diff --git a/.travis.yml b/.travis.yml
index 93953b75a..5f1bba0d2 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -85,3 +85,4 @@ script:
   - python test/array_test.py
   - python test/failure_test.py
   - python test/microbenchmarks.py
+  - python test/stress_tests.py
diff --git a/lib/python/ray/services.py b/lib/python/ray/services.py
index 5ba7564c8..5e42fcd63 100644
--- a/lib/python/ray/services.py
+++ b/lib/python/ray/services.py
@@ -66,6 +66,9 @@ def cleanup():
     print("Ray did not shut down properly.")
   all_processes = []
 
+def all_processes_alive():
+  return all([p.poll() is None for p in all_processes])
+
 def start_redis(num_retries=20, cleanup=True):
   """Start a Redis server.
 
diff --git a/test/stress_tests.py b/test/stress_tests.py
new file mode 100644
index 000000000..385a3aceb
--- /dev/null
+++ b/test/stress_tests.py
@@ -0,0 +1,109 @@
+from __future__ import print_function
+
+import unittest
+import ray
+import numpy as np
+import time
+
+class TaskTests(unittest.TestCase):
+
+  def testSubmittingTasks(self):
+    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.remote
+        def f(x):
+          return x
+
+        for _ in range(1):
+          ray.get([f.remote(1) for _ in range(1000)])
+
+        for _ in range(10):
+          ray.get([f.remote(1) for _ in range(100)])
+
+        for _ in range(100):
+          ray.get([f.remote(1) for _ in range(10)])
+
+        for _ in range(1000):
+          ray.get([f.remote(1) for _ in range(1)])
+
+        self.assertTrue(ray.services.all_processes_alive())
+        ray.worker.cleanup()
+
+  def testDependencies(self):
+    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.remote
+        def f(x):
+          return x
+
+        x = 1
+        for _ in range(1000):
+          x = f.remote(x)
+        ray.get(x)
+
+        @ray.remote
+        def g(*xs):
+          return 1
+
+        xs = [g.remote(1)]
+        for _ in range(100):
+          xs.append(g.remote(*xs))
+          xs.append(g.remote(1))
+        ray.get(xs)
+
+        self.assertTrue(ray.services.all_processes_alive())
+        ray.worker.cleanup()
+
+  def testGettingAndPutting(self):
+    ray.init(start_ray_local=True, num_workers=1)
+
+    for n in range(8):
+      x = np.zeros(10 ** n)
+
+      for _ in range(100):
+        ray.put(x)
+
+      x_id = ray.put(x)
+      for _ in range(1000):
+        ray.get(x_id)
+
+    self.assertTrue(ray.services.all_processes_alive())
+    ray.worker.cleanup()
+
+  def testWait(self):
+    # TODO(rkn): Use more local schedulers once the distributed wait
+    # implementation is in place.
+    for num_local_schedulers in [1]:
+      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.remote
+        def f(x):
+          return x
+
+        x_ids = [f.remote(i) for i in range(1000)]
+        for i in range(len(x_ids)):
+          ray.wait([x_ids[i]])
+        for i in range(len(x_ids) - 1):
+          ray.wait(x_ids[i:])
+
+        @ray.remote
+        def g(x):
+          time.sleep(x)
+
+        for i in range(1, 5):
+          x_ids = [g.remote(np.random.uniform(0, i)) for _ in range(4 * num_workers)]
+          ray.wait(x_ids, num_returns=len(x_ids))
+
+        self.assertTrue(ray.services.all_processes_alive())
+        ray.worker.cleanup()
+
+if __name__ == "__main__":
+  unittest.main(verbosity=2)