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https://github.com/wassname/ray.git
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Sven
60d4d5e1aa
* Remove all __future__ imports from RLlib. * Remove (object) again from tf_run_builder.py::TFRunBuilder. * Fix 2xLINT warnings. * Fix broken appo_policy import (must be appo_tf_policy) * Remove future imports from all other ray files (not just RLlib). * Remove future imports from all other ray files (not just RLlib). * Remove future import blocks that contain `unicode_literals` as well. Revert appo_tf_policy.py to appo_policy.py (belongs to another PR). * Add two empty lines before Schedule class. * Put back __future__ imports into determine_tests_to_run.py. Fails otherwise on a py2/print related error.
108 lines
2.4 KiB
Python
108 lines
2.4 KiB
Python
import numpy as np
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import pytest
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import time
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import ray
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from ray.cluster_utils import Cluster
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@pytest.fixture(params=[(1, 4), (4, 4)])
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def ray_start_combination(request):
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num_nodes = request.param[0]
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num_workers_per_scheduler = request.param[1]
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# Start the Ray processes.
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cluster = Cluster(
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initialize_head=True,
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head_node_args={
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"num_cpus": 10,
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"redis_max_memory": 10**7
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})
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for i in range(num_nodes - 1):
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cluster.add_node(num_cpus=10)
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ray.init(address=cluster.address)
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yield num_nodes, num_workers_per_scheduler, cluster
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# The code after the yield will run as teardown code.
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ray.shutdown()
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cluster.shutdown()
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def test_submitting_tasks(ray_start_combination):
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_, _, cluster = ray_start_combination
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@ray.remote
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def f(x):
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return x
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for _ in range(1):
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ray.get([f.remote(1) for _ in range(1000)])
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for _ in range(10):
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ray.get([f.remote(1) for _ in range(100)])
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for _ in range(100):
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ray.get([f.remote(1) for _ in range(10)])
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for _ in range(1000):
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ray.get([f.remote(1) for _ in range(1)])
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assert cluster.remaining_processes_alive()
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def test_dependencies(ray_start_combination):
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_, _, cluster = ray_start_combination
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@ray.remote
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def f(x):
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return x
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x = 1
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for _ in range(1000):
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x = f.remote(x)
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ray.get(x)
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@ray.remote
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def g(*xs):
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return 1
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xs = [g.remote(1)]
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for _ in range(100):
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xs.append(g.remote(*xs))
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xs.append(g.remote(1))
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ray.get(xs)
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assert cluster.remaining_processes_alive()
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def test_wait(ray_start_combination):
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num_nodes, num_workers_per_scheduler, cluster = ray_start_combination
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num_workers = num_nodes * num_workers_per_scheduler
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@ray.remote
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def f(x):
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return x
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x_ids = [f.remote(i) for i in range(100)]
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for i in range(len(x_ids)):
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ray.wait([x_ids[i]])
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for i in range(len(x_ids) - 1):
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ray.wait(x_ids[i:])
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@ray.remote
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def g(x):
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time.sleep(x)
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for i in range(1, 5):
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x_ids = [
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g.remote(np.random.uniform(0, i)) for _ in range(2 * num_workers)
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]
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ray.wait(x_ids, num_returns=len(x_ids))
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assert cluster.remaining_processes_alive()
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if __name__ == "__main__":
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import pytest
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import sys
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sys.exit(pytest.main(["-v", __file__]))
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