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24,600 | ray-project/ray | python/ray/experimental/async_plasma.py | PlasmaEventHandler.process_notifications | def process_notifications(self, messages):
"""Process notifications."""
for object_id, object_size, metadata_size in messages:
if object_size > 0 and object_id in self._waiting_dict:
linked_list = self._waiting_dict[object_id]
self._complete_future(linked_list... | python | def process_notifications(self, messages):
"""Process notifications."""
for object_id, object_size, metadata_size in messages:
if object_size > 0 and object_id in self._waiting_dict:
linked_list = self._waiting_dict[object_id]
self._complete_future(linked_list... | [
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24,601 | ray-project/ray | python/ray/experimental/async_plasma.py | PlasmaEventHandler.as_future | def as_future(self, object_id, check_ready=True):
"""Turn an object_id into a Future object.
Args:
object_id: A Ray's object_id.
check_ready (bool): If true, check if the object_id is ready.
Returns:
PlasmaObjectFuture: A future object that waits the object_... | python | def as_future(self, object_id, check_ready=True):
"""Turn an object_id into a Future object.
Args:
object_id: A Ray's object_id.
check_ready (bool): If true, check if the object_id is ready.
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24,602 | ray-project/ray | python/ray/tune/web_server.py | TuneClient.get_all_trials | def get_all_trials(self):
"""Returns a list of all trials' information."""
response = requests.get(urljoin(self._path, "trials"))
return self._deserialize(response) | python | def get_all_trials(self):
"""Returns a list of all trials' information."""
response = requests.get(urljoin(self._path, "trials"))
return self._deserialize(response) | [
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24,603 | ray-project/ray | python/ray/tune/web_server.py | TuneClient.get_trial | def get_trial(self, trial_id):
"""Returns trial information by trial_id."""
response = requests.get(
urljoin(self._path, "trials/{}".format(trial_id)))
return self._deserialize(response) | python | def get_trial(self, trial_id):
"""Returns trial information by trial_id."""
response = requests.get(
urljoin(self._path, "trials/{}".format(trial_id)))
return self._deserialize(response) | [
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24,604 | ray-project/ray | python/ray/tune/web_server.py | TuneClient.stop_trial | def stop_trial(self, trial_id):
"""Requests to stop trial by trial_id."""
response = requests.put(
urljoin(self._path, "trials/{}".format(trial_id)))
return self._deserialize(response) | python | def stop_trial(self, trial_id):
"""Requests to stop trial by trial_id."""
response = requests.put(
urljoin(self._path, "trials/{}".format(trial_id)))
return self._deserialize(response) | [
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24,605 | ray-project/ray | python/ray/experimental/sgd/sgd.py | DistributedSGD.foreach_worker | def foreach_worker(self, fn):
"""Apply the given function to each remote worker.
Returns:
List of results from applying the function.
"""
results = ray.get([w.foreach_worker.remote(fn) for w in self.workers])
return results | python | def foreach_worker(self, fn):
"""Apply the given function to each remote worker.
Returns:
List of results from applying the function.
"""
results = ray.get([w.foreach_worker.remote(fn) for w in self.workers])
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24,606 | ray-project/ray | python/ray/experimental/sgd/sgd.py | DistributedSGD.foreach_model | def foreach_model(self, fn):
"""Apply the given function to each model replica in each worker.
Returns:
List of results from applying the function.
"""
results = ray.get([w.foreach_model.remote(fn) for w in self.workers])
out = []
for r in results:
... | python | def foreach_model(self, fn):
"""Apply the given function to each model replica in each worker.
Returns:
List of results from applying the function.
"""
results = ray.get([w.foreach_model.remote(fn) for w in self.workers])
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24,607 | ray-project/ray | python/ray/experimental/sgd/sgd.py | DistributedSGD.for_model | def for_model(self, fn):
"""Apply the given function to a single model replica.
Returns:
Result from applying the function.
"""
return ray.get(self.workers[0].for_model.remote(fn)) | python | def for_model(self, fn):
"""Apply the given function to a single model replica.
Returns:
Result from applying the function.
"""
return ray.get(self.workers[0].for_model.remote(fn)) | [
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24,608 | ray-project/ray | python/ray/experimental/sgd/sgd.py | DistributedSGD.step | def step(self, fetch_stats=False):
"""Run a single SGD step.
Arguments:
fetch_stats (bool): Whether to return stats from the step. This can
slow down the computation by acting as a global barrier.
"""
if self.strategy == "ps":
return _distributed_... | python | def step(self, fetch_stats=False):
"""Run a single SGD step.
Arguments:
fetch_stats (bool): Whether to return stats from the step. This can
slow down the computation by acting as a global barrier.
"""
if self.strategy == "ps":
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24,609 | ray-project/ray | python/ray/experimental/serve/router/__init__.py | start_router | def start_router(router_class, router_name):
"""Wrapper for starting a router and register it.
Args:
router_class: The router class to instantiate.
router_name: The name to give to the router.
Returns:
A handle to newly started router actor.
"""
handle = router_class.remote... | python | def start_router(router_class, router_name):
"""Wrapper for starting a router and register it.
Args:
router_class: The router class to instantiate.
router_name: The name to give to the router.
Returns:
A handle to newly started router actor.
"""
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24,610 | ray-project/ray | python/ray/tune/automl/search_space.py | SearchSpace.generate_random_one_hot_encoding | def generate_random_one_hot_encoding(self):
"""Returns a list of one-hot encodings for all parameters.
1 one-hot np.array for 1 parameter,
and the 1's place is randomly chosen.
"""
encoding = []
for ps in self.param_list:
one_hot = np.zeros(ps.choices_count()... | python | def generate_random_one_hot_encoding(self):
"""Returns a list of one-hot encodings for all parameters.
1 one-hot np.array for 1 parameter,
and the 1's place is randomly chosen.
"""
encoding = []
for ps in self.param_list:
one_hot = np.zeros(ps.choices_count()... | [
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24,611 | ray-project/ray | python/ray/tune/automl/search_space.py | SearchSpace.apply_one_hot_encoding | def apply_one_hot_encoding(self, one_hot_encoding):
"""Apply one hot encoding to generate a specific config.
Arguments:
one_hot_encoding (list): A list of one hot encodings,
1 for each parameter. The shape of each encoding
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"""Apply one hot encoding to generate a specific config.
Arguments:
one_hot_encoding (list): A list of one hot encodings,
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24,612 | ray-project/ray | python/ray/tune/util.py | pin_in_object_store | def pin_in_object_store(obj):
"""Pin an object in the object store.
It will be available as long as the pinning process is alive. The pinned
object can be retrieved by calling get_pinned_object on the identifier
returned by this call.
"""
obj_id = ray.put(_to_pinnable(obj))
_pinned_objects... | python | def pin_in_object_store(obj):
"""Pin an object in the object store.
It will be available as long as the pinning process is alive. The pinned
object can be retrieved by calling get_pinned_object on the identifier
returned by this call.
"""
obj_id = ray.put(_to_pinnable(obj))
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24,613 | ray-project/ray | python/ray/tune/util.py | get_pinned_object | def get_pinned_object(pinned_id):
"""Retrieve a pinned object from the object store."""
from ray import ObjectID
return _from_pinnable(
ray.get(
ObjectID(base64.b64decode(pinned_id[len(PINNED_OBJECT_PREFIX):])))) | python | def get_pinned_object(pinned_id):
"""Retrieve a pinned object from the object store."""
from ray import ObjectID
return _from_pinnable(
ray.get(
ObjectID(base64.b64decode(pinned_id[len(PINNED_OBJECT_PREFIX):])))) | [
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24,614 | ray-project/ray | python/ray/tune/util.py | merge_dicts | def merge_dicts(d1, d2):
"""Returns a new dict that is d1 and d2 deep merged."""
merged = copy.deepcopy(d1)
deep_update(merged, d2, True, [])
return merged | python | def merge_dicts(d1, d2):
"""Returns a new dict that is d1 and d2 deep merged."""
merged = copy.deepcopy(d1)
deep_update(merged, d2, True, [])
return merged | [
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24,615 | ray-project/ray | python/ray/tune/util.py | deep_update | def deep_update(original, new_dict, new_keys_allowed, whitelist):
"""Updates original dict with values from new_dict recursively.
If new key is introduced in new_dict, then if new_keys_allowed is not
True, an error will be thrown. Further, for sub-dicts, if the key is
in the whitelist, then new subkeys ... | python | def deep_update(original, new_dict, new_keys_allowed, whitelist):
"""Updates original dict with values from new_dict recursively.
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24,616 | ray-project/ray | python/ray/rllib/utils/actors.py | TaskPool.completed_prefetch | def completed_prefetch(self, blocking_wait=False, max_yield=999):
"""Similar to completed but only returns once the object is local.
Assumes obj_id only is one id."""
for worker, obj_id in self.completed(blocking_wait=blocking_wait):
plasma_id = ray.pyarrow.plasma.ObjectID(obj_id.b... | python | def completed_prefetch(self, blocking_wait=False, max_yield=999):
"""Similar to completed but only returns once the object is local.
Assumes obj_id only is one id."""
for worker, obj_id in self.completed(blocking_wait=blocking_wait):
plasma_id = ray.pyarrow.plasma.ObjectID(obj_id.b... | [
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24,617 | ray-project/ray | python/ray/rllib/utils/actors.py | TaskPool.reset_evaluators | def reset_evaluators(self, evaluators):
"""Notify that some evaluators may be removed."""
for obj_id, ev in self._tasks.copy().items():
if ev not in evaluators:
del self._tasks[obj_id]
del self._objects[obj_id]
ok = []
for ev, obj_id in self._f... | python | def reset_evaluators(self, evaluators):
"""Notify that some evaluators may be removed."""
for obj_id, ev in self._tasks.copy().items():
if ev not in evaluators:
del self._tasks[obj_id]
del self._objects[obj_id]
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24,618 | ray-project/ray | python/ray/rllib/optimizers/aso_aggregator.py | AggregationWorkerBase.iter_train_batches | def iter_train_batches(self, max_yield=999):
"""Iterate over train batches.
Arguments:
max_yield (int): Max number of batches to iterate over in this
cycle. Setting this avoids iter_train_batches returning too
much data at once.
"""
for ev, s... | python | def iter_train_batches(self, max_yield=999):
"""Iterate over train batches.
Arguments:
max_yield (int): Max number of batches to iterate over in this
cycle. Setting this avoids iter_train_batches returning too
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24,619 | ray-project/ray | python/ray/autoscaler/commands.py | create_or_update_cluster | def create_or_update_cluster(config_file, override_min_workers,
override_max_workers, no_restart, restart_only,
yes, override_cluster_name):
"""Create or updates an autoscaling Ray cluster from a config json."""
config = yaml.load(open(config_file).read(... | python | def create_or_update_cluster(config_file, override_min_workers,
override_max_workers, no_restart, restart_only,
yes, override_cluster_name):
"""Create or updates an autoscaling Ray cluster from a config json."""
config = yaml.load(open(config_file).read(... | [
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24,620 | ray-project/ray | python/ray/autoscaler/commands.py | teardown_cluster | def teardown_cluster(config_file, yes, workers_only, override_cluster_name):
"""Destroys all nodes of a Ray cluster described by a config json."""
config = yaml.load(open(config_file).read())
if override_cluster_name is not None:
config["cluster_name"] = override_cluster_name
validate_config(co... | python | def teardown_cluster(config_file, yes, workers_only, override_cluster_name):
"""Destroys all nodes of a Ray cluster described by a config json."""
config = yaml.load(open(config_file).read())
if override_cluster_name is not None:
config["cluster_name"] = override_cluster_name
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24,621 | ray-project/ray | python/ray/autoscaler/commands.py | kill_node | def kill_node(config_file, yes, override_cluster_name):
"""Kills a random Raylet worker."""
config = yaml.load(open(config_file).read())
if override_cluster_name is not None:
config["cluster_name"] = override_cluster_name
config = _bootstrap_config(config)
confirm("This will kill a node in... | python | def kill_node(config_file, yes, override_cluster_name):
"""Kills a random Raylet worker."""
config = yaml.load(open(config_file).read())
if override_cluster_name is not None:
config["cluster_name"] = override_cluster_name
config = _bootstrap_config(config)
confirm("This will kill a node in... | [
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24,622 | ray-project/ray | python/ray/autoscaler/commands.py | attach_cluster | def attach_cluster(config_file, start, use_tmux, override_cluster_name, new):
"""Attaches to a screen for the specified cluster.
Arguments:
config_file: path to the cluster yaml
start: whether to start the cluster if it isn't up
use_tmux: whether to use tmux as multiplexer
overr... | python | def attach_cluster(config_file, start, use_tmux, override_cluster_name, new):
"""Attaches to a screen for the specified cluster.
Arguments:
config_file: path to the cluster yaml
start: whether to start the cluster if it isn't up
use_tmux: whether to use tmux as multiplexer
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24,623 | ray-project/ray | python/ray/autoscaler/commands.py | exec_cluster | def exec_cluster(config_file, cmd, docker, screen, tmux, stop, start,
override_cluster_name, port_forward):
"""Runs a command on the specified cluster.
Arguments:
config_file: path to the cluster yaml
cmd: command to run
docker: whether to run command in docker containe... | python | def exec_cluster(config_file, cmd, docker, screen, tmux, stop, start,
override_cluster_name, port_forward):
"""Runs a command on the specified cluster.
Arguments:
config_file: path to the cluster yaml
cmd: command to run
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24,624 | ray-project/ray | python/ray/autoscaler/commands.py | rsync | def rsync(config_file, source, target, override_cluster_name, down):
"""Rsyncs files.
Arguments:
config_file: path to the cluster yaml
source: source dir
target: target dir
override_cluster_name: set the name of the cluster
down: whether we're syncing remote -> local
... | python | def rsync(config_file, source, target, override_cluster_name, down):
"""Rsyncs files.
Arguments:
config_file: path to the cluster yaml
source: source dir
target: target dir
override_cluster_name: set the name of the cluster
down: whether we're syncing remote -> local
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24,625 | ray-project/ray | python/ray/autoscaler/commands.py | get_head_node_ip | def get_head_node_ip(config_file, override_cluster_name):
"""Returns head node IP for given configuration file if exists."""
config = yaml.load(open(config_file).read())
if override_cluster_name is not None:
config["cluster_name"] = override_cluster_name
provider = get_node_provider(config["pr... | python | def get_head_node_ip(config_file, override_cluster_name):
"""Returns head node IP for given configuration file if exists."""
config = yaml.load(open(config_file).read())
if override_cluster_name is not None:
config["cluster_name"] = override_cluster_name
provider = get_node_provider(config["pr... | [
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24,626 | ray-project/ray | python/ray/autoscaler/commands.py | get_worker_node_ips | def get_worker_node_ips(config_file, override_cluster_name):
"""Returns worker node IPs for given configuration file."""
config = yaml.load(open(config_file).read())
if override_cluster_name is not None:
config["cluster_name"] = override_cluster_name
provider = get_node_provider(config["provid... | python | def get_worker_node_ips(config_file, override_cluster_name):
"""Returns worker node IPs for given configuration file."""
config = yaml.load(open(config_file).read())
if override_cluster_name is not None:
config["cluster_name"] = override_cluster_name
provider = get_node_provider(config["provid... | [
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24,627 | ray-project/ray | python/ray/experimental/sgd/tfbench/model.py | Model.build_network | def build_network(self,
images,
phase_train=True,
nclass=1001,
image_depth=3,
data_type=tf.float32,
data_format="NCHW",
use_tf_layers=True,
fp16... | python | def build_network(self,
images,
phase_train=True,
nclass=1001,
image_depth=3,
data_type=tf.float32,
data_format="NCHW",
use_tf_layers=True,
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24,628 | ray-project/ray | python/ray/rllib/utils/__init__.py | renamed_class | def renamed_class(cls):
"""Helper class for renaming Agent => Trainer with a warning."""
class DeprecationWrapper(cls):
def __init__(self, config=None, env=None, logger_creator=None):
old_name = cls.__name__.replace("Trainer", "Agent")
new_name = cls.__name__
logger.... | python | def renamed_class(cls):
"""Helper class for renaming Agent => Trainer with a warning."""
class DeprecationWrapper(cls):
def __init__(self, config=None, env=None, logger_creator=None):
old_name = cls.__name__.replace("Trainer", "Agent")
new_name = cls.__name__
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24,629 | ray-project/ray | python/ray/profiling.py | profile | 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.
This function can be used as follows (both on the driver or within a task).
.. code-block:: python
with ray.profile("custom... | python | 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.
This function can be used as follows (both on the driver or within a task).
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24,630 | ray-project/ray | python/ray/profiling.py | Profiler._periodically_flush_profile_events | def _periodically_flush_profile_events(self):
"""Drivers run this as a thread to flush profile data in the
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# Note(rkn): This is run on a background thread in the driver. It uses
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24,631 | ray-project/ray | python/ray/profiling.py | Profiler.flush_profile_data | def flush_profile_data(self):
"""Push the logged profiling data to the global control store."""
with self.lock:
events = self.events
self.events = []
if self.worker.mode == ray.WORKER_MODE:
component_type = "worker"
else:
component_type = ... | python | def flush_profile_data(self):
"""Push the logged profiling data to the global control store."""
with self.lock:
events = self.events
self.events = []
if self.worker.mode == ray.WORKER_MODE:
component_type = "worker"
else:
component_type = ... | [
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24,632 | ray-project/ray | python/ray/profiling.py | RayLogSpanRaylet.set_attribute | def set_attribute(self, key, value):
"""Add a key-value pair to the extra_data dict.
This can be used to add attributes that are not available when
ray.profile was called.
Args:
key: The attribute name.
value: The attribute value.
"""
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"""Add a key-value pair to the extra_data dict.
This can be used to add attributes that are not available when
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Args:
key: The attribute name.
value: The attribute value.
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24,633 | ray-project/ray | python/ray/tune/log_sync.py | _LogSyncer.sync_to_worker_if_possible | def sync_to_worker_if_possible(self):
"""Syncs the local logdir on driver to worker if possible.
Requires ray cluster to be started with the autoscaler. Also requires
rsync to be installed.
"""
if self.worker_ip == self.local_ip:
return
ssh_key = get_ssh_key(... | python | def sync_to_worker_if_possible(self):
"""Syncs the local logdir on driver to worker if possible.
Requires ray cluster to be started with the autoscaler. Also requires
rsync to be installed.
"""
if self.worker_ip == self.local_ip:
return
ssh_key = get_ssh_key(... | [
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24,634 | ray-project/ray | python/ray/rllib/agents/qmix/mixers.py | QMixer.forward | def forward(self, agent_qs, states):
"""Forward pass for the mixer.
Arguments:
agent_qs: Tensor of shape [B, T, n_agents, n_actions]
states: Tensor of shape [B, T, state_dim]
"""
bs = agent_qs.size(0)
states = states.reshape(-1, self.state_dim)
ag... | python | def forward(self, agent_qs, states):
"""Forward pass for the mixer.
Arguments:
agent_qs: Tensor of shape [B, T, n_agents, n_actions]
states: Tensor of shape [B, T, state_dim]
"""
bs = agent_qs.size(0)
states = states.reshape(-1, self.state_dim)
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24,635 | ray-project/ray | python/ray/tune/suggest/sigopt.py | SigOptSearch.on_trial_complete | def on_trial_complete(self,
trial_id,
result=None,
error=False,
early_terminated=False):
"""Passes the result to SigOpt unless early terminated or errored.
If a trial fails, it will be reported as a ... | python | def on_trial_complete(self,
trial_id,
result=None,
error=False,
early_terminated=False):
"""Passes the result to SigOpt unless early terminated or errored.
If a trial fails, it will be reported as a ... | [
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24,636 | ray-project/ray | python/ray/experimental/sgd/tfbench/resnet_model.py | bottleneck_block_v1 | def bottleneck_block_v1(cnn, depth, depth_bottleneck, stride):
"""Bottleneck block with identity short-cut for ResNet v1.
Args:
cnn: the network to append bottleneck blocks.
depth: the number of output filters for this bottleneck block.
depth_bottleneck: the number of bottleneck filters for this bloc... | python | def bottleneck_block_v1(cnn, depth, depth_bottleneck, stride):
"""Bottleneck block with identity short-cut for ResNet v1.
Args:
cnn: the network to append bottleneck blocks.
depth: the number of output filters for this bottleneck block.
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24,637 | ray-project/ray | python/ray/experimental/sgd/tfbench/resnet_model.py | bottleneck_block | def bottleneck_block(cnn, depth, depth_bottleneck, stride, pre_activation):
"""Bottleneck block with identity short-cut.
Args:
cnn: the network to append bottleneck blocks.
depth: the number of output filters for this bottleneck block.
depth_bottleneck: the number of bottleneck filters for this block... | python | def bottleneck_block(cnn, depth, depth_bottleneck, stride, pre_activation):
"""Bottleneck block with identity short-cut.
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24,638 | ray-project/ray | python/ray/experimental/sgd/tfbench/resnet_model.py | residual_block | def residual_block(cnn, depth, stride, pre_activation):
"""Residual block with identity short-cut.
Args:
cnn: the network to append residual blocks.
depth: the number of output filters for this residual block.
stride: Stride used in the first layer of the residual block.
pre_activation: use pre_a... | python | def residual_block(cnn, depth, stride, pre_activation):
"""Residual block with identity short-cut.
Args:
cnn: the network to append residual blocks.
depth: the number of output filters for this residual block.
stride: Stride used in the first layer of the residual block.
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24,639 | ray-project/ray | python/ray/rllib/utils/filter.py | MeanStdFilter.apply_changes | def apply_changes(self, other, with_buffer=False):
"""Applies updates from the buffer of another filter.
Params:
other (MeanStdFilter): Other filter to apply info from
with_buffer (bool): Flag for specifying if the buffer should be
copied from other.
Exa... | python | def apply_changes(self, other, with_buffer=False):
"""Applies updates from the buffer of another filter.
Params:
other (MeanStdFilter): Other filter to apply info from
with_buffer (bool): Flag for specifying if the buffer should be
copied from other.
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24,640 | ray-project/ray | python/ray/rllib/utils/filter.py | MeanStdFilter.sync | def sync(self, other):
"""Syncs all fields together from other filter.
Examples:
>>> a = MeanStdFilter(())
>>> a(1)
>>> a(2)
>>> print([a.rs.n, a.rs.mean, a.buffer.n])
[2, array(1.5), 2]
>>> b = MeanStdFilter(())
>>> b(... | python | def sync(self, other):
"""Syncs all fields together from other filter.
Examples:
>>> a = MeanStdFilter(())
>>> a(1)
>>> a(2)
>>> print([a.rs.n, a.rs.mean, a.buffer.n])
[2, array(1.5), 2]
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24,641 | ray-project/ray | python/ray/rllib/utils/filter.py | ConcurrentMeanStdFilter.as_serializable | def as_serializable(self):
"""Returns non-concurrent version of current class"""
other = MeanStdFilter(self.shape)
other.sync(self)
return other | python | def as_serializable(self):
"""Returns non-concurrent version of current class"""
other = MeanStdFilter(self.shape)
other.sync(self)
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24,642 | ray-project/ray | python/ray/experimental/sgd/modified_allreduce.py | parse_general_int | def parse_general_int(s):
"""Parse integer with power-of-2 suffix eg. 32k."""
mo = re.match(r"(\d+)([KkMGT]?)$", s)
if mo:
i, suffix = mo.group(1, 2)
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if suffix:
if suffix == "K" or suffix == "k":
v *= 1024
elif suffix == "M":
... | python | def parse_general_int(s):
"""Parse integer with power-of-2 suffix eg. 32k."""
mo = re.match(r"(\d+)([KkMGT]?)$", s)
if mo:
i, suffix = mo.group(1, 2)
v = int(i)
if suffix:
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24,643 | ray-project/ray | python/ray/experimental/sgd/modified_allreduce.py | parse_all_reduce_spec | def parse_all_reduce_spec(all_reduce_spec):
"""Parse all_reduce_spec.
Args:
all_reduce_spec: a string specifying a combination of all-reduce
algorithms to apply for gradient reduction.
Returns:
a list of AllReduceSpecTuple.
Raises:
ValueError: all_reduce_spec is not well-formed.
An all... | python | def parse_all_reduce_spec(all_reduce_spec):
"""Parse all_reduce_spec.
Args:
all_reduce_spec: a string specifying a combination of all-reduce
algorithms to apply for gradient reduction.
Returns:
a list of AllReduceSpecTuple.
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24,644 | ray-project/ray | python/ray/experimental/sgd/modified_allreduce.py | build_all_reduce_device_prefixes | def build_all_reduce_device_prefixes(job_name, num_tasks):
"""Build list of device prefix names for all_reduce.
Args:
job_name: "worker", "ps" or "localhost".
num_tasks: number of jobs across which device names should be generated.
Returns:
A list of device name prefix strings. Each element spell... | python | def build_all_reduce_device_prefixes(job_name, num_tasks):
"""Build list of device prefix names for all_reduce.
Args:
job_name: "worker", "ps" or "localhost".
num_tasks: number of jobs across which device names should be generated.
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24,645 | ray-project/ray | python/ray/experimental/sgd/modified_allreduce.py | group_device_names | def group_device_names(devices, group_size):
"""Group device names into groups of group_size.
Args:
devices: list of strings naming devices.
group_size: int >= 1
Returns:
list of lists of devices, where each inner list is group_size long,
and each device appears at least once in an inner lis... | python | def group_device_names(devices, group_size):
"""Group device names into groups of group_size.
Args:
devices: list of strings naming devices.
group_size: int >= 1
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24,646 | ray-project/ray | python/ray/experimental/sgd/modified_allreduce.py | split_grads_by_size | def split_grads_by_size(threshold_size, device_grads):
"""Break gradients into two sets according to tensor size.
Args:
threshold_size: int size cutoff for small vs large tensor.
device_grads: List of lists of (gradient, variable) tuples. The outer
list is over devices. The inner list is over in... | python | def split_grads_by_size(threshold_size, device_grads):
"""Break gradients into two sets according to tensor size.
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threshold_size: int size cutoff for small vs large tensor.
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24,647 | ray-project/ray | python/ray/experimental/sgd/modified_allreduce.py | aggregate_single_gradient | def aggregate_single_gradient(grad_and_vars, use_mean, check_inf_nan):
"""Calculate the average gradient for a shared variable across all towers.
Note that this function provides a synchronization point across all towers.
Args:
grad_and_vars: A list or tuple of (gradient, variable) tuples. Each
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"""Calculate the average gradient for a shared variable across all towers.
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24,648 | ray-project/ray | python/ray/experimental/sgd/modified_allreduce.py | aggregate_gradients_using_copy_with_device_selection | def aggregate_gradients_using_copy_with_device_selection(
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"""Aggregate gradients, controlling device for the aggregation.
Args:
tower_grads: List of lists of (gradient, variable) tuples. The outer list
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24,649 | ray-project/ray | python/ray/experimental/sgd/modified_allreduce.py | extract_ranges | def extract_ranges(index_list, range_size_limit=32):
"""Extract consecutive ranges and singles from index_list.
Args:
index_list: List of monotone increasing non-negative integers.
range_size_limit: Largest size range to return. If a larger
consecutive range exists it will be returned as multiple
... | python | def extract_ranges(index_list, range_size_limit=32):
"""Extract consecutive ranges and singles from index_list.
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index_list: List of monotone increasing non-negative integers.
range_size_limit: Largest size range to return. If a larger
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24,650 | ray-project/ray | python/ray/experimental/sgd/modified_allreduce.py | pack_range | def pack_range(key, packing, grad_vars, rng):
"""Form the concatenation of a specified range of gradient tensors.
Args:
key: Value under which to store meta-data in packing that will be used
later to restore the grad_var list structure.
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key: Value under which to store meta-data in packing that will be used
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24,651 | ray-project/ray | python/ray/experimental/sgd/modified_allreduce.py | unpack_grad_tuple | def unpack_grad_tuple(gv, gpt):
"""Unpack a previously packed collection of gradient tensors.
Args:
gv: A (grad, var) pair to be unpacked.
gpt: A GradPackTuple describing the packing operation that produced gv.
Returns:
A list of (grad, var) pairs corresponding to the values that were
origina... | python | def unpack_grad_tuple(gv, gpt):
"""Unpack a previously packed collection of gradient tensors.
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gv: A (grad, var) pair to be unpacked.
gpt: A GradPackTuple describing the packing operation that produced gv.
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24,652 | ray-project/ray | python/ray/experimental/sgd/modified_allreduce.py | pack_small_tensors | def pack_small_tensors(tower_grads, max_bytes=0):
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Does binpacking
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tower_grads: List of lists of (gradient, variable) tuples.
max_bytes: Int giving max number of bytes in a tensor that
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"""
assert max_bytes >... | python | def pack_small_tensors(tower_grads, max_bytes=0):
"""Concatenate gradients together more intelligently.
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tower_grads: List of lists of (gradient, variable) tuples.
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24,653 | ray-project/ray | python/ray/experimental/sgd/modified_allreduce.py | unpack_small_tensors | def unpack_small_tensors(tower_grads, packing):
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Args:
tower_grads: List of List of (grad, var) tuples.
packing: A dict generated by pack_small_tensors describing the changes
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tower_grads: List of List of (grad, var) tuples.
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24,654 | ray-project/ray | python/ray/tune/logger.py | CSVLogger._init | def _init(self):
"""CSV outputted with Headers as first set of results."""
# Note that we assume params.json was already created by JsonLogger
progress_file = os.path.join(self.logdir, "progress.csv")
self._continuing = os.path.exists(progress_file)
self._file = open(progress_fil... | python | def _init(self):
"""CSV outputted with Headers as first set of results."""
# Note that we assume params.json was already created by JsonLogger
progress_file = os.path.join(self.logdir, "progress.csv")
self._continuing = os.path.exists(progress_file)
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24,655 | ray-project/ray | python/ray/tune/logger.py | UnifiedLogger.sync_results_to_new_location | def sync_results_to_new_location(self, worker_ip):
"""Sends the current log directory to the remote node.
Syncing will not occur if the cluster is not started
with the Ray autoscaler.
"""
if worker_ip != self._log_syncer.worker_ip:
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"""Sends the current log directory to the remote node.
Syncing will not occur if the cluster is not started
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"""
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24,656 | ray-project/ray | python/ray/tune/automl/search_policy.py | deep_insert | def deep_insert(path_list, value, config):
"""Inserts value into config by path, generating intermediate dictionaries.
Example:
>>> deep_insert(path.split("."), value, {})
"""
if len(path_list) > 1:
inside_config = config.setdefault(path_list[0], {})
deep_insert(path_list[1:], v... | python | def deep_insert(path_list, value, config):
"""Inserts value into config by path, generating intermediate dictionaries.
Example:
>>> deep_insert(path.split("."), value, {})
"""
if len(path_list) > 1:
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24,657 | ray-project/ray | python/ray/function_manager.py | FunctionDescriptor.from_bytes_list | def from_bytes_list(cls, function_descriptor_list):
"""Create a FunctionDescriptor instance from list of bytes.
This function is used to create the function descriptor from
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Args:
cls: Current class which is required argument for classmethod.
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24,658 | ray-project/ray | python/ray/function_manager.py | FunctionDescriptor.from_function | def from_function(cls, function):
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24,659 | ray-project/ray | python/ray/function_manager.py | FunctionDescriptor.from_class | def from_class(cls, target_class):
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24,660 | ray-project/ray | python/ray/function_manager.py | FunctionDescriptor.is_for_driver_task | def is_for_driver_task(self):
"""See whether this function descriptor is for a driver or not.
Returns:
True if this function descriptor is for driver tasks.
"""
return all(
len(x) == 0
for x in [self.module_name, self.class_name, self.function_name]) | python | def is_for_driver_task(self):
"""See whether this function descriptor is for a driver or not.
Returns:
True if this function descriptor is for driver tasks.
"""
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24,661 | ray-project/ray | python/ray/function_manager.py | FunctionDescriptor._get_function_id | def _get_function_id(self):
"""Calculate the function id of current function descriptor.
This function id is calculated from all the fields of function
descriptor.
Returns:
ray.ObjectID to represent the function descriptor.
"""
if self.is_for_driver_task:
... | python | def _get_function_id(self):
"""Calculate the function id of current function descriptor.
This function id is calculated from all the fields of function
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Returns:
ray.ObjectID to represent the function descriptor.
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24,662 | ray-project/ray | python/ray/function_manager.py | FunctionDescriptor.get_function_descriptor_list | def get_function_descriptor_list(self):
"""Return a list of bytes representing the function descriptor.
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Returns:
A list of bytes.
"""
descriptor_list = []
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... | python | def get_function_descriptor_list(self):
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Returns:
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24,663 | ray-project/ray | python/ray/function_manager.py | FunctionActorManager.export_cached | def export_cached(self):
"""Export cached remote functions
Note: this should be called only once when worker is connected.
"""
for remote_function in self._functions_to_export:
self._do_export(remote_function)
self._functions_to_export = None
for info in self... | python | def export_cached(self):
"""Export cached remote functions
Note: this should be called only once when worker is connected.
"""
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self._functions_to_export = None
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24,664 | ray-project/ray | python/ray/function_manager.py | FunctionActorManager.export | def export(self, remote_function):
"""Export a remote function.
Args:
remote_function: the RemoteFunction object.
"""
if self._worker.mode is None:
# If the worker isn't connected, cache the function
# and export it later.
self._functions_... | python | def export(self, remote_function):
"""Export a remote function.
Args:
remote_function: the RemoteFunction object.
"""
if self._worker.mode is None:
# If the worker isn't connected, cache the function
# and export it later.
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24,665 | ray-project/ray | python/ray/function_manager.py | FunctionActorManager._do_export | def _do_export(self, remote_function):
"""Pickle a remote function and export it to redis.
Args:
remote_function: the RemoteFunction object.
"""
if self._worker.load_code_from_local:
return
# Work around limitations of Python pickling.
function = ... | python | def _do_export(self, remote_function):
"""Pickle a remote function and export it to redis.
Args:
remote_function: the RemoteFunction object.
"""
if self._worker.load_code_from_local:
return
# Work around limitations of Python pickling.
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24,666 | ray-project/ray | python/ray/function_manager.py | FunctionActorManager.fetch_and_register_remote_function | def fetch_and_register_remote_function(self, key):
"""Import a remote function."""
(driver_id_str, function_id_str, function_name, serialized_function,
num_return_vals, module, resources,
max_calls) = self._worker.redis_client.hmget(key, [
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"""Import a remote function."""
(driver_id_str, function_id_str, function_name, serialized_function,
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max_calls) = self._worker.redis_client.hmget(key, [
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24,667 | ray-project/ray | python/ray/function_manager.py | FunctionActorManager.get_execution_info | def get_execution_info(self, driver_id, function_descriptor):
"""Get the FunctionExecutionInfo of a remote function.
Args:
driver_id: ID of the driver that the function belongs to.
function_descriptor: The FunctionDescriptor of the function to get.
Returns:
... | python | def get_execution_info(self, driver_id, function_descriptor):
"""Get the FunctionExecutionInfo of a remote function.
Args:
driver_id: ID of the driver that the function belongs to.
function_descriptor: The FunctionDescriptor of the function to get.
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24,668 | ray-project/ray | python/ray/function_manager.py | FunctionActorManager._wait_for_function | def _wait_for_function(self, function_descriptor, driver_id, timeout=10):
"""Wait until the function to be executed is present on this worker.
This method will simply loop until the import thread has imported the
relevant function. If we spend too long in this loop, that may indicate
a ... | python | def _wait_for_function(self, function_descriptor, driver_id, timeout=10):
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24,669 | ray-project/ray | python/ray/function_manager.py | FunctionActorManager._publish_actor_class_to_key | def _publish_actor_class_to_key(self, key, actor_class_info):
"""Push an actor class definition to Redis.
The is factored out as a separate function because it is also called
on cached actor class definitions when a worker connects for the first
time.
Args:
key: The... | python | def _publish_actor_class_to_key(self, key, actor_class_info):
"""Push an actor class definition to Redis.
The is factored out as a separate function because it is also called
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24,670 | ray-project/ray | python/ray/function_manager.py | FunctionActorManager.load_actor_class | def load_actor_class(self, driver_id, function_descriptor):
"""Load the actor class.
Args:
driver_id: Driver ID of the actor.
function_descriptor: Function descriptor of the actor constructor.
Returns:
The actor class.
"""
function_id = funct... | python | def load_actor_class(self, driver_id, function_descriptor):
"""Load the actor class.
Args:
driver_id: Driver ID of the actor.
function_descriptor: Function descriptor of the actor constructor.
Returns:
The actor class.
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24,671 | ray-project/ray | python/ray/function_manager.py | FunctionActorManager._load_actor_from_local | def _load_actor_from_local(self, driver_id, function_descriptor):
"""Load actor class from local code."""
module_name, class_name = (function_descriptor.module_name,
function_descriptor.class_name)
try:
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... | python | def _load_actor_from_local(self, driver_id, function_descriptor):
"""Load actor class from local code."""
module_name, class_name = (function_descriptor.module_name,
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24,672 | ray-project/ray | python/ray/function_manager.py | FunctionActorManager._load_actor_class_from_gcs | def _load_actor_class_from_gcs(self, driver_id, function_descriptor):
"""Load actor class from GCS."""
key = (b"ActorClass:" + driver_id.binary() + b":" +
function_descriptor.function_id.binary())
# Wait for the actor class key to have been imported by the
# import thread.... | python | def _load_actor_class_from_gcs(self, driver_id, function_descriptor):
"""Load actor class from GCS."""
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24,673 | ray-project/ray | python/ray/function_manager.py | FunctionActorManager._make_actor_method_executor | def _make_actor_method_executor(self, method_name, method, actor_imported):
"""Make an executor that wraps a user-defined actor method.
The wrapped method updates the worker's internal state and performs any
necessary checkpointing operations.
Args:
method_name (str): The n... | python | def _make_actor_method_executor(self, method_name, method, actor_imported):
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24,674 | ray-project/ray | python/ray/function_manager.py | FunctionActorManager._save_and_log_checkpoint | def _save_and_log_checkpoint(self, actor):
"""Save an actor checkpoint if necessary and log any errors.
Args:
actor: The actor to checkpoint.
Returns:
The result of the actor's user-defined `save_checkpoint` method.
"""
actor_id = self._worker.actor_id
... | python | def _save_and_log_checkpoint(self, actor):
"""Save an actor checkpoint if necessary and log any errors.
Args:
actor: The actor to checkpoint.
Returns:
The result of the actor's user-defined `save_checkpoint` method.
"""
actor_id = self._worker.actor_id
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24,675 | ray-project/ray | python/ray/function_manager.py | FunctionActorManager._restore_and_log_checkpoint | def _restore_and_log_checkpoint(self, actor):
"""Restore an actor from a checkpoint if available and log any errors.
This should only be called on workers that have just executed an actor
creation task.
Args:
actor: The actor to restore from a checkpoint.
"""
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Args:
actor: The actor to restore from a checkpoint.
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24,676 | ray-project/ray | python/ray/rllib/evaluation/sampler.py | _env_runner | def _env_runner(base_env, extra_batch_callback, policies, policy_mapping_fn,
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clip_rewards, clip_actions, pack, callbacks, tf_sess,
perf_stats, soft_horizon):
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24,677 | ray-project/ray | python/ray/rllib/evaluation/sampler.py | _do_policy_eval | def _do_policy_eval(tf_sess, to_eval, policies, active_episodes):
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24,678 | ray-project/ray | python/ray/rllib/evaluation/sampler.py | _process_policy_eval_results | def _process_policy_eval_results(to_eval, eval_results, active_episodes,
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24,679 | ray-project/ray | python/ray/rllib/evaluation/sampler.py | _fetch_atari_metrics | def _fetch_atari_metrics(base_env):
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"""
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if not unwrapped:
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... | python | def _fetch_atari_metrics(base_env):
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"""
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24,680 | google/flatbuffers | android/jni/msbuild.py | compare_version | def compare_version(a, b):
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24,681 | google/flatbuffers | conanfile.py | FlatbuffersConan.configure_cmake | def configure_cmake(self):
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cmake = CMake(self)
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24,691 | google/flatbuffers | python/flatbuffers/table.py | Table.GetVOffsetTSlot | def GetVOffsetTSlot(self, slot, d):
"""
GetVOffsetTSlot retrieves the VOffsetT that the given vtable location
points to. If the vtable value is zero, the default value `d`
will be returned.
"""
N.enforce_number(slot, N.VOffsetTFlags)
N.enforce_number(d, N.VOffset... | python | def GetVOffsetTSlot(self, slot, d):
"""
GetVOffsetTSlot retrieves the VOffsetT that the given vtable location
points to. If the vtable value is zero, the default value `d`
will be returned.
"""
N.enforce_number(slot, N.VOffsetTFlags)
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24,692 | google/flatbuffers | android/jni/run_flatc.py | main | def main():
"""Script that finds and runs flatc built from source."""
if len(sys.argv) < 2:
sys.stderr.write('Usage: run_flatc.py flatbuffers_dir [flatc_args]\n')
return 1
cwd = os.getcwd()
flatc = ''
flatbuffers_dir = sys.argv[1]
for path in FLATC_SEARCH_PATHS:
current = os.path.join(flatbuffer... | python | def main():
"""Script that finds and runs flatc built from source."""
if len(sys.argv) < 2:
sys.stderr.write('Usage: run_flatc.py flatbuffers_dir [flatc_args]\n')
return 1
cwd = os.getcwd()
flatc = ''
flatbuffers_dir = sys.argv[1]
for path in FLATC_SEARCH_PATHS:
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24,693 | google/flatbuffers | python/flatbuffers/compat.py | import_numpy | def import_numpy():
"""
Returns the numpy module if it exists on the system,
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"""
try:
imp.find_module('numpy')
numpy_exists = True
except ImportError:
numpy_exists = False
if numpy_exists:
# We do this outside of try/except block in ca... | python | def import_numpy():
"""
Returns the numpy module if it exists on the system,
otherwise returns None.
"""
try:
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numpy_exists = True
except ImportError:
numpy_exists = False
if numpy_exists:
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24,694 | google/flatbuffers | python/flatbuffers/builder.py | vtableEqual | def vtableEqual(a, objectStart, b):
"""vtableEqual compares an unwritten vtable to a written vtable."""
N.enforce_number(objectStart, N.UOffsetTFlags)
if len(a) * N.VOffsetTFlags.bytewidth != len(b):
return False
for i, elem in enumerate(a):
x = encode.Get(packer.voffset, b, i * N.VOf... | python | def vtableEqual(a, objectStart, b):
"""vtableEqual compares an unwritten vtable to a written vtable."""
N.enforce_number(objectStart, N.UOffsetTFlags)
if len(a) * N.VOffsetTFlags.bytewidth != len(b):
return False
for i, elem in enumerate(a):
x = encode.Get(packer.voffset, b, i * N.VOf... | [
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24,695 | google/flatbuffers | python/flatbuffers/builder.py | Builder.StartObject | def StartObject(self, numfields):
"""StartObject initializes bookkeeping for writing a new object."""
self.assertNotNested()
# use 32-bit offsets so that arithmetic doesn't overflow.
self.current_vtable = [0 for _ in range_func(numfields)]
self.objectEnd = self.Offset()
... | python | def StartObject(self, numfields):
"""StartObject initializes bookkeeping for writing a new object."""
self.assertNotNested()
# use 32-bit offsets so that arithmetic doesn't overflow.
self.current_vtable = [0 for _ in range_func(numfields)]
self.objectEnd = self.Offset()
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24,696 | google/flatbuffers | python/flatbuffers/builder.py | Builder.WriteVtable | def WriteVtable(self):
"""
WriteVtable serializes the vtable for the current object, if needed.
Before writing out the vtable, this checks pre-existing vtables for
equality to this one. If an equal vtable is found, point the object to
the existing vtable and return.
Bec... | python | def WriteVtable(self):
"""
WriteVtable serializes the vtable for the current object, if needed.
Before writing out the vtable, this checks pre-existing vtables for
equality to this one. If an equal vtable is found, point the object to
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24,697 | google/flatbuffers | python/flatbuffers/builder.py | Builder.Pad | def Pad(self, n):
"""Pad places zeros at the current offset."""
for i in range_func(n):
self.Place(0, N.Uint8Flags) | python | def Pad(self, n):
"""Pad places zeros at the current offset."""
for i in range_func(n):
self.Place(0, N.Uint8Flags) | [
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24,698 | google/flatbuffers | python/flatbuffers/builder.py | Builder.PrependSOffsetTRelative | def PrependSOffsetTRelative(self, off):
"""
PrependSOffsetTRelative prepends an SOffsetT, relative to where it
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"""
# Ensure alignment is already done:
self.Prep(N.SOffsetTFlags.bytewidth, 0)
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"""
PrependSOffsetTRelative prepends an SOffsetT, relative to where it
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"""
# Ensure alignment is already done:
self.Prep(N.SOffsetTFlags.bytewidth, 0)
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24,699 | google/flatbuffers | python/flatbuffers/builder.py | Builder.PrependUOffsetTRelative | def PrependUOffsetTRelative(self, off):
"""Prepends an unsigned offset into vector data, relative to where it
will be written.
"""
# Ensure alignment is already done:
self.Prep(N.UOffsetTFlags.bytewidth, 0)
if not (off <= self.Offset()):
msg = "flatbuffers: O... | python | def PrependUOffsetTRelative(self, off):
"""Prepends an unsigned offset into vector data, relative to where it
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# Ensure alignment is already done:
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