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171dd131c08deb5bb39f2be4e6a794a24cd7ca4b | kisuke95/ray | python/ray/serve/http_proxy.py | [
"Apache-2.0"
] | Python | ready | <not_specific> | async def ready(self):
"""Returns when HTTP proxy is ready to serve traffic.
Or throw exception when it is not able to serve traffic.
"""
done_set, _ = await asyncio.wait(
[
# Either the HTTP setup has completed.
# The event is set inside self.... | Returns when HTTP proxy is ready to serve traffic.
Or throw exception when it is not able to serve traffic.
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fc8076c10d57023ffc869146d347cf95686c33c2 | kisuke95/ray | python/ray/serve/tests/test_application.py | [
"Apache-2.0"
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self, deployments, responses, blocking=True, client=None
):
"""
Helper function that deploys the list of deployments, calls them with
their handles, and checks whether they return the objects in responses.
If blocking is False, this function us... |
Helper function that deploys the list of deployments, calls them with
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If blocking is False, this function uses a non-blocking deploy and uses
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9f4223345f2fcfd25acca530276d8bb5a0f0825d | kisuke95/ray | python/ray/state.py | [
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"""Ensure that the object has been initialized before it is used.
This lazily initializes clients needed for state accessors.
Raises:
RuntimeError: An exception is raised if ray.init() has not been
called yet.
"""
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This lazily initializes clients needed for state accessors.
Raises:
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9f4223345f2fcfd25acca530276d8bb5a0f0825d | kisuke95/ray | python/ray/state.py | [
"Apache-2.0"
] | Python | disconnect | null | def disconnect(self):
"""Disconnect global state from GCS."""
self.gcs_options = None
if self.global_state_accessor is not None:
self.global_state_accessor.disconnect()
self.global_state_accessor = None | Disconnect global state from GCS. | Disconnect global state from GCS. | [
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9f4223345f2fcfd25acca530276d8bb5a0f0825d | kisuke95/ray | python/ray/state.py | [
"Apache-2.0"
] | Python | _initialize_global_state | null | def _initialize_global_state(self, gcs_options):
"""Set args for lazily initialization of the GlobalState object.
It's possible that certain keys in gcs kv may not have been fully
populated yet. In this case, we will retry this method until they have
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9f4223345f2fcfd25acca530276d8bb5a0f0825d | kisuke95/ray | python/ray/state.py | [
"Apache-2.0"
] | Python | actor_table | <not_specific> | def actor_table(self, actor_id):
"""Fetch and parse the actor table information for a single actor ID.
Args:
actor_id: A hex string of the actor ID to fetch information about.
If this is None, then the actor table is fetched.
Returns:
Information from th... | Fetch and parse the actor table information for a single actor ID.
Args:
actor_id: A hex string of the actor ID to fetch information about.
If this is None, then the actor table is fetched.
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Information from the actor table.
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self._check_connected()
if actor_id is not None:
actor_id = ray.ActorID(hex_to_binary(actor_id))
actor_info = self.global_state_accessor.get_actor_info(actor_id)
if actor_info is None:
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9f4223345f2fcfd25acca530276d8bb5a0f0825d | kisuke95/ray | python/ray/state.py | [
"Apache-2.0"
] | Python | node_resource_table | <not_specific> | def node_resource_table(self, node_id=None):
"""Fetch and parse the node resource table info for one.
Args:
node_id: An node ID to fetch information about.
Returns:
Information from the node resource table.
"""
self._check_connected()
node_id = ... | Fetch and parse the node resource table info for one.
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node_id: An node ID to fetch information about.
Returns:
Information from the node resource table.
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self._check_connected()
node_id = ray.NodeID(hex_to_binary(node_id))
node_resource_bytes = self.global_state_accessor.get_node_resource_info(node_id)
if node_resource_bytes is None:
return {}
else:
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9f4223345f2fcfd25acca530276d8bb5a0f0825d | kisuke95/ray | python/ray/state.py | [
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] | Python | node_table | <not_specific> | def node_table(self):
"""Fetch and parse the Gcs node info table.
Returns:
Information about the node in the cluster.
"""
self._check_connected()
node_table = self.global_state_accessor.get_node_table()
results = []
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Returns:
Information about the node in the cluster.
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self._check_connected()
node_table = self.global_state_accessor.get_node_table()
results = []
for node_info_item in node_table:
item = gcs_utils.GcsNodeInfo.FromString(node_info_item)
node_info = {
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9f4223345f2fcfd25acca530276d8bb5a0f0825d | kisuke95/ray | python/ray/state.py | [
"Apache-2.0"
] | Python | chrome_tracing_dump | <not_specific> | def chrome_tracing_dump(self, filename=None):
"""Return a list of profiling events that can viewed as a timeline.
To view this information as a timeline, simply dump it as a json file
by passing in "filename" or using using json.dump, and then load go to
chrome://tracing in the Chrome w... | Return a list of profiling events that can viewed as a timeline.
To view this information as a timeline, simply dump it as a json file
by passing in "filename" or using using json.dump, and then load go to
chrome://tracing in the Chrome web browser and load the dumped file.
Make sure to... | Return a list of profiling events that can viewed as a timeline.
To view this information as a timeline, simply dump it as a json file
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self._check_connected()
profile_table = self.profile_table()
all_events = []
for component_id_hex, component_events in profile_table.items():
component_type = component_events[0]["component_type"]
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9f4223345f2fcfd25acca530276d8bb5a0f0825d | kisuke95/ray | python/ray/state.py | [
"Apache-2.0"
] | Python | chrome_tracing_object_transfer_dump | <not_specific> | def chrome_tracing_object_transfer_dump(self, filename=None):
"""Return a list of transfer events that can viewed as a timeline.
To view this information as a timeline, simply dump it as a json file
by passing in "filename" or using using json.dump, and then load go to
chrome://tracing ... | Return a list of transfer events that can viewed as a timeline.
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by passing in "filename" or using using json.dump, and then load go to
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9f4223345f2fcfd25acca530276d8bb5a0f0825d | kisuke95/ray | python/ray/state.py | [
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"""Get a dictionary mapping worker ID to worker information."""
self._check_connected()
# Get all data in worker table
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9f4223345f2fcfd25acca530276d8bb5a0f0825d | kisuke95/ray | python/ray/state.py | [
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9f4223345f2fcfd25acca530276d8bb5a0f0825d | kisuke95/ray | python/ray/state.py | [
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"""Returns a dictionary mapping node id to avaiable resources."""
self._check_connected()
available_resources_by_id = {}
all_available_resources = (
self.global_state_accessor.get_all_available_resources()
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185a6ea2e988c21e6681a8940a036cc0235bda41 | kisuke95/ray | rllib/agents/dqn/r2d2.py | [
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] | Python | validate_config | None | def validate_config(self, config: TrainerConfigDict) -> None:
"""Checks and updates the config based on settings.
Rewrites rollout_fragment_length to take into account burn-in and
max_seq_len truncation.
"""
# Call super's validation method.
super().validate_config(confi... | Checks and updates the config based on settings.
Rewrites rollout_fragment_length to take into account burn-in and
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32a7762fc8248fc4723627c77a8f8a06d1c764f8 | kisuke95/ray | python/ray/_private/test_utils.py | [
"Apache-2.0"
] | Python | run_string_as_driver | <not_specific> | def run_string_as_driver(driver_script: str, env: Dict = None, encode: str = "utf-8"):
"""Run a driver as a separate process.
Args:
driver_script (str): A string to run as a Python script.
env (dict): The environment variables for the driver.
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The script's output.
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32a7762fc8248fc4723627c77a8f8a06d1c764f8 | kisuke95/ray | python/ray/_private/test_utils.py | [
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] | Python | run_string_as_driver_nonblocking | <not_specific> | def run_string_as_driver_nonblocking(driver_script, env: Dict = None):
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driver_script: A string to run as a Python script.
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32a7762fc8248fc4723627c77a8f8a06d1c764f8 | kisuke95/ray | python/ray/_private/test_utils.py | [
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] | Python | wait_for_condition | <not_specific> | def wait_for_condition(
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32a7762fc8248fc4723627c77a8f8a06d1c764f8 | kisuke95/ray | python/ray/_private/test_utils.py | [
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32a7762fc8248fc4723627c77a8f8a06d1c764f8 | kisuke95/ray | python/ray/_private/test_utils.py | [
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32a7762fc8248fc4723627c77a8f8a06d1c764f8 | kisuke95/ray | python/ray/_private/test_utils.py | [
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"""Compares to dicts whose values may be floating point numbers."""
if dict1.keys() != dict2.keys():
return False
for k, v in dict1.items():
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if dict1.keys() != dict2.keys():
return False
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if (
isinstance(v, float)
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32a7762fc8248fc4723627c77a8f8a06d1c764f8 | kisuke95/ray | python/ray/_private/test_utils.py | [
"Apache-2.0"
] | Python | init_error_pubsub | <not_specific> | def init_error_pubsub():
"""Initialize error info pub/sub"""
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s.subscribe()
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32a7762fc8248fc4723627c77a8f8a06d1c764f8 | kisuke95/ray | python/ray/_private/test_utils.py | [
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print_interval_s: int = 30,
record_interval_s: int = 5,
warning_threshold: float = 0.9,
):
"""Run the memory monitor actor that prints the memory usage.
The monitor will run on the same node as this function is called.
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interval_s (int): The interval me... | Run the memory monitor actor that prints the memory usage.
The monitor will run on the same node as this function is called.
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66f4795b78a0fe69b2708da41523680f9b92e387 | kisuke95/ray | rllib/agents/trainer_config.py | [
"Apache-2.0"
] | Python | to_dict | TrainerConfigDict | def to_dict(self) -> TrainerConfigDict:
"""Converts all settings into a legacy config dict for backward compatibility.
Returns:
A complete TrainerConfigDict, usable in backward-compatible Tune/RLlib
use cases, e.g. w/ `tune.run()`.
"""
config = copy.deepcopy(vars... | Converts all settings into a legacy config dict for backward compatibility.
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A complete TrainerConfigDict, usable in backward-compatible Tune/RLlib
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config = copy.deepcopy(vars(self))
config.pop("trainer_class")
if "lambda_" in config:
assert hasattr(self, "lambda_")
config["lambda"] = getattr(self, "lambda_")
config.pop("lambda_")
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66f4795b78a0fe69b2708da41523680f9b92e387 | kisuke95/ray | rllib/agents/trainer_config.py | [
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] | Python | build | "Trainer" | def build(
self,
env: Optional[Union[str, EnvType]] = None,
logger_creator: Optional[Callable[[], Logger]] = None,
) -> "Trainer":
"""Builds a Trainer from the TrainerConfig.
Args:
env: Name of the environment to use (e.g. a gym-registered str),
a... | Builds a Trainer from the TrainerConfig.
Args:
env: Name of the environment to use (e.g. a gym-registered str),
a full class path (e.g.
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env: Optional[Union[str, EnvType]] = None,
logger_creator: Optional[Callable[[], Logger]] = None,
) -> "Trainer":
if env is not None:
self.env = env
if self.evaluation_config is not None:
self.evaluation_config["env"] = env
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66f4795b78a0fe69b2708da41523680f9b92e387 | kisuke95/ray | rllib/agents/trainer_config.py | [
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] | Python | python_environment | "TrainerConfig" | def python_environment(
self,
*,
extra_python_environs_for_driver: Optional[dict] = None,
extra_python_environs_for_worker: Optional[dict] = None,
) -> "TrainerConfig":
"""Sets the config's python environment settings.
Args:
extra_python_environs_for_driv... | Sets the config's python environment settings.
Args:
extra_python_environs_for_driver: Any extra python env vars to set in the
trainer process, e.g., {"OMP_NUM_THREADS": "16"}.
extra_python_environs_for_worker: The extra python environments need to set
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extra_python_environs_for_worker: Optional[dict] = None,
) -> "TrainerConfig":
if extra_python_environs_for_driver is not None:
self.extra_python_environs_for_driver = extra_... | [
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66f4795b78a0fe69b2708da41523680f9b92e387 | kisuke95/ray | rllib/agents/trainer_config.py | [
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] | Python | resources | "TrainerConfig" | def resources(
self,
*,
num_gpus: Optional[Union[float, int]] = None,
_fake_gpus: Optional[bool] = None,
num_cpus_per_worker: Optional[int] = None,
num_gpus_per_worker: Optional[Union[float, int]] = None,
num_cpus_for_local_worker: Optional[int] = None,
cu... | Specifies resources allocated for a Trainer and its ray actors/workers.
Args:
num_gpus: Number of GPUs to allocate to the trainer process.
Note that not all algorithms can take advantage of trainer GPUs.
Support for multi-GPU is currently only available for
... | Specifies resources allocated for a Trainer and its ray actors/workers. | [
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num_cpus_per_worker: Optional[int] = None,
num_gpus_per_worker: Optional[Union[float, int]] = None,
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66f4795b78a0fe69b2708da41523680f9b92e387 | kisuke95/ray | rllib/agents/trainer_config.py | [
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] | Python | framework | "TrainerConfig" | def framework(
self,
framework: Optional[str] = None,
*,
eager_tracing: Optional[bool] = None,
eager_max_retraces: Optional[int] = None,
tf_session_args: Optional[Dict[str, Any]] = None,
local_tf_session_args: Optional[Dict[str, Any]] = None,
) -> "TrainerConf... | Sets the config's DL framework settings.
Args:
framework: tf: TensorFlow (static-graph); tf2: TensorFlow 2.x
(eager or traced, if eager_tracing=True); torch: PyTorch
eager_tracing: Enable tracing in eager mode. This greatly improves
performance (speedup ~... | Sets the config's DL framework settings. | [
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self,
framework: Optional[str] = None,
*,
eager_tracing: Optional[bool] = None,
eager_max_retraces: Optional[int] = None,
tf_session_args: Optional[Dict[str, Any]] = None,
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66f4795b78a0fe69b2708da41523680f9b92e387 | kisuke95/ray | rllib/agents/trainer_config.py | [
"Apache-2.0"
] | Python | environment | "TrainerConfig" | def environment(
self,
*,
env: Optional[Union[str, EnvType]] = None,
env_config: Optional[EnvConfigDict] = None,
observation_space: Optional[gym.spaces.Space] = None,
action_space: Optional[gym.spaces.Space] = None,
env_task_fn: Optional[Callable[[ResultDict, EnvT... | Sets the config's RL-environment settings.
Args:
env: The environment specifier. This can either be a tune-registered env,
via `tune.register_env([name], lambda env_ctx: [env object])`,
or a string specifier of an RLlib supported type. In the latter case,
... | Sets the config's RL-environment settings. | [
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] | def environment(
self,
*,
env: Optional[Union[str, EnvType]] = None,
env_config: Optional[EnvConfigDict] = None,
observation_space: Optional[gym.spaces.Space] = None,
action_space: Optional[gym.spaces.Space] = None,
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66f4795b78a0fe69b2708da41523680f9b92e387 | kisuke95/ray | rllib/agents/trainer_config.py | [
"Apache-2.0"
] | Python | rollouts | "TrainerConfig" | def rollouts(
self,
*,
num_rollout_workers: Optional[int] = None,
num_envs_per_worker: Optional[int] = None,
create_env_on_local_worker: Optional[bool] = None,
sample_collector: Optional[Type[SampleCollector]] = None,
sample_async: Optional[bool] = None,
r... | Sets the rollout worker configuration.
Args:
num_rollout_workers: Number of rollout worker actors to create for
parallel sampling. Setting this to 0 will force rollouts to be done in
the local worker (driver process or the Trainer actor when using Tune).
... | Sets the rollout worker configuration. | [
"Sets",
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"rollout",
"worker",
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] | def rollouts(
self,
*,
num_rollout_workers: Optional[int] = None,
num_envs_per_worker: Optional[int] = None,
create_env_on_local_worker: Optional[bool] = None,
sample_collector: Optional[Type[SampleCollector]] = None,
sample_async: Optional[bool] = None,
r... | [
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66f4795b78a0fe69b2708da41523680f9b92e387 | kisuke95/ray | rllib/agents/trainer_config.py | [
"Apache-2.0"
] | Python | training | "TrainerConfig" | def training(
self,
gamma: Optional[float] = None,
lr: Optional[float] = None,
train_batch_size: Optional[int] = None,
model: Optional[dict] = None,
optimizer: Optional[dict] = None,
) -> "TrainerConfig":
"""Sets the training related configuration.
Ar... | Sets the training related configuration.
Args:
gamma: Float specifying the discount factor of the Markov Decision process.
lr: The default learning rate.
train_batch_size: Training batch size, if applicable.
model: Arguments passed into the policy model. See mode... | Sets the training related configuration. | [
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"configuration",
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] | def training(
self,
gamma: Optional[float] = None,
lr: Optional[float] = None,
train_batch_size: Optional[int] = None,
model: Optional[dict] = None,
optimizer: Optional[dict] = None,
) -> "TrainerConfig":
if gamma is not None:
self.gamma = gamma
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66f4795b78a0fe69b2708da41523680f9b92e387 | kisuke95/ray | rllib/agents/trainer_config.py | [
"Apache-2.0"
] | Python | exploration | "TrainerConfig" | def exploration(
self,
*,
explore: Optional[bool] = None,
exploration_config: Optional[dict] = None,
) -> "TrainerConfig":
"""Sets the config's exploration settings.
Args:
explore: Default exploration behavior, iff `explore`=None is passed into
... | Sets the config's exploration settings.
Args:
explore: Default exploration behavior, iff `explore`=None is passed into
compute_action(s). Set to False for no exploration behavior (e.g.,
for evaluation).
exploration_config: A dict specifying the Exploratio... | Sets the config's exploration settings. | [
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"config",
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"exploration",
"settings",
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] | def exploration(
self,
*,
explore: Optional[bool] = None,
exploration_config: Optional[dict] = None,
) -> "TrainerConfig":
if explore is not None:
self.explore = explore
if exploration_config is not None:
self.exploration_config = exploration_c... | [
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66f4795b78a0fe69b2708da41523680f9b92e387 | kisuke95/ray | rllib/agents/trainer_config.py | [
"Apache-2.0"
] | Python | evaluation | "TrainerConfig" | def evaluation(
self,
*,
evaluation_interval: Optional[int] = None,
evaluation_duration: Optional[int] = None,
evaluation_duration_unit: Optional[str] = None,
evaluation_parallel_to_training: Optional[bool] = None,
evaluation_config: Optional[
Union["T... | Sets the config's evaluation settings.
Args:
evaluation_interval: Evaluate with every `evaluation_interval` training
iterations. The evaluation stats will be reported under the "evaluation"
metric key. Note that for Ape-X metrics are already only reported for
... | Sets the config's evaluation settings. | [
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] | def evaluation(
self,
*,
evaluation_interval: Optional[int] = None,
evaluation_duration: Optional[int] = None,
evaluation_duration_unit: Optional[str] = None,
evaluation_parallel_to_training: Optional[bool] = None,
evaluation_config: Optional[
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66f4795b78a0fe69b2708da41523680f9b92e387 | kisuke95/ray | rllib/agents/trainer_config.py | [
"Apache-2.0"
] | Python | offline_data | "TrainerConfig" | def offline_data(
self,
*,
input_=None,
input_config=None,
actions_in_input_normalized=None,
input_evaluation=None,
postprocess_inputs=None,
shuffle_buffer_size=None,
output=None,
output_config=None,
output_compress_columns=None,
... | Sets the config's offline data settings.
Args:
input_: Specify how to generate experiences:
- "sampler": Generate experiences via online (env) simulation (default).
- A local directory or file glob expression (e.g., "/tmp/*.json").
- A list of individual file ... | Sets the config's offline data settings. | [
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"config",
"'",
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"settings",
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] | def offline_data(
self,
*,
input_=None,
input_config=None,
actions_in_input_normalized=None,
input_evaluation=None,
postprocess_inputs=None,
shuffle_buffer_size=None,
output=None,
output_config=None,
output_compress_columns=None,
... | [
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66f4795b78a0fe69b2708da41523680f9b92e387 | kisuke95/ray | rllib/agents/trainer_config.py | [
"Apache-2.0"
] | Python | multi_agent | "TrainerConfig" | def multi_agent(
self,
*,
policies=None,
policy_map_capacity=None,
policy_map_cache=None,
policy_mapping_fn=None,
policies_to_train=None,
observation_fn=None,
replay_mode=None,
count_steps_by=None,
) -> "TrainerConfig":
"""Sets ... | Sets the config's multi-agent settings.
Args:
policies: Map of type MultiAgentPolicyConfigDict from policy ids to tuples
of (policy_cls, obs_space, act_space, config). This defines the
observation and action spaces of the policies and any extra config.
po... | Sets the config's multi-agent settings. | [
"Sets",
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"-",
"agent",
"settings",
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] | def multi_agent(
self,
*,
policies=None,
policy_map_capacity=None,
policy_map_cache=None,
policy_mapping_fn=None,
policies_to_train=None,
observation_fn=None,
replay_mode=None,
count_steps_by=None,
) -> "TrainerConfig":
if polic... | [
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66f4795b78a0fe69b2708da41523680f9b92e387 | kisuke95/ray | rllib/agents/trainer_config.py | [
"Apache-2.0"
] | Python | reporting | "TrainerConfig" | def reporting(
self,
*,
keep_per_episode_custom_metrics: Optional[bool] = None,
metrics_episode_collection_timeout_s: Optional[int] = None,
metrics_num_episodes_for_smoothing: Optional[int] = None,
min_time_s_per_reporting: Optional[int] = None,
min_train_timestep... | Sets the config's reporting settings.
Args:
keep_per_episode_custom_metrics: Store raw custom metrics without
calculating max, min, mean
metrics_episode_collection_timeout_s: Wait for metric batches for at most
this many seconds. Those that have not retur... | Sets the config's reporting settings. | [
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"config",
"'",
"s",
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"settings",
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] | def reporting(
self,
*,
keep_per_episode_custom_metrics: Optional[bool] = None,
metrics_episode_collection_timeout_s: Optional[int] = None,
metrics_num_episodes_for_smoothing: Optional[int] = None,
min_time_s_per_reporting: Optional[int] = None,
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66f4795b78a0fe69b2708da41523680f9b92e387 | kisuke95/ray | rllib/agents/trainer_config.py | [
"Apache-2.0"
] | Python | debugging | "TrainerConfig" | def debugging(
self,
*,
logger_config: Optional[dict] = None,
log_level: Optional[str] = None,
log_sys_usage: Optional[bool] = None,
fake_sampler: Optional[bool] = None,
seed: Optional[int] = None,
) -> "TrainerConfig":
"""Sets the config's debugging s... | Sets the config's debugging settings.
Args:
logger_config: Define logger-specific configuration to be used inside Logger
Default value None allows overwriting with nested dicts.
log_level: Set the ray.rllib.* log level for the agent process and its
worker... | Sets the config's debugging settings. | [
"Sets",
"the",
"config",
"'",
"s",
"debugging",
"settings",
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] | def debugging(
self,
*,
logger_config: Optional[dict] = None,
log_level: Optional[str] = None,
log_sys_usage: Optional[bool] = None,
fake_sampler: Optional[bool] = None,
seed: Optional[int] = None,
) -> "TrainerConfig":
if logger_config is not None:
... | [
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66f4795b78a0fe69b2708da41523680f9b92e387 | kisuke95/ray | rllib/agents/trainer_config.py | [
"Apache-2.0"
] | Python | experimental | "TrainerConfig" | def experimental(
self,
*,
_tf_policy_handles_more_than_one_loss=None,
_disable_preprocessor_api=None,
_disable_action_flattening=None,
_disable_execution_plan_api=None,
) -> "TrainerConfig":
"""Sets the config's experimental settings.
Args:
... | Sets the config's experimental settings.
Args:
_tf_policy_handles_more_than_one_loss: Experimental flag.
If True, TFPolicy will handle more than one loss/optimizer.
Set this to True, if you would like to return more than
one loss term from your `loss_... | Sets the config's experimental settings. | [
"Sets",
"the",
"config",
"'",
"s",
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"settings",
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] | def experimental(
self,
*,
_tf_policy_handles_more_than_one_loss=None,
_disable_preprocessor_api=None,
_disable_action_flattening=None,
_disable_execution_plan_api=None,
) -> "TrainerConfig":
if _tf_policy_handles_more_than_one_loss is not None:
se... | [
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0b7d80145fa0c8e25ff6cc89b9f08fe0bbaadf67 | kisuke95/ray | python/ray/tune/progress_reporter.py | [
"Apache-2.0"
] | Python | _progress_str | <not_specific> | def _progress_str(
self,
trials: List[Trial],
done: bool,
*sys_info: Dict,
fmt: str = "psql",
delim: str = "\n",
):
"""Returns full progress string.
This string contains a progress table and error table. The progress
table describes the progre... | Returns full progress string.
This string contains a progress table and error table. The progress
table describes the progress of each trial. The error table lists
the error file, if any, corresponding to each trial. The latter only
exists if errors have occurred.
Args:
... | Returns full progress string.
This string contains a progress table and error table. The progress
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if not self._metrics_override:
user_metrics = self._infer_user_metrics(trials, self._infer_limit)
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0b7d80145fa0c8e25ff6cc89b9f08fe0bbaadf67 | kisuke95/ray | python/ray/tune/progress_reporter.py | [
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] | Python | trial_progress_str | <not_specific> | def trial_progress_str(
trials: List[Trial],
metric_columns: Union[List[str], Dict[str, str]],
parameter_columns: Optional[Union[List[str], Dict[str, str]]] = None,
total_samples: int = 0,
force_table: bool = False,
fmt: str = "psql",
max_rows: Optional[int] = None,
done: bool = False,
... | Returns a human readable message for printing to the console.
This contains a table where each row represents a trial, its parameters
and the current values of its metrics.
Args:
trials: List of trials to get progress string for.
metric_columns: Names of metrics to include.
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total_samples: int = 0,
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8f1595756078aa7d95134947abf60e9d44076e94 | kisuke95/ray | python/ray/ml/checkpoint.py | [
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"""Create a checkpoint from the given byte string.
Args:
data (bytes): Data object containing pickled checkpoint data.
Returns:
Checkpoint: checkpoint object.
"""
bytes_data = pickle.loads(data)
i... | Create a checkpoint from the given byte string.
Args:
data (bytes): Data object containing pickled checkpoint data.
Returns:
Checkpoint: checkpoint object.
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bytes_data = pickle.loads(data)
if isinstance(bytes_data, dict):
data_dict = bytes_data
else:
data_dict = {_BYTES_DATA_KEY: bytes_data}
return cls.from_dict(data_dict) | [
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8f1595756078aa7d95134947abf60e9d44076e94 | kisuke95/ray | python/ray/ml/checkpoint.py | [
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] | Python | to_bytes | bytes | def to_bytes(self) -> bytes:
"""Return Checkpoint serialized as bytes object.
Returns:
bytes: Bytes object containing checkpoint data.
"""
# Todo: Add support for stream in the future (to_bytes(file_like))
data_dict = self.to_dict()
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data_dict = self.to_dict()
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return data_dict["bytes_data"]
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8f1595756078aa7d95134947abf60e9d44076e94 | kisuke95/ray | python/ray/ml/checkpoint.py | [
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"""Create checkpoint object from dictionary.
Args:
data (dict): Dictionary containing checkpoint data.
Returns:
Checkpoint: checkpoint object.
"""
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8f1595756078aa7d95134947abf60e9d44076e94 | kisuke95/ray | python/ray/ml/checkpoint.py | [
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Returns:
dict: Dictionary containing checkpoint data.
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8f1595756078aa7d95134947abf60e9d44076e94 | kisuke95/ray | python/ray/ml/checkpoint.py | [
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Args:
obj_ref (ray.ObjectRef): ObjectRef pointing to checkpoint data.
Returns:
Checkpoint: checkpoint object.
"""
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8f1595756078aa7d95134947abf60e9d44076e94 | kisuke95/ray | python/ray/ml/checkpoint.py | [
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ray.ObjectRef: ObjectRef pointing to checkpoint data.
"""
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8f1595756078aa7d95134947abf60e9d44076e94 | kisuke95/ray | python/ray/ml/checkpoint.py | [
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"""Create checkpoint object from directory.
Args:
path (str): Directory containing checkpoint data.
Returns:
Checkpoint: checkpoint object.
"""
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8f1595756078aa7d95134947abf60e9d44076e94 | kisuke95/ray | python/ray/ml/checkpoint.py | [
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Args:
path (str): Target directory to restore data in.
Returns:
str: Directory containing checkpoint data.
"""
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path = path if path is not None else _temporary_checkpoint_dir()
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open(os.path.join(path, ".is_checkpoint"), "a").close()
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8f1595756078aa7d95134947abf60e9d44076e94 | kisuke95/ray | python/ray/ml/checkpoint.py | [
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"""Return checkpoint directory path in a context.
This function makes checkpoint data available as a directory while avoiding
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If the checkpoint is already a directory checkpoint, it will return
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8f1595756078aa7d95134947abf60e9d44076e94 | kisuke95/ray | python/ray/ml/checkpoint.py | [
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8f1595756078aa7d95134947abf60e9d44076e94 | kisuke95/ray | python/ray/ml/checkpoint.py | [
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"""Write checkpoint data to location URI (e.g. cloud storage).
Args:
uri (str): Target location URI to write data to.
Returns:
str: Cloud location containing checkpoint data.
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8f1595756078aa7d95134947abf60e9d44076e94 | kisuke95/ray | python/ray/ml/checkpoint.py | [
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8f1595756078aa7d95134947abf60e9d44076e94 | kisuke95/ray | python/ray/ml/checkpoint.py | [
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cc55375ffac9294f1eb9885643b02990fbb203ec | kisuke95/ray | python/ray/serve/pipeline/api.py | [
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produce a runnable and deployable serve pipeline application from a valid
DAG authored with Ray DAG API.
This should be the only user facing API that user interacts with.
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b5f7b618d56d3241e7ffdaec7ea5b33493c5213d | kisuke95/ray | python/ray/data/datasource/file_based_datasource.py | [
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self,
paths: List[str],
schema: Optional[Union[type, "pyarrow.lib.Schema"]],
*,
rows_per_file: Optional[int],
file_sizes: List[Optional[int]],
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... | Resolves and returns block metadata for the given file paths.
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b5f7b618d56d3241e7ffdaec7ea5b33493c5213d | kisuke95/ray | python/ray/data/datasource/file_based_datasource.py | [
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"""Expands all paths into concrete file paths by walking directories.
Also returns a sidecar of file sizes.
The input paths will be ... | Expands all paths into concrete file paths by walking directories.
Also returns a sidecar of file sizes.
The input paths will be normalized for compatibility with the input
filesystem prior to invocation.
Args:
paths: A list of file and/or directory paths compatible wit... | Expands all paths into concrete file paths by walking directories.
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b5f7b618d56d3241e7ffdaec7ea5b33493c5213d | kisuke95/ray | python/ray/data/datasource/file_based_datasource.py | [
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parallelism: int,
paths: Union[str, List[str]],
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schema: Optional[Union[type, "pyarrow.lib.Schema"]] = None,
open_stream_args: Optional[Dict[str, Any]] = None,
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schema: Optional[Union[type, "pyarrow.lib.Schema"]] = None,
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b5f7b618d56d3241e7ffdaec7ea5b33493c5213d | kisuke95/ray | python/ray/data/datasource/file_based_datasource.py | [
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self,
blocks: List[ObjectRef[Block]],
metadata: List[BlockMetadata],
path: str,
dataset_uuid: str,
filesystem: Optional["pyarrow.fs.FileSystem"] = None,
try_create_dir: bool = True,
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... |
4dae4310b570e8c0bb53bd071bc84f551a9f8411 | kisuke95/ray | python/ray/experimental/state/api.py | [
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] | Python | _list | <not_specific> | def _list(resource_name: str, options: ListApiOptions, api_server_url: str = None):
"""Query the API server in address to list "resource_name" states.
Args:
resource_name: The name of the resource. E.g., actor, task.
options: The options for the REST API that are translated to query strings.
... | Query the API server in address to list "resource_name" states.
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resource_name: The name of the resource. E.g., actor, task.
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a33eb17f0036fc04fb2741c88774ade82979865f | kisuke95/ray | rllib/utils/debug/memory.py | [
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"""Diagnoses the given trainer for possible memory leaks.
Isolates single components inside the trainer's local worker, e.g. the en... | Diagnoses the given trainer for possible memory leaks.
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... | Diagnoses the given trainer for possible memory leaks.
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fe3c9a42f60aadad6cd7ece275dd4c4feea528d5 | kisuke95/ray | python/ray/data/impl/simple_block.py | [
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] | Python | _apply_accum | Optional[U] | def _apply_accum(
self,
init: AggType,
accum: Callable[[AggType, T], AggType],
on: KeyFn,
ignore_nulls: bool,
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"""Helper providing null handling around applying an aggregation."""
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fe3c9a42f60aadad6cd7ece275dd4c4feea528d5 | kisuke95/ray | python/ray/data/impl/simple_block.py | [
"Apache-2.0"
] | Python | combine | Block[Tuple[KeyType, AggType]] | def combine(
self, key: KeyFn, aggs: Tuple[AggregateFn]
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This assumes the block is already sorted by key in ascending order.
Args:
key: The key function that returns the key from ... | Combine rows with the same key into an accumulator.
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Args:
key: The key function that returns the key from the row
or None for global aggregation.
agg: The aggregations to do.
Returns:
... | Combine rows with the same key into an accumulator.
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fe3c9a42f60aadad6cd7ece275dd4c4feea528d5 | kisuke95/ray | python/ray/data/impl/simple_block.py | [
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] | Python | iter_groups | Iterator[Tuple[KeyType, Block]] | def iter_groups() -> Iterator[Tuple[KeyType, Block]]:
"""Creates an iterator over zero-copy group views."""
if key is None:
# Global aggregation consists of a single "group", so we short-circuit.
yield None, self.to_block()
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fe3c9a42f60aadad6cd7ece275dd4c4feea528d5 | kisuke95/ray | python/ray/data/impl/simple_block.py | [
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] | Python | aggregate_combined_blocks | Tuple[Block[Tuple[KeyType, U]], BlockMetadata] | def aggregate_combined_blocks(
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6a2e26e103fa753b6a8ffa55a8049dd8b75ed958 | kisuke95/ray | python/ray/experimental/dag/input_node.py | [
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fc085dbf3fbe4e28eaf96b37675319ef0d0639b1 | kisuke95/ray | python/ray/tune/tuner.py | [
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path: The path where the previous failed run is checkpointed.
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... | Restores Tuner after a previously failed run.
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fc085dbf3fbe4e28eaf96b37675319ef0d0639b1 | kisuke95/ray | python/ray/tune/tuner.py | [
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For the kind of exception that happens during the execution of a trial,
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See ``Resu... | Executes hyperparameter tuning job as configured and returns result.
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4339eed6b07870b20bca2b24712c63652e4123a1 | kisuke95/ray | python/ray/tune/schedulers/resource_changing_scheduler.py | [
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self, base_trial_resource: PlacementGroupFactory, result: Dict[str, Any]
) -> bool:
"""Return False if we should keep the current resources outright."""
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4339eed6b07870b20bca2b24712c63652e4123a1 | kisuke95/ray | python/ray/tune/schedulers/resource_changing_scheduler.py | [
"Apache-2.0"
] | Python | _get_total_available_resources | Tuple[float, float] | def _get_total_available_resources(
self, trial_runner: "trial_runner.TrialRunner"
) -> Tuple[float, float]:
"""Get the number of CPUs and GPUs avaialble in total (not just free)"""
total_available_cpus = (
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4339eed6b07870b20bca2b24712c63652e4123a1 | kisuke95/ray | python/ray/tune/schedulers/resource_changing_scheduler.py | [
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] | Python | _get_used_cpus_and_gpus | Tuple[float, float] | def _get_used_cpus_and_gpus(self, t: Trial) -> Tuple[float, float]:
"""Check how many CPUs and GPUs a trial is using currently"""
return (
t.placement_group_factory.required_resources.get("CPU", 0),
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4339eed6b07870b20bca2b24712c63652e4123a1 | kisuke95/ray | python/ray/tune/schedulers/resource_changing_scheduler.py | [
"Apache-2.0"
] | Python | _get_resources_from_bundles | Dict[str, float] | def _get_resources_from_bundles(
self, bundles: List[Dict[str, float]]
) -> Dict[str, float]:
"""Get total sums of resources in bundles"""
if not bundles:
return {"CPU": 0, "GPU": 0}
pgf = PlacementGroupFactory(bundles)
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if not bundles:
return {"CPU": 0, "GPU": 0}
pgf = PlacementGroupFactory(bundles)
return pgf.required_resources | [
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4339eed6b07870b20bca2b24712c63652e4123a1 | kisuke95/ray | python/ray/tune/schedulers/resource_changing_scheduler.py | [
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] | Python | _get_added_bundles | List[Dict[str, float]] | def _get_added_bundles(
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4339eed6b07870b20bca2b24712c63652e4123a1 | kisuke95/ray | python/ray/tune/schedulers/resource_changing_scheduler.py | [
"Apache-2.0"
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trial_runner: "trial_runner.TrialRunner",
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result: Dict[str, Any],
scheduler: "ResourceChangingScheduler",
) -> Optional[PlacementGroupFactory]:
"""This is a basic uniform resource allocating function.
This function is used by default in ``ResourceC... | This is a basic uniform resource allocating function.
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The function naively balances free resources (CPUs and GPUs) between
trials, giving them all equal priority, ensuring that all resources
are always being used. All of the resources ... | This is a basic uniform resource allocating function.
This function is used by default in ``ResourceChangingScheduler``.
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4339eed6b07870b20bca2b24712c63652e4123a1 | kisuke95/ray | python/ray/tune/schedulers/resource_changing_scheduler.py | [
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] | Python | evenly_distribute_cpus_gpus_distributed | Optional[PlacementGroupFactory] | def evenly_distribute_cpus_gpus_distributed(
trial_runner: "trial_runner.TrialRunner",
trial: Trial,
result: Dict[str, Any],
scheduler: "ResourceChangingScheduler",
) -> Optional[PlacementGroupFactory]:
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The function naively balances free resources (CPUs and GPUs) between
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4339eed6b07870b20bca2b24712c63652e4123a1 | kisuke95/ray | python/ray/tune/schedulers/resource_changing_scheduler.py | [
"Apache-2.0"
] | Python | _are_resources_the_same | bool | def _are_resources_the_same(
self,
trial: Trial,
new_resources,
) -> bool:
"""Returns True if trial's resources are value equal to new_resources.
Only checks for PlacementGroupFactories at this moment.
"""
if (
isinstance(new_resources, PlacementG... | Returns True if trial's resources are value equal to new_resources.
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4339eed6b07870b20bca2b24712c63652e4123a1 | kisuke95/ray | python/ray/tune/schedulers/resource_changing_scheduler.py | [
"Apache-2.0"
] | Python | reallocate_trial_resources_if_needed | Optional[Union[dict, PlacementGroupFactory]] | def reallocate_trial_resources_if_needed(
self, trial_runner: "trial_runner.TrialRunner", trial: Trial, result: Dict
) -> Optional[Union[dict, PlacementGroupFactory]]:
"""Calls user defined resources_allocation_function. If the returned
resources are not none and not the same as currently pr... | Calls user defined resources_allocation_function. If the returned
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94096e6917f57dc164deafb80da329e4140e637f | kisuke95/ray | python/ray/_private/gcs_utils.py | [
"Apache-2.0"
] | Python | check_health | bool | def check_health(address: str, timeout=2) -> bool:
"""Checks Ray cluster health, before / without actually connecting to the
cluster via ray.init().
Args:
address: Ray cluster / GCS address string, e.g. ip:port.
timeout: request timeout.
Returns:
Returns True if the cluster is r... | Checks Ray cluster health, before / without actually connecting to the
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Args:
address: Ray cluster / GCS address string, e.g. ip:port.
timeout: request timeout.
Returns:
Returns True if the cluster is running and has matching Ray version.
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channel = create_gcs_channel(address)
stub = gcs_service_pb2_grpc.HeartbeatInfoGcsServiceStub(channel)
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94096e6917f57dc164deafb80da329e4140e637f | kisuke95/ray | python/ray/_private/gcs_utils.py | [
"Apache-2.0"
] | Python | use_gcs_for_bootstrap | <not_specific> | def use_gcs_for_bootstrap():
"""In the current version of Ray, we always use the GCS to bootstrap.
(This was previously controlled by a feature flag.)
This function is included for the purposes of backwards compatibility.
"""
return True | In the current version of Ray, we always use the GCS to bootstrap.
(This was previously controlled by a feature flag.)
This function is included for the purposes of backwards compatibility.
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(This was previously controlled by a feature flag.)
This function is included for the purposes of backwards compatibility. | [
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} |
6d32ec4dc204e34f8fdf850f0bf33e41dca550f9 | kisuke95/ray | python/ray/remote_function.py | [
"Apache-2.0"
] | Python | options | <not_specific> | def options(self, **task_options):
"""Configures and overrides the task invocation parameters.
The arguments are the same as those that can be passed to :obj:`ray.remote`.
Overriding `max_calls` is not supported.
Examples:
.. code-block:: python
@ray.remote(num_gp... | Configures and overrides the task invocation parameters.
The arguments are the same as those that can be passed to :obj:`ray.remote`.
Overriding `max_calls` is not supported.
Examples:
.. code-block:: python
@ray.remote(num_gpus=1, max_calls=1, num_returns=2)
... | Configures and overrides the task invocation parameters.
The arguments are the same as those that can be passed to :obj:`ray.remote`.
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default_options.pop("max_calls", None)
updated_options = {**default_options, **task_options}
ray_option_utils.validate_task_options(updated_options, in_options=True)
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6d32ec4dc204e34f8fdf850f0bf33e41dca550f9 | kisuke95/ray | python/ray/remote_function.py | [
"Apache-2.0"
] | Python | bind | <not_specific> | def bind(self, *args, **kwargs):
"""
**Experimental**
For ray DAG building. Implementation and interface subject to changes.
"""
from ray.experimental.dag.function_node import FunctionNode
return FunctionNode(func_cls._fun... |
**Experimental**
For ray DAG building. Implementation and interface subject to changes.
| Experimental
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] | def bind(self, *args, **kwargs):
from ray.experimental.dag.function_node import FunctionNode
return FunctionNode(func_cls._function, args, kwargs, updated_options) | [
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} |
6d32ec4dc204e34f8fdf850f0bf33e41dca550f9 | kisuke95/ray | python/ray/remote_function.py | [
"Apache-2.0"
] | Python | _remote | <not_specific> | def _remote(self, args=None, kwargs=None, **task_options):
"""Submit the remote function for execution."""
# We pop the "max_calls" coming from "@ray.remote" here. We no longer need
# it in "_remote()".
task_options.pop("max_calls", None)
if client_mode_should_convert(auto_init=T... | Submit the remote function for execution. | Submit the remote function for execution. | [
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] | def _remote(self, args=None, kwargs=None, **task_options):
task_options.pop("max_calls", None)
if client_mode_should_convert(auto_init=True):
return client_mode_convert_function(self, args, kwargs, **task_options)
worker = ray.worker.global_worker
worker.check_connected()
... | [
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6d32ec4dc204e34f8fdf850f0bf33e41dca550f9 | kisuke95/ray | python/ray/remote_function.py | [
"Apache-2.0"
] | Python | bind | <not_specific> | def bind(self, *args, **kwargs):
"""
**Experimental**
For ray DAG building. Implementation and interface subject to changes.
"""
from ray.experimental.dag.function_node import FunctionNode
return FunctionNode(self._function, args, kwargs, self._default_options) |
**Experimental**
For ray DAG building. Implementation and interface subject to changes.
| Experimental
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] | def bind(self, *args, **kwargs):
from ray.experimental.dag.function_node import FunctionNode
return FunctionNode(self._function, args, kwargs, self._default_options) | [
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} |
4d2f0569a8ddd270ce8186bf8a7004cdc18a8312 | kisuke95/ray | python/ray/util/collective/collective_group/gloo_util.py | [
"Apache-2.0"
] | Python | copy_tensor | null | def copy_tensor(dst_tensor, src_tensor):
"""Copy the content from src_tensor to dst_tensor.
Args:
dst_tensor: the tensor to copy from.
src_tensor: the tensor to copy to.
Returns:
None
"""
copied = True
if isinstance(dst_tensor, numpy.ndarray) and isinstance(src_tensor, ... | Copy the content from src_tensor to dst_tensor.
Args:
dst_tensor: the tensor to copy from.
src_tensor: the tensor to copy to.
Returns:
None
| Copy the content from src_tensor to dst_tensor. | [
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copied = True
if isinstance(dst_tensor, numpy.ndarray) and isinstance(src_tensor, numpy.ndarray):
numpy.copyto(dst_tensor, src_tensor)
elif torch_available():
if isinstance(dst_tensor, torch.Tensor) and isinstance(
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4d3a032bae314404db42165cc78fb007f2c00316 | kisuke95/ray | rllib/agents/ppo/ddppo.py | [
"Apache-2.0"
] | Python | execution_plan | LocalIterator[dict] | def execution_plan(
workers: WorkerSet, config: TrainerConfigDict, **kwargs
) -> LocalIterator[dict]:
"""Execution plan of the DD-PPO algorithm. Defines the distributed dataflow.
Args:
workers (WorkerSet): The WorkerSet for training the Polic(y/ies)
of the Traine... | Execution plan of the DD-PPO algorithm. Defines the distributed dataflow.
Args:
workers (WorkerSet): The WorkerSet for training the Polic(y/ies)
of the Trainer.
config (TrainerConfigDict): The trainer's configuration dict.
Returns:
LocalIterator[dict... | Execution plan of the DD-PPO algorithm. Defines the distributed dataflow. | [
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] | def execution_plan(
workers: WorkerSet, config: TrainerConfigDict, **kwargs
) -> LocalIterator[dict]:
assert (
len(kwargs) == 0
), "DDPPO execution_plan does NOT take any additional parameters"
rollouts = ParallelRollouts(workers, mode="raw")
ip = ray.get(workers.... | [
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8f7ed64aa593b15e33cdc1e93d8be0f3cd6ead76 | kisuke95/ray | python/ray/autoscaler/_private/kuberay/autoscaling_config.py | [
"Apache-2.0"
] | Python | _generate_provider_config | Dict[str, Any] | def _generate_provider_config(ray_cluster_namespace: str) -> Dict[str, Any]:
"""Generates the `provider` field of the autoscaling config, which carries data
required to instantiate the KubeRay node provider.
"""
return {
"type": "kuberay",
"namespace": ray_cluster_namespace,
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return {
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"disable_launch_config_check": True,
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8f7ed64aa593b15e33cdc1e93d8be0f3cd6ead76 | kisuke95/ray | python/ray/autoscaler/_private/kuberay/autoscaling_config.py | [
"Apache-2.0"
] | Python | _generate_legacy_autoscaling_config_fields | Dict[str, Any] | def _generate_legacy_autoscaling_config_fields() -> Dict[str, Any]:
"""Generates legacy autoscaling config fields required for compatibiliy."""
return {
"file_mounts": {},
"cluster_synced_files": [],
"file_mounts_sync_continuously": False,
"initialization_commands": [],
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] | def _generate_legacy_autoscaling_config_fields() -> Dict[str, Any]:
return {
"file_mounts": {},
"cluster_synced_files": [],
"file_mounts_sync_continuously": False,
"initialization_commands": [],
"setup_commands": [],
"head_setup_commands": [],
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8f7ed64aa593b15e33cdc1e93d8be0f3cd6ead76 | kisuke95/ray | python/ray/autoscaler/_private/kuberay/autoscaling_config.py | [
"Apache-2.0"
] | Python | _generate_available_node_types_from_ray_cr_spec | Dict[str, Any] | def _generate_available_node_types_from_ray_cr_spec(
ray_cr_spec: Dict[str, Any]
) -> Dict[str, Any]:
"""Formats autoscaler "available_node_types" field based on the Ray CR's group
specs.
"""
headGroupSpec = ray_cr_spec["headGroupSpec"]
return {
_HEAD_GROUP_NAME: _node_type_from_group_sp... | Formats autoscaler "available_node_types" field based on the Ray CR's group
specs.
| Formats autoscaler "available_node_types" field based on the Ray CR's group
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) -> Dict[str, Any]:
headGroupSpec = ray_cr_spec["headGroupSpec"]
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8f7ed64aa593b15e33cdc1e93d8be0f3cd6ead76 | kisuke95/ray | python/ray/autoscaler/_private/kuberay/autoscaling_config.py | [
"Apache-2.0"
] | Python | _node_type_from_group_spec | Dict[str, Any] | def _node_type_from_group_spec(
group_spec: Dict[str, Any], is_head: bool
) -> Dict[str, Any]:
"""Converts CR group spec to autoscaler node type."""
if is_head:
# The head node type has no workers because the head is not a worker.
min_workers = max_workers = 0
else:
# `minReplica... | Converts CR group spec to autoscaler node type. | Converts CR group spec to autoscaler node type. | [
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if is_head:
min_workers = max_workers = 0
else:
min_workers = group_spec["minReplicas"]
max_workers = group_spec["maxReplicas"]
resources = _get_ray_resources_from_group_spec(group_spec... | [
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8f7ed64aa593b15e33cdc1e93d8be0f3cd6ead76 | kisuke95/ray | python/ray/autoscaler/_private/kuberay/autoscaling_config.py | [
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) -> Dict[str, int]:
"""
Infers Ray resources from rayStartCommands and K8s limits.
The resources extracted are used in autoscaling calculations.
TODO: Expose a better interface in the RayCluster CRD for Ray resource ... |
Infers Ray resources from rayStartCommands and K8s limits.
The resources extracted are used in autoscaling calculations.
TODO: Expose a better interface in the RayCluster CRD for Ray resource annotations.
For now, we take the rayStartParams as the primary source of truth.
| Infers Ray resources from rayStartCommands and K8s limits.
The resources extracted are used in autoscaling calculations.
Expose a better interface in the RayCluster CRD for Ray resource annotations.
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8f7ed64aa593b15e33cdc1e93d8be0f3cd6ead76 | kisuke95/ray | python/ray/autoscaler/_private/kuberay/autoscaling_config.py | [
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] | Python | _get_memory | Optional[int] | def _get_memory(
ray_start_params: Dict[str, str], k8s_resource_limits: Dict[str, Any]
) -> Optional[int]:
"""Get memory resource annotation from ray_start_params, if it is set there.
TODO, maybe: Consider container resource limits as in
https://github.com/ray-project/ray/pull/14567/files
"""
i... | Get memory resource annotation from ray_start_params, if it is set there.
TODO, maybe: Consider container resource limits as in
https://github.com/ray-project/ray/pull/14567/files
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8f7ed64aa593b15e33cdc1e93d8be0f3cd6ead76 | kisuke95/ray | python/ray/autoscaler/_private/kuberay/autoscaling_config.py | [
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ray_start_params: Dict[str, str],
k8s_resource_limits: Dict[str, Any],
group_name: str,
) -> Optional[int]:
"""Read the number of GPUs from the Ray start params.
Potential TODO: Read GPU info from the container spec, here and in the
Ray Operator.
"""
if "num-gpus" in... | Read the number of GPUs from the Ray start params.
Potential TODO: Read GPU info from the container spec, here and in the
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8f7ed64aa593b15e33cdc1e93d8be0f3cd6ead76 | kisuke95/ray | python/ray/autoscaler/_private/kuberay/autoscaling_config.py | [
"Apache-2.0"
] | Python | _get_custom_resources | Dict[str, int] | def _get_custom_resources(
ray_start_params: Dict[str, Any], group_name: str
) -> Dict[str, int]:
"""Format custom resources based on the `resources` Ray start param.
For the current prototype, the value of the `resources` field must
be formatted as follows:
'"{\"Custom1\": 1, \"Custom2\": 5}"'.
... | Format custom resources based on the `resources` Ray start param.
For the current prototype, the value of the `resources` field must
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We intend to provide a better interface soon.
This method first converts the input to a correctly for... | Format custom resources based on the `resources` Ray start param.
We intend to provide a better interface soon.
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77b4ac8b92d898ecb53acc0b0ff428534588f3a8 | kisuke95/ray | python/ray/ml/preprocessor.py | [
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Args:
df (DataBatchType): Input data batch.
Returns:
DataBatchType: The transformed data batch.
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ec136008be24b88cdb1960d13f0a84202e837227 | kisuke95/ray | python/ray/serve/pipeline/json_serde.py | [
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The returned object can safely be `json.dumps`'d to a string.
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Raises: TypeError if the object contains fields that cannot be
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ec136008be24b88cdb1960d13f0a84202e837227 | kisuke95/ray | python/ray/serve/pipeline/json_serde.py | [
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"\"\"\"Converts a JSON-safe object to one that contains Serve special types.\n\n The provided object should have been serialized using\n convert_to_json_safe_obj. Any special-cased objects such as ServeHandles\n will be recovered on this pass.\n \"\"\""
] | [
{
"param": "obj",
"type": "Any"
},
{
"param": "err_key",
"type": "str"
}
] | {
"returns": [],
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"identifier": "err_key",
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"docstring": null,
"docstring_tokens... |
ec136008be24b88cdb1960d13f0a84202e837227 | kisuke95/ray | python/ray/serve/pipeline/json_serde.py | [
"Apache-2.0"
] | Python | dagnode_from_json | Union[DAGNode, RayServeHandle, Any] | def dagnode_from_json(input_json: Any) -> Union[DAGNode, RayServeHandle, Any]:
"""
Decode a DAGNode from given input json dictionary. JSON serialization is
only used and enforced in ray serve from ray core API authored DAGNode(s).
Covers both RayServeHandle and DAGNode types.
Assumptions:
... |
Decode a DAGNode from given input json dictionary. JSON serialization is
only used and enforced in ray serve from ray core API authored DAGNode(s).
Covers both RayServeHandle and DAGNode types.
Assumptions:
- User object's JSON dict does not have keys that collide with our
reserve... | Decode a DAGNode from given input json dictionary. JSON serialization is
only used and enforced in ray serve from ray core API authored DAGNode(s).
User object's JSON dict does not have keys that collide with our
reserved DAGNODE_TYPE_KEY
RayServeHandle and Deployment can be re-constructed without losing
states need... | [
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if SERVE_HANDLE_JSON_KEY in input_json:
return serve_handle_from_json_dict(input_json)
elif DAGNODE_TYPE_KEY not in input_json:
return input_json
elif input_json[DAGNODE_TYPE_KEY] == RayServeDAGHandle.__name__:
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"outlier_params": [],
"others": []
} |
4dd6a9147c12df497d3b3773df14b4992b50d758 | kisuke95/ray | rllib/evaluation/worker_set.py | [
"Apache-2.0"
] | Python | sync_weights | None | def sync_weights(
self,
policies: Optional[List[PolicyID]] = None,
from_worker: Optional[RolloutWorker] = None,
global_vars: Optional[Dict[str, TensorType]] = None,
) -> None:
"""Syncs model weights from the local worker to all remote workers.
Args:
polic... | Syncs model weights from the local worker to all remote workers.
Args:
policies: Optional list of PolicyIDs to sync weights for.
If None (default), sync weights to/from all policies.
from_worker: Optional RolloutWorker instance to sync from.
If None (defa... | Syncs model weights from the local worker to all remote workers. | [
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] | def sync_weights(
self,
policies: Optional[List[PolicyID]] = None,
from_worker: Optional[RolloutWorker] = None,
global_vars: Optional[Dict[str, TensorType]] = None,
) -> None:
if self.local_worker() is None and from_worker is None:
raise TypeError(
... | [
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"returns": [],
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4dd6a9147c12df497d3b3773df14b4992b50d758 | kisuke95/ray | rllib/evaluation/worker_set.py | [
"Apache-2.0"
] | Python | add_workers | None | def add_workers(self, num_workers: int) -> None:
"""Creates and adds a number of remote workers to this worker set.
Can be called several times on the same WorkerSet to add more
RolloutWorkers to the set.
Args:
num_workers: The number of remote Workers to add to this
... | Creates and adds a number of remote workers to this worker set.
Can be called several times on the same WorkerSet to add more
RolloutWorkers to the set.
Args:
num_workers: The number of remote Workers to add to this
WorkerSet.
| Creates and adds a number of remote workers to this worker set.
Can be called several times on the same WorkerSet to add more
RolloutWorkers to the set. | [
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] | def add_workers(self, num_workers: int) -> None:
old_num_workers = len(self._remote_workers)
self._remote_workers.extend(
[
self._make_worker(
cls=self._cls,
env_creator=self._env_creator,
validate_env=None,
... | [
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] | [
{
"param": "self",
"type": null
},
{
"param": "num_workers",
"type": "int"
}
] | {
"returns": [],
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{
"identifier": "self",
"type": null,
"docstring": null,
"docstring_tokens": [],
"default": null,
"is_optional": null
},
{
"identifier": "num_workers",
"type": "int",
"docstring": "The number of remote Wor... |
4dd6a9147c12df497d3b3773df14b4992b50d758 | kisuke95/ray | rllib/evaluation/worker_set.py | [
"Apache-2.0"
] | Python | _worker_health_check | List[int] | def _worker_health_check(self) -> List[int]:
"""Performs a health-check on each remote worker.
Returns:
List of indices (into `self._remote_workers` list) of faulty workers.
Note that index=1 is the 0th item in `self._remote_workers`.
"""
logger.info("Health chec... | Performs a health-check on each remote worker.
Returns:
List of indices (into `self._remote_workers` list) of faulty workers.
Note that index=1 is the 0th item in `self._remote_workers`.
| Performs a health-check on each remote worker. | [
"Performs",
"a",
"health",
"-",
"check",
"on",
"each",
"remote",
"worker",
"."
] | def _worker_health_check(self) -> List[int]:
logger.info("Health checking all workers ...")
checks = []
for worker in self.remote_workers():
_, obj_ref = worker.sample_with_count.remote()
checks.append(obj_ref)
faulty_worker_indices = []
for i, obj_ref in ... | [
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"# TODO: Maybe find a better way to probe for healthiness. Perfor... | [
{
"param": "self",
"type": null
}
] | {
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"docstring": "List of indices (into `self._remote_workers` list) of faulty workers.\nNote that index=1 is the 0th item in `self._remote_workers`.",
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... |
79b38d5e824adb8e3497e80d18fd58f63a1de41b | kisuke95/ray | python/ray/data/dataset.py | [
"Apache-2.0"
] | Python | map | "Dataset[U]" | def map(
self,
fn: Union[CallableClass, Callable[[T], U]],
*,
compute: Optional[str] = None,
**ray_remote_args,
) -> "Dataset[U]":
"""Apply the given function to each record of this dataset.
This is a blocking operation. Note that mapping individual records
... | Apply the given function to each record of this dataset.
This is a blocking operation. Note that mapping individual records
can be quite slow. Consider using `.map_batches()` for performance.
Examples:
>>> import ray
>>> # Transform python objects.
>>> ds = ... | Apply the given function to each record of this dataset.
This is a blocking operation. Note that mapping individual records
can be quite slow. Consider using `.map_batches()` for performance. | [
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"."... | def map(
self,
fn: Union[CallableClass, Callable[[T], U]],
*,
compute: Optional[str] = None,
**ray_remote_args,
) -> "Dataset[U]":
self._warn_slow()
fn = cache_wrapper(fn, compute)
context = DatasetContext.get_current()
def transform(block: Blo... | [
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"docstring":... |
79b38d5e824adb8e3497e80d18fd58f63a1de41b | kisuke95/ray | python/ray/data/dataset.py | [
"Apache-2.0"
] | Python | map_batches | "Dataset[Any]" | def map_batches(
self,
fn: Union[CallableClass, Callable[[BatchType], BatchType]],
*,
batch_size: Optional[int] = 4096,
compute: Union[str, ComputeStrategy] = None,
batch_format: str = "native",
**ray_remote_args,
) -> "Dataset[Any]":
"""Apply the give... | Apply the given function to batches of records of this dataset.
This is a blocking operation.
Examples:
>>> import ray
>>> # Transform python objects.
>>> ds = ray.data.range(1000) # doctest: +SKIP
>>> # Transform batches in parallel.
>>> ds.... | Apply the given function to batches of records of this dataset.
This is a blocking operation. | [
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"."
] | def map_batches(
self,
fn: Union[CallableClass, Callable[[BatchType], BatchType]],
*,
batch_size: Optional[int] = 4096,
compute: Union[str, ComputeStrategy] = None,
batch_format: str = "native",
**ray_remote_args,
) -> "Dataset[Any]":
import pyarrow as... | [
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},
{
"param": "compute",
"type": "Union[str, ComputeStrategy]"
},
{
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"returns": [],
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"default": null,
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},
{
"identifier": "fn",
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... |
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