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ray-project/ray | python/ray/rllib/agents/ars/utils.py | compute_ranks | def compute_ranks(x):
"""Returns ranks in [0, len(x))
Note: This is different from scipy.stats.rankdata, which returns ranks in
[1, len(x)].
"""
assert x.ndim == 1
ranks = np.empty(len(x), dtype=int)
ranks[x.argsort()] = np.arange(len(x))
return ranks | python | def compute_ranks(x):
"""Returns ranks in [0, len(x))
Note: This is different from scipy.stats.rankdata, which returns ranks in
[1, len(x)].
"""
assert x.ndim == 1
ranks = np.empty(len(x), dtype=int)
ranks[x.argsort()] = np.arange(len(x))
return ranks | [
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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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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.
Args:
cnn: the network to append bottleneck blocks.
depth: the number of output filters for this bottleneck block.
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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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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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ray-project/ray | python/ray/rllib/utils/filter.py | MeanStdFilter.copy | def copy(self):
"""Returns a copy of Filter."""
other = MeanStdFilter(self.shape)
other.sync(self)
return other | python | def copy(self):
"""Returns a copy of Filter."""
other = MeanStdFilter(self.shape)
other.sync(self)
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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):
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Examples:
>>> a = MeanStdFilter(())
>>> a(1)
>>> a(2)
>>> print([a.rs.n, a.rs.mean, a.buffer.n])
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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)
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"""Returns non-concurrent version of current class"""
other = MeanStdFilter(self.shape)
other.sync(self)
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ray-project/ray | python/ray/rllib/utils/filter.py | ConcurrentMeanStdFilter.copy | def copy(self):
"""Returns a copy of Filter."""
other = ConcurrentMeanStdFilter(self.shape)
other.sync(self)
return other | python | def copy(self):
"""Returns a copy of Filter."""
other = ConcurrentMeanStdFilter(self.shape)
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ray-project/ray | python/ray/tune/examples/genetic_example.py | michalewicz_function | def michalewicz_function(config, reporter):
"""f(x) = -sum{sin(xi) * [sin(i*xi^2 / pi)]^(2m)}"""
import numpy as np
x = np.array(
[config["x1"], config["x2"], config["x3"], config["x4"], config["x5"]])
sin_x = np.sin(x)
z = (np.arange(1, 6) / np.pi * (x * x))
sin_z = np.power(np.sin(z), ... | python | def michalewicz_function(config, reporter):
"""f(x) = -sum{sin(xi) * [sin(i*xi^2 / pi)]^(2m)}"""
import numpy as np
x = np.array(
[config["x1"], config["x2"], config["x3"], config["x4"], config["x5"]])
sin_x = np.sin(x)
z = (np.arange(1, 6) / np.pi * (x * x))
sin_z = np.power(np.sin(z), ... | [
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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]?)$", s)
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... | python | def parse_general_int(s):
"""Parse integer with power-of-2 suffix eg. 32k."""
mo = re.match(r"(\d+)([KkMGT]?)$", s)
if mo:
i, suffix = mo.group(1, 2)
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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
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Returns:
a list of AllReduceSpecTuple.
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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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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
Returns:
list of lists of devices, where each inner list is group_size long,
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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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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
(gra... | python | def aggregate_single_gradient(grad_and_vars, use_mean, check_inf_nan):
"""Calculate the average gradient for a shared variable across all towers.
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grad_and_vars: A list or tuple of (gradient, variable) tuples. Each
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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(
tower_grads, avail_devices, use_mean=True, check_inf_nan=False):
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tower_grads: List of lists of (gradient, variable) tuples. The outer list
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ray-project/ray | python/ray/experimental/sgd/modified_allreduce.py | sum_grad_and_var_all_reduce | def sum_grad_and_var_all_reduce(grad_and_vars,
num_workers,
alg,
gpu_indices,
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num_shards=1):
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aux_devices=None,
num_shards=1):
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ray-project/ray | python/ray/experimental/sgd/modified_allreduce.py | sum_gradients_all_reduce | def sum_gradients_all_reduce(dev_prefixes,
tower_grads,
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num_shards,
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agg_small_grads_max_bytes=0):
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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
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... | 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.
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ray-project/ray | python/ray/experimental/sgd/modified_allreduce.py | pack_range | def pack_range(key, packing, grad_vars, rng):
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key: Value under which to store meta-data in packing that will be used
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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.
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A list of (grad, var) pairs corresponding to the values that were
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"""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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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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tower_grads: List of lists of (gradient, variable) tuples.
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"""
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tower_grads: List of lists of (gradient, variable) tuples.
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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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ray-project/ray | python/ray/tune/logger.py | CSVLogger._init | def _init(self):
"""CSV outputted with Headers as first set of results."""
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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")
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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.
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"""
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ray-project/ray | python/ray/tune/automl/search_policy.py | deep_insert | def deep_insert(path_list, value, config):
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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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ray-project/ray | python/ray/function_manager.py | FunctionDescriptor.from_bytes_list | def from_bytes_list(cls, function_descriptor_list):
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This function is used to create the function descriptor from
backend data.
Args:
cls: Current class which is required argument for classmethod.
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ray-project/ray | python/ray/function_manager.py | FunctionDescriptor.from_function | def from_function(cls, function):
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ray-project/ray | python/ray/function_manager.py | FunctionDescriptor.from_class | def from_class(cls, target_class):
"""Create a FunctionDescriptor from a class.
Args:
cls: Current class which is required argument for classmethod.
target_class: the python class used to create the function
descriptor.
Returns:
The FunctionD... | python | def from_class(cls, target_class):
"""Create a FunctionDescriptor from a class.
Args:
cls: Current class which is required argument for classmethod.
target_class: the python class used to create the function
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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
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"""See whether this function descriptor is for a driver or not.
Returns:
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"""
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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:
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"""Calculate the function id of current function descriptor.
This function id is calculated from all the fields of function
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ray.ObjectID to represent the function descriptor.
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ray-project/ray | python/ray/function_manager.py | FunctionDescriptor.get_function_descriptor_list | def get_function_descriptor_list(self):
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Returns:
A list of bytes.
"""
descriptor_list = []
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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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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.
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"""Export a remote function.
Args:
remote_function: the RemoteFunction object.
"""
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# If the worker isn't connected, cache the function
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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:
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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."""
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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):
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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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ray-project/ray | python/ray/function_manager.py | FunctionActorManager._wait_for_function | def _wait_for_function(self, function_descriptor, driver_id, timeout=10):
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This method will simply loop until the import thread has imported the
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a ... | python | def _wait_for_function(self, function_descriptor, driver_id, timeout=10):
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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):
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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
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Args:
key: The... | python | def _publish_actor_class_to_key(self, key, actor_class_info):
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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):
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Args:
driver_id: Driver ID of the actor.
function_descriptor: Function descriptor of the actor constructor.
Returns:
The actor class.
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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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try:
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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
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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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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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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
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Args:
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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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ray-project/ray | python/ray/rllib/evaluation/sampler.py | _process_observations | def _process_observations(base_env, policies, batch_builder_pool,
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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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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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ray-project/ray | python/ray/rllib/evaluation/sampler.py | _fetch_atari_metrics | def _fetch_atari_metrics(base_env):
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"""Union initializes any Table-derived type to point to the union at
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assert type(t2) is Table
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"""Union initializes any Table-derived type to point to the union at
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google/flatbuffers | python/flatbuffers/table.py | Table.GetVectorAsNumpy | def GetVectorAsNumpy(self, flags, off):
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google/flatbuffers | python/flatbuffers/encode.py | GetVectorAsNumpy | def GetVectorAsNumpy(numpy_type, buf, count, offset):
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google/flatbuffers | python/flatbuffers/encode.py | Write | def Write(packer_type, buf, head, n):
""" Write encodes `n` at buf[head] using `packer_type`. """
packer_type.pack_into(buf, head, n) | python | def Write(packer_type, buf, head, n):
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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]
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"""Script that finds and runs flatc built from source."""
if len(sys.argv) < 2:
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return 1
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google/flatbuffers | python/flatbuffers/compat.py | import_numpy | def import_numpy():
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"""
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Returns the numpy module if it exists on the system,
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google/flatbuffers | python/flatbuffers/builder.py | vtableEqual | def vtableEqual(a, objectStart, b):
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google/flatbuffers | python/flatbuffers/builder.py | Builder.StartObject | def StartObject(self, numfields):
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google/flatbuffers | python/flatbuffers/builder.py | Builder.WriteVtable | def WriteVtable(self):
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WriteVtable serializes the vtable for the current object, if needed.
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WriteVtable serializes the vtable for the current object, if needed.
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google/flatbuffers | python/flatbuffers/builder.py | Builder.PrependSOffsetTRelative | def PrependSOffsetTRelative(self, off):
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google/flatbuffers | python/flatbuffers/builder.py | Builder.PrependUOffsetTRelative | def PrependUOffsetTRelative(self, off):
"""Prepends an unsigned offset into vector data, relative to where it
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"""
# 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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google/flatbuffers | python/flatbuffers/builder.py | Builder.StartVector | def StartVector(self, elemSize, numElems, alignment):
"""
StartVector initializes bookkeeping for writing a new vector.
A vector has the following format:
- <UOffsetT: number of elements in this vector>
- <T: data>+, where T is the type of elements of this vector.
""... | python | def StartVector(self, elemSize, numElems, alignment):
"""
StartVector initializes bookkeeping for writing a new vector.
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google/flatbuffers | python/flatbuffers/builder.py | Builder.EndVector | def EndVector(self, vectorNumElems):
"""EndVector writes data necessary to finish vector construction."""
self.assertNested()
## @cond FLATBUFFERS_INTERNAL
self.nested = False
## @endcond
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"""EndVector writes data necessary to finish vector construction."""
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google/flatbuffers | python/flatbuffers/builder.py | Builder.CreateString | def CreateString(self, s, encoding='utf-8', errors='strict'):
"""CreateString writes a null-terminated byte string as a vector."""
self.assertNotNested()
## @cond FLATBUFFERS_INTERNAL
self.nested = True
## @endcond
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"""CreateString writes a null-terminated byte string as a vector."""
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google/flatbuffers | python/flatbuffers/builder.py | Builder.CreateByteVector | def CreateByteVector(self, x):
"""CreateString writes a byte vector."""
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"""CreateString writes a byte vector."""
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google/flatbuffers | python/flatbuffers/builder.py | Builder.CreateNumpyVector | def CreateNumpyVector(self, x):
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google/flatbuffers | python/flatbuffers/builder.py | Builder.assertStructIsInline | def assertStructIsInline(self, obj):
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google/flatbuffers | python/flatbuffers/builder.py | Builder.Slot | def Slot(self, slotnum):
"""
Slot sets the vtable key `voffset` to the current location in the
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"""
self.assertNested()
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Slot sets the vtable key `voffset` to the current location in the
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google/flatbuffers | python/flatbuffers/builder.py | Builder.__Finish | def __Finish(self, rootTable, sizePrefix):
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google/flatbuffers | python/flatbuffers/builder.py | Builder.PrependUOffsetTRelativeSlot | def PrependUOffsetTRelativeSlot(self, o, x, d):
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PrependUOffsetTRelativeSlot prepends an UOffsetT onto the object at
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google/flatbuffers | python/flatbuffers/builder.py | Builder.Place | def Place(self, x, flags):
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google/flatbuffers | python/flatbuffers/builder.py | Builder.PlaceVOffsetT | def PlaceVOffsetT(self, x):
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google/flatbuffers | python/flatbuffers/builder.py | Builder.PlaceSOffsetT | def PlaceSOffsetT(self, x):
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google/flatbuffers | python/flatbuffers/builder.py | Builder.PlaceUOffsetT | def PlaceUOffsetT(self, x):
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pypa/pipenv | pipenv/vendor/appdirs.py | site_data_dir | def site_data_dir(appname=None, appauthor=None, version=None, multipath=False):
r"""Return full path to the user-shared data dir for this application.
"appname" is the name of application.
If None, just the system directory is returned.
"appauthor" (only used on Windows) is the name of ... | python | def site_data_dir(appname=None, appauthor=None, version=None, multipath=False):
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pypa/pipenv | pipenv/vendor/appdirs.py | user_config_dir | def user_config_dir(appname=None, appauthor=None, version=None, roaming=False):
r"""Return full path to the user-specific config dir for this application.
"appname" is the name of application.
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pypa/pipenv | pipenv/vendor/requests/api.py | request | def request(method, url, **kwargs):
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:param method: method for the new :class:`Request` object.
:param url: URL for the new :class:`Request` object.
:param params: (optional) Dictionary, list of tuples or bytes to send
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"""Constructs and sends a :class:`Request <Request>`.
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pypa/pipenv | pipenv/vendor/requests/api.py | get | def get(url, params=None, **kwargs):
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:param params: (optional) Dictionary, list of tuples or bytes to send
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:param \*\*kwargs: Optional arguments that ``request`` takes.
:return... | python | def get(url, params=None, **kwargs):
r"""Sends a GET request.
:param url: URL for the new :class:`Request` object.
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pypa/pipenv | pipenv/vendor/toml/encoder.py | dump | def dump(o, f):
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Args:
o: Object to dump into toml
f: File descriptor where the toml should be stored
Returns:
String containing the toml corresponding to dictionary
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pypa/pipenv | pipenv/vendor/toml/encoder.py | dumps | def dumps(o, encoder=None):
"""Stringifies input dict as toml
Args:
o: Object to dump into toml
preserve: Boolean parameter. If true, preserve inline tables.
Returns:
String containing the toml corresponding to dict
"""
retval = ""
if encoder is None:
encoder ... | python | def dumps(o, encoder=None):
"""Stringifies input dict as toml
Args:
o: Object to dump into toml
preserve: Boolean parameter. If true, preserve inline tables.
Returns:
String containing the toml corresponding to dict
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pypa/pipenv | pipenv/vendor/toml/encoder.py | TomlEncoder.dump_inline_table | def dump_inline_table(self, section):
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https://github.com/toml-lang/toml#user-content-inline-table
"""
retval = ""
if isinstance(section, dict):
val_list = []
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"""Preserve inline table in its compact syntax instead of expanding
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https://github.com/toml-lang/toml#user-content-inline-table
"""
retval = ""
if isinstance(section, dict):
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pypa/pipenv | pipenv/environments.py | _is_env_truthy | def _is_env_truthy(name):
"""An environment variable is truthy if it exists and isn't one of (0, false, no, off)
"""
if name not in os.environ:
return False
return os.environ.get(name).lower() not in ("0", "false", "no", "off") | python | def _is_env_truthy(name):
"""An environment variable is truthy if it exists and isn't one of (0, false, no, off)
"""
if name not in os.environ:
return False
return os.environ.get(name).lower() not in ("0", "false", "no", "off") | [
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pypa/pipenv | pipenv/environments.py | is_in_virtualenv | def is_in_virtualenv():
"""
Check virtualenv membership dynamically
:return: True or false depending on whether we are in a regular virtualenv or not
:rtype: bool
"""
pipenv_active = os.environ.get("PIPENV_ACTIVE", False)
virtual_env = None
use_system = False
ignore_virtualenvs = b... | python | def is_in_virtualenv():
"""
Check virtualenv membership dynamically
:return: True or false depending on whether we are in a regular virtualenv or not
:rtype: bool
"""
pipenv_active = os.environ.get("PIPENV_ACTIVE", False)
virtual_env = None
use_system = False
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pypa/pipenv | pipenv/patched/notpip/_vendor/msgpack/fallback.py | unpackb | def unpackb(packed, **kwargs):
"""
Unpack an object from `packed`.
Raises `ExtraData` when `packed` contains extra bytes.
See :class:`Unpacker` for options.
"""
unpacker = Unpacker(None, **kwargs)
unpacker.feed(packed)
try:
ret = unpacker._unpack()
except OutOfData:
... | python | def unpackb(packed, **kwargs):
"""
Unpack an object from `packed`.
Raises `ExtraData` when `packed` contains extra bytes.
See :class:`Unpacker` for options.
"""
unpacker = Unpacker(None, **kwargs)
unpacker.feed(packed)
try:
ret = unpacker._unpack()
except OutOfData:
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