self.gpu_ids))
|
|
|
|
# Create some actors.
|
|
actors2 = [Actor2.remote() for _ in range(num_local_schedulers)]
|
|
# Make sure that no two actors are assigned to the same GPU.
|
|
locations_and_ids = ray.get(
|
|
[actor.get_location_and_ids.remote() for actor in actors2])
|
|
names = {location for location, gpu_id in locations_and_ids}
|
|
assert node_names == names
|
|
for location, gpu_ids in locations_and_ids:
|
|
gpus_in_use[location].extend(gpu_ids)
|
|
for node_name in node_names:
|
|
assert len(gpus_in_use[node_name]) == 5
|
|
assert set(gpus_in_use[node_name]) == set(range(5))
|
|
|
|
# Creating a new actor should fail because all of the GPUs are being
|
|
# used.
|
|
a = Actor2.remote()
|
|
ready_ids, _ = ray.wait([a.get_location_and_ids.remote()], timeout=10)
|
|
assert ready_ids == []
|
|
|
|
@unittest.skipIf(
|
|
os.environ.get("RAY_USE_XRAY") == "1",
|
|
"This test does not work with xray yet.")
|
|
def testActorDifferentNumbersOfGPUs(self):
|
|
# Test that we can create actors on two nodes that have different
|
|
# numbers of GPUs.
|
|
ray.worker._init(
|
|
start_ray_local=True,
|
|
num_workers=0,
|
|
num_local_schedulers=3,
|
|
num_cpus=[10, 10, 10],
|
|
num_gpus=[0, 5, 10])
|
|
|
|
@ray.remote(num_gpus=1)
|
|
class Actor1(object):
|
|
def __init__(self):
|
|
self.gpu_ids = ray.get_gpu_ids()
|
|
|
|
def get_location_and_ids(self):
|
|
return (
|
|
ray.worker.global_worker.plasma_client.store_socket_name,
|
|
tuple(self.gpu_ids))
|
|
|
|
# Create some actors.
|
|
actors = [Actor1.remote() for _ in range(0 + 5 + 10)]
|
|
# Make sure that no two actors are assigned to the same GPU.
|
|
locations_and_ids = ray.get(
|
|
[actor.get_location_and_ids.remote() for actor in actors])
|
|
node_names = {location for location, gpu_id in locations_and_ids}
|
|
assert len(node_names) == 2
|
|
for node_name in node_names:
|
|
node_gpu_ids = [
|
|
gpu_id for location, gpu_id in locations_and_ids
|
|
if location == node_name
|
|
]
|
|
assert len(node_gpu_ids) in [5, 10]
|
|
assert set(node_gpu_ids) == {(i, )
|
|
for i in range(len(node_gpu_ids))}
|
|
|
|
# Creating a new actor should fail because all of the GPUs are being
|
|
# used.
|
|
a = Actor1.remote()
|
|
ready_ids, _ = ray.wait([a.get_location_and_ids.remote()], timeout=10)
|
|
assert ready_ids == []
|
|
|
|
@unittest.skipIf(
|
|
os.environ.get("RAY_USE_XRAY") == "1",
|
|
"This test does not work with xray yet.")
|
|
def testActorMultipleGPUsFromMultipleTasks(self):
|
|
num_local_schedulers = 10
|
|
num_gpus_per_scheduler = 10
|
|
ray.worker._init(
|
|
start_ray_local=True,
|
|
num_workers=0,
|
|
num_local_schedulers=num_local_schedulers,
|
|
redirect_output=True,
|
|
num_cpus=(num_local_schedulers * [10 * num_gpus_per_scheduler]),
|
|
num_gpus=(num_local_schedulers * [num_gpus_per_scheduler]))
|
|
|
|
@ray.remote
|
|
def create_actors(n):
|
|
@ray.remote(num_gpus=1)
|
|
class Actor(object):
|
|
def __init__(self):
|
|
self.gpu_ids = ray.get_gpu_ids()
|
|
|
|
def get_location_and_ids(self):
|
|
return ((ray.worker.global_worker.plasma_client.
|
|
store_socket_name), tuple(self.gpu_ids))
|
|
|
|
# Create n actors.
|
|
for _ in range(n):
|
|
Actor.remote()
|
|
|
|
ray.get([
|
|
create_actors.remote(num_gpus_per_scheduler)
|
|
for _ in range(num_local_schedulers)
|
|
])
|
|
|
|
@ray.remote(num_gpus=1)
|
|
class Actor(object):
|
|
def __init__(self):
|
|
self.gpu_ids = ray.get_gpu_ids()
|
|
|
|
def get_location_and_ids(self):
|
|
return (
|
|
ray.worker.global_worker.plasma_client.store_socket_name,
|
|
tuple(self.gpu_ids))
|
|
|
|
# All the GPUs should be used up now.
|
|
a = Actor.remote()
|
|
ready_ids, _ = ray.wait([a.get_location_and_ids.remote()], timeout=10)
|
|
assert ready_ids == []
|
|
|
|
@unittest.skipIf(sys.version_info < (3, 0), "This test requires Python 3.")
|
|
@unittest.skipIf(
|
|
os.environ.get("RAY_USE_XRAY") == "1",
|
|
"This test does not work with xray yet.")
|
|
def testActorsAndTasksWithGPUs(self):
|
|
num_local_schedulers = 3
|
|
num_gpus_per_scheduler = 6
|
|
ray.worker._init(
|
|
start_ray_local=True,
|
|
num_workers=0,
|
|
num_local_schedulers=num_local_schedulers,
|
|
num_cpus=num_gpus_per_scheduler,
|
|
num_gpus=(num_local_schedulers * [num_gpus_per_scheduler]))
|
|
|
|
def check_intervals_non_overlapping(list_of_intervals):
|
|
for i in range(len(list_of_intervals)):
|
|
for j in range(i):
|
|
first_interval = list_of_intervals[i]
|
|
second_interval = list_of_intervals[j]
|
|
# Check that list_of_intervals[i] and list_of_intervals[j]
|
|
# don't overlap.
|
|
assert first_interval[0] < first_interval[1]
|
|
assert second_interval[0] < second_interval[1]
|
|
intervals_nonoverlapping = (
|
|
first_interval[1] <= second_interval[0]
|
|
or second_interval[1] <= first_interval[0])
|
|
assert intervals_nonoverlapping, (
|
|
"Intervals {} and {} are overlapping.".format(
|
|
first_interval, second_interval))
|
|
|
|
@ray.remote(num_gpus=1)
|
|
def f1():
|
|
t1 = time.monotonic()
|
|
time.sleep(0.1)
|
|
t2 = time.monotonic()
|
|
gpu_ids = ray.get_gpu_ids()
|
|
assert len(gpu_ids) == 1
|
|
assert gpu_ids[0] in range(num_gpus_per_scheduler)
|
|
return (ray.worker.global_worker.plasma_client.store_socket_name,
|
|
tuple(gpu_ids), [t1, t2])
|
|
|
|
@ray.remote(num_gpus=2)
|
|
def f2():
|
|
t1 = time.monotonic()
|
|
time.sleep(0.1)
|
|
t2 = time.monotonic()
|
|
gpu_ids = ray.get_gpu_ids()
|
|
assert len(gpu_ids) == 2
|
|
assert gpu_ids[0] in range(num_gpus_per_scheduler)
|
|
assert gpu_ids[1] in range(num_gpus_per_scheduler)
|
|
return (ray.worker.global_worker.plasma_client.store_socket_name,
|
|
tuple(gpu_ids), [t1, t2])
|
|
|
|
@ray.remote(num_gpus=1)
|
|
class Actor1(object):
|
|
def __init__(self):
|
|
self.gpu_ids = ray.get_gpu_ids()
|
|
assert len(self.gpu_ids) == 1
|
|
assert self.gpu_ids[0] in range(num_gpus_per_scheduler)
|
|
|
|
def get_location_and_ids(self):
|
|
assert ray.get_gpu_ids() == self.gpu_ids
|
|
return (
|
|
ray.worker.global_worker.plasma_client.store_socket_name,
|
|
tuple(self.gpu_ids))
|
|
|
|
def locations_to_intervals_for_many_tasks():
|
|
# Launch a bunch of GPU tasks.
|
|
locations_ids_and_intervals = ray.get([
|
|
f1.remote() for _ in range(5 * num_local_schedulers *
|
|
num_gpus_per_scheduler)
|
|
] + [
|
|
f2.remote() for _ in range(5 * num_local_schedulers *
|
|
num_gpus_per_scheduler)
|
|
] + [
|
|
f1.remote() for _ in range(5 * num_local_schedulers *
|
|
num_gpus_per_scheduler)
|
|
])
|
|
|
|
locations_to_intervals = collections.defaultdict(lambda: [])
|
|
for location, gpu_ids, interval in locations_ids_and_intervals:
|
|
for gpu_id in gpu_ids:
|
|
locations_to_intervals[(location, gpu_id)].append(interval)
|
|
return locations_to_intervals
|
|
|
|
# Run a bunch of GPU tasks.
|
|
locations_to_intervals = locations_to_intervals_for_many_tasks()
|
|
# Make sure that all GPUs were used.
|
|
assert (len(locations_to_intervals) == num_local_schedulers *
|
|
num_gpus_per_scheduler)
|
|
# For each GPU, verify that the set of tasks that used this specific
|
|
# GPU did not overlap in time.
|
|
for locations in locations_to_intervals:
|
|
check_intervals_non_overlapping(locations_to_intervals[locations])
|
|
|
|
# Create an actor that uses a GPU.
|
|
a = Actor1.remote()
|
|
actor_location = ray.get(a.get_location_and_ids.remote())
|
|
actor_location = (actor_location[0], actor_location[1][0])
|
|
# This check makes sure that actor_location is formatted the same way
|
|
# that the keys of locations_to_intervals are formatted.
|
|
assert actor_location in locations_to_intervals
|
|
|
|
# Run a bunch of GPU tasks.
|
|
locations_to_intervals = locations_to_intervals_for_many_tasks()
|
|
# Make sure that all but one of the GPUs were used.
|
|
assert (len(locations_to_intervals) ==
|
|
num_local_schedulers * num_gpus_per_scheduler - 1)
|
|
# For each GPU, verify that the set of tasks that used this specific
|
|
# GPU did not overlap in time.
|
|
for locations in locations_to_intervals:
|
|
check_intervals_non_overlapping(locations_to_intervals[locations])
|
|
# Make sure that the actor's GPU was not used.
|
|
assert actor_location not in locations_to_intervals
|
|
|
|
# Create several more actors that use GPUs.
|
|
actors = [Actor1.remote() for _ in range(3)]
|
|
actor_locations = ray.get(
|
|
[actor.get_location_and_ids.remote() for actor in actors])
|
|
|
|
# Run a bunch of GPU tasks.
|
|
locations_to_intervals = locations_to_intervals_for_many_tasks()
|
|
# Make sure that all but 11 of the GPUs were used.
|
|
assert (len(locations_to_intervals) ==
|
|
num_local_schedulers * num_gpus_per_scheduler - 1 - 3)
|
|
# For each GPU, verify that the set of tasks that used this specific
|
|
# GPU did not overlap in time.
|
|
for locations in locations_to_intervals:
|
|
check_intervals_non_overlapping(locations_to_intervals[locations])
|
|
# Make sure that the GPUs were not used.
|
|
assert actor_location not in locations_to_intervals
|
|
for location in actor_locations:
|
|
assert location not in locations_to_intervals
|
|
|
|
# Create more actors to fill up all the GPUs.
|
|
more_actors = [
|
|
Actor1.remote()
|
|
for _ in range(num_local_schedulers * num_gpus_per_scheduler - 1 -
|
|
3)
|
|
]
|
|
# Wait for the actors to finish being created.
|
|
ray.get([actor.get_location_and_ids.remote() for actor in more_actors])
|
|
|
|
# Now if we run some GPU tasks, they should not be scheduled.
|
|
results = [f1.remote() for _ in range(30)]
|
|
ready_ids, remaining_ids = ray.wait(results, timeout=1000)
|
|
assert len(ready_ids) == 0
|
|
|
|
def testActorsAndTasksWithGPUsVersionTwo(self):
|
|
# Create tasks and actors that both use GPUs and make sure that they
|
|
# are given different GPUs
|
|
ray.init(num_cpus=10, num_gpus=10)
|
|
|
|
@ray.remote(num_gpus=1)
|
|
def f():
|
|
time.sleep(4)
|
|
gpu_ids = ray.get_gpu_ids()
|
|
assert len(gpu_ids) == 1
|
|
return gpu_ids[0]
|
|
|
|
@ray.remote(num_gpus=1)
|
|
class Actor(object):
|
|
def __init__(self):
|
|
self.gpu_ids = ray.get_gpu_ids()
|
|
assert len(self.gpu_ids) == 1
|
|
|
|
def get_gpu_id(self):
|
|
assert ray.get_gpu_ids() == self.gpu_ids
|
|
return self.gpu_ids[0]
|
|
|
|
results = []
|
|
actors = []
|
|
for _ in range(5):
|
|
results.append(f.remote())
|
|
a = Actor.remote()
|
|
results.append(a.get_gpu_id.remote())
|
|
# Prevent the actor handle from going out of scope so that its GPU
|
|
# resources don't get released.
|
|
actors.append(a)
|
|
|
|
gpu_ids = ray.get(results)
|
|
assert set(gpu_ids) == set(range(10))
|
|
|
|
@unittest.skipIf(sys.version_info < (3, 0), "This test requires Python 3.")
|
|
def testActorsAndTaskResourceBookkeeping(self):
|
|
ray.init(num_cpus=1)
|
|
|
|
@ray.remote
|
|
class Foo(object):
|
|
def __init__(self):
|
|
start = time.monotonic()
|
|
time.sleep(0.1)
|
|
end = time.monotonic()
|
|
self.interval = (start, end)
|
|
|
|
def get_interval(self):
|
|
return self.interval
|
|
|
|
def sleep(self):
|
|
start = time.monotonic()
|
|
time.sleep(0.01)
|
|
end = time.monotonic()
|
|
return start, end
|
|
|
|
# First make sure that we do not have more actor methods running at a
|
|
# time than we have CPUs.
|
|
actors = [Foo.remote() for _ in range(4)]
|
|
interval_ids = []
|
|
interval_ids += [actor.get_interval.remote() for actor in actors]
|
|
for _ in range(4):
|
|
interval_ids += [actor.sleep.remote() for actor in actors]
|
|
|
|
# Make sure that the intervals don't overlap.
|
|
intervals = ray.get(interval_ids)
|
|
intervals.sort(key=lambda x: x[0])
|
|
for interval1, interval2 in zip(intervals[:-1], intervals[1:]):
|
|
assert interval1[0] < interval1[1]
|
|
assert interval1[1] < interval2[0]
|
|
assert interval2[0] < interval2[1]
|
|
|
|
def testBlockingActorTask(self):
|
|
ray.init(num_cpus=1, num_gpus=1)
|
|
|
|
@ray.remote(num_gpus=1)
|
|
def f():
|
|
return 1
|
|
|
|
@ray.remote
|
|
class Foo(object):
|
|
def __init__(self):
|
|
pass
|
|
|
|
def blocking_method(self):
|
|
ray.get(f.remote())
|
|
|
|
# Make sure we can execute a blocking actor method even if there is
|
|
# only one CPU.
|
|
actor = Foo.remote()
|
|
ray.get(actor.blocking_method.remote())
|
|
|
|
@ray.remote(num_cpus=1)
|
|
class CPUFoo(object):
|
|
def __init__(self):
|
|
pass
|
|
|
|
def blocking_method(self):
|
|
ray.get(f.remote())
|
|
|
|
# Make sure that lifetime CPU resources are not released when actors
|
|
# block.
|
|
actor = CPUFoo.remote()
|
|
x_id = actor.blocking_method.remote()
|
|
ready_ids, remaining_ids = ray.wait([x_id], timeout=1000)
|
|
assert ready_ids == []
|
|
assert remaining_ids == [x_id]
|
|
|
|
@ray.remote(num_gpus=1)
|
|
class GPUFoo(object):
|
|
def __init__(self):
|
|
pass
|
|
|
|
def blocking_method(self):
|
|
ray.get(f.remote())
|
|
|
|
# Make sure that GPU resources are not released when actors block.
|
|
actor = GPUFoo.remote()
|
|
x_id = actor.blocking_method.remote()
|
|
ready_ids, remaining_ids = ray.wait([x_id], timeout=1000)
|
|
assert ready_ids == []
|
|
assert remaining_ids == [x_id]
|
|
|
|
|
|
@unittest.skipIf(
|
|
os.environ.get("RAY_USE_XRAY") == "1",
|
|
"This test does not work with xray yet.")
|
|
class ActorReconstruction(unittest.TestCase):
|
|
def tearDown(self):
|
|
ray.shutdown()
|
|
|
|
@unittest.skipIf(
|
|
os.environ.get('RAY_USE_NEW_GCS', False), "Hanging with new GCS API.")
|
|
def testLocalSchedulerDying(self):
|
|
ray.worker._init(
|
|
start_ray_local=True,
|
|
num_local_schedulers=2,
|
|
num_workers=0,
|
|
redirect_output=True)
|
|
|
|
@ray.remote
|
|
class Counter(object):
|
|
def __init__(self):
|
|
self.x = 0
|
|
|
|
def local_plasma(self):
|
|
return ray.worker.global_worker.plasma_client.store_socket_name
|
|
|
|
def inc(self):
|
|
self.x += 1
|
|
return self.x
|
|
|
|
local_plasma = ray.worker.global_worker.plasma_client.store_socket_name
|
|
|
|
# Create an actor that is not on the local scheduler.
|
|
actor = Counter.remote()
|
|
while ray.get(actor.local_plasma.remote()) == local_plasma:
|
|
actor = Counter.remote()
|
|
|
|
ids = [actor.inc.remote() for _ in range(100)]
|
|
|
|
# Wait for the last task to finish running.
|
|
ray.get(ids[-1])
|
|
|
|
# Kill the second plasma store to get rid of the cached objects and
|
|
# trigger the corresponding local scheduler to exit.
|
|
process = ray.services.all_processes[
|
|
ray.services.PROCESS_TYPE_PLASMA_STORE][1]
|
|
process.kill()
|
|
process.wait()
|
|
|
|
# Get all of the results
|
|
results = ray.get(ids)
|
|
|
|
assert results == list(range(1, 1 + len(results)))
|
|
|
|
@unittest.skipIf(
|
|
os.environ.get('RAY_USE_NEW_GCS', False), "Hanging with new GCS API.")
|
|
def testManyLocalSchedulersDying(self):
|
|
# This test can be made more stressful by increasing the numbers below.
|
|
# The total number of actors created will be
|
|
# num_actors_at_a_time * num_local_schedulers.
|
|
num_local_schedulers = 5
|
|
num_actors_at_a_time = 3
|
|
num_function_calls_at_a_time = 10
|
|
|
|
ray.worker._init(
|
|
start_ray_local=True,
|
|
num_local_schedulers=num_local_schedulers,
|
|
num_workers=0,
|
|
redirect_output=True)
|
|
|
|
@ray.remote
|
|
class SlowCounter(object):
|
|
def __init__(self):
|
|
self.x = 0
|
|
|
|
def inc(self, duration):
|
|
time.sleep(duration)
|
|
self.x += 1
|
|
return self.x
|
|
|
|
# Create some initial actors.
|
|
actors = [SlowCounter.remote() for _ in range(num_actors_at_a_time)]
|
|
|
|
# Wait for the actors to start up.
|
|
time.sleep(1)
|
|
|
|
# This is a mapping from actor handles to object IDs returned by
|
|
# methods on that actor.
|
|
result_ids = collections.defaultdict(lambda: [])
|
|
|
|
# In a loop we are going to create some actors, run some methods, kill
|
|
# a local scheduler, and run some more methods.
|
|
for i in range(num_local_schedulers - 1):
|
|
# Create some actors.
|
|
actors.extend(
|
|
[SlowCounter.remote() for _ in range(num_actors_at_a_time)])
|
|
# Run some methods.
|
|
for j in range(len(actors)):
|
|
actor = actors[j]
|
|
for _ in range(num_function_calls_at_a_time):
|
|
result_ids[actor].append(actor.inc.remote(j**2 * 0.000001))
|
|
# Kill a plasma store to get rid of the cached objects and trigger
|
|
# exit of the corresponding local scheduler. Don't kill the first
|
|
# local scheduler since that is the one that the driver is
|
|
# connected to.
|
|
process = ray.services.all_processes[
|
|
ray.services.PROCESS_TYPE_PLASMA_STORE][i + 1]
|
|
process.kill()
|
|
process.wait()
|
|
|
|
# Run some more methods.
|
|
for j in range(len(actors)):
|
|
actor = actors[j]
|
|
for _ in range(num_function_calls_at_a_time):
|
|
result_ids[actor].append(actor.inc.remote(j**2 * 0.000001))
|
|
|
|
# Get the results and check that they have the correct values.
|
|
for _, result_id_list in result_ids.items():
|
|
results = list(range(1, len(result_id_list) + 1))
|
|
assert ray.get(result_id_list) == results
|
|
|
|
def setup_counter_actor(self,
|
|
test_checkpoint=False,
|
|
save_exception=False,
|
|
resume_exception=False):
|
|
ray.worker._init(
|
|
start_ray_local=True,
|
|
num_local_schedulers=2,
|
|
num_workers=0,
|
|
redirect_output=True)
|
|
|
|
# Only set the checkpoint interval if we're testing with checkpointing.
|
|
checkpoint_interval = -1
|
|
if test_checkpoint:
|
|
checkpoint_interval = 5
|
|
|
|
@ray.remote(checkpoint_interval=checkpoint_interval)
|
|
class Counter(object):
|
|
_resume_exception = resume_exception
|
|
|
|
def __init__(self, save_exception):
|
|
self.x = 0
|
|
self.num_inc_calls = 0
|
|
self.save_exception = save_exception
|
|
self.restored = False
|
|
|
|
def local_plasma(self):
|
|
return ray.worker.global_worker.plasma_client.store_socket_name
|
|
|
|
def inc(self, *xs):
|
|
self.x += 1
|
|
self.num_inc_calls += 1
|
|
return self.x
|
|
|
|
def get_num_inc_calls(self):
|
|
return self.num_inc_calls
|
|
|
|
def test_restore(self):
|
|
# This method will only return True if __ray_restore__ has been
|
|
# called.
|
|
return self.restored
|
|
|
|
def __ray_save__(self):
|
|
if self.save_exception:
|
|
raise Exception("Exception raised in checkpoint save")
|
|
return self.x, self.save_exception
|
|
|
|
def __ray_restore__(self, checkpoint):
|
|
if self._resume_exception:
|
|
raise Exception("Exception raised in checkpoint resume")
|
|
self.x, self.save_exception = checkpoint
|
|
self.num_inc_calls = 0
|
|
self.restored = True
|
|
|
|
local_plasma = ray.worker.global_worker.plasma_client.store_socket_name
|
|
|
|
# Create an actor that is not on the local scheduler.
|
|
actor = Counter.remote(save_exception)
|
|
while ray.get(actor.local_plasma.remote()) == local_plasma:
|
|
actor = Counter.remote(save_exception)
|
|
|
|
args = [ray.put(0) for _ in range(100)]
|
|
ids = [actor.inc.remote(*args[i:]) for i in range(100)]
|
|
|
|
return actor, ids
|
|
|
|
@unittest.skipIf(
|
|
os.environ.get('RAY_USE_NEW_GCS', False), "Hanging with new GCS API.")
|
|
def testCheckpointing(self):
|
|
actor, ids = self.setup_counter_actor(test_checkpoint=True)
|
|
# Wait for the last task to finish running.
|
|
ray.get(ids[-1])
|
|
|
|
# Kill the corresponding plasma store to get rid of the cached objects.
|
|
process = ray.services.all_processes[
|
|
ray.services.PROCESS_TYPE_PLASMA_STORE][1]
|
|
process.kill()
|
|
process.wait()
|
|
|
|
# Check that the actor restored from a checkpoint.
|
|
assert ray.get(actor.test_restore.remote())
|
|
# Check that we can submit another call on the actor and get the
|
|
# correct counter result.
|
|
x = ray.get(actor.inc.remote())
|
|
assert x == 101
|
|
# Check that the number of inc calls since actor initialization is less
|
|
# than the counter value, since the actor initialized from a
|
|
# checkpoint.
|
|
num_inc_calls = ray.get(actor.get_num_inc_calls.remote())
|
|
assert num_inc_calls < x
|
|
|
|
@unittest.skipIf(
|
|
os.environ.get('RAY_USE_NEW_GCS', False), "Hanging with new GCS API.")
|
|
def testRemoteCheckpoint(self):
|
|
actor, ids = self.setup_counter_actor(test_checkpoint=True)
|
|
|
|
# Do a remote checkpoint call and wait for it to finish.
|
|
ray.get(actor.__ray_checkpoint__.remote())
|
|
|
|
# Kill the corresponding plasma store to get rid of the cached objects.
|
|
process = ray.services.all_processes[
|
|
ray.services.PROCESS_TYPE_PLASMA_STORE][1]
|
|
process.kill()
|
|
process.wait()
|
|
|
|
# Check that the actor restored from a checkpoint.
|
|
assert ray.get(actor.test_restore.remote())
|
|
# Check that the number of inc calls since actor initialization is
|
|
# exactly zero, since there could not have been another inc call since
|
|
# the remote checkpoint.
|
|
num_inc_calls = ray.get(actor.get_num_inc_calls.remote())
|
|
assert num_inc_calls == 0
|
|
# Check that we can submit another call on the actor and get the
|
|
# correct counter result.
|
|
x = ray.get(actor.inc.remote())
|
|
assert x == 101
|
|
|
|
@unittest.skipIf(
|
|
os.environ.get('RAY_USE_NEW_GCS', False), "Hanging with new GCS API.")
|
|
def testLostCheckpoint(self):
|
|
actor, ids = self.setup_counter_actor(test_checkpoint=True)
|
|
# Wait for the first fraction of tasks to finish running.
|
|
ray.get(ids[len(ids) // 10])
|
|
|
|
# Kill the corresponding plasma store to get rid of the cached objects.
|
|
process = ray.services.all_processes[
|
|
ray.services.PROCESS_TYPE_PLASMA_STORE][1]
|
|
process.kill()
|
|
process.wait()
|
|
|
|
# Check that the actor restored from a checkpoint.
|
|
assert ray.get(actor.test_restore.remote())
|
|
# Check that we can submit another call on the actor and get the
|
|
# correct counter result.
|
|
x = ray.get(actor.inc.remote())
|
|
assert x == 101
|
|
# Check that the number of inc calls since actor initialization is less
|
|
# than the counter value, since the actor initialized from a
|
|
# checkpoint.
|
|
num_inc_calls = ray.get(actor.get_num_inc_calls.remote())
|
|
assert num_inc_calls < x
|
|
assert 5 < num_inc_calls
|
|
|
|
@unittest.skipIf(
|
|
os.environ.get('RAY_USE_NEW_GCS', False), "Hanging with new GCS API.")
|
|
def testCheckpointException(self):
|
|
actor, ids = self.setup_counter_actor(
|
|
test_checkpoint=True, save_exception=True)
|
|
# Wait for the last task to finish running.
|
|
ray.get(ids[-1])
|
|
|
|
# Kill the corresponding plasma store to get rid of the cached objects.
|
|
process = ray.services.all_processes[
|
|
ray.services.PROCESS_TYPE_PLASMA_STORE][1]
|
|
process.kill()
|
|
process.wait()
|
|
|
|
# Check that we can submit another call on the actor and get the
|
|
# correct counter result.
|
|
x = ray.get(actor.inc.remote())
|
|
assert x == 101
|
|
# Check that the number of inc calls since actor initialization is
|
|
# equal to the counter value, since the actor did not initialize from a
|
|
# checkpoint.
|
|
num_inc_calls = ray.get(actor.get_num_inc_calls.remote())
|
|
assert num_inc_calls == x
|
|
# Check that errors were raised when trying to save the checkpoint.
|
|
errors = ray.error_info()
|
|
assert 0 < len(errors)
|
|
for error in errors:
|
|
assert error["type"] == ray_constants.CHECKPOINT_PUSH_ERROR
|
|
|
|
@unittest.skipIf(
|
|
os.environ.get('RAY_USE_NEW_GCS', False), "Hanging with new GCS API.")
|
|
def testCheckpointResumeException(self):
|
|
actor, ids = self.setup_counter_actor(
|
|
test_checkpoint=True, resume_exception=True)
|
|
# Wait for the last task to finish running.
|
|
ray.get(ids[-1])
|
|
|
|
# Kill the corresponding plasma store to get rid of the cached objects.
|
|
process = ray.services.all_processes[
|
|
ray.services.PROCESS_TYPE_PLASMA_STORE][1]
|
|
process.kill()
|
|
process.wait()
|
|
|
|
# Check that we can submit another call on the actor and get the
|
|
# correct counter result.
|
|
x = ray.get(actor.inc.remote())
|
|
assert x == 101
|
|
# Check that the number of inc calls since actor initialization is
|
|
# equal to the counter value, since the actor did not initialize from a
|
|
# checkpoint.
|
|
num_inc_calls = ray.get(actor.get_num_inc_calls.remote())
|
|
assert num_inc_calls == x
|
|
# Check that an error was raised when trying to resume from the
|
|
# checkpoint.
|
|
errors = ray.error_info()
|
|
assert len(errors) == 1
|
|
for error in errors:
|
|
assert error["type"] == ray_constants.CHECKPOINT_PUSH_ERROR
|
|
|
|
@unittest.skip("Fork/join consistency not yet implemented.")
|
|
def testDistributedHandle(self):
|
|
counter, ids = self.setup_counter_actor(test_checkpoint=False)
|
|
|
|
@ray.remote
|
|
def fork_many_incs(counter, num_incs):
|
|
x = None
|
|
for _ in range(num_incs):
|
|
x = counter.inc.remote()
|
|
# Only call ray.get() on the last task submitted.
|
|
return ray.get(x)
|
|
|
|
# Fork num_iters times.
|
|
count = ray.get(ids[-1])
|
|
num_incs = 100
|
|
num_iters = 10
|
|
forks = [
|
|
fork_many_incs.remote(counter, num_incs) for _ in range(num_iters)
|
|
]
|
|
ray.wait(forks, num_returns=len(forks))
|
|
count += num_incs * num_iters
|
|
|
|
# Kill the second plasma store to get rid of the cached objects and
|
|
# trigger the corresponding local scheduler to exit.
|
|
process = ray.services.all_processes[
|
|
ray.services.PROCESS_TYPE_PLASMA_STORE][1]
|
|
process.kill()
|
|
process.wait()
|
|
|
|
# Check that the actor did not restore from a checkpoint.
|
|
assert not ray.get(counter.test_restore.remote())
|
|
# Check that we can submit another call on the actor and get the
|
|
# correct counter result.
|
|
x = ray.get(counter.inc.remote())
|
|
assert x == count + 1
|
|
|
|
@unittest.skipIf(
|
|
os.environ.get('RAY_USE_NEW_GCS', False), "Hanging with new GCS API.")
|
|
def testRemoteCheckpointDistributedHandle(self):
|
|
counter, ids = self.setup_counter_actor(test_checkpoint=True)
|
|
|
|
@ray.remote
|
|
def fork_many_incs(counter, num_incs):
|
|
x = None
|
|
for _ in range(num_incs):
|
|
x = counter.inc.remote()
|
|
# Only call ray.get() on the last task submitted.
|
|
return ray.get(x)
|
|
|
|
# Fork num_iters times.
|
|
count = ray.get(ids[-1])
|
|
num_incs = 100
|
|
num_iters = 10
|
|
forks = [
|
|
fork_many_incs.remote(counter, num_incs) for _ in range(num_iters)
|
|
]
|
|
ray.wait(forks, num_returns=len(forks))
|
|
ray.wait([counter.__ray_checkpoint__.remote()])
|
|
count += num_incs * num_iters
|
|
|
|
# Kill the second plasma store to get rid of the cached objects and
|
|
# trigger the corresponding local scheduler to exit.
|
|
process = ray.services.all_processes[
|
|
ray.services.PROCESS_TYPE_PLASMA_STORE][1]
|
|
process.kill()
|
|
process.wait()
|
|
|
|
# Check that the actor restored from a checkpoint.
|
|
assert ray.get(counter.test_restore.remote())
|
|
# Check that the number of inc calls since actor initialization is
|
|
# exactly zero, since there could not have been another inc call since
|
|
# the remote checkpoint.
|
|
num_inc_calls = ray.get(counter.get_num_inc_calls.remote())
|
|
assert num_inc_calls == 0
|
|
# Check that we can submit another call on the actor and get the
|
|
# correct counter result.
|
|
x = ray.get(counter.inc.remote())
|
|
assert x == count + 1
|
|
|
|
@unittest.skip("Fork/join consistency not yet implemented.")
|
|
def testCheckpointDistributedHandle(self):
|
|
counter, ids = self.setup_counter_actor(test_checkpoint=True)
|
|
|
|
@ray.remote
|
|
def fork_many_incs(counter, num_incs):
|
|
x = None
|
|
for _ in range(num_incs):
|
|
x = counter.inc.remote()
|
|
# Only call ray.get() on the last task submitted.
|
|
return ray.get(x)
|
|
|
|
# Fork num_iters times.
|
|
count = ray.get(ids[-1])
|
|
num_incs = 100
|
|
num_iters = 10
|
|
forks = [
|
|
fork_many_incs.remote(counter, num_incs) for _ in range(num_iters)
|
|
]
|
|
ray.wait(forks, num_returns=len(forks))
|
|
count += num_incs * num_iters
|
|
|
|
# Kill the second plasma store to get rid of the cached objects and
|
|
# trigger the corresponding local scheduler to exit.
|
|
process = ray.services.all_processes[
|
|
ray.services.PROCESS_TYPE_PLASMA_STORE][1]
|
|
process.kill()
|
|
process.wait()
|
|
|
|
# Check that the actor restored from a checkpoint.
|
|
assert ray.get(counter.test_restore.remote())
|
|
# Check that we can submit another call on the actor and get the
|
|
# correct counter result.
|
|
x = ray.get(counter.inc.remote())
|
|
assert x == count + 1
|
|
|
|
def _testNondeterministicReconstruction(
|
|
self, num_forks, num_items_per_fork, num_forks_to_wait):
|
|
ray.worker._init(
|
|
start_ray_local=True,
|
|
num_local_schedulers=2,
|
|
num_workers=0,
|
|
redirect_output=True)
|
|
|
|
# Make a shared queue.
|
|
@ray.remote
|
|
class Queue(object):
|
|
def __init__(self):
|
|
self.queue = []
|
|
|
|
def local_plasma(self):
|
|
return ray.worker.global_worker.plasma_client.store_socket_name
|
|
|
|
def push(self, item):
|
|
self.queue.append(item)
|
|
|
|
def read(self):
|
|
return self.queue
|
|
|
|
# Schedule the shared queue onto the remote local scheduler.
|
|
local_plasma = ray.worker.global_worker.plasma_client.store_socket_name
|
|
actor = Queue.remote()
|
|
while ray.get(actor.local_plasma.remote()) == local_plasma:
|
|
actor = Queue.remote()
|
|
|
|
# A task that takes in the shared queue and a list of items to enqueue,
|
|
# one by one.
|
|
@ray.remote
|
|
def enqueue(queue, items):
|
|
done = None
|
|
for item in items:
|
|
done = queue.push.remote(item)
|
|
# TODO(swang): Return the object ID returned by the last method
|
|
# called on the shared queue, so that the caller of enqueue can
|
|
# wait for all of the queue methods to complete. This can be
|
|
# removed once join consistency is implemented.
|
|
return [done]
|
|
|
|
# Call the enqueue task num_forks times, each with num_items_per_fork
|
|
# unique objects to push onto the shared queue.
|
|
enqueue_tasks = []
|
|
for fork in range(num_forks):
|
|
enqueue_tasks.append(
|
|
enqueue.remote(actor,
|
|
[(fork, i) for i in range(num_items_per_fork)]))
|
|
# Wait for the forks to complete their tasks.
|
|
enqueue_tasks = ray.get(enqueue_tasks)
|
|
enqueue_tasks = [fork_ids[0] for fork_ids in enqueue_tasks]
|
|
ray.wait(enqueue_tasks, num_returns=num_forks_to_wait)
|
|
|
|
# Read the queue to get the initial order of execution.
|
|
queue = ray.get(actor.read.remote())
|
|
|
|
# Kill the second plasma store to get rid of the cached objects and
|
|
# trigger the corresponding local scheduler to exit.
|
|
process = ray.services.all_processes[
|
|
ray.services.PROCESS_TYPE_PLASMA_STORE][1]
|
|
process.kill()
|
|
process.wait()
|
|
|
|
# Read the queue again and check for deterministic reconstruction.
|
|
ray.get(enqueue_tasks)
|
|
reconstructed_queue = ray.get(actor.read.remote())
|
|
# Make sure the final queue has all items from all forks.
|
|
assert len(reconstructed_queue) == num_forks * num_items_per_fork
|
|
# Make sure that the prefix of the final queue matches the queue from
|
|
# the initial execution.
|
|
assert queue == reconstructed_queue[:len(queue)]
|
|
|
|
@unittest.skipIf(
|
|
os.environ.get('RAY_USE_NEW_GCS', False),
|
|
"Currently doesn't work with the new GCS.")
|
|
def testNondeterministicReconstruction(self):
|
|
self._testNondeterministicReconstruction(10, 100, 10)
|
|
|
|
@unittest.skip("Nondeterministic reconstruction currently not supported "
|
|
"when there are concurrent forks that didn't finish "
|
|
"initial execution.")
|
|
def testNondeterministicReconstructionConcurrentForks(self):
|
|
self._testNondeterministicReconstruction(10, 100, 1)
|
|
|
|
|
|
class DistributedActorHandles(unittest.TestCase):
|
|
def tearDown(self):
|
|
ray.shutdown()
|
|
|
|
def setup_queue_actor(self):
|
|
ray.init()
|
|
|
|
@ray.remote
|
|
class Queue(object):
|
|
def __init__(self):
|
|
self.queue = []
|
|
|
|
def enqueue(self, key, item):
|
|
self.queue.append((key, item))
|
|
|
|
def read(self):
|
|
return self.queue
|
|
|
|
return Queue.remote()
|
|
|
|
@unittest.skipIf(
|
|
os.environ.get("RAY_USE_XRAY") == "1",
|
|
"This test does not work with xray yet.")
|
|
def testFork(self):
|
|
queue = self.setup_queue_actor()
|
|
|
|
@ray.remote
|
|
def fork(queue, key, item):
|
|
return ray.get(queue.enqueue.remote(key, item))
|
|
|
|
# Fork num_iters times.
|
|
num_iters = 100
|
|
ray.get([fork.remote(queue, i, 0) for i in range(num_iters)])
|
|
items = ray.get(queue.read.remote())
|
|
for i in range(num_iters):
|
|
filtered_items = [item[1] for item in items if item[0] == i]
|
|
assert filtered_items == list(range(1))
|
|
|
|
@unittest.skipIf(
|
|
os.environ.get("RAY_USE_XRAY") == "1",
|
|
"This test does not work with xray yet.")
|
|
def testForkConsistency(self):
|
|
queue = self.setup_queue_actor()
|
|
|
|
@ray.remote
|
|
def fork(queue, key, num_items):
|
|
x = None
|
|
for item in range(num_items):
|
|
x = queue.enqueue.remote(key, item)
|
|
return ray.get(x)
|
|
|
|
# Fork num_iters times.
|
|
num_forks = 10
|
|
num_items_per_fork = 100
|
|
ray.get([
|
|
fork.remote(queue, i, num_items_per_fork) for i in range(num_forks)
|
|
])
|
|
items = ray.get(queue.read.remote())
|
|
for i in range(num_forks):
|
|
filtered_items = [item[1] for item in items if item[0] == i]
|
|
assert filtered_items == list(range(num_items_per_fork))
|
|
|
|
@unittest.skip("Garbage collection for distributed actor handles not "
|
|
"implemented.")
|
|
def testGarbageCollection(self):
|
|
queue = self.setup_queue_actor()
|
|
|
|
@ray.remote
|
|
def fork(queue):
|
|
for i in range(10):
|
|
x = queue.enqueue.remote(0, i)
|
|
time.sleep(0.1)
|
|
return ray.get(x)
|
|
|
|
x = fork.remote(queue)
|
|
ray.get(queue.read.remote())
|
|
del queue
|
|
|
|
print(ray.get(x))
|
|
|
|
def testCallingPutOnActorHandle(self):
|
|
ray.worker.init(num_workers=1)
|
|
|
|
@ray.remote
|
|
class Counter(object):
|
|
def __init__(self):
|
|
self.x = 0
|
|
|
|
def inc(self):
|
|
self.x += 1
|
|
return self.x
|
|
|
|
@ray.remote
|
|
def f():
|
|
return Counter.remote()
|
|
|
|
@ray.remote
|
|
def g():
|
|
return [Counter.remote()]
|
|
|
|
# Currently, calling ray.put on an actor handle is allowed, but is
|
|
# there a good use case?
|
|
counter = Counter.remote()
|
|
counter_id = ray.put(counter)
|
|
new_counter = ray.get(counter_id)
|
|
assert ray.get(new_counter.inc.remote()) == 1
|
|
assert ray.get(counter.inc.remote()) == 2
|
|
assert ray.get(new_counter.inc.remote()) == 3
|
|
|
|
with pytest.raises(Exception):
|
|
ray.get(f.remote())
|
|
|
|
# The below test works, but do we want to disallow this usage?
|
|
ray.get(g.remote())
|
|
|
|
def testPicklingActorHandle(self):
|
|
ray.worker.init(num_workers=1)
|
|
|
|
@ray.remote
|
|
class Foo(object):
|
|
def method(self):
|
|
pass
|
|
|
|
f = Foo.remote()
|
|
new_f = ray.worker.pickle.loads(ray.worker.pickle.dumps(f))
|
|
# Verify that we can call a method on the unpickled handle. TODO(rkn):
|
|
# we should also test this from a different driver.
|
|
ray.get(new_f.method.remote())
|
|
|
|
def testRegisterAndGetNamedActors(self):
|
|
# TODO(heyucongtom): We should test this from another driver.
|
|
ray.worker.init(num_workers=1)
|
|
|
|
@ray.remote
|
|
class Foo(object):
|
|
def __init__(self):
|
|
self.x = 0
|
|
|
|
def method(self):
|
|
self.x += 1
|
|
return self.x
|
|
|
|
f1 = Foo.remote()
|
|
# Test saving f.
|
|
ray.experimental.register_actor("f1", f1)
|
|
# Test getting f.
|
|
f2 = ray.experimental.get_actor("f1")
|
|
assert f1._actor_id == f2._actor_id
|
|
|
|
# Test same name register shall raise error.
|
|
with pytest.raises(ValueError):
|
|
ray.experimental.register_actor("f1", f2)
|
|
|
|
# Test register with wrong object type.
|
|
with pytest.raises(TypeError):
|
|
ray.experimental.register_actor("f3", 1)
|
|
|
|
# Test getting a nonexistent actor.
|
|
with pytest.raises(ValueError):
|
|
ray.experimental.get_actor("nonexistent")
|
|
|
|
# Test method
|
|
assert ray.get(f1.method.remote()) == 1
|
|
assert ray.get(f2.method.remote()) == 2
|
|
assert ray.get(f1.method.remote()) == 3
|
|
assert ray.get(f2.method.remote()) == 4
|
|
|
|
|
|
@pytest.fixture
|
|
def ray_stop():
|
|
# The initialization code depends on the test that is run.
|
|
yield None
|
|
# The code after the yield will run as teardown code.
|
|
ray.shutdown()
|
|
|
|
|
|
@unittest.skipIf(
|
|
os.environ.get("RAY_USE_XRAY") == "1" or sys.version_info < (3, 0),
|
|
"This test does not work with xray yet"
|
|
" and is currently failing on Python 2.7.")
|
|
def testLifetimeAndTransientResources(ray_stop):
|
|
ray.init(num_cpus=1)
|
|
|
|
# This actor acquires resources only when running methods.
|
|
@ray.remote
|
|
class Actor1(object):
|
|
def method(self):
|
|
pass
|
|
|
|
# This actor acquires resources for its lifetime.
|
|
@ray.remote(num_cpus=1)
|
|
class Actor2(object):
|
|
def method(self):
|
|
pass
|
|
|
|
actor1s = [Actor1.remote() for _ in range(10)]
|
|
ray.get([a.method.remote() for a in actor1s])
|
|
|
|
actor2s = [Actor2.remote() for _ in range(2)]
|
|
results = [a.method.remote() for a in actor2s]
|
|
ready_ids, remaining_ids = ray.wait(
|
|
results, num_returns=len(results), timeout=1000)
|
|
assert len(ready_ids) == 1
|
|
|
|
|
|
def testCustomLabelPlacement(ray_stop):
|
|
ray.worker._init(
|
|
start_ray_local=True,
|
|
num_local_schedulers=2,
|
|
num_workers=0,
|
|
resources=[{
|
|
"CustomResource1": 2
|
|
}, {
|
|
"CustomResource2": 2
|
|
}])
|
|
|
|
@ray.remote(resources={"CustomResource1": 1})
|
|
class ResourceActor1(object):
|
|
def get_location(self):
|
|
return ray.worker.global_worker.plasma_client.store_socket_name
|
|
|
|
@ray.remote(resources={"CustomResource2": 1})
|
|
class ResourceActor2(object):
|
|
def get_location(self):
|
|
return ray.worker.global_worker.plasma_client.store_socket_name
|
|
|
|
local_plasma = ray.worker.global_worker.plasma_client.store_socket_name
|
|
|
|
# Create some actors.
|
|
actors1 = [ResourceActor1.remote() for _ in range(2)]
|
|
actors2 = [ResourceActor2.remote() for _ in range(2)]
|
|
locations1 = ray.get([a.get_location.remote() for a in actors1])
|
|
locations2 = ray.get([a.get_location.remote() for a in actors2])
|
|
for location in locations1:
|
|
assert location == local_plasma
|
|
for location in locations2:
|
|
assert location != local_plasma
|
|
|
|
|
|
def testCreatingMoreActorsThanResources(ray_stop):
|
|
ray.init(
|
|
num_workers=0,
|
|
num_cpus=10,
|
|
num_gpus=2,
|
|
resources={"CustomResource1": 1})
|
|
|
|
@ray.remote(num_gpus=1)
|
|
class ResourceActor1(object):
|
|
def method(self):
|
|
return ray.get_gpu_ids()[0]
|
|
|
|
@ray.remote(resources={"CustomResource1": 1})
|
|
class ResourceActor2(object):
|
|
def method(self):
|
|
pass
|
|
|
|
# Make sure the first two actors get created and the third one does
|
|
# not.
|
|
actor1 = ResourceActor1.remote()
|
|
result1 = actor1.method.remote()
|
|
ray.wait([result1])
|
|
actor2 = ResourceActor1.remote()
|
|
result2 = actor2.method.remote()
|
|
ray.wait([result2])
|
|
actor3 = ResourceActor1.remote()
|
|
result3 = actor3.method.remote()
|
|
ready_ids, _ = ray.wait([result3], timeout=200)
|
|
assert len(ready_ids) == 0
|
|
|
|
# By deleting actor1, we free up resources to create actor3.
|
|
del actor1
|
|
|
|
results = ray.get([result1, result2, result3])
|
|
assert results[0] == results[2]
|
|
assert set(results) == {0, 1}
|
|
|
|
# Make sure that when one actor goes out of scope a new actor is
|
|
# created because some resources have been freed up.
|
|
results = []
|
|
for _ in range(3):
|
|
actor = ResourceActor2.remote()
|
|
object_id = actor.method.remote()
|
|
results.append(object_id)
|
|
# Wait for the task to execute. We do this because otherwise it may
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# be possible for the __ray_terminate__ task to execute before the
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# method.
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ray.wait([object_id])
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ray.get(results)
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if __name__ == "__main__":
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unittest.main(verbosity=2)
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