mirror of
https://github.com/vale981/ray
synced 2025-03-07 02:51:39 -05:00
e0867c8845
* 4 space indentation for actor.py. * 4 space indentation for worker.py. * 4 space indentation for more files. * 4 space indentation for some test files. * Check indentation in Travis. * 4 space indentation for some rl files. * Fix failure test. * Fix multi_node_test. * 4 space indentation for more files. * 4 space indentation for remaining files. * Fixes.
275 lines
10 KiB
Python
275 lines
10 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import time
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import ray
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from ray.test.test_utils import (_wait_for_nodes_to_join,
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_broadcast_event,
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_wait_for_event,
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wait_for_pid_to_exit)
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# This test should be run with 5 nodes, which have 0, 1, 2, 3, and 4 GPUs for a
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# total of 10 GPUs. It should be run with 7 drivers. Drivers 2 through 6 must
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# run on different nodes so they can check if all the relevant workers on all
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# the nodes have been killed.
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total_num_nodes = 5
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def actor_event_name(driver_index, actor_index):
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return "DRIVER_{}_ACTOR_{}_RUNNING".format(driver_index, actor_index)
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def remote_function_event_name(driver_index, task_index):
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return "DRIVER_{}_TASK_{}_RUNNING".format(driver_index, task_index)
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@ray.remote
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def long_running_task(driver_index, task_index, redis_address):
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_broadcast_event(remote_function_event_name(driver_index, task_index),
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redis_address,
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data=(ray.services.get_node_ip_address(), os.getpid()))
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# Loop forever.
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while True:
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time.sleep(100)
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num_long_running_tasks_per_driver = 2
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@ray.remote
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class Actor0(object):
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def __init__(self, driver_index, actor_index, redis_address):
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_broadcast_event(actor_event_name(driver_index, actor_index),
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redis_address,
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data=(ray.services.get_node_ip_address(),
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os.getpid()))
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assert len(ray.get_gpu_ids()) == 0
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def check_ids(self):
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assert len(ray.get_gpu_ids()) == 0
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def long_running_method(self):
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# Loop forever.
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while True:
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time.sleep(100)
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@ray.remote(num_gpus=1)
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class Actor1(object):
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def __init__(self, driver_index, actor_index, redis_address):
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_broadcast_event(actor_event_name(driver_index, actor_index),
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redis_address,
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data=(ray.services.get_node_ip_address(),
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os.getpid()))
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assert len(ray.get_gpu_ids()) == 1
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def check_ids(self):
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assert len(ray.get_gpu_ids()) == 1
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def long_running_method(self):
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# Loop forever.
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while True:
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time.sleep(100)
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@ray.remote(num_gpus=2)
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class Actor2(object):
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def __init__(self, driver_index, actor_index, redis_address):
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_broadcast_event(actor_event_name(driver_index, actor_index),
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redis_address,
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data=(ray.services.get_node_ip_address(),
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os.getpid()))
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assert len(ray.get_gpu_ids()) == 2
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def check_ids(self):
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assert len(ray.get_gpu_ids()) == 2
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def long_running_method(self):
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# Loop forever.
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while True:
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time.sleep(100)
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def driver_0(redis_address, driver_index):
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"""The script for driver 0.
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This driver should create five actors that each use one GPU and some actors
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that use no GPUs. After a while, it should exit.
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"""
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ray.init(redis_address=redis_address)
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# Wait for all the nodes to join the cluster.
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_wait_for_nodes_to_join(total_num_nodes)
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# Start some long running task. Driver 2 will make sure the worker running
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# this task has been killed.
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for i in range(num_long_running_tasks_per_driver):
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long_running_task.remote(driver_index, i, redis_address)
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# Create some actors that require one GPU.
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actors_one_gpu = [Actor1.remote(driver_index, i, redis_address)
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for i in range(5)]
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# Create some actors that don't require any GPUs.
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actors_no_gpus = [Actor0.remote(driver_index, 5 + i, redis_address)
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for i in range(5)]
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for _ in range(1000):
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ray.get([actor.check_ids.remote() for actor in actors_one_gpu])
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ray.get([actor.check_ids.remote() for actor in actors_no_gpus])
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# Start a long-running method on one actor and make sure this doesn't
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# affect anything.
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actors_no_gpus[0].long_running_method.remote()
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_broadcast_event("DRIVER_0_DONE", redis_address)
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def driver_1(redis_address, driver_index):
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"""The script for driver 1.
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This driver should create one actor that uses two GPUs, three actors that
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each use one GPU (the one requiring two must be created first), and some
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actors that don't use any GPUs. After a while, it should exit.
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"""
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ray.init(redis_address=redis_address)
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# Wait for all the nodes to join the cluster.
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_wait_for_nodes_to_join(total_num_nodes)
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# Start some long running task. Driver 2 will make sure the worker running
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# this task has been killed.
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for i in range(num_long_running_tasks_per_driver):
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long_running_task.remote(driver_index, i, redis_address)
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# Create an actor that requires two GPUs.
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actors_two_gpus = [Actor2.remote(driver_index, i, redis_address)
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for i in range(1)]
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# Create some actors that require one GPU.
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actors_one_gpu = [Actor1.remote(driver_index, 1 + i, redis_address)
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for i in range(3)]
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# Create some actors that don't require any GPUs.
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actors_no_gpus = [Actor0.remote(driver_index, 1 + 3 + i, redis_address)
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for i in range(5)]
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for _ in range(1000):
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ray.get([actor.check_ids.remote() for actor in actors_two_gpus])
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ray.get([actor.check_ids.remote() for actor in actors_one_gpu])
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ray.get([actor.check_ids.remote() for actor in actors_no_gpus])
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# Start a long-running method on one actor and make sure this doesn't
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# affect anything.
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actors_one_gpu[0].long_running_method.remote()
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_broadcast_event("DRIVER_1_DONE", redis_address)
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def cleanup_driver(redis_address, driver_index):
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"""The script for drivers 2 through 6.
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This driver should wait for the first two drivers to finish. Then it should
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create some actors that use a total of ten GPUs.
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"""
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ray.init(redis_address=redis_address)
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# Only one of the cleanup drivers should create more actors.
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if driver_index == 2:
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# We go ahead and create some actors that don't require any GPUs. We
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# don't need to wait for the other drivers to finish. We call methods
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# on these actors later to make sure they haven't been killed.
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actors_no_gpus = [Actor0.remote(driver_index, i, redis_address)
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for i in range(10)]
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_wait_for_event("DRIVER_0_DONE", redis_address)
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_wait_for_event("DRIVER_1_DONE", redis_address)
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def try_to_create_actor(actor_class, driver_index, actor_index,
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timeout=20):
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# Try to create an actor, but allow failures while we wait for the
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# monitor to release the resources for the removed drivers.
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start_time = time.time()
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while time.time() - start_time < timeout:
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try:
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actor = actor_class.remote(driver_index, actor_index,
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redis_address)
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except Exception as e:
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time.sleep(0.1)
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else:
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return actor
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# If we are here, then we timed out while looping.
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raise Exception("Timed out while trying to create actor.")
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# Only one of the cleanup drivers should create more actors.
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if driver_index == 2:
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# Create some actors that require two GPUs.
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actors_two_gpus = []
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for i in range(3):
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actors_two_gpus.append(try_to_create_actor(Actor2, driver_index,
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10 + i))
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# Create some actors that require one GPU.
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actors_one_gpu = []
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for i in range(4):
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actors_one_gpu.append(try_to_create_actor(Actor1, driver_index,
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10 + 3 + i))
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removed_workers = 0
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# Make sure that the PIDs for the long-running tasks from driver 0 and
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# driver 1 have been killed.
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for i in range(num_long_running_tasks_per_driver):
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node_ip_address, pid = _wait_for_event(
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remote_function_event_name(0, i), redis_address)
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if node_ip_address == ray.services.get_node_ip_address():
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wait_for_pid_to_exit(pid)
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removed_workers += 1
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for i in range(num_long_running_tasks_per_driver):
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node_ip_address, pid = _wait_for_event(
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remote_function_event_name(1, i), redis_address)
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if node_ip_address == ray.services.get_node_ip_address():
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wait_for_pid_to_exit(pid)
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removed_workers += 1
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# Make sure that the PIDs for the actors from driver 0 and driver 1 have
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# been killed.
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for i in range(10):
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node_ip_address, pid = _wait_for_event(actor_event_name(0, i),
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redis_address)
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if node_ip_address == ray.services.get_node_ip_address():
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wait_for_pid_to_exit(pid)
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removed_workers += 1
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for i in range(9):
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node_ip_address, pid = _wait_for_event(actor_event_name(1, i),
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redis_address)
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if node_ip_address == ray.services.get_node_ip_address():
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wait_for_pid_to_exit(pid)
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removed_workers += 1
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print("{} workers/actors were removed on this node."
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.format(removed_workers))
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# Only one of the cleanup drivers should create and use more actors.
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if driver_index == 2:
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for _ in range(1000):
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ray.get([actor.check_ids.remote() for actor in actors_two_gpus])
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ray.get([actor.check_ids.remote() for actor in actors_one_gpu])
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ray.get([actor.check_ids.remote() for actor in actors_no_gpus])
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_broadcast_event("DRIVER_{}_DONE".format(driver_index), redis_address)
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if __name__ == "__main__":
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driver_index = int(os.environ["RAY_DRIVER_INDEX"])
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redis_address = os.environ["RAY_REDIS_ADDRESS"]
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print("Driver {} started at {}.".format(driver_index, time.time()))
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if driver_index == 0:
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driver_0(redis_address, driver_index)
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elif driver_index == 1:
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driver_1(redis_address, driver_index)
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elif driver_index in [2, 3, 4, 5, 6]:
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cleanup_driver(redis_address, driver_index)
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else:
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raise Exception("This code should be unreachable.")
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print("Driver {} finished at {}.".format(driver_index, time.time()))
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