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API cleanups. Remove worker argument. Remove some deprecated arguments. (#4025)

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* Remove worker argument from API methods.

* Remove deprecated arguments and deprecate redirect_output and redirect_worker_output.

* Fix
This commit is contained in:
Robert Nishihara
2019-02-15 10:49:16 -08:00
committed by Philipp Moritz
parent 042ad84573
commit 5f71751891
24 changed files with 171 additions and 217 deletions
Download Patch File Download Diff File Expand all files Collapse all files
doc/source/tempfile.rst
+2 -2
View File
@@ -47,7 +47,7 @@ A typical layout of temporary files could look like this:
│   ├── monitor.out
│   ├── plasma_store_0.err # array of plasma stores' outputs
│   ├── plasma_store_0.out
│   ├── raylet_0.err # array of raylets' outputs. Control it with `--no-redirect-worker-output` (in Ray's command line) or `redirect_worker_output` (in ray.init())
│   ├── raylet_0.err
│   ├── raylet_0.out
│   ├── redis-shard_0.err # array of redis shards' outputs
│   ├── redis-shard_0.out
@@ -80,4 +80,4 @@ The path you specified will be given as it is without being affected any other p
Notes
-----
Temporary file policies are defined in ``python/ray/node.py``.
Temporary file policies are defined in ``python/ray/node.py``.
examples/lbfgs/driver.py
+19 -11
View File
@@ -35,6 +35,7 @@ class LinearModel(object):
variables (TensorFlowVariables): Extracted variables and methods to
manipulate them.
"""
def __init__(self, shape):
"""Creates a LinearModel object."""
x = tf.placeholder(tf.float32, [None, shape[0]])
@@ -46,26 +47,33 @@ class LinearModel(object):
y = tf.nn.softmax(tf.matmul(x, w) + b)
y_ = tf.placeholder(tf.float32, [None, shape[1]])
self.y_ = y_
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y),
reduction_indices=[1]))
cross_entropy = tf.reduce_mean(
-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
self.cross_entropy = cross_entropy
self.cross_entropy_grads = tf.gradients(cross_entropy, [w, b])
self.sess = tf.Session()
# In order to get and set the weights, we pass in the loss function to
# Ray's TensorFlowVariables to automatically create methods to modify
# the weights.
self.variables = ray.experimental.TensorFlowVariables(cross_entropy,
self.sess)
self.variables = ray.experimental.TensorFlowVariables(
cross_entropy, self.sess)
def loss(self, xs, ys):
"""Computes the loss of the network."""
return float(self.sess.run(self.cross_entropy,
feed_dict={self.x: xs, self.y_: ys}))
return float(
self.sess.run(
self.cross_entropy, feed_dict={
self.x: xs,
self.y_: ys
}))
def grad(self, xs, ys):
"""Computes the gradients of the network."""
return self.sess.run(self.cross_entropy_grads,
feed_dict={self.x: xs, self.y_: ys})
return self.sess.run(
self.cross_entropy_grads, feed_dict={
self.x: xs,
self.y_: ys
})
@ray.remote
@@ -110,7 +118,7 @@ def full_grad(theta):
if __name__ == "__main__":
ray.init(redirect_output=True)
ray.init()
# From the perspective of scipy.optimize.fmin_l_bfgs_b, full_loss is simply
# a function which takes some parameters theta, and computes a loss.
@@ -136,5 +144,5 @@ if __name__ == "__main__":
# Use L-BFGS to minimize the loss function.
print("Running L-BFGS.")
result = scipy.optimize.fmin_l_bfgs_b(full_loss, theta_init, maxiter=10,
fprime=full_grad, disp=True)
result = scipy.optimize.fmin_l_bfgs_b(
full_loss, theta_init, maxiter=10, fprime=full_grad, disp=True)
examples/resnet/resnet_main.py
+55 -37
View File
@@ -23,22 +23,41 @@ if (tf_major < 1) or (tf_major == 1 and tf_minor < 2):
"update Tensorflow to the latest version.")
parser = argparse.ArgumentParser(description="Run the ResNet example.")
parser.add_argument("--dataset", default="cifar10", type=str,
help="Dataset to use: cifar10 or cifar100.")
parser.add_argument("--train_data_path",
default="cifar-10-batches-bin/data_batch*", type=str,
help="Data path for the training data.")
parser.add_argument("--eval_data_path",
default="cifar-10-batches-bin/test_batch.bin", type=str,
help="Data path for the testing data.")
parser.add_argument("--eval_dir", default="/tmp/resnet-model/eval", type=str,
help="Data path for the tensorboard logs.")
parser.add_argument("--eval_batch_count", default=50, type=int,
help="Number of batches to evaluate over.")
parser.add_argument("--num_gpus", default=0, type=int,
help="Number of GPUs to use for training.")
parser.add_argument("--redis-address", default=None, type=str,
help="The Redis address of the cluster.")
parser.add_argument(
"--dataset",
default="cifar10",
type=str,
help="Dataset to use: cifar10 or cifar100.")
parser.add_argument(
"--train_data_path",
default="cifar-10-batches-bin/data_batch*",
type=str,
help="Data path for the training data.")
parser.add_argument(
"--eval_data_path",
default="cifar-10-batches-bin/test_batch.bin",
type=str,
help="Data path for the testing data.")
parser.add_argument(
"--eval_dir",
default="/tmp/resnet-model/eval",
type=str,
help="Data path for the tensorboard logs.")
parser.add_argument(
"--eval_batch_count",
default=50,
type=int,
help="Number of batches to evaluate over.")
parser.add_argument(
"--num_gpus",
default=0,
type=int,
help="Number of GPUs to use for training.")
parser.add_argument(
"--redis-address",
default=None,
type=str,
help="The Redis address of the cluster.")
FLAGS = parser.parse_args()
@@ -87,9 +106,8 @@ class ResNetTrainActor(object):
with tf.device("/gpu:0" if num_gpus > 0 else "/cpu:0"):
# Build the model.
images, labels = cifar_input.build_input(data,
hps.batch_size, dataset,
False)
images, labels = cifar_input.build_input(data, hps.batch_size,
dataset, False)
self.model = resnet_model.ResNet(hps, images, labels, "train")
self.model.build_graph()
config = tf.ConfigProto(allow_soft_placement=True)
@@ -131,9 +149,8 @@ class ResNetTestActor(object):
num_gpus=0)
with tf.device("/cpu:0"):
# Builds the testing network.
images, labels = cifar_input.build_input(data,
hps.batch_size, dataset,
False)
images, labels = cifar_input.build_input(data, hps.batch_size,
dataset, False)
self.model = resnet_model.ResNet(hps, images, labels, "eval")
self.model.build_graph()
config = tf.ConfigProto(allow_soft_placement=True)
@@ -159,8 +176,7 @@ class ResNetTestActor(object):
sess = self.model.variables.sess
for _ in range(self.eval_batch_count):
summaries, loss, predictions, truth = sess.run(
[model.summaries, model.cost, model.predictions,
model.labels])
[model.summaries, model.cost, model.predictions, model.labels])
truth = np.argmax(truth, axis=1)
predictions = np.argmax(predictions, axis=1)
@@ -170,8 +186,7 @@ class ResNetTestActor(object):
precision = 1.0 * correct_prediction / total_prediction
self.best_precision = max(precision, self.best_precision)
precision_summ = tf.Summary()
precision_summ.value.add(
tag="Precision", simple_value=precision)
precision_summ.value.add(tag="Precision", simple_value=precision)
self.summary_writer.add_summary(precision_summ, train_step)
best_precision_summ = tf.Summary()
best_precision_summ.value.add(
@@ -192,7 +207,7 @@ class ResNetTestActor(object):
def train():
num_gpus = FLAGS.num_gpus
if FLAGS.redis_address is None:
ray.init(num_gpus=num_gpus, redirect_output=True)
ray.init(num_gpus=num_gpus)
else:
ray.init(redis_address=FLAGS.redis_address)
train_data = get_data.remote(FLAGS.train_data_path, 50000, FLAGS.dataset)
@@ -200,15 +215,16 @@ def train():
# Creates an actor for each gpu, or one if only using the cpu. Each actor
# has access to the dataset.
if FLAGS.num_gpus > 0:
train_actors = [ResNetTrainActor.remote(train_data, FLAGS.dataset,
num_gpus)
for _ in range(num_gpus)]
train_actors = [
ResNetTrainActor.remote(train_data, FLAGS.dataset, num_gpus)
for _ in range(num_gpus)
]
else:
train_actors = [ResNetTrainActor.remote(train_data, FLAGS.dataset, 0)]
test_actor = ResNetTestActor.remote(test_data, FLAGS.dataset,
FLAGS.eval_batch_count, FLAGS.eval_dir)
print("The log files for tensorboard are stored at ip {}."
.format(ray.get(test_actor.get_ip_addr.remote())))
print("The log files for tensorboard are stored at ip {}.".format(
ray.get(test_actor.get_ip_addr.remote())))
step = 0
weight_id = train_actors[0].get_weights.remote()
acc_id = test_actor.accuracy.remote(weight_id, step)
@@ -218,11 +234,13 @@ def train():
print("Starting training loop. Use Ctrl-C to exit.")
try:
while True:
all_weights = ray.get([actor.compute_steps.remote(weight_id)
for actor in train_actors])
mean_weights = {k: (sum(weights[k] for weights in all_weights) /
num_gpus)
for k in all_weights[0]}
all_weights = ray.get([
actor.compute_steps.remote(weight_id) for actor in train_actors
])
mean_weights = {
k: (sum(weights[k] for weights in all_weights) / num_gpus)
for k in all_weights[0]
}
weight_id = ray.put(mean_weights)
step += 10
if step % 200 == 0:
examples/rl_pong/driver.py
+21 -12
View File
@@ -146,17 +146,26 @@ class PongEnv(object):
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Train an RL agent on Pong.")
parser.add_argument("--batch-size", default=10, type=int,
help="The number of rollouts to do per batch.")
parser.add_argument("--redis-address", default=None, type=str,
help="The Redis address of the cluster.")
parser.add_argument("--iterations", default=-1, type=int,
help="The number of model updates to perform. By "
"default, training will not terminate.")
parser.add_argument(
"--batch-size",
default=10,
type=int,
help="The number of rollouts to do per batch.")
parser.add_argument(
"--redis-address",
default=None,
type=str,
help="The Redis address of the cluster.")
parser.add_argument(
"--iterations",
default=-1,
type=int,
help="The number of model updates to perform. By "
"default, training will not terminate.")
args = parser.parse_args()
batch_size = args.batch_size
ray.init(redis_address=args.redis_address, redirect_output=True)
ray.init(redis_address=args.redis_address)
# Run the reinforcement learning.
@@ -187,8 +196,8 @@ if __name__ == "__main__":
# Accumulate the gradient over batch.
for k in model:
grad_buffer[k] += grad[k]
running_reward = (reward_sum if running_reward is None
else running_reward * 0.99 + reward_sum * 0.01)
running_reward = (reward_sum if running_reward is None else
running_reward * 0.99 + reward_sum * 0.01)
end_time = time.time()
print("Batch {} computed {} rollouts in {} seconds, "
"running mean is {}".format(batch_num, batch_size,
@@ -196,8 +205,8 @@ if __name__ == "__main__":
running_reward))
for k, v in model.items():
g = grad_buffer[k]
rmsprop_cache[k] = (decay_rate * rmsprop_cache[k] +
(1 - decay_rate) * g ** 2)
rmsprop_cache[k] = (
decay_rate * rmsprop_cache[k] + (1 - decay_rate) * g**2)
model[k] += learning_rate * g / (np.sqrt(rmsprop_cache[k]) + 1e-5)
# Reset the batch gradient buffer.
grad_buffer[k] = np.zeros_like(v)
python/ray/actor.py
-21
View File
@@ -125,12 +125,6 @@ class ActorMethod(object):
def remote(self, *args, **kwargs):
return self._remote(args, kwargs)
def _submit(self, args, kwargs, num_return_vals=None):
logger.warning(
"WARNING: _submit() is being deprecated. Please use _remote().")
return self._remote(
args=args, kwargs=kwargs, num_return_vals=num_return_vals)
def _remote(self, args, kwargs, num_return_vals=None):
if num_return_vals is None:
num_return_vals = self._num_return_vals
@@ -238,21 +232,6 @@ class ActorClass(object):
"""
return self._remote(args=args, kwargs=kwargs)
def _submit(self,
args,
kwargs,
num_cpus=None,
num_gpus=None,
resources=None):
logger.warning(
"WARNING: _submit() is being deprecated. Please use _remote().")
return self._remote(
args=args,
kwargs=kwargs,
num_cpus=num_cpus,
num_gpus=num_gpus,
resources=resources)
def _remote(self,
args,
kwargs,
python/ray/experimental/api.py
+6 -14
View File
@@ -6,7 +6,7 @@ import ray
import numpy as np
def get(object_ids, worker=None):
def get(object_ids):
"""Get a single or a collection of remote objects from the object store.
This method is identical to `ray.get` except it adds support for tuples,
@@ -19,11 +19,8 @@ def get(object_ids, worker=None):
Returns:
A Python object, a list of Python objects or a dict of {key: object}.
"""
# There is a dependency on ray.worker which prevents importing
# global_worker at the top of this file
worker = ray.worker.global_worker if worker is None else worker
if isinstance(object_ids, (tuple, np.ndarray)):
return ray.get(list(object_ids), worker)
return ray.get(list(object_ids))
elif isinstance(object_ids, dict):
keys_to_get = [
k for k, v in object_ids.items() if isinstance(v, ray.ObjectID)
@@ -38,10 +35,10 @@ def get(object_ids, worker=None):
result[key] = value
return result
else:
return ray.get(object_ids, worker)
return ray.get(object_ids)
def wait(object_ids, num_returns=1, timeout=None, worker=None):
def wait(object_ids, num_returns=1, timeout=None):
"""Return a list of IDs that are ready and a list of IDs that are not.
This method is identical to `ray.wait` except it adds support for tuples
@@ -59,13 +56,8 @@ def wait(object_ids, num_returns=1, timeout=None, worker=None):
A list of object IDs that are ready and a list of the remaining object
IDs.
"""
worker = ray.worker.global_worker if worker is None else worker
if isinstance(object_ids, (tuple, np.ndarray)):
return ray.wait(
list(object_ids),
num_returns=num_returns,
timeout=timeout,
worker=worker)
list(object_ids), num_returns=num_returns, timeout=timeout)
return ray.wait(
object_ids, num_returns=num_returns, timeout=timeout, worker=worker)
return ray.wait(object_ids, num_returns=num_returns, timeout=timeout)
python/ray/function_manager.py
+1 -1
View File
@@ -441,7 +441,7 @@ class FunctionActorManager(object):
# we spend too long in this loop.
# The driver function may not be found in sys.path. Try to load
# the function from GCS.
with profiling.profile("wait_for_function", worker=self._worker):
with profiling.profile("wait_for_function"):
self._wait_for_function(function_descriptor, driver_id)
try:
info = self._function_execution_info[driver_id][function_id]
python/ray/import_thread.py
+3 -6
View File
@@ -91,21 +91,18 @@ class ImportThread(object):
# Handle the driver case first.
if self.mode != ray.WORKER_MODE:
if key.startswith(b"FunctionsToRun"):
with profiling.profile(
"fetch_and_run_function", worker=self.worker):
with profiling.profile("fetch_and_run_function"):
self.fetch_and_execute_function_to_run(key)
# Return because FunctionsToRun are the only things that
# the driver should import.
return
if key.startswith(b"RemoteFunction"):
with profiling.profile(
"register_remote_function", worker=self.worker):
with profiling.profile("register_remote_function"):
(self.worker.function_actor_manager.
fetch_and_register_remote_function(key))
elif key.startswith(b"FunctionsToRun"):
with profiling.profile(
"fetch_and_run_function", worker=self.worker):
with profiling.profile("fetch_and_run_function"):
self.fetch_and_execute_function_to_run(key)
elif key.startswith(b"ActorClass"):
# Keep track of the fact that this actor class has been
python/ray/internal/internal_api.py
+3 -4
View File
@@ -8,7 +8,7 @@ from ray import profiling
__all__ = ["free"]
def free(object_ids, local_only=False, worker=None):
def free(object_ids, local_only=False):
"""Free a list of IDs from object stores.
This function is a low-level API which should be used in restricted
@@ -26,8 +26,7 @@ def free(object_ids, local_only=False, worker=None):
local_only (bool): Whether only deleting the list of objects in local
object store or all object stores.
"""
if worker is None:
worker = ray.worker.get_global_worker()
worker = ray.worker.get_global_worker()
if isinstance(object_ids, ray.ObjectID):
object_ids = [object_ids]
@@ -37,7 +36,7 @@ def free(object_ids, local_only=False, worker=None):
type(object_ids)))
worker.check_connected()
with profiling.profile("ray.free", worker=worker):
with profiling.profile("ray.free"):
if len(object_ids) == 0:
return
python/ray/node.py
+4 -4
View File
@@ -197,7 +197,7 @@ class Node(object):
raise FileExistsError(errno.EEXIST,
"No usable temporary filename found")
def new_log_files(self, name, redirect_output=None):
def new_log_files(self, name, redirect_output=True):
"""Generate partially randomized filenames for log files.
Args:
@@ -262,7 +262,7 @@ class Node(object):
redis_shard_ports=self._ray_params.redis_shard_ports,
num_redis_shards=self._ray_params.num_redis_shards,
redis_max_clients=self._ray_params.redis_max_clients,
redirect_worker_output=self._ray_params.redirect_worker_output,
redirect_worker_output=True,
password=self._ray_params.redis_password,
redis_max_memory=self._ray_params.redis_max_memory)
assert (
@@ -272,7 +272,7 @@ class Node(object):
def start_log_monitor(self):
"""Start the log monitor."""
stdout_file, stderr_file = self.new_log_files("log_monitor", True)
stdout_file, stderr_file = self.new_log_files("log_monitor")
process_info = ray.services.start_log_monitor(
self.redis_address,
self._logs_dir,
@@ -286,7 +286,7 @@ class Node(object):
def start_ui(self):
"""Start the web UI."""
stdout_file, stderr_file = self.new_log_files("webui", True)
stdout_file, stderr_file = self.new_log_files("webui")
notebook_name = self._make_inc_temp(
suffix=".ipynb", prefix="ray_ui", directory_name=self._temp_dir)
self._webui_url, process_info = ray.services.start_ui(
python/ray/parameter.py
+11 -8
View File
@@ -90,11 +90,10 @@ class RayParams(object):
node_manager_port=None,
node_ip_address=None,
object_id_seed=None,
num_workers=None,
local_mode=False,
driver_mode=None,
redirect_worker_output=True,
redirect_output=True,
redirect_worker_output=None,
redirect_output=None,
num_redis_shards=None,
redis_max_clients=None,
redis_password=None,
@@ -124,7 +123,6 @@ class RayParams(object):
self.object_manager_port = object_manager_port
self.node_manager_port = node_manager_port
self.node_ip_address = node_ip_address
self.num_workers = num_workers
self.local_mode = local_mode
self.driver_mode = driver_mode
self.redirect_worker_output = redirect_worker_output
@@ -186,10 +184,15 @@ class RayParams(object):
"'GPU' should not be included in the resource dictionary. Use "
"num_gpus instead.")
if self.num_workers is not None:
raise ValueError(
"The 'num_workers' argument is deprecated. Please use "
"'num_cpus' instead.")
if self.redirect_worker_output is not None:
raise DeprecationWarning(
"The redirect_worker_output argument is deprecated. To "
"control logging to the driver, use the 'log_to_driver' "
"argument to 'ray.init()'")
if self.redirect_output is not None:
raise DeprecationWarning(
"The redirect_output argument is deprecated.")
if self.include_java is None and self.java_worker_options is not None:
raise ValueError("Should not specify `java-worker-options` "
python/ray/profiling.py
+2 -3
View File
@@ -27,7 +27,7 @@ class _NullLogSpan(object):
NULL_LOG_SPAN = _NullLogSpan()
def profile(event_type, extra_data=None, worker=None):
def profile(event_type, extra_data=None):
"""Profile a span of time so that it appears in the timeline visualization.
Note that this only works in the raylet code path.
@@ -57,8 +57,7 @@ def profile(event_type, extra_data=None, worker=None):
Returns:
An object that can profile a span of time via a "with" statement.
"""
if worker is None:
worker = ray.worker.global_worker
worker = ray.worker.global_worker
return RayLogSpanRaylet(worker.profiler, event_type, extra_data=extra_data)
python/ray/rllib/examples/carla/train_ppo.py
+1 -1
View File
@@ -21,7 +21,7 @@ env_config.update({
})
register_carla_model()
ray.init(redirect_output=True)
ray.init()
run_experiments({
"carla": {
"run": "PPO",
python/ray/scripts/scripts.py
+6 -14
View File
@@ -123,15 +123,6 @@ def cli(logging_level, logging_format):
"limit is exceeded, redis will start LRU eviction of entries. This only "
"applies to the sharded redis tables (task, object, and profile tables). "
"By default this is capped at 10GB but can be set higher.")
@click.option(
"--num-workers",
required=False,
type=int,
help=("The initial number of workers to start on this node, "
"note that the local scheduler may start additional "
"workers. If you wish to control the total number of "
"concurent tasks, then use --resources instead and "
"specify the CPU field."))
@click.option(
"--num-cpus",
required=False,
@@ -220,8 +211,8 @@ def cli(logging_level, logging_format):
def start(node_ip_address, redis_address, redis_port, num_redis_shards,
redis_max_clients, redis_password, redis_shard_ports,
object_manager_port, node_manager_port, object_store_memory,
redis_max_memory, num_workers, num_cpus, num_gpus, resources, head,
no_ui, block, plasma_directory, huge_pages, autoscaling_config,
redis_max_memory, num_cpus, num_gpus, resources, head, no_ui, block,
plasma_directory, huge_pages, autoscaling_config,
no_redirect_worker_output, no_redirect_output,
plasma_store_socket_name, raylet_socket_name, temp_dir, include_java,
java_worker_options, internal_config):
@@ -239,15 +230,16 @@ def start(node_ip_address, redis_address, redis_port, num_redis_shards,
" --resources='{\"CustomResource1\": 3, "
"\"CustomReseource2\": 2}'")
redirect_worker_output = None if not no_redirect_worker_output else True
redirect_output = None if not no_redirect_output else True
ray_params = ray.parameter.RayParams(
node_ip_address=node_ip_address,
object_manager_port=object_manager_port,
node_manager_port=node_manager_port,
num_workers=num_workers,
object_store_memory=object_store_memory,
redis_password=redis_password,
redirect_worker_output=not no_redirect_worker_output,
redirect_output=not no_redirect_output,
redirect_worker_output=redirect_worker_output,
redirect_output=redirect_output,
num_cpus=num_cpus,
num_gpus=num_gpus,
resources=resources,
python/ray/tune/examples/bayesopt_example.py
+1 -1
View File
@@ -30,7 +30,7 @@ if __name__ == '__main__':
parser.add_argument(
"--smoke-test", action="store_true", help="Finish quickly for testing")
args, _ = parser.parse_known_args()
ray.init(redirect_output=True)
ray.init()
register_trainable("exp", easy_objective)
python/ray/tune/examples/genetic_example.py
+1 -1
View File
@@ -34,7 +34,7 @@ if __name__ == '__main__':
parser.add_argument(
"--smoke-test", action="store_true", help="Finish quickly for testing")
args, _ = parser.parse_known_args()
ray.init(redirect_output=True)
ray.init()
register_trainable("exp", michalewicz_function)
python/ray/tune/examples/hyperopt_example.py
+1 -1
View File
@@ -33,7 +33,7 @@ if __name__ == '__main__':
parser.add_argument(
"--smoke-test", action="store_true", help="Finish quickly for testing")
args, _ = parser.parse_known_args()
ray.init(redirect_output=True)
ray.init()
register_trainable("exp", easy_objective)
python/ray/tune/examples/nevergrad_example.py
+1 -1
View File
@@ -31,7 +31,7 @@ if __name__ == '__main__':
parser.add_argument(
"--smoke-test", action="store_true", help="Finish quickly for testing")
args, _ = parser.parse_known_args()
ray.init(redirect_output=True)
ray.init()
register_trainable("exp", easy_objective)
python/ray/tune/examples/sigopt_example.py
+1 -1
View File
@@ -34,7 +34,7 @@ if __name__ == '__main__':
parser.add_argument(
"--smoke-test", action="store_true", help="Finish quickly for testing")
args, _ = parser.parse_known_args()
ray.init(redirect_output=True)
ray.init()
register_trainable("exp", easy_objective)
python/ray/tune/examples/skopt_example.py
+1 -1
View File
@@ -31,7 +31,7 @@ if __name__ == '__main__':
parser.add_argument(
"--smoke-test", action="store_true", help="Finish quickly for testing")
args, _ = parser.parse_known_args()
ray.init(redirect_output=True)
ray.init()
register_trainable("exp", easy_objective)
python/ray/worker.py
+28 -52
View File
@@ -579,7 +579,7 @@ class Worker(object):
Returns:
The return object IDs for this task.
"""
with profiling.profile("submit_task", worker=self):
with profiling.profile("submit_task"):
if actor_id is None:
assert actor_handle_id is None
actor_id = ActorID.nil()
@@ -828,7 +828,7 @@ class Worker(object):
if function_name != "__ray_terminate__":
self.reraise_actor_init_error()
self.memory_monitor.raise_if_low_memory()
with profiling.profile("task:deserialize_arguments", worker=self):
with profiling.profile("task:deserialize_arguments"):
arguments = self._get_arguments_for_execution(
function_name, args)
except RayTaskError as e:
@@ -844,7 +844,7 @@ class Worker(object):
# Execute the task.
try:
with profiling.profile("task:execute", worker=self):
with profiling.profile("task:execute"):
if (task.actor_id().is_nil()
and task.actor_creation_id().is_nil()):
outputs = function_executor(*arguments)
@@ -867,7 +867,7 @@ class Worker(object):
# Store the outputs in the local object store.
try:
with profiling.profile("task:store_outputs", worker=self):
with profiling.profile("task:store_outputs"):
# If this is an actor task, then the last object ID returned by
# the task is a dummy output, not returned by the function
# itself. Decrement to get the correct number of return values.
@@ -952,7 +952,7 @@ class Worker(object):
title = "ray_{}:{}()".format(actor.__class__.__name__,
function_name)
next_title = "ray_{}".format(actor.__class__.__name__)
with profiling.profile("task", extra_data=extra_data, worker=self):
with profiling.profile("task", extra_data=extra_data):
with _changeproctitle(title, next_title):
self._process_task(task, execution_info)
# Reset the state fields so the next task can run.
@@ -981,7 +981,7 @@ class Worker(object):
Returns:
A task from the local scheduler.
"""
with profiling.profile("worker_idle", worker=self):
with profiling.profile("worker_idle"):
task = self.raylet_client.get_task()
# Automatically restrict the GPUs available to this task.
@@ -993,7 +993,7 @@ class Worker(object):
"""The main loop a worker runs to receive and execute tasks."""
def exit(signum, frame):
shutdown(worker=self)
shutdown()
sys.exit(0)
signal.signal(signal.SIGTERM, exit)
@@ -1109,8 +1109,9 @@ def print_failed_task(task_status):
task_status["error_message"]))
def error_info(worker=global_worker):
def error_info():
"""Return information about failed tasks."""
worker = global_worker
worker.check_connected()
return (global_state.error_messages(job_id=worker.task_driver_id) +
global_state.error_messages(job_id=DriverID.nil()))
@@ -1251,11 +1252,9 @@ def init(redis_address=None,
log_to_driver=True,
node_ip_address=None,
object_id_seed=None,
num_workers=None,
local_mode=False,
driver_mode=None,
redirect_worker_output=True,
redirect_output=True,
redirect_worker_output=None,
redirect_output=None,
ignore_reinit_error=False,
num_redis_shards=None,
redis_max_clients=None,
@@ -1270,8 +1269,7 @@ def init(redis_address=None,
plasma_store_socket_name=None,
raylet_socket_name=None,
temp_dir=None,
_internal_config=None,
use_raylet=None):
_internal_config=None):
"""Connect to an existing Ray cluster or start one and connect to it.
This method handles two cases. Either a Ray cluster already exists and we
@@ -1320,10 +1318,6 @@ def init(redis_address=None,
manner. However, the same ID should not be used for different jobs.
local_mode (bool): True if the code should be executed serially
without Ray. This is useful for debugging.
redirect_worker_output: True if the stdout and stderr of worker
processes should be redirected to files.
redirect_output (bool): True if stdout and stderr for non-worker
processes should be redirected to files and false otherwise.
ignore_reinit_error: True if we should suppress errors from calling
ray.init() a second time.
num_redis_shards: The number of Redis shards to start in addition to
@@ -1364,18 +1358,6 @@ def init(redis_address=None,
if configure_logging:
setup_logger(logging_level, logging_format)
# Add the use_raylet option for backwards compatibility.
if use_raylet is not None:
if use_raylet:
logger.warning("WARNING: The use_raylet argument has been "
"deprecated. Please remove it.")
else:
raise DeprecationWarning("The use_raylet argument is deprecated. "
"Please remove it.")
if driver_mode is not None:
raise Exception("The 'driver_mode' argument has been deprecated. "
"To run Ray in local mode, pass in local_mode=True.")
if local_mode:
driver_mode = LOCAL_MODE
else:
@@ -1424,7 +1406,6 @@ def init(redis_address=None,
ray_params = ray.parameter.RayParams(
redis_address=redis_address,
node_ip_address=node_ip_address,
num_workers=num_workers,
object_id_seed=object_id_seed,
local_mode=local_mode,
driver_mode=driver_mode,
@@ -1458,9 +1439,6 @@ def init(redis_address=None,
address_info["raylet_socket_name"] = _global_node.raylet_socket_name
else:
# In this case, we are connecting to an existing cluster.
if num_workers is not None:
raise Exception("When connecting to an existing cluster, "
"num_workers must not be provided.")
if num_cpus is not None or num_gpus is not None:
raise Exception("When connecting to an existing cluster, num_cpus "
"and num_gpus must not be provided.")
@@ -1548,13 +1526,7 @@ def init(redis_address=None,
_post_init_hooks = []
def cleanup(worker=global_worker):
raise DeprecationWarning(
"The function ray.worker.cleanup() has been deprecated. Instead, "
"please call ray.shutdown().")
def shutdown(worker=global_worker):
def shutdown():
"""Disconnect the worker, and terminate processes started by ray.init().
This will automatically run at the end when a Python process that uses Ray
@@ -1567,7 +1539,7 @@ def shutdown(worker=global_worker):
need to redefine them. If they were defined in an imported module, then you
will need to reload the module.
"""
disconnect(worker)
disconnect()
# Shut down the Ray processes.
global _global_node
@@ -1575,7 +1547,7 @@ def shutdown(worker=global_worker):
_global_node.kill_all_processes(check_alive=False, allow_graceful=True)
_global_node = None
worker.set_mode(None)
global_worker.set_mode(None)
atexit.register(shutdown)
@@ -2037,12 +2009,13 @@ def connect(info,
worker.cached_functions_to_run = None
def disconnect(worker=global_worker):
def disconnect():
"""Disconnect this worker from the scheduler and object store."""
# Reset the list of cached remote functions and actors so that if more
# remote functions or actors are defined and then connect is called again,
# the remote functions will be exported. This is mostly relevant for the
# tests.
worker = global_worker
if worker.connected:
# Shutdown all of the threads that we've started. TODO(rkn): This
# should be handled cleanly in the worker object's destructor and not
@@ -2129,8 +2102,7 @@ def register_custom_serializer(cls,
deserializer=None,
local=False,
driver_id=None,
class_id=None,
worker=global_worker):
class_id=None):
"""Enable serialization and deserialization for a particular class.
This method runs the register_class function defined below on every worker,
@@ -2159,6 +2131,7 @@ def register_custom_serializer(cls,
be efficiently serialized by Ray. This can also raise an exception
if use_dict is true and cls is not pickleable.
"""
worker = global_worker
assert (serializer is None) == (deserializer is None), (
"The serializer/deserializer arguments must both be provided or "
"both not be provided.")
@@ -2225,7 +2198,7 @@ def register_custom_serializer(cls,
register_class_for_serialization({"worker": worker})
def get(object_ids, worker=global_worker):
def get(object_ids):
"""Get a remote object or a list of remote objects from the object store.
This method blocks until the object corresponding to the object ID is
@@ -2245,8 +2218,9 @@ def get(object_ids, worker=global_worker):
Exception: An exception is raised if the task that created the object
or that created one of the objects raised an exception.
"""
worker = global_worker
worker.check_connected()
with profiling.profile("ray.get", worker=worker):
with profiling.profile("ray.get"):
if worker.mode == LOCAL_MODE:
# In LOCAL_MODE, ray.get is the identity operation (the input will
# actually be a value not an objectid).
@@ -2270,7 +2244,7 @@ def get(object_ids, worker=global_worker):
return value
def put(value, worker=global_worker):
def put(value):
"""Store an object in the object store.
Args:
@@ -2279,8 +2253,9 @@ def put(value, worker=global_worker):
Returns:
The object ID assigned to this value.
"""
worker = global_worker
worker.check_connected()
with profiling.profile("ray.put", worker=worker):
with profiling.profile("ray.put"):
if worker.mode == LOCAL_MODE:
# In LOCAL_MODE, ray.put is the identity operation.
return value
@@ -2293,7 +2268,7 @@ def put(value, worker=global_worker):
return object_id
def wait(object_ids, num_returns=1, timeout=None, worker=global_worker):
def wait(object_ids, num_returns=1, timeout=None):
"""Return a list of IDs that are ready and a list of IDs that are not.
.. warning::
@@ -2327,6 +2302,7 @@ def wait(object_ids, num_returns=1, timeout=None, worker=global_worker):
A list of object IDs that are ready and a list of the remaining object
IDs.
"""
worker = global_worker
if isinstance(object_ids, ObjectID):
raise TypeError(
@@ -2356,7 +2332,7 @@ def wait(object_ids, num_returns=1, timeout=None, worker=global_worker):
worker.check_connected()
# TODO(swang): Check main thread.
with profiling.profile("ray.wait", worker=worker):
with profiling.profile("ray.wait"):
# When Ray is run in LOCAL_MODE, all functions are run immediately,
# so all objects in object_id are ready.
if worker.mode == LOCAL_MODE:
test/runtest.py
+1 -1
View File
@@ -2502,7 +2502,7 @@ def test_not_logging_to_driver(shutdown_only):
reason="New GCS API doesn't have a Python API yet.")
def test_workers(shutdown_only):
num_workers = 3
ray.init(redirect_worker_output=True, num_cpus=num_workers)
ray.init(num_cpus=num_workers)
@ray.remote
def f():
test/stress_tests.py
-2
View File
@@ -204,7 +204,6 @@ def ray_start_reconstruction(request):
"num_cpus": 1,
"object_store_memory": plasma_store_memory // num_nodes,
"redis_max_memory": 10**7,
"redirect_output": True,
"_internal_config": json.dumps({
"initial_reconstruction_timeout_milliseconds": 200
})
@@ -213,7 +212,6 @@ def ray_start_reconstruction(request):
cluster.add_node(
num_cpus=1,
object_store_memory=plasma_store_memory // num_nodes,
redirect_output=True,
_internal_config=json.dumps({
"initial_reconstruction_timeout_milliseconds": 200
}))
test/tempfile_test.py
+2 -18
View File
@@ -64,7 +64,7 @@ def test_temp_plasma_store_socket():
def test_raylet_tempfiles():
ray.init(redirect_worker_output=False)
ray.init(num_cpus=0)
node = ray.worker._global_node
top_levels = set(os.listdir(node.get_temp_dir_path()))
assert top_levels == {"ray_ui.ipynb", "sockets", "logs"}
@@ -80,23 +80,7 @@ def test_raylet_tempfiles():
assert socket_files == {"plasma_store", "raylet"}
ray.shutdown()
ray.init(redirect_worker_output=True, num_cpus=0)
node = ray.worker._global_node
top_levels = set(os.listdir(node.get_temp_dir_path()))
assert top_levels == {"ray_ui.ipynb", "sockets", "logs"}
log_files = set(os.listdir(node.get_logs_dir_path()))
assert log_files == {
"log_monitor.out", "log_monitor.err", "plasma_store.out",
"plasma_store.err", "webui.out", "webui.err", "monitor.out",
"monitor.err", "raylet_monitor.out", "raylet_monitor.err",
"redis-shard_0.out", "redis-shard_0.err", "redis.out", "redis.err",
"raylet.out", "raylet.err"
} # with raylet logs
socket_files = set(os.listdir(node.get_sockets_dir_path()))
assert socket_files == {"plasma_store", "raylet"}
ray.shutdown()
ray.init(redirect_worker_output=True, num_cpus=2)
ray.init(num_cpus=2)
node = ray.worker._global_node
top_levels = set(os.listdir(node.get_temp_dir_path()))
assert top_levels == {"ray_ui.ipynb", "sockets", "logs"}