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Allow ray.init to take in address information about existing services. (#161)

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* Refactor ray.init and ray.services to allow processes that are already running

* Fix indexing error

* Address Robert's comments
This commit is contained in:
Stephanie Wang authored and Robert Nishihara committed 2016-12-28 14:17:29 -08:00
1 parent baf835efcd
commit c403ab11ab
4 files changed
+328 -133
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test/array_test.py
+1 -1
View File
@@ -66,7 +66,7 @@ class DistributedArrayTest(unittest.TestCase):
def testMethods(self):
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=10, num_local_schedulers=2)
ray.worker._init(start_ray_local=True, num_workers=10, num_local_schedulers=2)
x = da.zeros.remote([9, 25, 51], "float")
assert_equal(ray.get(da.assemble.remote(x)), np.zeros([9, 25, 51]))
test/stress_tests.py
+6 -3
View File
@@ -13,7 +13,8 @@ class TaskTests(unittest.TestCase):
for num_local_schedulers in [1, 4]:
for num_workers_per_scheduler in [4]:
num_workers = num_local_schedulers * num_workers_per_scheduler
ray.init(start_ray_local=True, num_workers=num_workers, num_local_schedulers=num_local_schedulers)
ray.worker._init(start_ray_local=True, num_workers=num_workers,
num_local_schedulers=num_local_schedulers)
@ray.remote
def f(x):
@@ -38,7 +39,8 @@ class TaskTests(unittest.TestCase):
for num_local_schedulers in [1, 4]:
for num_workers_per_scheduler in [4]:
num_workers = num_local_schedulers * num_workers_per_scheduler
ray.init(start_ray_local=True, num_workers=num_workers, num_local_schedulers=num_local_schedulers)
ray.worker._init(start_ray_local=True, num_workers=num_workers,
num_local_schedulers=num_local_schedulers)
@ray.remote
def f(x):
@@ -82,7 +84,8 @@ class TaskTests(unittest.TestCase):
for num_local_schedulers in [1, 4]:
for num_workers_per_scheduler in [4]:
num_workers = num_local_schedulers * num_workers_per_scheduler
ray.init(start_ray_local=True, num_workers=num_workers, num_local_schedulers=num_local_schedulers)
ray.worker._init(start_ray_local=True, num_workers=num_workers,
num_local_schedulers=num_local_schedulers)
@ray.remote
def f(x):