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ray/test/runtest.py
Yuhong Guo b9e1977fae Fix failure of test_free_objects_multi_node (#3481) 
It is possible that `test_free_objects_multi_node` would fail sometimes. If we run this test 20 times, we may found at least one failure.

The cause is that the test is based on function tasks. One raylet may create more than one worker to execute the tasks. So flush operations may be separated to several workers and not clean all the worker objects held by the plasma client.

In this PR, I change function task to actor tasks, which guarantee all the tasks are executed in one worker of a raylet.
2018-12-06 15:55:49 -05:00

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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import logging
import os
import re
import setproctitle
import string
import subprocess
import sys
import threading
import time
from collections import defaultdict, namedtuple, OrderedDict
import numpy as np
import pytest
import ray
import ray.ray_constants as ray_constants
import ray.test.cluster_utils
import ray.test.test_utils
logger = logging.getLogger(__name__)
def assert_equal(obj1, obj2):
module_numpy = (type(obj1).__module__ == np.__name__
or type(obj2).__module__ == np.__name__)
if module_numpy:
empty_shape = ((hasattr(obj1, "shape") and obj1.shape == ())
or (hasattr(obj2, "shape") and obj2.shape == ()))
if empty_shape:
# This is a special case because currently np.testing.assert_equal
# fails because we do not properly handle different numerical
# types.
assert obj1 == obj2, ("Objects {} and {} are "
"different.".format(obj1, obj2))
else:
np.testing.assert_equal(obj1, obj2)
elif hasattr(obj1, "__dict__") and hasattr(obj2, "__dict__"):
special_keys = ["_pytype_"]
assert (set(list(obj1.__dict__.keys()) + special_keys) == set(
list(obj2.__dict__.keys()) + special_keys)), ("Objects {} "
"and {} are "
"different.".format(
obj1, obj2))
for key in obj1.__dict__.keys():
if key not in special_keys:
assert_equal(obj1.__dict__[key], obj2.__dict__[key])
elif type(obj1) is dict or type(obj2) is dict:
assert_equal(obj1.keys(), obj2.keys())
for key in obj1.keys():
assert_equal(obj1[key], obj2[key])
elif type(obj1) is list or type(obj2) is list:
assert len(obj1) == len(obj2), ("Objects {} and {} are lists with "
"different lengths.".format(
obj1, obj2))
for i in range(len(obj1)):
assert_equal(obj1[i], obj2[i])
elif type(obj1) is tuple or type(obj2) is tuple:
assert len(obj1) == len(obj2), ("Objects {} and {} are tuples with "
"different lengths.".format(
obj1, obj2))
for i in range(len(obj1)):
assert_equal(obj1[i], obj2[i])
elif (ray.serialization.is_named_tuple(type(obj1))
or ray.serialization.is_named_tuple(type(obj2))):
assert len(obj1) == len(obj2), ("Objects {} and {} are named tuples "
"with different lengths.".format(
obj1, obj2))
for i in range(len(obj1)):
assert_equal(obj1[i], obj2[i])
else:
assert obj1 == obj2, "Objects {} and {} are different.".format(
obj1, obj2)
if sys.version_info >= (3, 0):
long_extras = [0, np.array([["hi", u"hi"], [1.3, 1]])]
else:
long_extras = [
long(0), # noqa: E501,F821
np.array([
["hi", u"hi"],
[1.3, long(1)] # noqa: E501,F821
])
]
PRIMITIVE_OBJECTS = [
0, 0.0, 0.9, 1 << 62, 1 << 100, 1 << 999, [1 << 100, [1 << 100]], "a",
string.printable, "\u262F", u"hello world", u"\xff\xfe\x9c\x001\x000\x00",
None, True, False, [], (), {},
np.int8(3),
np.int32(4),
np.int64(5),
np.uint8(3),
np.uint32(4),
np.uint64(5),
np.float32(1.9),
np.float64(1.9),
np.zeros([100, 100]),
np.random.normal(size=[100, 100]),
np.array(["hi", 3]),
np.array(["hi", 3], dtype=object)
] + long_extras
COMPLEX_OBJECTS = [
[[[[[[[[[[[[]]]]]]]]]]]],
{"obj{}".format(i): np.random.normal(size=[100, 100])
for i in range(10)},
# {(): {(): {(): {(): {(): {(): {(): {(): {(): {(): {
# (): {(): {}}}}}}}}}}}}},
(
(((((((((), ), ), ), ), ), ), ), ), ),
{
"a": {
"b": {
"c": {
"d": {}
}
}
}
}
]
class Foo(object):
def __init__(self, value=0):
self.value = value
def __hash__(self):
return hash(self.value)
def __eq__(self, other):
return other.value == self.value
class Bar(object):
def __init__(self):
for i, val in enumerate(PRIMITIVE_OBJECTS + COMPLEX_OBJECTS):
setattr(self, "field{}".format(i), val)
class Baz(object):
def __init__(self):
self.foo = Foo()
self.bar = Bar()
def method(self, arg):
pass
class Qux(object):
def __init__(self):
self.objs = [Foo(), Bar(), Baz()]
class SubQux(Qux):
def __init__(self):
Qux.__init__(self)
class CustomError(Exception):
pass
Point = namedtuple("Point", ["x", "y"])
NamedTupleExample = namedtuple("Example",
"field1, field2, field3, field4, field5")
CUSTOM_OBJECTS = [
Exception("Test object."),
CustomError(),
Point(11, y=22),
Foo(),
Bar(),
Baz(), # Qux(), SubQux(),
NamedTupleExample(1, 1.0, "hi", np.zeros([3, 5]), [1, 2, 3])
]
BASE_OBJECTS = PRIMITIVE_OBJECTS + COMPLEX_OBJECTS + CUSTOM_OBJECTS
LIST_OBJECTS = [[obj] for obj in BASE_OBJECTS]
TUPLE_OBJECTS = [(obj, ) for obj in BASE_OBJECTS]
# The check that type(obj).__module__ != "numpy" should be unnecessary, but
# otherwise this seems to fail on Mac OS X on Travis.
DICT_OBJECTS = (
[{
obj: obj
} for obj in PRIMITIVE_OBJECTS
if (obj.__hash__ is not None and type(obj).__module__ != "numpy")] + [{
0: obj
} for obj in BASE_OBJECTS] + [{
Foo(123): Foo(456)
}])
RAY_TEST_OBJECTS = BASE_OBJECTS + LIST_OBJECTS + TUPLE_OBJECTS + DICT_OBJECTS
@pytest.fixture
def ray_start():
# Start the Ray processes.
ray.init(num_cpus=1)
yield None
# The code after the yield will run as teardown code.
ray.shutdown()
@pytest.fixture
def shutdown_only():
yield None
# The code after the yield will run as teardown code.
ray.shutdown()
def test_passing_arguments_by_value(ray_start):
@ray.remote
def f(x):
return x
# Check that we can pass arguments by value to remote functions and
# that they are uncorrupted.
for obj in RAY_TEST_OBJECTS:
assert_equal(obj, ray.get(f.remote(obj)))
def test_ray_recursive_objects(ray_start):
class ClassA(object):
pass
# Make a list that contains itself.
lst = []
lst.append(lst)
# Make an object that contains itself as a field.
a1 = ClassA()
a1.field = a1
# Make two objects that contain each other as fields.
a2 = ClassA()
a3 = ClassA()
a2.field = a3
a3.field = a2
# Make a dictionary that contains itself.
d1 = {}
d1["key"] = d1
# Create a list of recursive objects.
recursive_objects = [lst, a1, a2, a3, d1]
# Check that exceptions are thrown when we serialize the recursive
# objects.
for obj in recursive_objects:
with pytest.raises(Exception):
ray.put(obj)
def test_passing_arguments_by_value_out_of_the_box(ray_start):
@ray.remote
def f(x):
return x
# Test passing lambdas.
def temp():
return 1
assert ray.get(f.remote(temp))() == 1
assert ray.get(f.remote(lambda x: x + 1))(3) == 4
# Test sets.
assert ray.get(f.remote(set())) == set()
s = {1, (1, 2, "hi")}
assert ray.get(f.remote(s)) == s
# Test types.
assert ray.get(f.remote(int)) == int
assert ray.get(f.remote(float)) == float
assert ray.get(f.remote(str)) == str
class Foo(object):
def __init__(self):
pass
# Make sure that we can put and get a custom type. Note that the result
# won't be "equal" to Foo.
ray.get(ray.put(Foo))
def test_putting_object_that_closes_over_object_id(ray_start):
# This test is here to prevent a regression of
# https://github.com/ray-project/ray/issues/1317.
class Foo(object):
def __init__(self):
self.val = ray.put(0)
def method(self):
f
f = Foo()
with pytest.raises(ray.raylet.common_error):
ray.put(f)
def test_python_workers(shutdown_only):
# Test the codepath for starting workers from the Python script,
# instead of the local scheduler. This codepath is for debugging
# purposes only.
num_workers = 4
ray.worker._init(
num_cpus=num_workers,
start_workers_from_local_scheduler=False,
start_ray_local=True)
@ray.remote
def f(x):
return x
values = ray.get([f.remote(1) for i in range(num_workers * 2)])
assert values == [1] * (num_workers * 2)
def test_put_get(shutdown_only):
ray.init(num_cpus=0)
for i in range(100):
value_before = i * 10**6
objectid = ray.put(value_before)
value_after = ray.get(objectid)
assert value_before == value_after
for i in range(100):
value_before = i * 10**6 * 1.0
objectid = ray.put(value_before)
value_after = ray.get(objectid)
assert value_before == value_after
for i in range(100):
value_before = "h" * i
objectid = ray.put(value_before)
value_after = ray.get(objectid)
assert value_before == value_after
for i in range(100):
value_before = [1] * i
objectid = ray.put(value_before)
value_after = ray.get(objectid)
assert value_before == value_after
def test_custom_serializers(shutdown_only):
ray.init(num_cpus=1)
class Foo(object):
def __init__(self):
self.x = 3
def custom_serializer(obj):
return 3, "string1", type(obj).__name__
def custom_deserializer(serialized_obj):
return serialized_obj, "string2"
ray.register_custom_serializer(
Foo, serializer=custom_serializer, deserializer=custom_deserializer)
assert ray.get(ray.put(Foo())) == ((3, "string1", Foo.__name__), "string2")
class Bar(object):
def __init__(self):
self.x = 3
ray.register_custom_serializer(
Bar, serializer=custom_serializer, deserializer=custom_deserializer)
@ray.remote
def f():
return Bar()
assert ray.get(f.remote()) == ((3, "string1", Bar.__name__), "string2")
def test_serialization_final_fallback(ray_start):
pytest.importorskip("catboost")
# This test will only run when "catboost" is installed.
from catboost import CatBoostClassifier
model = CatBoostClassifier(
iterations=2,
depth=2,
learning_rate=1,
loss_function="Logloss",
logging_level="Verbose")
reconstructed_model = ray.get(ray.put(model))
assert set(model.get_params().items()) == set(
reconstructed_model.get_params().items())
def test_register_class(shutdown_only):
ray.init(num_cpus=2)
# Check that putting an object of a class that has not been registered
# throws an exception.
class TempClass(object):
pass
ray.get(ray.put(TempClass()))
# Test subtypes of dictionaries.
value_before = OrderedDict([("hello", 1), ("world", 2)])
object_id = ray.put(value_before)
assert value_before == ray.get(object_id)
value_before = defaultdict(lambda: 0, [("hello", 1), ("world", 2)])
object_id = ray.put(value_before)
assert value_before == ray.get(object_id)
value_before = defaultdict(lambda: [], [("hello", 1), ("world", 2)])
object_id = ray.put(value_before)
assert value_before == ray.get(object_id)
# Test passing custom classes into remote functions from the driver.
@ray.remote
def f(x):
return x
foo = ray.get(f.remote(Foo(7)))
assert foo == Foo(7)
regex = re.compile(r"\d+\.\d*")
new_regex = ray.get(f.remote(regex))
# This seems to fail on the system Python 3 that comes with
# Ubuntu, so it is commented out for now:
# assert regex == new_regex
# Instead, we do this:
assert regex.pattern == new_regex.pattern
# Test returning custom classes created on workers.
@ray.remote
def g():
return SubQux(), Qux()
subqux, qux = ray.get(g.remote())
assert subqux.objs[2].foo.value == 0
# Test exporting custom class definitions from one worker to another
# when the worker is blocked in a get.
class NewTempClass(object):
def __init__(self, value):
self.value = value
@ray.remote
def h1(x):
return NewTempClass(x)
@ray.remote
def h2(x):
return ray.get(h1.remote(x))
assert ray.get(h2.remote(10)).value == 10
# Test registering multiple classes with the same name.
@ray.remote(num_return_vals=3)
def j():
class Class0(object):
def method0(self):
pass
c0 = Class0()
class Class0(object):
def method1(self):
pass
c1 = Class0()
class Class0(object):
def method2(self):
pass
c2 = Class0()
return c0, c1, c2
results = []
for _ in range(5):
results += j.remote()
for i in range(len(results) // 3):
c0, c1, c2 = ray.get(results[(3 * i):(3 * (i + 1))])
c0.method0()
c1.method1()
c2.method2()
assert not hasattr(c0, "method1")
assert not hasattr(c0, "method2")
assert not hasattr(c1, "method0")
assert not hasattr(c1, "method2")
assert not hasattr(c2, "method0")
assert not hasattr(c2, "method1")
@ray.remote
def k():
class Class0(object):
def method0(self):
pass
c0 = Class0()
class Class0(object):
def method1(self):
pass
c1 = Class0()
class Class0(object):
def method2(self):
pass
c2 = Class0()
return c0, c1, c2
results = ray.get([k.remote() for _ in range(5)])
for c0, c1, c2 in results:
c0.method0()
c1.method1()
c2.method2()
assert not hasattr(c0, "method1")
assert not hasattr(c0, "method2")
assert not hasattr(c1, "method0")
assert not hasattr(c1, "method2")
assert not hasattr(c2, "method0")
assert not hasattr(c2, "method1")
def test_keyword_args(shutdown_only):
@ray.remote
def keyword_fct1(a, b="hello"):
return "{} {}".format(a, b)
@ray.remote
def keyword_fct2(a="hello", b="world"):
return "{} {}".format(a, b)
@ray.remote
def keyword_fct3(a, b, c="hello", d="world"):
return "{} {} {} {}".format(a, b, c, d)
ray.init(num_cpus=1)
x = keyword_fct1.remote(1)
assert ray.get(x) == "1 hello"
x = keyword_fct1.remote(1, "hi")
assert ray.get(x) == "1 hi"
x = keyword_fct1.remote(1, b="world")
assert ray.get(x) == "1 world"
x = keyword_fct1.remote(a=1, b="world")
assert ray.get(x) == "1 world"
x = keyword_fct2.remote(a="w", b="hi")
assert ray.get(x) == "w hi"
x = keyword_fct2.remote(b="hi", a="w")
assert ray.get(x) == "w hi"
x = keyword_fct2.remote(a="w")
assert ray.get(x) == "w world"
x = keyword_fct2.remote(b="hi")
assert ray.get(x) == "hello hi"
x = keyword_fct2.remote("w")
assert ray.get(x) == "w world"
x = keyword_fct2.remote("w", "hi")
assert ray.get(x) == "w hi"
x = keyword_fct3.remote(0, 1, c="w", d="hi")
assert ray.get(x) == "0 1 w hi"
x = keyword_fct3.remote(0, b=1, c="w", d="hi")
assert ray.get(x) == "0 1 w hi"
x = keyword_fct3.remote(a=0, b=1, c="w", d="hi")
assert ray.get(x) == "0 1 w hi"
x = keyword_fct3.remote(0, 1, d="hi", c="w")
assert ray.get(x) == "0 1 w hi"
x = keyword_fct3.remote(0, 1, c="w")
assert ray.get(x) == "0 1 w world"
x = keyword_fct3.remote(0, 1, d="hi")
assert ray.get(x) == "0 1 hello hi"
x = keyword_fct3.remote(0, 1)
assert ray.get(x) == "0 1 hello world"
x = keyword_fct3.remote(a=0, b=1)
assert ray.get(x) == "0 1 hello world"
# Check that we cannot pass invalid keyword arguments to functions.
@ray.remote
def f1():
return
@ray.remote
def f2(x, y=0, z=0):
return
# Make sure we get an exception if too many arguments are passed in.
with pytest.raises(Exception):
f1.remote(3)
with pytest.raises(Exception):
f1.remote(x=3)
with pytest.raises(Exception):
f2.remote(0, w=0)
with pytest.raises(Exception):
f2.remote(3, x=3)
# Make sure we get an exception if too many arguments are passed in.
with pytest.raises(Exception):
f2.remote(1, 2, 3, 4)
@ray.remote
def f3(x):
return x
assert ray.get(f3.remote(4)) == 4
def test_variable_number_of_args(shutdown_only):
@ray.remote
def varargs_fct1(*a):
return " ".join(map(str, a))
@ray.remote
def varargs_fct2(a, *b):
return " ".join(map(str, b))
try:
@ray.remote
def kwargs_throw_exception(**c):
return ()
kwargs_exception_thrown = False
except Exception:
kwargs_exception_thrown = True
ray.init(num_cpus=1)
x = varargs_fct1.remote(0, 1, 2)
assert ray.get(x) == "0 1 2"
x = varargs_fct2.remote(0, 1, 2)
assert ray.get(x) == "1 2"
assert kwargs_exception_thrown
@ray.remote
def f1(*args):
return args
@ray.remote
def f2(x, y, *args):
return x, y, args
assert ray.get(f1.remote()) == ()
assert ray.get(f1.remote(1)) == (1, )
assert ray.get(f1.remote(1, 2, 3)) == (1, 2, 3)
with pytest.raises(Exception):
f2.remote()
with pytest.raises(Exception):
f2.remote(1)
assert ray.get(f2.remote(1, 2)) == (1, 2, ())
assert ray.get(f2.remote(1, 2, 3)) == (1, 2, (3, ))
assert ray.get(f2.remote(1, 2, 3, 4)) == (1, 2, (3, 4))
def testNoArgs(self):
@ray.remote
def no_op():
pass
self.init_ray()
ray.get(no_op.remote())
def test_defining_remote_functions(shutdown_only):
ray.init(num_cpus=3)
# Test that we can define a remote function in the shell.
@ray.remote
def f(x):
return x + 1
assert ray.get(f.remote(0)) == 1
# Test that we can redefine the remote function.
@ray.remote
def f(x):
return x + 10
while True:
val = ray.get(f.remote(0))
assert val in [1, 10]
if val == 10:
break
else:
logger.info("Still using old definition of f, trying again.")
# Test that we can close over plain old data.
data = [
np.zeros([3, 5]), (1, 2, "a"), [0.0, 1.0, 1 << 62], 1 << 60, {
"a": np.zeros(3)
}
]
@ray.remote
def g():
return data
ray.get(g.remote())
# Test that we can close over modules.
@ray.remote
def h():
return np.zeros([3, 5])
assert_equal(ray.get(h.remote()), np.zeros([3, 5]))
@ray.remote
def j():
return time.time()
ray.get(j.remote())
# Test that we can define remote functions that call other remote
# functions.
@ray.remote
def k(x):
return x + 1
@ray.remote
def k2(x):
return ray.get(k.remote(x))
@ray.remote
def m(x):
return ray.get(k2.remote(x))
assert ray.get(k.remote(1)) == 2
assert ray.get(k2.remote(1)) == 2
assert ray.get(m.remote(1)) == 2
def test_submit_api(shutdown_only):
ray.init(num_cpus=1, num_gpus=1, resources={"Custom": 1})
@ray.remote
def f(n):
return list(range(n))
@ray.remote
def g():
return ray.get_gpu_ids()
assert f._remote([0], num_return_vals=0) is None
id1 = f._remote(args=[1], num_return_vals=1)
assert ray.get(id1) == [0]
id1, id2 = f._remote(args=[2], num_return_vals=2)
assert ray.get([id1, id2]) == [0, 1]
id1, id2, id3 = f._remote(args=[3], num_return_vals=3)
assert ray.get([id1, id2, id3]) == [0, 1, 2]
assert ray.get(
g._remote(
args=[], num_cpus=1, num_gpus=1,
resources={"Custom": 1})) == [0]
infeasible_id = g._remote(args=[], resources={"NonexistentCustom": 1})
ready_ids, remaining_ids = ray.wait([infeasible_id], timeout=50)
assert len(ready_ids) == 0
assert len(remaining_ids) == 1
@ray.remote
class Actor(object):
def __init__(self, x, y=0):
self.x = x
self.y = y
def method(self, a, b=0):
return self.x, self.y, a, b
def gpu_ids(self):
return ray.get_gpu_ids()
a = Actor._remote(
args=[0], kwargs={"y": 1}, num_gpus=1, resources={"Custom": 1})
id1, id2, id3, id4 = a.method._remote(
args=["test"], kwargs={"b": 2}, num_return_vals=4)
assert ray.get([id1, id2, id3, id4]) == [0, 1, "test", 2]
def test_get_multiple(shutdown_only):
ray.init(num_cpus=1)
object_ids = [ray.put(i) for i in range(10)]
assert ray.get(object_ids) == list(range(10))
# Get a random choice of object IDs with duplicates.
indices = list(np.random.choice(range(10), 5))
indices += indices
results = ray.get([object_ids[i] for i in indices])
assert results == indices
def test_get_multiple_experimental(shutdown_only):
ray.init(num_cpus=1)
object_ids = [ray.put(i) for i in range(10)]
object_ids_tuple = tuple(object_ids)
assert ray.experimental.get(object_ids_tuple) == list(range(10))
object_ids_nparray = np.array(object_ids)
assert ray.experimental.get(object_ids_nparray) == list(range(10))
def test_get_dict(shutdown_only):
ray.init(num_cpus=1)
d = {str(i): ray.put(i) for i in range(5)}
for i in range(5, 10):
d[str(i)] = i
result = ray.experimental.get(d)
expected = {str(i): i for i in range(10)}
assert result == expected
def test_wait(shutdown_only):
ray.init(num_cpus=1)
@ray.remote
def f(delay):
time.sleep(delay)
return 1
objectids = [f.remote(1.0), f.remote(0.5), f.remote(0.5), f.remote(0.5)]
ready_ids, remaining_ids = ray.wait(objectids)
assert len(ready_ids) == 1
assert len(remaining_ids) == 3
ready_ids, remaining_ids = ray.wait(objectids, num_returns=4)
assert set(ready_ids) == set(objectids)
assert remaining_ids == []
objectids = [f.remote(0.5), f.remote(0.5), f.remote(0.5), f.remote(0.5)]
start_time = time.time()
ready_ids, remaining_ids = ray.wait(objectids, timeout=1750, num_returns=4)
assert time.time() - start_time < 2
assert len(ready_ids) == 3
assert len(remaining_ids) == 1
ray.wait(objectids)
objectids = [f.remote(1.0), f.remote(0.5), f.remote(0.5), f.remote(0.5)]
start_time = time.time()
ready_ids, remaining_ids = ray.wait(objectids, timeout=5000)
assert time.time() - start_time < 5
assert len(ready_ids) == 1
assert len(remaining_ids) == 3
# Verify that calling wait with duplicate object IDs throws an
# exception.
x = ray.put(1)
with pytest.raises(Exception):
ray.wait([x, x])
# Make sure it is possible to call wait with an empty list.
ready_ids, remaining_ids = ray.wait([])
assert ready_ids == []
assert remaining_ids == []
# Test semantics of num_returns with no timeout.
oids = [ray.put(i) for i in range(10)]
(found, rest) = ray.wait(oids, num_returns=2)
assert len(found) == 2
assert len(rest) == 8
# Verify that incorrect usage raises a TypeError.
x = ray.put(1)
with pytest.raises(TypeError):
ray.wait(x)
with pytest.raises(TypeError):
ray.wait(1)
with pytest.raises(TypeError):
ray.wait([1])
def test_wait_iterables(shutdown_only):
ray.init(num_cpus=1)
@ray.remote
def f(delay):
time.sleep(delay)
return 1
objectids = (f.remote(1.0), f.remote(0.5), f.remote(0.5), f.remote(0.5))
ready_ids, remaining_ids = ray.experimental.wait(objectids)
assert len(ready_ids) == 1
assert len(remaining_ids) == 3
objectids = np.array(
[f.remote(1.0),
f.remote(0.5),
f.remote(0.5),
f.remote(0.5)])
ready_ids, remaining_ids = ray.experimental.wait(objectids)
assert len(ready_ids) == 1
assert len(remaining_ids) == 3
def test_multiple_waits_and_gets(shutdown_only):
# It is important to use three workers here, so that the three tasks
# launched in this experiment can run at the same time.
ray.init(num_cpus=3)
@ray.remote
def f(delay):
time.sleep(delay)
return 1
@ray.remote
def g(l):
# The argument l should be a list containing one object ID.
ray.wait([l[0]])
@ray.remote
def h(l):
# The argument l should be a list containing one object ID.
ray.get(l[0])
# Make sure that multiple wait requests involving the same object ID
# all return.
x = f.remote(1)
ray.get([g.remote([x]), g.remote([x])])
# Make sure that multiple get requests involving the same object ID all
# return.
x = f.remote(1)
ray.get([h.remote([x]), h.remote([x])])
def test_caching_functions_to_run(shutdown_only):
# Test that we export functions to run on all workers before the driver
# is connected.
def f(worker_info):
sys.path.append(1)
ray.worker.global_worker.run_function_on_all_workers(f)
def f(worker_info):
sys.path.append(2)
ray.worker.global_worker.run_function_on_all_workers(f)
def g(worker_info):
sys.path.append(3)
ray.worker.global_worker.run_function_on_all_workers(g)
def f(worker_info):
sys.path.append(4)
ray.worker.global_worker.run_function_on_all_workers(f)
ray.init(num_cpus=1)
@ray.remote
def get_state():
time.sleep(1)
return sys.path[-4], sys.path[-3], sys.path[-2], sys.path[-1]
res1 = get_state.remote()
res2 = get_state.remote()
assert ray.get(res1) == (1, 2, 3, 4)
assert ray.get(res2) == (1, 2, 3, 4)
# Clean up the path on the workers.
def f(worker_info):
sys.path.pop()
sys.path.pop()
sys.path.pop()
sys.path.pop()
ray.worker.global_worker.run_function_on_all_workers(f)
def test_running_function_on_all_workers(shutdown_only):
ray.init(num_cpus=1)
def f(worker_info):
sys.path.append("fake_directory")
ray.worker.global_worker.run_function_on_all_workers(f)
@ray.remote
def get_path1():
return sys.path
assert "fake_directory" == ray.get(get_path1.remote())[-1]
def f(worker_info):
sys.path.pop(-1)
ray.worker.global_worker.run_function_on_all_workers(f)
# Create a second remote function to guarantee that when we call
# get_path2.remote(), the second function to run will have been run on
# the worker.
@ray.remote
def get_path2():
return sys.path
assert "fake_directory" not in ray.get(get_path2.remote())
def test_profiling_api(shutdown_only):
ray.init(num_cpus=2)
@ray.remote
def f():
with ray.profile(
"custom_event",
extra_data={"name": "custom name"}) as ray_prof:
ray_prof.set_attribute("key", "value")
ray.put(1)
object_id = f.remote()
ray.wait([object_id])
ray.get(object_id)
# Wait until all of the profiling information appears in the profile
# table.
timeout_seconds = 20
start_time = time.time()
while True:
if time.time() - start_time > timeout_seconds:
raise Exception("Timed out while waiting for information in "
"profile table.")
profile_data = ray.global_state.chrome_tracing_dump()
event_types = {event["cat"] for event in profile_data}
expected_types = [
"worker_idle",
"task",
"task:deserialize_arguments",
"task:execute",
"task:store_outputs",
"wait_for_function",
"ray.get",
"ray.put",
"ray.wait",
"submit_task",
"fetch_and_run_function",
"register_remote_function",
"custom_event", # This is the custom one from ray.profile.
]
if all(expected_type in event_types
for expected_type in expected_types):
break
@pytest.fixture()
def ray_start_cluster():
cluster = ray.test.cluster_utils.Cluster()
yield cluster
# The code after the yield will run as teardown code.
ray.shutdown()
cluster.shutdown()
def test_object_transfer_dump(ray_start_cluster):
cluster = ray_start_cluster
num_nodes = 3
for i in range(num_nodes):
cluster.add_node(resources={str(i): 1}, object_store_memory=10**9)
ray.init(redis_address=cluster.redis_address)
@ray.remote
def f(x):
return
# These objects will live on different nodes.
object_ids = [
f._remote(args=[1], resources={str(i): 1}) for i in range(num_nodes)
]
# Broadcast each object from each machine to each other machine.
for object_id in object_ids:
ray.get([
f._remote(args=[object_id], resources={str(i): 1})
for i in range(num_nodes)
])
# The profiling information only flushes once every second.
time.sleep(1.1)
transfer_dump = ray.global_state.chrome_tracing_object_transfer_dump()
# Make sure the transfer dump can be serialized with JSON.
json.loads(json.dumps(transfer_dump))
assert len(transfer_dump) >= num_nodes**2
assert len({
event["pid"]
for event in transfer_dump if event["name"] == "transfer_receive"
}) == num_nodes
assert len({
event["pid"]
for event in transfer_dump if event["name"] == "transfer_send"
}) == num_nodes
def test_identical_function_names(shutdown_only):
# Define a bunch of remote functions and make sure that we don't
# accidentally call an older version.
ray.init(num_cpus=1)
num_calls = 200
@ray.remote
def f():
return 1
results1 = [f.remote() for _ in range(num_calls)]
@ray.remote
def f():
return 2
results2 = [f.remote() for _ in range(num_calls)]
@ray.remote
def f():
return 3
results3 = [f.remote() for _ in range(num_calls)]
@ray.remote
def f():
return 4
results4 = [f.remote() for _ in range(num_calls)]
@ray.remote
def f():
return 5
results5 = [f.remote() for _ in range(num_calls)]
assert ray.get(results1) == num_calls * [1]
assert ray.get(results2) == num_calls * [2]
assert ray.get(results3) == num_calls * [3]
assert ray.get(results4) == num_calls * [4]
assert ray.get(results5) == num_calls * [5]
@ray.remote
def g():
return 1
@ray.remote # noqa: F811
def g():
return 2
@ray.remote # noqa: F811
def g():
return 3
@ray.remote # noqa: F811
def g():
return 4
@ray.remote # noqa: F811
def g():
return 5
result_values = ray.get([g.remote() for _ in range(num_calls)])
assert result_values == num_calls * [5]
def test_illegal_api_calls(shutdown_only):
ray.init(num_cpus=1)
# Verify that we cannot call put on an ObjectID.
x = ray.put(1)
with pytest.raises(Exception):
ray.put(x)
# Verify that we cannot call get on a regular value.
with pytest.raises(Exception):
ray.get(3)
def test_multithreading(shutdown_only):
# This test requires at least 2 CPUs to finish since the worker does not
# relase resources when joining the threads.
ray.init(num_cpus=2)
@ray.remote
def f():
pass
def g(n):
for _ in range(1000 // n):
ray.get([f.remote() for _ in range(n)])
res = [ray.put(i) for i in range(1000 // n)]
ray.wait(res, len(res))
def test_multi_threading():
threads = [
threading.Thread(target=g, args=(n, ))
for n in [1, 5, 10, 100, 1000]
]
[thread.start() for thread in threads]
[thread.join() for thread in threads]
@ray.remote
def test_multi_threading_in_worker():
test_multi_threading()
def block(args, n):
ray.wait(args, num_returns=n)
ray.get(args[:n])
@ray.remote
class MultithreadedActor(object):
def __init__(self):
pass
def spawn(self):
objects = [f.remote() for _ in range(1000)]
self.threads = [
threading.Thread(target=block, args=(objects, n))
for n in [1, 5, 10, 100, 1000]
]
[thread.start() for thread in self.threads]
def join(self):
[thread.join() for thread in self.threads]
# test multi-threading in the driver
test_multi_threading()
# test multi-threading in the worker
ray.get(test_multi_threading_in_worker.remote())
# test multi-threading in the actor
a = MultithreadedActor.remote()
ray.get(a.spawn.remote())
ray.get(a.join.remote())
def test_free_objects_multi_node(shutdown_only):
# This test will do following:
# 1. Create 3 raylets that each hold an actor.
# 2. Each actor creates an object which is the deletion target.
# 3. Invoke 64 methods on each actor to flush plasma client.
# 4. After flushing, the plasma client releases the targets.
# 5. Check that the deletion targets have been deleted.
# Caution: if remote functions are used instead of actor methods,
# one raylet may create more than one worker to execute the
# tasks, so the flushing operations may be executed in different
# workers and the plasma client holding the deletion target
# may not be flushed.
config = json.dumps({"object_manager_repeated_push_delay_ms": 1000})
ray.worker._init(
start_ray_local=True,
num_local_schedulers=3,
num_cpus=[1, 1, 1],
resources=[{
"Custom0": 1
}, {
"Custom1": 1
}, {
"Custom2": 1
}],
_internal_config=config)
@ray.remote(resources={"Custom0": 1})
class ActorOnNode0(object):
def get(self):
return ray.worker.global_worker.plasma_client.store_socket_name
@ray.remote(resources={"Custom1": 1})
class ActorOnNode1(object):
def get(self):
return ray.worker.global_worker.plasma_client.store_socket_name
@ray.remote(resources={"Custom2": 1})
class ActorOnNode2(object):
def get(self):
return ray.worker.global_worker.plasma_client.store_socket_name
def create(actors):
a = actors[0].get.remote()
b = actors[1].get.remote()
c = actors[2].get.remote()
(l1, l2) = ray.wait([a, b, c], num_returns=3)
assert len(l1) == 3
assert len(l2) == 0
return (a, b, c)
def flush(actors):
# Flush the Release History.
# Current Plasma Client Cache will maintain 64-item list.
# If the number changed, this will fail.
logger.info("Start Flush!")
for i in range(64):
ray.get([actor.get.remote() for actor in actors])
logger.info("Flush finished!")
def run_one_test(actors, local_only):
(a, b, c) = create(actors)
# The three objects should be generated on different object stores.
assert ray.get(a) != ray.get(b)
assert ray.get(a) != ray.get(c)
assert ray.get(c) != ray.get(b)
ray.internal.free([a, b, c], local_only=local_only)
flush(actors)
return (a, b, c)
actors = [
ActorOnNode0.remote(),
ActorOnNode1.remote(),
ActorOnNode2.remote()
]
# Case 1: run this local_only=False. All 3 objects will be deleted.
(a, b, c) = run_one_test(actors, False)
(l1, l2) = ray.wait([a, b, c], timeout=10, num_returns=1)
# All the objects are deleted.
assert len(l1) == 0
assert len(l2) == 3
# Case 2: run this local_only=True. Only 1 object will be deleted.
(a, b, c) = run_one_test(actors, True)
(l1, l2) = ray.wait([a, b, c], timeout=10, num_returns=3)
# One object is deleted and 2 objects are not.
assert len(l1) == 2
assert len(l2) == 1
# The deleted object will have the same store with the driver.
local_return = ray.worker.global_worker.plasma_client.store_socket_name
for object_id in l1:
assert ray.get(object_id) != local_return
def test_local_mode(shutdown_only):
@ray.remote
def local_mode_f():
return np.array([0, 0])
@ray.remote
def local_mode_g(x):
x[0] = 1
return x
ray.init(local_mode=True)
@ray.remote
def f():
return np.ones([3, 4, 5])
xref = f.remote()
# Remote functions should return by value.
assert_equal(xref, np.ones([3, 4, 5]))
# Check that ray.get is the identity.
assert_equal(xref, ray.get(xref))
y = np.random.normal(size=[11, 12])
# Check that ray.put is the identity.
assert_equal(y, ray.put(y))
# Make sure objects are immutable, this example is why we need to copy
# arguments before passing them into remote functions in python mode
aref = local_mode_f.remote()
assert_equal(aref, np.array([0, 0]))
bref = local_mode_g.remote(aref)
# Make sure local_mode_g does not mutate aref.
assert_equal(aref, np.array([0, 0]))
assert_equal(bref, np.array([1, 0]))
# wait should return the first num_returns values passed in as the
# first list and the remaining values as the second list
num_returns = 5
object_ids = [ray.put(i) for i in range(20)]
ready, remaining = ray.wait(
object_ids, num_returns=num_returns, timeout=None)
assert_equal(ready, object_ids[:num_returns])
assert_equal(remaining, object_ids[num_returns:])
# Test actors in LOCAL_MODE.
@ray.remote
class LocalModeTestClass(object):
def __init__(self, array):
self.array = array
def set_array(self, array):
self.array = array
def get_array(self):
return self.array
def modify_and_set_array(self, array):
array[0] = -1
self.array = array
test_actor = LocalModeTestClass.remote(np.arange(10))
# Remote actor functions should return by value
assert_equal(test_actor.get_array.remote(), np.arange(10))
test_array = np.arange(10)
# Remote actor functions should not mutate arguments
test_actor.modify_and_set_array.remote(test_array)
assert_equal(test_array, np.arange(10))
# Remote actor functions should keep state
test_array[0] = -1
assert_equal(test_array, test_actor.get_array.remote())
# Check that actor handles work in Python mode.
@ray.remote
def use_actor_handle(handle):
array = np.ones(10)
handle.set_array.remote(array)
assert np.alltrue(array == ray.get(handle.get_array.remote()))
ray.get(use_actor_handle.remote(test_actor))
def test_resource_constraints(shutdown_only):
num_workers = 20
ray.init(num_cpus=10, num_gpus=2)
@ray.remote(num_cpus=0)
def get_worker_id():
time.sleep(0.1)
return os.getpid()
# Attempt to wait for all of the workers to start up.
while True:
if len(
set(
ray.get([
get_worker_id.remote() for _ in range(num_workers)
]))) == num_workers:
break
time_buffer = 0.3
# At most 10 copies of this can run at once.
@ray.remote(num_cpus=1)
def f(n):
time.sleep(n)
start_time = time.time()
ray.get([f.remote(0.5) for _ in range(10)])
duration = time.time() - start_time
assert duration < 0.5 + time_buffer
assert duration > 0.5
start_time = time.time()
ray.get([f.remote(0.5) for _ in range(11)])
duration = time.time() - start_time
assert duration < 1 + time_buffer
assert duration > 1
@ray.remote(num_cpus=3)
def f(n):
time.sleep(n)
start_time = time.time()
ray.get([f.remote(0.5) for _ in range(3)])
duration = time.time() - start_time
assert duration < 0.5 + time_buffer
assert duration > 0.5
start_time = time.time()
ray.get([f.remote(0.5) for _ in range(4)])
duration = time.time() - start_time
assert duration < 1 + time_buffer
assert duration > 1
@ray.remote(num_gpus=1)
def f(n):
time.sleep(n)
start_time = time.time()
ray.get([f.remote(0.5) for _ in range(2)])
duration = time.time() - start_time
assert duration < 0.5 + time_buffer
assert duration > 0.5
start_time = time.time()
ray.get([f.remote(0.5) for _ in range(3)])
duration = time.time() - start_time
assert duration < 1 + time_buffer
assert duration > 1
start_time = time.time()
ray.get([f.remote(0.5) for _ in range(4)])
duration = time.time() - start_time
assert duration < 1 + time_buffer
assert duration > 1
def test_multi_resource_constraints(shutdown_only):
num_workers = 20
ray.init(num_cpus=10, num_gpus=10)
@ray.remote(num_cpus=0)
def get_worker_id():
time.sleep(0.1)
return os.getpid()
# Attempt to wait for all of the workers to start up.
while True:
if len(
set(
ray.get([
get_worker_id.remote() for _ in range(num_workers)
]))) == num_workers:
break
@ray.remote(num_cpus=1, num_gpus=9)
def f(n):
time.sleep(n)
@ray.remote(num_cpus=9, num_gpus=1)
def g(n):
time.sleep(n)
time_buffer = 0.3
start_time = time.time()
ray.get([f.remote(0.5), g.remote(0.5)])
duration = time.time() - start_time
assert duration < 0.5 + time_buffer
assert duration > 0.5
start_time = time.time()
ray.get([f.remote(0.5), f.remote(0.5)])
duration = time.time() - start_time
assert duration < 1 + time_buffer
assert duration > 1
start_time = time.time()
ray.get([g.remote(0.5), g.remote(0.5)])
duration = time.time() - start_time
assert duration < 1 + time_buffer
assert duration > 1
start_time = time.time()
ray.get([f.remote(0.5), f.remote(0.5), g.remote(0.5), g.remote(0.5)])
duration = time.time() - start_time
assert duration < 1 + time_buffer
assert duration > 1
def test_gpu_ids(shutdown_only):
num_gpus = 10
ray.init(num_cpus=10, num_gpus=num_gpus)
@ray.remote(num_gpus=0)
def f0():
time.sleep(0.1)
gpu_ids = ray.get_gpu_ids()
assert len(gpu_ids) == 0
assert (os.environ["CUDA_VISIBLE_DEVICES"] == ",".join(
[str(i) for i in gpu_ids]))
for gpu_id in gpu_ids:
assert gpu_id in range(num_gpus)
return gpu_ids
@ray.remote(num_gpus=1)
def f1():
time.sleep(0.1)
gpu_ids = ray.get_gpu_ids()
assert len(gpu_ids) == 1
assert (os.environ["CUDA_VISIBLE_DEVICES"] == ",".join(
[str(i) for i in gpu_ids]))
for gpu_id in gpu_ids:
assert gpu_id in range(num_gpus)
return gpu_ids
@ray.remote(num_gpus=2)
def f2():
time.sleep(0.1)
gpu_ids = ray.get_gpu_ids()
assert len(gpu_ids) == 2
assert (os.environ["CUDA_VISIBLE_DEVICES"] == ",".join(
[str(i) for i in gpu_ids]))
for gpu_id in gpu_ids:
assert gpu_id in range(num_gpus)
return gpu_ids
@ray.remote(num_gpus=3)
def f3():
time.sleep(0.1)
gpu_ids = ray.get_gpu_ids()
assert len(gpu_ids) == 3
assert (os.environ["CUDA_VISIBLE_DEVICES"] == ",".join(
[str(i) for i in gpu_ids]))
for gpu_id in gpu_ids:
assert gpu_id in range(num_gpus)
return gpu_ids
@ray.remote(num_gpus=4)
def f4():
time.sleep(0.1)
gpu_ids = ray.get_gpu_ids()
assert len(gpu_ids) == 4
assert (os.environ["CUDA_VISIBLE_DEVICES"] == ",".join(
[str(i) for i in gpu_ids]))
for gpu_id in gpu_ids:
assert gpu_id in range(num_gpus)
return gpu_ids
@ray.remote(num_gpus=5)
def f5():
time.sleep(0.1)
gpu_ids = ray.get_gpu_ids()
assert len(gpu_ids) == 5
assert (os.environ["CUDA_VISIBLE_DEVICES"] == ",".join(
[str(i) for i in gpu_ids]))
for gpu_id in gpu_ids:
assert gpu_id in range(num_gpus)
return gpu_ids
# Wait for all workers to start up.
@ray.remote
def f():
time.sleep(0.1)
return os.getpid()
start_time = time.time()
while True:
if len(set(ray.get([f.remote() for _ in range(10)]))) == 10:
break
if time.time() > start_time + 10:
raise Exception("Timed out while waiting for workers to start "
"up.")
list_of_ids = ray.get([f0.remote() for _ in range(10)])
assert list_of_ids == 10 * [[]]
list_of_ids = ray.get([f1.remote() for _ in range(10)])
set_of_ids = {tuple(gpu_ids) for gpu_ids in list_of_ids}
assert set_of_ids == {(i, ) for i in range(10)}
list_of_ids = ray.get([f2.remote(), f4.remote(), f4.remote()])
all_ids = [gpu_id for gpu_ids in list_of_ids for gpu_id in gpu_ids]
assert set(all_ids) == set(range(10))
remaining = [f5.remote() for _ in range(20)]
for _ in range(10):
t1 = time.time()
ready, remaining = ray.wait(remaining, num_returns=2)
t2 = time.time()
# There are only 10 GPUs, and each task uses 2 GPUs, so there
# should only be 2 tasks scheduled at a given time, so if we wait
# for 2 tasks to finish, then it should take at least 0.1 seconds
# for each pair of tasks to finish.
assert t2 - t1 > 0.09
list_of_ids = ray.get(ready)
all_ids = [gpu_id for gpu_ids in list_of_ids for gpu_id in gpu_ids]
# Commenting out the below assert because it seems to fail a lot.
# assert set(all_ids) == set(range(10))
# Test that actors have CUDA_VISIBLE_DEVICES set properly.
@ray.remote
class Actor0(object):
def __init__(self):
gpu_ids = ray.get_gpu_ids()
assert len(gpu_ids) == 0
assert (os.environ["CUDA_VISIBLE_DEVICES"] == ",".join(
[str(i) for i in gpu_ids]))
# Set self.x to make sure that we got here.
self.x = 1
def test(self):
gpu_ids = ray.get_gpu_ids()
assert len(gpu_ids) == 0
assert (os.environ["CUDA_VISIBLE_DEVICES"] == ",".join(
[str(i) for i in gpu_ids]))
return self.x
@ray.remote(num_gpus=1)
class Actor1(object):
def __init__(self):
gpu_ids = ray.get_gpu_ids()
assert len(gpu_ids) == 1
assert (os.environ["CUDA_VISIBLE_DEVICES"] == ",".join(
[str(i) for i in gpu_ids]))
# Set self.x to make sure that we got here.
self.x = 1
def test(self):
gpu_ids = ray.get_gpu_ids()
assert len(gpu_ids) == 1
assert (os.environ["CUDA_VISIBLE_DEVICES"] == ",".join(
[str(i) for i in gpu_ids]))
return self.x
a0 = Actor0.remote()
ray.get(a0.test.remote())
a1 = Actor1.remote()
ray.get(a1.test.remote())
def test_zero_cpus(shutdown_only):
ray.init(num_cpus=0)
@ray.remote(num_cpus=0)
def f():
return 1
# The task should be able to execute.
ray.get(f.remote())
def test_zero_cpus_actor(shutdown_only):
ray.worker._init(
start_ray_local=True, num_local_schedulers=2, num_cpus=[0, 2])
local_plasma = ray.worker.global_worker.plasma_client.store_socket_name
@ray.remote
class Foo(object):
def method(self):
return ray.worker.global_worker.plasma_client.store_socket_name
# Make sure tasks and actors run on the remote local scheduler.
a = Foo.remote()
assert ray.get(a.method.remote()) != local_plasma
def test_fractional_resources(shutdown_only):
ray.init(num_cpus=6, num_gpus=3, resources={"Custom": 1})
@ray.remote(num_gpus=0.5)
class Foo1(object):
def method(self):
gpu_ids = ray.get_gpu_ids()
assert len(gpu_ids) == 1
return gpu_ids[0]
foos = [Foo1.remote() for _ in range(6)]
gpu_ids = ray.get([f.method.remote() for f in foos])
for i in range(3):
assert gpu_ids.count(i) == 2
del foos
@ray.remote
class Foo2(object):
def method(self):
pass
# Create an actor that requires 0.7 of the custom resource.
f1 = Foo2._remote([], {}, resources={"Custom": 0.7})
ray.get(f1.method.remote())
# Make sure that we cannot create an actor that requires 0.7 of the
# custom resource. TODO(rkn): Re-enable this once ray.wait is
# implemented.
f2 = Foo2._remote([], {}, resources={"Custom": 0.7})
ready, _ = ray.wait([f2.method.remote()], timeout=500)
assert len(ready) == 0
# Make sure we can start an actor that requries only 0.3 of the custom
# resource.
f3 = Foo2._remote([], {}, resources={"Custom": 0.3})
ray.get(f3.method.remote())
del f1, f3
# Make sure that we get exceptions if we submit tasks that require a
# fractional number of resources greater than 1.
@ray.remote(num_cpus=1.5)
def test():
pass
with pytest.raises(ValueError):
test.remote()
with pytest.raises(ValueError):
Foo2._remote([], {}, resources={"Custom": 1.5})
def test_multiple_local_schedulers(shutdown_only):
# This test will define a bunch of tasks that can only be assigned to
# specific local schedulers, and we will check that they are assigned
# to the correct local schedulers.
address_info = ray.worker._init(
start_ray_local=True,
num_local_schedulers=3,
num_cpus=[11, 5, 10],
num_gpus=[0, 5, 1])
# Define a bunch of remote functions that all return the socket name of
# the plasma store. Since there is a one-to-one correspondence between
# plasma stores and local schedulers (at least right now), this can be
# used to identify which local scheduler the task was assigned to.
# This must be run on the zeroth local scheduler.
@ray.remote(num_cpus=11)
def run_on_0():
return ray.worker.global_worker.plasma_client.store_socket_name
# This must be run on the first local scheduler.
@ray.remote(num_gpus=2)
def run_on_1():
return ray.worker.global_worker.plasma_client.store_socket_name
# This must be run on the second local scheduler.
@ray.remote(num_cpus=6, num_gpus=1)
def run_on_2():
return ray.worker.global_worker.plasma_client.store_socket_name
# This can be run anywhere.
@ray.remote(num_cpus=0, num_gpus=0)
def run_on_0_1_2():
return ray.worker.global_worker.plasma_client.store_socket_name
# This must be run on the first or second local scheduler.
@ray.remote(num_gpus=1)
def run_on_1_2():
return ray.worker.global_worker.plasma_client.store_socket_name
# This must be run on the zeroth or second local scheduler.
@ray.remote(num_cpus=8)
def run_on_0_2():
return ray.worker.global_worker.plasma_client.store_socket_name
def run_lots_of_tasks():
names = []
results = []
for i in range(100):
index = np.random.randint(6)
if index == 0:
names.append("run_on_0")
results.append(run_on_0.remote())
elif index == 1:
names.append("run_on_1")
results.append(run_on_1.remote())
elif index == 2:
names.append("run_on_2")
results.append(run_on_2.remote())
elif index == 3:
names.append("run_on_0_1_2")
results.append(run_on_0_1_2.remote())
elif index == 4:
names.append("run_on_1_2")
results.append(run_on_1_2.remote())
elif index == 5:
names.append("run_on_0_2")
results.append(run_on_0_2.remote())
return names, results
store_names = address_info["object_store_addresses"]
def validate_names_and_results(names, results):
for name, result in zip(names, ray.get(results)):
if name == "run_on_0":
assert result in [store_names[0]]
elif name == "run_on_1":
assert result in [store_names[1]]
elif name == "run_on_2":
assert result in [store_names[2]]
elif name == "run_on_0_1_2":
assert (result in [
store_names[0], store_names[1], store_names[2]
])
elif name == "run_on_1_2":
assert result in [store_names[1], store_names[2]]
elif name == "run_on_0_2":
assert result in [store_names[0], store_names[2]]
else:
raise Exception("This should be unreachable.")
assert set(ray.get(results)) == set(store_names)
names, results = run_lots_of_tasks()
validate_names_and_results(names, results)
# Make sure the same thing works when this is nested inside of a task.
@ray.remote
def run_nested1():
names, results = run_lots_of_tasks()
return names, results
@ray.remote
def run_nested2():
names, results = ray.get(run_nested1.remote())
return names, results
names, results = ray.get(run_nested2.remote())
validate_names_and_results(names, results)
def test_custom_resources(shutdown_only):
ray.worker._init(
start_ray_local=True,
num_local_schedulers=2,
num_cpus=[3, 3],
resources=[{
"CustomResource": 0
}, {
"CustomResource": 1
}])
@ray.remote
def f():
time.sleep(0.001)
return ray.worker.global_worker.plasma_client.store_socket_name
@ray.remote(resources={"CustomResource": 1})
def g():
time.sleep(0.001)
return ray.worker.global_worker.plasma_client.store_socket_name
@ray.remote(resources={"CustomResource": 1})
def h():
ray.get([f.remote() for _ in range(5)])
return ray.worker.global_worker.plasma_client.store_socket_name
# The f tasks should be scheduled on both local schedulers.
assert len(set(ray.get([f.remote() for _ in range(50)]))) == 2
local_plasma = ray.worker.global_worker.plasma_client.store_socket_name
# The g tasks should be scheduled only on the second local scheduler.
local_scheduler_ids = set(ray.get([g.remote() for _ in range(50)]))
assert len(local_scheduler_ids) == 1
assert list(local_scheduler_ids)[0] != local_plasma
# Make sure that resource bookkeeping works when a task that uses a
# custom resources gets blocked.
ray.get([h.remote() for _ in range(5)])
def test_two_custom_resources(shutdown_only):
ray.worker._init(
start_ray_local=True,
num_local_schedulers=2,
num_cpus=[3, 3],
resources=[{
"CustomResource1": 1,
"CustomResource2": 2
}, {
"CustomResource1": 3,
"CustomResource2": 4
}])
@ray.remote(resources={"CustomResource1": 1})
def f():
time.sleep(0.001)
return ray.worker.global_worker.plasma_client.store_socket_name
@ray.remote(resources={"CustomResource2": 1})
def g():
time.sleep(0.001)
return ray.worker.global_worker.plasma_client.store_socket_name
@ray.remote(resources={"CustomResource1": 1, "CustomResource2": 3})
def h():
time.sleep(0.001)
return ray.worker.global_worker.plasma_client.store_socket_name
@ray.remote(resources={"CustomResource1": 4})
def j():
time.sleep(0.001)
return ray.worker.global_worker.plasma_client.store_socket_name
@ray.remote(resources={"CustomResource3": 1})
def k():
time.sleep(0.001)
return ray.worker.global_worker.plasma_client.store_socket_name
# The f and g tasks should be scheduled on both local schedulers.
assert len(set(ray.get([f.remote() for _ in range(50)]))) == 2
assert len(set(ray.get([g.remote() for _ in range(50)]))) == 2
local_plasma = ray.worker.global_worker.plasma_client.store_socket_name
# The h tasks should be scheduled only on the second local scheduler.
local_scheduler_ids = set(ray.get([h.remote() for _ in range(50)]))
assert len(local_scheduler_ids) == 1
assert list(local_scheduler_ids)[0] != local_plasma
# Make sure that tasks with unsatisfied custom resource requirements do
# not get scheduled.
ready_ids, remaining_ids = ray.wait([j.remote(), k.remote()], timeout=500)
assert ready_ids == []
def test_many_custom_resources(shutdown_only):
num_custom_resources = 10000
total_resources = {
str(i): np.random.randint(1, 7)
for i in range(num_custom_resources)
}
ray.init(num_cpus=5, resources=total_resources)
def f():
return 1
remote_functions = []
for _ in range(20):
num_resources = np.random.randint(0, num_custom_resources + 1)
permuted_resources = np.random.permutation(
num_custom_resources)[:num_resources]
random_resources = {
str(i): total_resources[str(i)]
for i in permuted_resources
}
remote_function = ray.remote(resources=random_resources)(f)
remote_functions.append(remote_function)
remote_functions.append(ray.remote(f))
remote_functions.append(ray.remote(resources=total_resources)(f))
results = []
for remote_function in remote_functions:
results.append(remote_function.remote())
results.append(remote_function.remote())
results.append(remote_function.remote())
ray.get(results)
@pytest.fixture
def save_gpu_ids_shutdown_only():
# Record the curent value of this environment variable so that we can
# reset it after the test.
original_gpu_ids = os.environ.get("CUDA_VISIBLE_DEVICES", None)
yield None
# The code after the yield will run as teardown code.
ray.shutdown()
# Reset the environment variable.
if original_gpu_ids is not None:
os.environ["CUDA_VISIBLE_DEVICES"] = original_gpu_ids
else:
del os.environ["CUDA_VISIBLE_DEVICES"]
def test_specific_gpus(save_gpu_ids_shutdown_only):
allowed_gpu_ids = [4, 5, 6]
os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(
[str(i) for i in allowed_gpu_ids])
ray.init(num_gpus=3)
@ray.remote(num_gpus=1)
def f():
gpu_ids = ray.get_gpu_ids()
assert len(gpu_ids) == 1
assert gpu_ids[0] in allowed_gpu_ids
@ray.remote(num_gpus=2)
def g():
gpu_ids = ray.get_gpu_ids()
assert len(gpu_ids) == 2
assert gpu_ids[0] in allowed_gpu_ids
assert gpu_ids[1] in allowed_gpu_ids
ray.get([f.remote() for _ in range(100)])
ray.get([g.remote() for _ in range(100)])
def test_blocking_tasks(shutdown_only):
ray.init(num_cpus=1)
@ray.remote
def f(i, j):
return (i, j)
@ray.remote
def g(i):
# Each instance of g submits and blocks on the result of another
# remote task.
object_ids = [f.remote(i, j) for j in range(2)]
return ray.get(object_ids)
@ray.remote
def h(i):
# Each instance of g submits and blocks on the result of another
# remote task using ray.wait.
object_ids = [f.remote(i, j) for j in range(2)]
return ray.wait(object_ids, num_returns=len(object_ids))
ray.get([h.remote(i) for i in range(4)])
@ray.remote
def _sleep(i):
time.sleep(0.01)
return (i)
@ray.remote
def sleep():
# Each instance of sleep submits and blocks on the result of
# another remote task, which takes some time to execute.
ray.get([_sleep.remote(i) for i in range(10)])
ray.get(sleep.remote())
def test_max_call_tasks(shutdown_only):
ray.init(num_cpus=1)
@ray.remote(max_calls=1)
def f():
return os.getpid()
pid = ray.get(f.remote())
ray.test.test_utils.wait_for_pid_to_exit(pid)
@ray.remote(max_calls=2)
def f():
return os.getpid()
pid1 = ray.get(f.remote())
pid2 = ray.get(f.remote())
assert pid1 == pid2
ray.test.test_utils.wait_for_pid_to_exit(pid1)
def attempt_to_load_balance(remote_function,
args,
total_tasks,
num_local_schedulers,
minimum_count,
num_attempts=100):
attempts = 0
while attempts < num_attempts:
locations = ray.get(
[remote_function.remote(*args) for _ in range(total_tasks)])
names = set(locations)
counts = [locations.count(name) for name in names]
logger.info("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
def test_load_balancing(shutdown_only):
# This test ensures that tasks are being assigned to all local
# schedulers in a roughly equal manner.
num_local_schedulers = 3
num_cpus = 7
ray.worker._init(
start_ray_local=True,
num_local_schedulers=num_local_schedulers,
num_cpus=num_cpus)
@ray.remote
def f():
time.sleep(0.01)
return ray.worker.global_worker.plasma_client.store_socket_name
attempt_to_load_balance(f, [], 100, num_local_schedulers, 10)
attempt_to_load_balance(f, [], 1000, num_local_schedulers, 100)
def test_load_balancing_with_dependencies(shutdown_only):
# This test ensures that tasks are being assigned to all local
# schedulers in a roughly equal manner even when the tasks have
# dependencies.
num_local_schedulers = 3
ray.worker._init(
start_ray_local=True,
num_local_schedulers=num_local_schedulers,
num_cpus=1)
@ray.remote
def f(x):
time.sleep(0.010)
return ray.worker.global_worker.plasma_client.store_socket_name
# This object will be local to one of the local schedulers. Make sure
# this doesn't prevent tasks from being scheduled on other local
# schedulers.
x = ray.put(np.zeros(1000000))
attempt_to_load_balance(f, [x], 100, num_local_schedulers, 25)
def wait_for_num_tasks(num_tasks, timeout=10):
start_time = time.time()
while time.time() - start_time < timeout:
if len(ray.global_state.task_table()) >= num_tasks:
return
time.sleep(0.1)
raise Exception("Timed out while waiting for global state.")
def wait_for_num_objects(num_objects, timeout=10):
start_time = time.time()
while time.time() - start_time < timeout:
if len(ray.global_state.object_table()) >= num_objects:
return
time.sleep(0.1)
raise Exception("Timed out while waiting for global state.")
@pytest.mark.skipif(
os.environ.get("RAY_USE_NEW_GCS") == "on",
reason="New GCS API doesn't have a Python API yet.")
def test_global_state_api(shutdown_only):
with pytest.raises(Exception):
ray.global_state.object_table()
with pytest.raises(Exception):
ray.global_state.task_table()
with pytest.raises(Exception):
ray.global_state.client_table()
with pytest.raises(Exception):
ray.global_state.function_table()
with pytest.raises(Exception):
ray.global_state.log_files()
ray.init(num_cpus=5, num_gpus=3, resources={"CustomResource": 1})
resources = {"CPU": 5, "GPU": 3, "CustomResource": 1}
assert ray.global_state.cluster_resources() == resources
assert ray.global_state.object_table() == {}
driver_id = ray.experimental.state.binary_to_hex(
ray.worker.global_worker.worker_id)
driver_task_id = ray.experimental.state.binary_to_hex(
ray.worker.global_worker.current_task_id.id())
# One task is put in the task table which corresponds to this driver.
wait_for_num_tasks(1)
task_table = ray.global_state.task_table()
assert len(task_table) == 1
assert driver_task_id == list(task_table.keys())[0]
task_spec = task_table[driver_task_id]["TaskSpec"]
assert task_spec["TaskID"] == driver_task_id
assert task_spec["ActorID"] == ray_constants.ID_SIZE * "ff"
assert task_spec["Args"] == []
assert task_spec["DriverID"] == driver_id
assert task_spec["FunctionID"] == ray_constants.ID_SIZE * "ff"
assert task_spec["ReturnObjectIDs"] == []
client_table = ray.global_state.client_table()
node_ip_address = ray.worker.global_worker.node_ip_address
assert len(client_table) == 1
assert client_table[0]["NodeManagerAddress"] == node_ip_address
@ray.remote
def f(*xs):
return 1
x_id = ray.put(1)
result_id = f.remote(1, "hi", x_id)
# Wait for one additional task to complete.
wait_for_num_tasks(1 + 1)
task_table = ray.global_state.task_table()
assert len(task_table) == 1 + 1
task_id_set = set(task_table.keys())
task_id_set.remove(driver_task_id)
task_id = list(task_id_set)[0]
function_table = ray.global_state.function_table()
task_spec = task_table[task_id]["TaskSpec"]
assert task_spec["ActorID"] == ray_constants.ID_SIZE * "ff"
assert task_spec["Args"] == [1, "hi", x_id]
assert task_spec["DriverID"] == driver_id
assert task_spec["ReturnObjectIDs"] == [result_id]
function_table_entry = function_table[task_spec["FunctionID"]]
assert function_table_entry["Name"] == "runtest.f"
assert function_table_entry["DriverID"] == driver_id
assert function_table_entry["Module"] == "runtest"
assert task_table[task_id] == ray.global_state.task_table(task_id)
# Wait for two objects, one for the x_id and one for result_id.
wait_for_num_objects(2)
def wait_for_object_table():
timeout = 10
start_time = time.time()
while time.time() - start_time < timeout:
object_table = ray.global_state.object_table()
tables_ready = (object_table[x_id]["ManagerIDs"] is not None and
object_table[result_id]["ManagerIDs"] is not None)
if tables_ready:
return
time.sleep(0.1)
raise Exception("Timed out while waiting for object table to "
"update.")
object_table = ray.global_state.object_table()
assert len(object_table) == 2
assert object_table[x_id]["IsEviction"][0] is False
assert object_table[result_id]["IsEviction"][0] is False
assert object_table[x_id] == ray.global_state.object_table(x_id)
object_table_entry = ray.global_state.object_table(result_id)
assert object_table[result_id] == object_table_entry
@pytest.mark.skipif(
os.environ.get("RAY_USE_NEW_GCS") == "on",
reason="New GCS API doesn't have a Python API yet.")
def test_log_file_api(shutdown_only):
ray.init(num_cpus=1, redirect_worker_output=True)
message = "unique message"
@ray.remote
def f():
logger.info(message)
# The call to sys.stdout.flush() seems to be necessary when using
# the system Python 2.7 on Ubuntu.
sys.stdout.flush()
ray.get(f.remote())
# Make sure that the message appears in the log files.
start_time = time.time()
found_message = False
while time.time() - start_time < 10:
log_files = ray.global_state.log_files()
for ip, innerdict in log_files.items():
for filename, contents in innerdict.items():
contents_str = "".join(contents)
if message in contents_str:
found_message = True
if found_message:
break
time.sleep(0.1)
assert found_message is True
@pytest.mark.skipif(
os.environ.get("RAY_USE_NEW_GCS") == "on",
reason="New GCS API doesn't have a Python API yet.")
def test_workers(shutdown_only):
num_workers = 3
ray.init(redirect_worker_output=True, num_cpus=num_workers)
@ray.remote
def f():
return id(ray.worker.global_worker), os.getpid()
# Wait until all of the workers have started.
worker_ids = set()
while len(worker_ids) != num_workers:
worker_ids = set(ray.get([f.remote() for _ in range(10)]))
worker_info = ray.global_state.workers()
assert len(worker_info) >= num_workers
for worker_id, info in worker_info.items():
assert "node_ip_address" in info
assert "plasma_store_socket" in info
assert "stderr_file" in info
assert "stdout_file" in info
def test_specific_driver_id():
dummy_driver_id = ray.ObjectID(b"00112233445566778899")
ray.init(driver_id=dummy_driver_id)
@ray.remote
def f():
return ray.worker.global_worker.task_driver_id.id()
assert_equal(dummy_driver_id.id(), ray.worker.global_worker.worker_id)
task_driver_id = ray.get(f.remote())
assert_equal(dummy_driver_id.id(), task_driver_id)
ray.shutdown()
@pytest.fixture
def shutdown_only_with_initialization_check():
yield None
# The code after the yield will run as teardown code.
ray.shutdown()
assert not ray.is_initialized()
def test_initialized(shutdown_only_with_initialization_check):
assert not ray.is_initialized()
ray.init(num_cpus=0)
assert ray.is_initialized()
def test_initialized_local_mode(shutdown_only_with_initialization_check):
assert not ray.is_initialized()
ray.init(num_cpus=0, local_mode=True)
assert ray.is_initialized()
def test_wait_reconstruction(shutdown_only):
ray.init(num_cpus=1, object_store_memory=10**8)
@ray.remote
def f():
return np.zeros(6 * 10**7, dtype=np.uint8)
x_id = f.remote()
ray.wait([x_id])
ray.wait([f.remote()])
assert not ray.worker.global_worker.plasma_client.contains(
ray.pyarrow.plasma.ObjectID(x_id.id()))
ready_ids, _ = ray.wait([x_id])
assert len(ready_ids) == 1
def test_ray_setproctitle(shutdown_only):
ray.init(num_cpus=2)
@ray.remote
class UniqueName(object):
def __init__(self):
assert setproctitle.getproctitle() == "ray_UniqueName:__init__()"
def f(self):
assert setproctitle.getproctitle() == "ray_UniqueName:f()"
@ray.remote
def unique_1():
assert setproctitle.getproctitle() == "ray_worker:runtest.unique_1()"
actor = UniqueName.remote()
ray.get(actor.f.remote())
ray.get(unique_1.remote())
def test_duplicate_error_messages(shutdown_only):
ray.init(num_cpus=0)
driver_id = ray.ray_constants.NIL_JOB_ID.id()
error_data = ray.gcs_utils.construct_error_message(driver_id, "test",
"message", 0)
# Push the same message to the GCS twice (they are the same because we
# do not include a timestamp).
r = ray.worker.global_worker.redis_client
r.execute_command("RAY.TABLE_APPEND", ray.gcs_utils.TablePrefix.ERROR_INFO,
ray.gcs_utils.TablePubsub.ERROR_INFO, driver_id,
error_data)
# Before https://github.com/ray-project/ray/pull/3316 this would
# give an error
r.execute_command("RAY.TABLE_APPEND", ray.gcs_utils.TablePrefix.ERROR_INFO,
ray.gcs_utils.TablePubsub.ERROR_INFO, driver_id,
error_data)
@pytest.mark.skipif(
os.getenv("TRAVIS") is None,
reason="This test should only be run on Travis.")
def test_ray_stack(shutdown_only):
ray.init(num_cpus=2)
def unique_name_1():
time.sleep(1000)
@ray.remote
def unique_name_2():
time.sleep(1000)
@ray.remote
def unique_name_3():
unique_name_1()
unique_name_2.remote()
unique_name_3.remote()
success = False
start_time = time.time()
while time.time() - start_time < 30:
# Attempt to parse the "ray stack" call.
output = ray.utils.decode(subprocess.check_output(["ray", "stack"]))
if ("unique_name_1" in output and "unique_name_2" in output
and "unique_name_3" in output):
success = True
break
if not success:
raise Exception("Failed to find necessary information with "
"'ray stack'")
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