def testGet(self):
ray.init(start_ray_local=True, num_workers=3)
for cls in [
Foo, Bar, Baz, Qux, SubQux, Exception, CustomError, Point,
NamedTupleExample
]:
ray.register_class(cls)
# Remote objects should be deallocated when the corresponding ObjectID goes
# out of scope, and all results of ray.get called on the ID go out of scope.
for val in RAY_TEST_OBJECTS:
x = ray.put(val)
objectid = x.id
xval = ray.get(x)
del x, xval
self.assertEqual(
ray.scheduler_info()["reference_counts"][objectid], -1)
# Remote objects that do not contain numpy arrays should be deallocated when
# the corresponding ObjectID goes out of scope, even if ray.get has been
# called on the ObjectID.
for val in [
True, False, None, 1, 1.0, 1L, "hi", u"hi", [1, 2, 3],
(1, 2, 3), [(), {
(): ()
}]
]:
x = ray.put(val)
objectid = x.id
xval = ray.get(x)
del x
self.assertEqual(
ray.scheduler_info()["reference_counts"][objectid], -1)
def testObjStore(self):
node_ip_address = "127.0.0.1"
scheduler_address = ray.services.start_ray_local(num_objstores=2, num_workers=0, worker_path=None)
ray.connect(node_ip_address, scheduler_address, mode=ray.SCRIPT_MODE)
objstore_addresses = [objstore_info["address"] for objstore_info in ray.scheduler_info()["objstores"]]
w1 = ray.worker.Worker()
w2 = ray.worker.Worker()
ray.reusables._cached_reusables = [] # This is a hack to make the test run.
ray.connect(node_ip_address, scheduler_address, objstore_address=objstore_addresses[0], mode=ray.SCRIPT_MODE, worker=w1)
ray.reusables._cached_reusables = [] # This is a hack to make the test run.
ray.connect(node_ip_address, scheduler_address, objstore_address=objstore_addresses[1], mode=ray.SCRIPT_MODE, worker=w2)
for cls in [Foo, Bar, Baz, Qux, SubQux, Exception, CustomError, Point, NamedTupleExample]:
ray.register_class(cls)
# putting and getting an object shouldn't change it
for data in RAY_TEST_OBJECTS:
objectid = ray.put(data, w1)
result = ray.get(objectid, w1)
assert_equal(result, data)
# putting an object, shipping it to another worker, and getting it shouldn't change it
for data in RAY_TEST_OBJECTS:
objectid = ray.put(data, w1)
result = ray.get(objectid, w2)
assert_equal(result, data)
# putting an object, shipping it to another worker, and getting it shouldn't change it
for data in RAY_TEST_OBJECTS:
objectid = ray.put(data, w2)
result = ray.get(objectid, w1)
assert_equal(result, data)
# This test fails. See https://github.com/ray-project/ray/issues/159.
# getting multiple times shouldn't matter
# for data in [np.zeros([10, 20]), np.random.normal(size=[45, 25]), np.zeros([10, 20], dtype=np.dtype("float64")), np.zeros([10, 20], dtype=np.dtype("float32")), np.zeros([10, 20], dtype=np.dtype("int64")), np.zeros([10, 20], dtype=np.dtype("int32"))]:
# objectid = worker.put(data, w1)
# result = worker.get(objectid, w2)
# result = worker.get(objectid, w2)
# result = worker.get(objectid, w2)
# assert_equal(result, data)
# Getting a buffer after modifying it before it finishes should return updated buffer
objectid = ray.libraylib.get_objectid(w1.handle)
buf = ray.libraylib.allocate_buffer(w1.handle, objectid, 100)
buf[0][0] = 1
ray.libraylib.finish_buffer(w1.handle, objectid, buf[1], 0)
completedbuffer = ray.libraylib.get_buffer(w1.handle, objectid)
self.assertEqual(completedbuffer[0][0], 1)
# We started multiple drivers manually, so we will disconnect them manually.
ray.disconnect(worker=w1)
ray.disconnect(worker=w2)
ray.worker.cleanup()
def testObjStore(self):
node_ip_address = "127.0.0.1"
scheduler_address = ray.services.start_ray_local(num_objstores=2, num_workers=0, worker_path=None)
ray.connect(node_ip_address, scheduler_address, mode=ray.SCRIPT_MODE)
objstore_addresses = [objstore_info["address"] for objstore_info in ray.scheduler_info()["objstores"]]
w1 = ray.worker.Worker()
w2 = ray.worker.Worker()
ray.reusables._cached_reusables = [] # This is a hack to make the test run.
ray.connect(node_ip_address, scheduler_address, objstore_address=objstore_addresses[0], mode=ray.SCRIPT_MODE, worker=w1)
ray.reusables._cached_reusables = [] # This is a hack to make the test run.
ray.connect(node_ip_address, scheduler_address, objstore_address=objstore_addresses[1], mode=ray.SCRIPT_MODE, worker=w2)
for cls in [Foo, Bar, Baz, Qux, SubQux, Exception, CustomError, Point, NamedTupleExample]:
ray.register_class(cls)
# putting and getting an object shouldn't change it
for data in RAY_TEST_OBJECTS:
objectid = ray.put(data, w1)
result = ray.get(objectid, w1)
assert_equal(result, data)
# putting an object, shipping it to another worker, and getting it shouldn't change it
for data in RAY_TEST_OBJECTS:
objectid = ray.put(data, w1)
result = ray.get(objectid, w2)
assert_equal(result, data)
# putting an object, shipping it to another worker, and getting it shouldn't change it
for data in RAY_TEST_OBJECTS:
objectid = ray.put(data, w2)
result = ray.get(objectid, w1)
assert_equal(result, data)
# This test fails. See https://github.com/ray-project/ray/issues/159.
# getting multiple times shouldn't matter
# for data in [np.zeros([10, 20]), np.random.normal(size=[45, 25]), np.zeros([10, 20], dtype=np.dtype("float64")), np.zeros([10, 20], dtype=np.dtype("float32")), np.zeros([10, 20], dtype=np.dtype("int64")), np.zeros([10, 20], dtype=np.dtype("int32"))]:
# objectid = worker.put(data, w1)
# result = worker.get(objectid, w2)
# result = worker.get(objectid, w2)
# result = worker.get(objectid, w2)
# assert_equal(result, data)
# Getting a buffer after modifying it before it finishes should return updated buffer
objectid = ray.libraylib.get_objectid(w1.handle)
buf = ray.libraylib.allocate_buffer(w1.handle, objectid, 100)
buf[0][0] = 1
ray.libraylib.finish_buffer(w1.handle, objectid, buf[1], 0)
completedbuffer = ray.libraylib.get_buffer(w1.handle, objectid)
self.assertEqual(completedbuffer[0][0], 1)
# We started multiple drivers manually, so we will disconnect them manually.
ray.disconnect(worker=w1)
ray.disconnect(worker=w2)
ray.worker.cleanup()
def testGet(self):
ray.init(start_ray_local=True, num_workers=3)
for cls in [Foo, Bar, Baz, Qux, SubQux, Exception, CustomError, Point, NamedTupleExample]:
ray.register_class(cls)
# Remote objects should be deallocated when the corresponding ObjectID goes
# out of scope, and all results of ray.get called on the ID go out of scope.
for val in RAY_TEST_OBJECTS:
x = ray.put(val)
objectid = x.id
xval = ray.get(x)
del x, xval
self.assertEqual(ray.scheduler_info()["reference_counts"][objectid], -1)
# Remote objects that do not contain numpy arrays should be deallocated when
# the corresponding ObjectID goes out of scope, even if ray.get has been
# called on the ObjectID.
for val in [True, False, None, 1, 1.0, 1L, "hi", u"hi", [1, 2, 3], (1, 2, 3), [(), {(): ()}]]:
x = ray.put(val)
objectid = x.id
xval = ray.get(x)
del x
self.assertEqual(ray.scheduler_info()["reference_counts"][objectid], -1)
def check_everything_deallocated(): reference_counts = ray.scheduler_info()["reference_counts"] self.assertEqual(reference_counts, len(reference_counts) * [-1])
def check_not_deallocated(object_ids):
reference_counts = ray.scheduler_info()["reference_counts"]
for object_id in object_ids:
self.assertGreater(reference_counts[object_id.id], 0)
class ReferenceCountingTest(unittest.TestCase):
def testDeallocation(self):
reload(test_functions)
for module in [ra.core, ra.random, ra.linalg, da.core, da.random, da.linalg]:
reload(module)
ray.init(start_ray_local=True, num_workers=1)
ray.register_class(da.DistArray)
def check_not_deallocated(object_ids):
reference_counts = ray.scheduler_info()["reference_counts"]
for object_id in object_ids:
self.assertGreater(reference_counts[object_id.id], 0)
def check_everything_deallocated():
reference_counts = ray.scheduler_info()["reference_counts"]
self.assertEqual(reference_counts, len(reference_counts) * [-1])
z = da.zeros.remote([da.BLOCK_SIZE, 2 * da.BLOCK_SIZE])
time.sleep(0.1)
objectid_val = z.id
time.sleep(0.1)
check_not_deallocated([z])
del z
time.sleep(0.1)
check_everything_deallocated()
x = ra.zeros.remote([10, 10])
y = ra.zeros.remote([10, 10])
z = ra.dot.remote(x, y)
objectid_val = x.id
time.sleep(0.1)
check_not_deallocated([x, y, z])
del x
time.sleep(0.1)
check_not_deallocated([y, z])
del y
time.sleep(0.1)
check_not_deallocated([z])
del z
time.sleep(0.1)
check_everything_deallocated()
z = da.zeros.remote([4 * da.BLOCK_SIZE])
time.sleep(0.1)
check_not_deallocated(ray.get(z).objectids.tolist())
del z
time.sleep(0.1)
check_everything_deallocated()
ray.worker.cleanup()
def testGet(self):
ray.init(start_ray_local=True, num_workers=3)
for cls in [Foo, Bar, Baz, Qux, SubQux, Exception, CustomError, Point, NamedTupleExample]:
ray.register_class(cls)
# Remote objects should be deallocated when the corresponding ObjectID goes
# out of scope, and all results of ray.get called on the ID go out of scope.
for val in RAY_TEST_OBJECTS:
x = ray.put(val)
objectid = x.id
xval = ray.get(x)
del x, xval
self.assertEqual(ray.scheduler_info()["reference_counts"][objectid], -1)
# Remote objects that do not contain numpy arrays should be deallocated when
# the corresponding ObjectID goes out of scope, even if ray.get has been
# called on the ObjectID.
for val in [True, False, None, 1, 1.0, 1L, "hi", u"hi", [1, 2, 3], (1, 2, 3), [(), {(): ()}]]:
x = ray.put(val)
objectid = x.id
xval = ray.get(x)
del x
self.assertEqual(ray.scheduler_info()["reference_counts"][objectid], -1)
# Remote objects that contain numpy arrays should not be deallocated when
# the corresponding ObjectID goes out of scope, if ray.get has been called
# on the ObjectID and the result of that call is still in scope.
for val in [np.zeros(10), [np.zeros(10)], (((np.zeros(10)),),), {(): np.zeros(10)}, [1, 2, 3, np.zeros(1)]]:
x = ray.put(val)
objectid = x.id
xval = ray.get(x)
del x
self.assertEqual(ray.scheduler_info()["reference_counts"][objectid], 1)
# Getting an object multiple times should not be a problem. And the remote
# object should not be deallocated until both of the results are out of scope.
for val in [np.zeros(10), [np.zeros(10)], (((np.zeros(10)),),), {(): np.zeros(10)}, [1, 2, 3, np.zeros(1)]]:
x = ray.put(val)
objectid = x.id
xval1 = ray.get(x)
xval2 = ray.get(x)
del xval1
# Make sure we can still access xval2.
xval2
del xval2
self.assertEqual(ray.scheduler_info()["reference_counts"][objectid], 1)
xval3 = ray.get(x)
xval4 = ray.get(x)
xval5 = ray.get(x)
del x
del xval4, xval5
# Make sure we can still access xval3.
xval3
self.assertEqual(ray.scheduler_info()["reference_counts"][objectid], 1)
del xval3
self.assertEqual(ray.scheduler_info()["reference_counts"][objectid], -1)
# Getting an object multiple times and assigning it to the same name should
# work. This was a problem in https://github.com/ray-project/ray/issues/159.
for val in [np.zeros(10), [np.zeros(10)], (((np.zeros(10)),),), {(): np.zeros(10)}, [1, 2, 3, np.zeros(1)]]:
x = ray.put(val)
objectid = x.id
xval = ray.get(x)
xval = ray.get(x)
xval = ray.get(x)
xval = ray.get(x)
self.assertEqual(ray.scheduler_info()["reference_counts"][objectid], 1)
del x
self.assertEqual(ray.scheduler_info()["reference_counts"][objectid], 1)
del xval
self.assertEqual(ray.scheduler_info()["reference_counts"][objectid], -1)
ray.worker.cleanup()
def check_everything_deallocated(): reference_counts = ray.scheduler_info()["reference_counts"] self.assertEqual(reference_counts, len(reference_counts) * [-1])
def check_not_deallocated(object_ids):
reference_counts = ray.scheduler_info()["reference_counts"]
for object_id in object_ids:
self.assertGreater(reference_counts[object_id.id], 0)
