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ab1cc8af10ed239587445b410df0435b41c4631e | kisuke95/ray | python/ray/serve/deployment_state.py | [
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"""Check if the actor is healthy.
self._healthy should *only* be modified in this method.
This is responsible for:
1) Checking the outstanding health check (if any).
2) Determining the replica health based on the health check results.
... | Check if the actor is healthy.
self._healthy should *only* be modified in this method.
This is responsible for:
1) Checking the outstanding health check (if any).
2) Determining the replica health based on the health check results.
3) Kicking off a new health check ... | Check if the actor is healthy.
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ab1cc8af10ed239587445b410df0435b41c4631e | kisuke95/ray | python/ray/serve/deployment_state.py | [
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"""Force the actor to exit without shutting down gracefully."""
try:
ray.kill(
ray.get_actor(self._actor_name, namespace=self._controller_namespace)
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except ValueError:
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ab1cc8af10ed239587445b410df0435b41c4631e | kisuke95/ray | python/ray/serve/deployment_state.py | [
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"""Clean up any remaining resources after the actor has exited.
Currently, this just removes the placement group.
"""
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return
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ab1cc8af10ed239587445b410df0435b41c4631e | kisuke95/ray | python/ray/serve/deployment_state.py | [
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"""
Start a new actor for current DeploymentReplica instance.
"""
self._actor.start(deployment_info, version)
self._start_time = time.time()
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ab1cc8af10ed239587445b410df0435b41c4631e | kisuke95/ray | python/ray/serve/deployment_state.py | [
"Apache-2.0"
] | Python | update_user_config | null | def update_user_config(self, user_config: Any):
"""
Update user config of existing actor behind current
DeploymentReplica instance.
"""
self._actor.update_user_config(user_config)
self._version = DeploymentVersion(
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self._actor.update_user_config(user_config)
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ab1cc8af10ed239587445b410df0435b41c4631e | kisuke95/ray | python/ray/serve/deployment_state.py | [
"Apache-2.0"
] | Python | recover | null | def recover(self):
"""
Recover states in DeploymentReplica instance by fetching running actor
status
"""
self._actor.recover()
self._start_time = time.time()
# Replica version is fetched from recovered replica dynamically in
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Recover states in DeploymentReplica instance by fetching running actor
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ab1cc8af10ed239587445b410df0435b41c4631e | kisuke95/ray | python/ray/serve/deployment_state.py | [
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Should handle the case where the replica has already stopped.
Returns:
status (ReplicaStartupStatus): Most recent state of replica by
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ab1cc8af10ed239587445b410df0435b41c4631e | kisuke95/ray | python/ray/serve/deployment_state.py | [
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"""Stop the replica.
Should handle the case where the replica is already stopped.
"""
timeout_s = self._actor.graceful_stop()
if not graceful:
timeout_s = 0
self._shutdown_deadline = time.time() + timeout_s | Stop the replica.
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ab1cc8af10ed239587445b410df0435b41c4631e | kisuke95/ray | python/ray/serve/deployment_state.py | [
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] | Python | check_stopped | bool | def check_stopped(self) -> bool:
"""Check if the replica has finished stopping."""
if self._actor.check_stopped():
# Clean up any associated resources (e.g., placement group).
self._actor.cleanup()
return True
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ab1cc8af10ed239587445b410df0435b41c4631e | kisuke95/ray | python/ray/serve/deployment_state.py | [
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"""Returns required and currently available resources.
Only resources with nonzero requirements will be included in the
required dict and only resources in the required dict will be
included in the available dict (filtered for relevanc... | Returns required and currently available resources.
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ab1cc8af10ed239587445b410df0435b41c4631e | kisuke95/ray | python/ray/serve/deployment_state.py | [
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"""Add the provided replica under the provided state.
Args:
state (ReplicaState): state to add the replica under.
replica (VersionedReplica): replica to add.
"""
assert isinstance(state, ReplicaState)... | Add the provided replica under the provided state.
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ab1cc8af10ed239587445b410df0435b41c4631e | kisuke95/ray | python/ray/serve/deployment_state.py | [
"Apache-2.0"
] | Python | pop | List[VersionedReplica] | def pop(
self,
exclude_version: Optional[DeploymentVersion] = None,
states: Optional[List[ReplicaState]] = None,
max_replicas: Optional[int] = math.inf,
ranking_function: Optional[
Callable[[List["DeploymentReplica"]], List["DeploymentReplica"]]
] = None,
... | Get and remove all replicas of the given states.
This removes the replicas from the container. Replicas are returned
in order of state as passed in.
Args:
exclude_version (DeploymentVersion): if specified, replicas of the
provided version will *not* be removed.
... | Get and remove all replicas of the given states.
This removes the replicas from the container. Replicas are returned
in order of state as passed in. | [
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exclude_version: Optional[DeploymentVersion] = None,
states: Optional[List[ReplicaState]] = None,
max_replicas: Optional[int] = math.inf,
ranking_function: Optional[
Callable[[List["DeploymentReplica"]], List["DeploymentReplica"]]
] = None,
... | [
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ab1cc8af10ed239587445b410df0435b41c4631e | kisuke95/ray | python/ray/serve/deployment_state.py | [
"Apache-2.0"
] | Python | count | <not_specific> | def count(
self,
exclude_version: Optional[DeploymentVersion] = None,
version: Optional[DeploymentVersion] = None,
states: Optional[List[ReplicaState]] = None,
):
"""Get the total count of replicas of the given states.
Args:
exclude_version(DeploymentVers... | Get the total count of replicas of the given states.
Args:
exclude_version(DeploymentVersion): version to exclude. If not
specified, all versions are considered.
version(DeploymentVersion): version to filter to. If not specified,
all versions are consider... | Get the total count of replicas of the given states. | [
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] | def count(
self,
exclude_version: Optional[DeploymentVersion] = None,
version: Optional[DeploymentVersion] = None,
states: Optional[List[ReplicaState]] = None,
):
if states is None:
states = ALL_REPLICA_STATES
assert isinstance(states, list)
assert... | [
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ab1cc8af10ed239587445b410df0435b41c4631e | kisuke95/ray | python/ray/serve/deployment_state.py | [
"Apache-2.0"
] | Python | _set_deployment_goal | None | def _set_deployment_goal(self, deployment_info: Optional[DeploymentInfo]) -> None:
"""
Set desirable state for a given deployment, identified by tag.
Args:
deployment_info (Optional[DeploymentInfo]): Contains deployment and
replica config, if passed in as None, we're... |
Set desirable state for a given deployment, identified by tag.
Args:
deployment_info (Optional[DeploymentInfo]): Contains deployment and
replica config, if passed in as None, we're marking
target deployment as shutting down.
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] | def _set_deployment_goal(self, deployment_info: Optional[DeploymentInfo]) -> None:
if deployment_info is not None:
self._target_info = deployment_info
self._target_replicas = deployment_info.deployment_config.num_replicas
self._target_version = DeploymentVersion(
... | [
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ab1cc8af10ed239587445b410df0435b41c4631e | kisuke95/ray | python/ray/serve/deployment_state.py | [
"Apache-2.0"
] | Python | deploy | bool | def deploy(self, deployment_info: DeploymentInfo) -> bool:
"""Deploy the deployment.
If the deployment already exists with the same version and config,
this is a no-op and returns False.
Returns:
bool: Whether or not the deployment is being updated.
"""
# En... | Deploy the deployment.
If the deployment already exists with the same version and config,
this is a no-op and returns False.
Returns:
bool: Whether or not the deployment is being updated.
| Deploy the deployment.
If the deployment already exists with the same version and config,
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existing_info = self._target_info
if existing_info is not None:
deployment_info.start_time_ms = existing_info.start_time_ms
if (
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ab1cc8af10ed239587445b410df0435b41c4631e | kisuke95/ray | python/ray/serve/deployment_state.py | [
"Apache-2.0"
] | Python | _stop_wrong_version_replicas | bool | def _stop_wrong_version_replicas(self) -> bool:
"""Stops replicas with outdated versions to implement rolling updates.
This includes both explicit code version updates and changes to the
user_config.
Returns whether any replicas were stopped.
"""
# Short circuit if targ... | Stops replicas with outdated versions to implement rolling updates.
This includes both explicit code version updates and changes to the
user_config.
Returns whether any replicas were stopped.
| Stops replicas with outdated versions to implement rolling updates.
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ab1cc8af10ed239587445b410df0435b41c4631e | kisuke95/ray | python/ray/serve/deployment_state.py | [
"Apache-2.0"
] | Python | _scale_deployment_replicas | bool | def _scale_deployment_replicas(self) -> bool:
"""Scale the given deployment to the number of replicas."""
assert (
self._target_replicas >= 0
), "Number of replicas must be greater than or equal to 0."
replicas_stopped = self._stop_wrong_version_replicas()
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assert (
self._target_replicas >= 0
), "Number of replicas must be greater than or equal to 0."
replicas_stopped = self._stop_wrong_version_replicas()
current_replicas = self._replicas.count(
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ab1cc8af10ed239587445b410df0435b41c4631e | kisuke95/ray | python/ray/serve/deployment_state.py | [
"Apache-2.0"
] | Python | _check_curr_status | bool | def _check_curr_status(self) -> bool:
"""Check the current deployment status.
Checks the difference between the target vs. running replica count for
the target version.
This will update the current deployment status depending on the state
of the replicas.
Returns:
... | Check the current deployment status.
Checks the difference between the target vs. running replica count for
the target version.
This will update the current deployment status depending on the state
of the replicas.
Returns:
was_deleted
| Check the current deployment status.
Checks the difference between the target vs. running replica count for
the target version.
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all_running_replica_cnt = self._replicas.count(states=[ReplicaState.RUNNING])
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ab1cc8af10ed239587445b410df0435b41c4631e | kisuke95/ray | python/ray/serve/deployment_state.py | [
"Apache-2.0"
] | Python | _check_startup_replicas | Tuple[List[Tuple[DeploymentReplica, ReplicaStartupStatus]], bool] | def _check_startup_replicas(
self, original_state: ReplicaState, stop_on_slow=False
) -> Tuple[List[Tuple[DeploymentReplica, ReplicaStartupStatus]], bool]:
"""
Common helper function for startup actions tracking and status
transition: STARTING, UPDATING and RECOVERING.
Args:... |
Common helper function for startup actions tracking and status
transition: STARTING, UPDATING and RECOVERING.
Args:
stop_on_slow: If we consider a replica failed upon observing it's
slow to reach running state.
| Common helper function for startup actions tracking and status
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self, original_state: ReplicaState, stop_on_slow=False
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slow_replicas = []
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ab1cc8af10ed239587445b410df0435b41c4631e | kisuke95/ray | python/ray/serve/deployment_state.py | [
"Apache-2.0"
] | Python | _check_and_update_replicas | bool | def _check_and_update_replicas(self) -> bool:
"""
Check current state of all DeploymentReplica being tracked, and compare
with state container from previous update() cycle to see if any state
transition happened.
Returns if any running replicas transitioned to another state.
... |
Check current state of all DeploymentReplica being tracked, and compare
with state container from previous update() cycle to see if any state
transition happened.
Returns if any running replicas transitioned to another state.
| Check current state of all DeploymentReplica being tracked, and compare
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running_replicas_changed = False
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self._replicas.add(ReplicaState.RUNNING, replica)
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ab1cc8af10ed239587445b410df0435b41c4631e | kisuke95/ray | python/ray/serve/deployment_state.py | [
"Apache-2.0"
] | Python | update | bool | def update(self) -> bool:
"""Attempts to reconcile this deployment to match its goal state.
This is an asynchronous call; it's expected to be called repeatedly.
Also updates the internal DeploymentStatusInfo based on the current
state of the system.
Returns true if this deploy... | Attempts to reconcile this deployment to match its goal state.
This is an asynchronous call; it's expected to be called repeatedly.
Also updates the internal DeploymentStatusInfo based on the current
state of the system.
Returns true if this deployment was successfully deleted.
... | Attempts to reconcile this deployment to match its goal state.
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ab1cc8af10ed239587445b410df0435b41c4631e | kisuke95/ray | python/ray/serve/deployment_state.py | [
"Apache-2.0"
] | Python | _map_actor_names_to_deployment | Dict[str, List[str]] | def _map_actor_names_to_deployment(
self, all_current_actor_names: List[str]
) -> Dict[str, List[str]]:
"""
Given a list of all actor names queried from current ray cluster,
map them to corresponding deployments.
Example:
Args:
[A#zxc123, B#xcv234... |
Given a list of all actor names queried from current ray cluster,
map them to corresponding deployments.
Example:
Args:
[A#zxc123, B#xcv234, A#qwe234]
Returns:
{
A: [A#zxc123, A#qwe234]
B: [B#xcv234... | Given a list of all actor names queried from current ray cluster,
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] | def _map_actor_names_to_deployment(
self, all_current_actor_names: List[str]
) -> Dict[str, List[str]]:
all_replica_names = [
actor_name
for actor_name in all_current_actor_names
if ReplicaName.is_replica_name(actor_name)
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ab1cc8af10ed239587445b410df0435b41c4631e | kisuke95/ray | python/ray/serve/deployment_state.py | [
"Apache-2.0"
] | Python | _recover_from_checkpoint | None | def _recover_from_checkpoint(self, all_current_actor_names: List[str]) -> None:
"""
Recover from checkpoint upon controller failure with all actor names
found in current cluster.
Each deployment resumes target state from checkpoint if available.
For current state it will priori... |
Recover from checkpoint upon controller failure with all actor names
found in current cluster.
Each deployment resumes target state from checkpoint if available.
For current state it will prioritize reconstructing from current
actor names found that matches deployment tag if a... | Recover from checkpoint upon controller failure with all actor names
found in current cluster.
Each deployment resumes target state from checkpoint if available.
For current state it will prioritize reconstructing from current
actor names found that matches deployment tag if applicable. | [
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deployment_to_current_replicas = self._map_actor_names_to_deployment(
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)
checkpoint = self._kv_store.get(CHECKPOINT_KEY)
if checkpoint is not None:
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ab1cc8af10ed239587445b410df0435b41c4631e | kisuke95/ray | python/ray/serve/deployment_state.py | [
"Apache-2.0"
] | Python | shutdown | null | def shutdown(self):
"""
Shutdown all running replicas by notifying the controller, and leave
it to the controller event loop to take actions afterwards.
Once shutdown signal is received, it will also prevent any new
deployments or replicas from being created.
One can se... |
Shutdown all running replicas by notifying the controller, and leave
it to the controller event loop to take actions afterwards.
Once shutdown signal is received, it will also prevent any new
deployments or replicas from being created.
One can send multiple shutdown signals bu... | Shutdown all running replicas by notifying the controller, and leave
it to the controller event loop to take actions afterwards.
Once shutdown signal is received, it will also prevent any new
deployments or replicas from being created.
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for deployment_state in self._deployment_states.values():
deployment_state.delete()
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ab1cc8af10ed239587445b410df0435b41c4631e | kisuke95/ray | python/ray/serve/deployment_state.py | [
"Apache-2.0"
] | Python | deploy | bool | def deploy(self, deployment_name: str, deployment_info: DeploymentInfo) -> bool:
"""Deploy the deployment.
If the deployment already exists with the same version and config,
this is a no-op and returns False.
Returns:
bool: Whether or not the deployment is being updated.
... | Deploy the deployment.
If the deployment already exists with the same version and config,
this is a no-op and returns False.
Returns:
bool: Whether or not the deployment is being updated.
| Deploy the deployment.
If the deployment already exists with the same version and config,
this is a no-op and returns False. | [
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del self._deleted_deployment_metadata[deployment_name]
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ab1cc8af10ed239587445b410df0435b41c4631e | kisuke95/ray | python/ray/serve/deployment_state.py | [
"Apache-2.0"
] | Python | update | null | def update(self):
"""Updates the state of all deployments to match their goal state."""
deleted_tags = []
for deployment_name, deployment_state in self._deployment_states.items():
deleted = deployment_state.update()
if deleted:
deleted_tags.append(deployme... | Updates the state of all deployments to match their goal state. | Updates the state of all deployments to match their goal state. | [
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] | def update(self):
deleted_tags = []
for deployment_name, deployment_state in self._deployment_states.items():
deleted = deployment_state.update()
if deleted:
deleted_tags.append(deployment_name)
deployment_info = deployment_state.target_info
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15ec7b653d9215be07afd1912e6d6a18ad9ed8dd | kisuke95/ray | dashboard/modules/job/sdk.py | [
"Apache-2.0"
] | Python | submit_job | str | def submit_job(
self,
*,
entrypoint: str,
job_id: Optional[str] = None,
runtime_env: Optional[Dict[str, Any]] = None,
metadata: Optional[Dict[str, str]] = None,
) -> str:
"""Submit and execute a job asynchronously.
When a job is submitted, it runs onc... | Submit and execute a job asynchronously.
When a job is submitted, it runs once to completion or failure. Retries or
different runs with different parameters should be handled by the
submitter. Jobs are bound to the lifetime of a Ray cluster, so if the
cluster goes down, all running jobs... | Submit and execute a job asynchronously.
When a job is submitted, it runs once to completion or failure. Retries or
different runs with different parameters should be handled by the
submitter. Jobs are bound to the lifetime of a Ray cluster, so if the
cluster goes down, all running jobs on that cluster will be terminat... | [
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runtime_env: Optional[Dict[str, Any]] = None,
metadata: Optional[Dict[str, str]] = None,
) -> str:
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metadata = metadata or {}
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15ec7b653d9215be07afd1912e6d6a18ad9ed8dd | kisuke95/ray | dashboard/modules/job/sdk.py | [
"Apache-2.0"
] | Python | stop_job | bool | def stop_job(
self,
job_id: str,
) -> bool:
"""Request a job to exit asynchronously.
Example:
>>> from ray.job_submission import JobSubmissionClient
>>> client = JobSubmissionClient("http://127.0.0.1:8265") # doctest: +SKIP
>>> job_id = client.sub... | Request a job to exit asynchronously.
Example:
>>> from ray.job_submission import JobSubmissionClient
>>> client = JobSubmissionClient("http://127.0.0.1:8265") # doctest: +SKIP
>>> job_id = client.submit_job(entrypoint="sleep 10") # doctest: +SKIP
>>> client.stop... | Request a job to exit asynchronously.
The job ID for the job to be stopped.
True if the job was running, otherwise False.
If the job does not exist or if the request to the
job server fails. | [
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r = self._do_request("POST", f"/api/jobs/{job_id}/stop")
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15ec7b653d9215be07afd1912e6d6a18ad9ed8dd | kisuke95/ray | dashboard/modules/job/sdk.py | [
"Apache-2.0"
] | Python | list_jobs | Dict[str, JobInfo] | def list_jobs(self) -> Dict[str, JobInfo]:
"""List all jobs along with their status and other information.
Lists all jobs that have ever run on the cluster, including jobs that are
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Lists all jobs that have ever run on the cluster, including jobs that are
currently running and jobs that are no longer running.
Example:
>>> from ray.job_submission import JobSubmissionClient
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jobs_info = {
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15ec7b653d9215be07afd1912e6d6a18ad9ed8dd | kisuke95/ray | dashboard/modules/job/sdk.py | [
"Apache-2.0"
] | Python | tail_job_logs | Iterator[str] | async def tail_job_logs(self, job_id: str) -> Iterator[str]:
"""Get an iterator that follows the logs of a job.
Example:
>>> from ray.job_submission import JobSubmissionClient
>>> client = JobSubmissionClient("http://127.0.0.1:8265") # doctest: +SKIP
>>> job_id = cli... | Get an iterator that follows the logs of a job.
Example:
>>> from ray.job_submission import JobSubmissionClient
>>> client = JobSubmissionClient("http://127.0.0.1:8265") # doctest: +SKIP
>>> job_id = client.submit_job( # doctest: +SKIP
... entrypoint="echo hi... | Get an iterator that follows the logs of a job.
The ID of the job whose logs are being requested.
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6b0a70ec8a5b4ecec4000b6e60f1b6841c9bf7f6 | kisuke95/ray | python/ray/data/impl/sort.py | [
"Apache-2.0"
] | Python | sample_boundaries | List[T] | def sample_boundaries(
blocks: List[ObjectRef[Block]], key: SortKeyT, num_reducers: int
) -> List[T]:
"""
Return (num_reducers - 1) items in ascending order from the blocks that
partition the domain into ranges with approximately equally many elements.
"""
# TODO(Clark): Support multiple boundar... |
Return (num_reducers - 1) items in ascending order from the blocks that
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158ca292f1018283d8351be1f1e7f66ae463b8a6 | kisuke95/ray | python/ray/ml/predictors/integrations/xgboost/xgboost_predictor.py | [
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] | Python | from_checkpoint | "XGBoostPredictor" | def from_checkpoint(cls, checkpoint: Checkpoint) -> "XGBoostPredictor":
"""Instantiate the predictor from a Checkpoint.
The checkpoint is expected to be a result of ``XGBoostTrainer``.
Args:
checkpoint (Checkpoint): The checkpoint to load the model and
preprocessor ... | Instantiate the predictor from a Checkpoint.
The checkpoint is expected to be a result of ``XGBoostTrainer``.
Args:
checkpoint (Checkpoint): The checkpoint to load the model and
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158ca292f1018283d8351be1f1e7f66ae463b8a6 | kisuke95/ray | python/ray/ml/predictors/integrations/xgboost/xgboost_predictor.py | [
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self,
data: DataBatchType,
feature_columns: Optional[Union[List[str], List[int]]] = None,
dmatrix_kwargs: Optional[Dict[str, Any]] = None,
**predict_kwargs,
) -> pd.DataFrame:
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data: A batch of input data. Either a pandas DataFrame or numpy
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dmatrix_kwargs = dmatrix_kwargs or {}
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158f492335f8f84e6f93ad9d95dcaaeb1c7e4846 | kisuke95/ray | python/ray/data/impl/block_list.py | [
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"""Raise an error if this BlockList has been previously cleared."""
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158f492335f8f84e6f93ad9d95dcaaeb1c7e4846 | kisuke95/ray | python/ray/data/impl/block_list.py | [
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"""Split this BlockList into multiple lists.
Args:
split_size: The number of lists to split into.
"""
self._check_if_cleared()
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blocks = np.array_split(self._blocks, num_splits)
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158f492335f8f84e6f93ad9d95dcaaeb1c7e4846 | kisuke95/ray | python/ray/data/impl/block_list.py | [
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158f492335f8f84e6f93ad9d95dcaaeb1c7e4846 | kisuke95/ray | python/ray/data/impl/block_list.py | [
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158f492335f8f84e6f93ad9d95dcaaeb1c7e4846 | kisuke95/ray | python/ray/data/impl/block_list.py | [
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block_idx: The block index to divide at.
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self._check_if_cleared()
return (
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158f492335f8f84e6f93ad9d95dcaaeb1c7e4846 | kisuke95/ray | python/ray/data/impl/block_list.py | [
"Apache-2.0"
] | Python | iter_blocks | Iterator[ObjectRef[Block]] | def iter_blocks(self) -> Iterator[ObjectRef[Block]]:
"""Iterate over the blocks of this block list.
This blocks on the execution of the tasks generating block outputs.
The length of this iterator is not known until execution.
"""
self._check_if_cleared()
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158f492335f8f84e6f93ad9d95dcaaeb1c7e4846 | kisuke95/ray | python/ray/data/impl/block_list.py | [
"Apache-2.0"
] | Python | iter_blocks_with_metadata | Iterator[Tuple[ObjectRef[Block], BlockMetadata]] | def iter_blocks_with_metadata(
self,
) -> Iterator[Tuple[ObjectRef[Block], BlockMetadata]]:
"""Iterate over the blocks along with their runtime metadata.
This blocks on the execution of the tasks generating block outputs.
The length of this iterator is not known until execution.
... | Iterate over the blocks along with their runtime metadata.
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158f492335f8f84e6f93ad9d95dcaaeb1c7e4846 | kisuke95/ray | python/ray/data/impl/block_list.py | [
"Apache-2.0"
] | Python | executed_num_blocks | int | def executed_num_blocks(self) -> int:
"""Returns the number of output blocks after execution.
This may differ from initial_num_blocks() for LazyBlockList, which
doesn't know how many blocks will be produced until tasks finish.
"""
return len(self.get_blocks()) | Returns the number of output blocks after execution.
This may differ from initial_num_blocks() for LazyBlockList, which
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1af9bb24baf891ccd742ee9c5d5cdf1f42802871 | kisuke95/ray | python/ray/serve/pipeline/deployment_method_node.py | [
"Apache-2.0"
] | Python | _execute_impl | <not_specific> | def _execute_impl(self, *args, **kwargs):
"""Executor of DeploymentMethodNode by ray.remote()"""
# Execute with bound args.
method_body = getattr(self._deployment_handle, self._deployment_method_name)
return method_body.remote(
*self._bound_args,
**self._bound_kwa... | Executor of DeploymentMethodNode by ray.remote() | Executor of DeploymentMethodNode by ray.remote() | [
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"DeploymentMethodNode",
"by",
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".",
"remote",
"()"
] | def _execute_impl(self, *args, **kwargs):
method_body = getattr(self._deployment_handle, self._deployment_method_name)
return method_body.remote(
*self._bound_args,
**self._bound_kwargs,
) | [
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} |
1af9bb24baf891ccd742ee9c5d5cdf1f42802871 | kisuke95/ray | python/ray/serve/pipeline/deployment_method_node.py | [
"Apache-2.0"
] | Python | _get_serve_deployment_handle | Union[RayServeHandle, RayServeSyncHandle] | def _get_serve_deployment_handle(
self,
deployment: Deployment,
bound_other_args_to_resolve: Dict[str, Any],
) -> Union[RayServeHandle, RayServeSyncHandle]:
"""
Return a sync or async handle of the encapsulated Deployment based on
config.
Args:
de... |
Return a sync or async handle of the encapsulated Deployment based on
config.
Args:
deployment (Deployment): Deployment instance wrapped in the DAGNode.
bound_other_args_to_resolve (Dict[str, Any]): Contains args used
to configure DeploymentNode.
... | Return a sync or async handle of the encapsulated Deployment based on
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] | def _get_serve_deployment_handle(
self,
deployment: Deployment,
bound_other_args_to_resolve: Dict[str, Any],
) -> Union[RayServeHandle, RayServeSyncHandle]:
if USE_SYNC_HANDLE_KEY not in bound_other_args_to_resolve:
return RayServeLazySyncHandle(deployment.name)
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238f37522e44380744b70cbb54103520bbfe347f | kisuke95/ray | python/ray/data/datasource/partitioning.py | [
"Apache-2.0"
] | Python | _normalize_base_dir | null | def _normalize_base_dir(self):
"""Normalizes the partition base directory for compatibility with the
given filesystem.
This should be called once a filesystem has been resolved to ensure that this
base directory is correctly discovered at the root of all partitioned file
paths.
... | Normalizes the partition base directory for compatibility with the
given filesystem.
This should be called once a filesystem has been resolved to ensure that this
base directory is correctly discovered at the root of all partitioned file
paths.
| Normalizes the partition base directory for compatibility with the
given filesystem.
This should be called once a filesystem has been resolved to ensure that this
base directory is correctly discovered at the root of all partitioned file
paths. | [
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from ray.data.datasource.file_based_datasource import (
_resolve_paths_and_filesystem,
)
paths, self._resolved_filesystem = _resolve_paths_and_filesystem(
self._base_dir,
self._filesystem,
)
assert (
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} |
238f37522e44380744b70cbb54103520bbfe347f | kisuke95/ray | python/ray/data/datasource/partitioning.py | [
"Apache-2.0"
] | Python | of | "PathPartitionEncoder" | def of(
style: PartitionStyle = PartitionStyle.HIVE,
base_dir: Optional[str] = None,
field_names: Optional[List[str]] = None,
filesystem: Optional["pyarrow.fs.FileSystem"] = None,
) -> "PathPartitionEncoder":
"""Creates a new partition path encoder.
Args:
... | Creates a new partition path encoder.
Args:
style: The partition style - may be either HIVE or DIRECTORY.
base_dir: "/"-delimited base directory that all partition paths will be
generated under (exclusive).
field_names: The partition key field names (i.e. colu... | Creates a new partition path encoder. | [
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"partition",
"path",
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"."
] | def of(
style: PartitionStyle = PartitionStyle.HIVE,
base_dir: Optional[str] = None,
field_names: Optional[List[str]] = None,
filesystem: Optional["pyarrow.fs.FileSystem"] = None,
) -> "PathPartitionEncoder":
scheme = PathPartitionScheme(style, base_dir, field_names, filesyst... | [
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"identifier": "style",
"type":... |
238f37522e44380744b70cbb54103520bbfe347f | kisuke95/ray | python/ray/data/datasource/partitioning.py | [
"Apache-2.0"
] | Python | _as_partition_dirs | List[str] | def _as_partition_dirs(self, values: List[str]) -> List[str]:
"""Creates a list of partition directory names for the given values."""
field_names = self._scheme.field_names
if field_names:
assert len(values) == len(field_names), (
f"Expected {len(field_names)} partiti... | Creates a list of partition directory names for the given values. | Creates a list of partition directory names for the given values. | [
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] | def _as_partition_dirs(self, values: List[str]) -> List[str]:
field_names = self._scheme.field_names
if field_names:
assert len(values) == len(field_names), (
f"Expected {len(field_names)} partition value(s) but found "
f"{len(values)}: {values}."
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238f37522e44380744b70cbb54103520bbfe347f | kisuke95/ray | python/ray/data/datasource/partitioning.py | [
"Apache-2.0"
] | Python | of | "PathPartitionParser" | def of(
style: PartitionStyle = PartitionStyle.HIVE,
base_dir: Optional[str] = None,
field_names: Optional[List[str]] = None,
filesystem: Optional["pyarrow.fs.FileSystem"] = None,
) -> "PathPartitionParser":
"""Creates a path-based partition parser using a flattened argument ... | Creates a path-based partition parser using a flattened argument list.
Args:
style: The partition style - may be either HIVE or DIRECTORY.
base_dir: "/"-delimited base directory to start searching for partitions
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field_names: Optional[List[str]] = None,
filesystem: Optional["pyarrow.fs.FileSystem"] = None,
) -> "PathPartitionParser":
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238f37522e44380744b70cbb54103520bbfe347f | kisuke95/ray | python/ray/data/datasource/partitioning.py | [
"Apache-2.0"
] | Python | _dir_path_trim_base | Optional[str] | def _dir_path_trim_base(self, path: str) -> Optional[str]:
"""Trims the normalized base directory and returns the directory path.
Returns None if the path does not start with the normalized base directory.
Simply returns the directory path if the base directory is undefined.
"""
... | Trims the normalized base directory and returns the directory path.
Returns None if the path does not start with the normalized base directory.
Simply returns the directory path if the base directory is undefined.
| Trims the normalized base directory and returns the directory path.
Returns None if the path does not start with the normalized base directory.
Simply returns the directory path if the base directory is undefined. | [
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path = path[len(self._scheme.normalized_base_dir) :]
return posixpath.dirname(path) | [
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238f37522e44380744b70cbb54103520bbfe347f | kisuke95/ray | python/ray/data/datasource/partitioning.py | [
"Apache-2.0"
] | Python | _parse_dir_path | Dict[str, str] | def _parse_dir_path(self, dir_path: str) -> Dict[str, str]:
"""Directory partition path parser.
Returns a dictionary mapping directory partition keys to values from a
partition path of the form "{value1}/{value2}/..." or an empty dictionary for
unpartitioned files.
Requires a c... | Directory partition path parser.
Returns a dictionary mapping directory partition keys to values from a
partition path of the form "{value1}/{value2}/..." or an empty dictionary for
unpartitioned files.
Requires a corresponding ordered list of partition key field names to map the
... | Directory partition path parser.
Returns a dictionary mapping directory partition keys to values from a
partition path of the form "{value1}/{value2}/..." or an empty dictionary for
unpartitioned files.
Requires a corresponding ordered list of partition key field names to map the
correct key to each value. | [
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field_names = self._scheme.field_names
assert not dirs or len(dirs) == len(field_names), (
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238f37522e44380744b70cbb54103520bbfe347f | kisuke95/ray | python/ray/data/datasource/partitioning.py | [
"Apache-2.0"
] | Python | of | "PathPartitionFilter" | def of(
filter_fn: Callable[[Dict[str, str]], bool],
style: PartitionStyle = PartitionStyle.HIVE,
base_dir: Optional[str] = None,
field_names: Optional[List[str]] = None,
filesystem: Optional["pyarrow.fs.FileSystem"] = None,
) -> "PathPartitionFilter":
"""Creates a pa... | Creates a path-based partition filter using a flattened argument list.
Args:
filter_fn: Callback used to filter partitions. Takes a dictionary mapping
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base_dir: Optional[str] = None,
field_names: Optional[List[str]] = None,
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99245cce50010f8a0bc1e0e3a06c7bad8a56fe7d | kisuke95/ray | python/ray/_private/runtime_env/packaging.py | [
"Apache-2.0"
] | Python | _dir_travel | null | def _dir_travel(
path: Path,
excludes: List[Callable],
handler: Callable,
logger: Optional[logging.Logger] = default_logger,
):
"""Travels the path recursively, calling the handler on each subpath.
Respects excludes, which will be called to check if this path is skipped.
"""
e = _get_gi... | Travels the path recursively, calling the handler on each subpath.
Respects excludes, which will be called to check if this path is skipped.
| Travels the path recursively, calling the handler on each subpath.
Respects excludes, which will be called to check if this path is skipped. | [
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] | def _dir_travel(
path: Path,
excludes: List[Callable],
handler: Callable,
logger: Optional[logging.Logger] = default_logger,
):
e = _get_gitignore(path)
if e is not None:
excludes.append(e)
skip = any(e(path) for e in excludes)
if not skip:
try:
handler(path)
... | [
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99245cce50010f8a0bc1e0e3a06c7bad8a56fe7d | kisuke95/ray | python/ray/_private/runtime_env/packaging.py | [
"Apache-2.0"
] | Python | _hash_directory | bytes | def _hash_directory(
root: Path,
relative_path: Path,
excludes: Optional[Callable],
logger: Optional[logging.Logger] = default_logger,
) -> bytes:
"""Helper function to create hash of a directory.
It'll go through all the files in the directory and xor
hash(file_name, file_content) to creat... | Helper function to create hash of a directory.
It'll go through all the files in the directory and xor
hash(file_name, file_content) to create a hash value.
| Helper function to create hash of a directory.
It'll go through all the files in the directory and xor
hash(file_name, file_content) to create a hash value. | [
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root: Path,
relative_path: Path,
excludes: Optional[Callable],
logger: Optional[logging.Logger] = default_logger,
) -> bytes:
hash_val = b"0" * 8
BUF_SIZE = 4096 * 1024
def handler(path: Path):
md5 = hashlib.md5()
md5.update(str(path.relative_to(relative_... | [
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99245cce50010f8a0bc1e0e3a06c7bad8a56fe7d | kisuke95/ray | python/ray/_private/runtime_env/packaging.py | [
"Apache-2.0"
] | Python | parse_uri | Tuple[Protocol, str] | def parse_uri(pkg_uri: str) -> Tuple[Protocol, str]:
"""
Parse resource uri into protocol and package name based on its format.
Note that the output of this function is not for handling actual IO, it's
only for setting up local directory folders by using package name as path.
For GCS URIs, netloc is... |
Parse resource uri into protocol and package name based on its format.
Note that the output of this function is not for handling actual IO, it's
only for setting up local directory folders by using package name as path.
For GCS URIs, netloc is the package name.
urlparse("gcs://_ray_pkg_029f88d5... | Parse resource uri into protocol and package name based on its format.
Note that the output of this function is not for handling actual IO, it's
only for setting up local directory folders by using package name as path.
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uri = urlparse(pkg_uri)
protocol = Protocol(uri.scheme)
if protocol == Protocol.S3 or protocol == Protocol.GS:
return (protocol, f"{protocol.value}_{uri.netloc}{uri.path.replace('/', '_')}")
elif protocol == Protocol.HTTPS:
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99245cce50010f8a0bc1e0e3a06c7bad8a56fe7d | kisuke95/ray | python/ray/_private/runtime_env/packaging.py | [
"Apache-2.0"
] | Python | _store_package_in_gcs | int | def _store_package_in_gcs(
pkg_uri: str,
data: bytes,
logger: Optional[logging.Logger] = default_logger,
) -> int:
"""Stores package data in the Global Control Store (GCS).
Args:
pkg_uri (str): The GCS key to store the data in.
data (bytes): The serialized package's bytes to store i... | Stores package data in the Global Control Store (GCS).
Args:
pkg_uri (str): The GCS key to store the data in.
data (bytes): The serialized package's bytes to store in the GCS.
logger (Optional[logging.Logger]): The logger used by this function.
Return:
int: Size of data
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pkg_uri: str,
data: bytes,
logger: Optional[logging.Logger] = default_logger,
) -> int:
file_size = len(data)
size_str = _mib_string(file_size)
if len(data) >= GCS_STORAGE_MAX_SIZE:
raise ValueError(
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99245cce50010f8a0bc1e0e3a06c7bad8a56fe7d | kisuke95/ray | python/ray/_private/runtime_env/packaging.py | [
"Apache-2.0"
] | Python | upload_package_if_needed | bool | def upload_package_if_needed(
pkg_uri: str,
base_directory: str,
directory: str,
include_parent_dir: bool = False,
excludes: Optional[List[str]] = None,
logger: Optional[logging.Logger] = default_logger,
) -> bool:
"""Upload the contents of the directory under the given URI.
This will f... | Upload the contents of the directory under the given URI.
This will first create a temporary zip file under the passed
base_directory.
If the package already exists in storage, this is a no-op.
Args:
pkg_uri: URI of the package to upload.
base_directory: Directory where package files ... | Upload the contents of the directory under the given URI.
This will first create a temporary zip file under the passed
base_directory.
If the package already exists in storage, this is a no-op. | [
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pkg_uri: str,
base_directory: str,
directory: str,
include_parent_dir: bool = False,
excludes: Optional[List[str]] = None,
logger: Optional[logging.Logger] = default_logger,
) -> bool:
if excludes is None:
excludes = []
if logger is None:
log... | [
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99245cce50010f8a0bc1e0e3a06c7bad8a56fe7d | kisuke95/ray | python/ray/_private/runtime_env/packaging.py | [
"Apache-2.0"
] | Python | download_and_unpack_package | str | def download_and_unpack_package(
pkg_uri: str,
base_directory: str,
logger: Optional[logging.Logger] = default_logger,
) -> str:
"""Download the package corresponding to this URI and unpack it if zipped.
Will be written to a file or directory named {base_directory}/{uri}.
Returns the path to th... | Download the package corresponding to this URI and unpack it if zipped.
Will be written to a file or directory named {base_directory}/{uri}.
Returns the path to this file or directory.
| Download the package corresponding to this URI and unpack it if zipped.
Will be written to a file or directory named {base_directory}/{uri}.
Returns the path to this file or directory. | [
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logger: Optional[logging.Logger] = default_logger,
) -> str:
pkg_file = Path(_get_local_path(base_directory, pkg_uri))
with FileLock(str(pkg_file) + ".lock"):
if logger is None:
logger = default_logger
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99245cce50010f8a0bc1e0e3a06c7bad8a56fe7d | kisuke95/ray | python/ray/_private/runtime_env/packaging.py | [
"Apache-2.0"
] | Python | remove_dir_from_filepaths | null | def remove_dir_from_filepaths(base_dir: str, rdir: str):
"""
base_dir: String path of the directory containing rdir
rdir: String path of directory relative to base_dir whose contents should
be moved to its base_dir, its parent directory
Removes rdir from the filepaths of all files and directo... |
base_dir: String path of the directory containing rdir
rdir: String path of directory relative to base_dir whose contents should
be moved to its base_dir, its parent directory
Removes rdir from the filepaths of all files and directories inside it.
In other words, moves all the files inside r... | String path of the directory containing rdir
rdir: String path of directory relative to base_dir whose contents should
be moved to its base_dir, its parent directory
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shutil.move(os.path.join(base_dir, rdir), os.path.join(tmp_dir, rdir))
rdir_children = os.listdir(os.path.join(tmp_dir, rdir))
for child in rdir_children:
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99245cce50010f8a0bc1e0e3a06c7bad8a56fe7d | kisuke95/ray | python/ray/_private/runtime_env/packaging.py | [
"Apache-2.0"
] | Python | unzip_package | null | def unzip_package(
package_path: str,
target_dir: str,
remove_top_level_directory: bool,
unlink_zip: bool,
logger: Optional[logging.Logger] = default_logger,
):
"""
Unzip the compressed package contained at package_path and store the
contents in target_dir. If remove_top_level_directory ... |
Unzip the compressed package contained at package_path and store the
contents in target_dir. If remove_top_level_directory is True, the function
will automatically remove the top_level_directory and store the contents
directly in target_dir. If unlink_zip is True, the function will unlink the
zip f... | Unzip the compressed package contained at package_path and store the
contents in target_dir. If remove_top_level_directory is True, the function
will automatically remove the top_level_directory and store the contents
directly in target_dir. If unlink_zip is True, the function will unlink the
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package_path: str,
target_dir: str,
remove_top_level_directory: bool,
unlink_zip: bool,
logger: Optional[logging.Logger] = default_logger,
):
try:
os.mkdir(target_dir)
except FileExistsError:
logger.info(f"Directory at {target_dir} already exists")
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99245cce50010f8a0bc1e0e3a06c7bad8a56fe7d | kisuke95/ray | python/ray/_private/runtime_env/packaging.py | [
"Apache-2.0"
] | Python | delete_package | Tuple[bool, int] | def delete_package(pkg_uri: str, base_directory: str) -> Tuple[bool, int]:
"""Deletes a specific URI from the local filesystem.
Args:
pkg_uri (str): URI to delete.
Returns:
bool: True if the URI was successfully deleted, else False.
"""
deleted = False
path = Path(_get_local_p... | Deletes a specific URI from the local filesystem.
Args:
pkg_uri (str): URI to delete.
Returns:
bool: True if the URI was successfully deleted, else False.
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deleted = False
path = Path(_get_local_path(base_directory, pkg_uri))
with FileLock(str(path) + ".lock"):
path = path.with_suffix("")
if path.exists():
if path.is_dir() and not path.is_symlink():
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d81ad90b6ea6ef3bcd84e0215dd89fe25a4d1166 | kisuke95/ray | dashboard/head.py | [
"Apache-2.0"
] | Python | check_once | bool | async def check_once(self) -> bool:
"""Ask the thread to perform a healthcheck."""
assert (
threading.current_thread != self
), "caller shouldn't be from the same thread as GCSHealthCheckThread."
future = Future()
self.work_queue.put(future)
return await asyn... | Ask the thread to perform a healthcheck. | Ask the thread to perform a healthcheck. | [
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assert (
threading.current_thread != self
), "caller shouldn't be from the same thread as GCSHealthCheckThread."
future = Future()
self.work_queue.put(future)
return await asyncio.wrap_future(future) | [
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28b48e106b84189bd2ec946f00cb8395ce0cc0b1 | kisuke95/ray | python/ray/data/impl/lazy_block_list.py | [
"Apache-2.0"
] | Python | clear | null | def clear(self):
"""Clears all object references (block partitions and base block partitions)
from this lazy block list.
"""
self._block_partition_refs = [None for _ in self._block_partition_refs]
self._block_partition_meta_refs = [
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28b48e106b84189bd2ec946f00cb8395ce0cc0b1 | kisuke95/ray | python/ray/data/impl/lazy_block_list.py | [
"Apache-2.0"
] | Python | _get_blocks_with_metadata | Tuple[List[ObjectRef[Block]], List[BlockMetadata]] | def _get_blocks_with_metadata(
self,
) -> Tuple[List[ObjectRef[Block]], List[BlockMetadata]]:
"""Get all underlying block futures and concrete metadata.
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28b48e106b84189bd2ec946f00cb8395ce0cc0b1 | kisuke95/ray | python/ray/data/impl/lazy_block_list.py | [
"Apache-2.0"
] | Python | compute_first_block | null | def compute_first_block(self):
"""Kick off computation for the first block in the list.
This is useful if looking to support rapid lightweight interaction with a small
amount of the dataset.
"""
if self._tasks:
self._get_or_compute(0) | Kick off computation for the first block in the list.
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28b48e106b84189bd2ec946f00cb8395ce0cc0b1 | kisuke95/ray | python/ray/data/impl/lazy_block_list.py | [
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] | Python | ensure_metadata_for_first_block | Optional[BlockMetadata] | def ensure_metadata_for_first_block(self) -> Optional[BlockMetadata]:
"""Ensure that the metadata is fetched and set for the first block.
This will only block execution in order to fetch the post-read metadata for the
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28b48e106b84189bd2ec946f00cb8395ce0cc0b1 | kisuke95/ray | python/ray/data/impl/lazy_block_list.py | [
"Apache-2.0"
] | Python | iter_blocks_with_metadata | Iterator[Tuple[ObjectRef[Block], BlockMetadata]] | def iter_blocks_with_metadata(
self,
block_for_metadata: bool = False,
) -> Iterator[Tuple[ObjectRef[Block], BlockMetadata]]:
"""Iterate over the blocks along with their metadata.
Note that, if block_for_metadata is False (default), this iterator returns
pre-read metadata fr... | Iterate over the blocks along with their metadata.
Note that, if block_for_metadata is False (default), this iterator returns
pre-read metadata from the ReadTasks given to this LazyBlockList so it doesn't
have to block on the execution of the read tasks. Therefore, the metadata may be
u... | Iterate over the blocks along with their metadata.
Note that, if block_for_metadata is False (default), this iterator returns
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) -> Iterator[Tuple[ObjectRef[Block], BlockMetadata]]:
context = DatasetContext.get_current()
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class Iter:
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28b48e106b84189bd2ec946f00cb8395ce0cc0b1 | kisuke95/ray | python/ray/data/impl/lazy_block_list.py | [
"Apache-2.0"
] | Python | _iter_block_partition_refs | Iterator[
Tuple[ObjectRef[MaybeBlockPartition], ObjectRef[BlockPartitionMetadata]]
] | def _iter_block_partition_refs(
self,
) -> Iterator[
Tuple[ObjectRef[MaybeBlockPartition], ObjectRef[BlockPartitionMetadata]]
]:
"""Iterate over the block futures and their corresponding metadata futures.
This does NOT block on the execution of each submitted task.
"""
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class Iter:
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28b48e106b84189bd2ec946f00cb8395ce0cc0b1 | kisuke95/ray | python/ray/data/impl/lazy_block_list.py | [
"Apache-2.0"
] | Python | _submit_task | Tuple[ObjectRef[MaybeBlockPartition], ObjectRef[BlockPartitionMetadata]] | def _submit_task(
self, task_idx: int
) -> Tuple[ObjectRef[MaybeBlockPartition], ObjectRef[BlockPartitionMetadata]]:
"""Submit the task with index task_idx."""
stats_actor = _get_or_create_stats_actor()
if not self._execution_started:
stats_actor.record_start.remote(self.... | Submit the task with index task_idx. | Submit the task with index task_idx. | [
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self, task_idx: int
) -> Tuple[ObjectRef[MaybeBlockPartition], ObjectRef[BlockPartitionMetadata]]:
stats_actor = _get_or_create_stats_actor()
if not self._execution_started:
stats_actor.record_start.remote(self._stats_uuid)
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0c4552a40bc1094a0ba193f44cb5dab944ab764f | kisuke95/ray | python/ray/util/client/dataclient.py | [
"Apache-2.0"
] | Python | chunk_put | null | def chunk_put(req: ray_client_pb2.DataRequest):
"""
Chunks a put request. Doing this lazily is important for large objects,
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into the result_queue, we would effe... |
Chunks a put request. Doing this lazily is important for large objects,
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if total_size >= OBJECT_TRANSFER_WARNING_SIZE and log_once(
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size_gb = total_size / 2 ** 30
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0c4552a40bc1094a0ba193f44cb5dab944ab764f | kisuke95/ray | python/ray/util/client/dataclient.py | [
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] | Python | _process_response | None | def _process_response(self, response: Any) -> None:
"""
Process responses from the data servicer.
"""
if response.req_id == 0:
# This is not being waited for.
logger.debug(f"Got unawaited response {response}")
return
if response.req_id in self.... |
Process responses from the data servicer.
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logger.debug(f"Got unawaited response {response}")
return
if response.req_id in self.asyncio_waiting_data:
can_remove = True
try:
callback = self.asyncio_waiting_d... | [
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0c4552a40bc1094a0ba193f44cb5dab944ab764f | kisuke95/ray | python/ray/util/client/dataclient.py | [
"Apache-2.0"
] | Python | _can_reconnect | bool | def _can_reconnect(self, e: grpc.RpcError) -> bool:
"""
Processes RPC errors that occur while reading from data stream.
Returns True if the error can be recovered from, False otherwise.
"""
if not self.client_worker._can_reconnect(e):
logger.error("Unrecoverable error... |
Processes RPC errors that occur while reading from data stream.
Returns True if the error can be recovered from, False otherwise.
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0c4552a40bc1094a0ba193f44cb5dab944ab764f | kisuke95/ray | python/ray/util/client/dataclient.py | [
"Apache-2.0"
] | Python | _acknowledge | None | def _acknowledge(self, req_id: int) -> None:
"""
Puts an acknowledge request on the request queue periodically.
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blocking response is received.
"""
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Puts an acknowledge request on the request queue periodically.
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0c4552a40bc1094a0ba193f44cb5dab944ab764f | kisuke95/ray | python/ray/util/client/dataclient.py | [
"Apache-2.0"
] | Python | _reconnect_channel | None | def _reconnect_channel(self) -> None:
"""
Attempts to reconnect the gRPC channel and resend outstanding
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still works. If the ping fails, then the current channel is closed
and replaced with a new one.
Onc... |
Attempts to reconnect the gRPC channel and resend outstanding
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and replaced with a new one.
Once a working channel is available, a new request q... | Attempts to reconnect the gRPC channel and resend outstanding
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ping_succeeded = False
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80fbbbc8f12bfe5433e5b231fcdbad67578d33af | kisuke95/ray | rllib/agents/dqn/dqn.py | [
"Apache-2.0"
] | Python | training_iteration | ResultDict | def training_iteration(self) -> ResultDict:
"""DQN training iteration function.
Each training iteration, we:
- Sample (MultiAgentBatch) from workers.
- Store new samples in replay buffer.
- Sample training batch (MultiAgentBatch) from replay buffer.
- Learn on training b... | DQN training iteration function.
Each training iteration, we:
- Sample (MultiAgentBatch) from workers.
- Store new samples in replay buffer.
- Sample training batch (MultiAgentBatch) from replay buffer.
- Learn on training batch.
- Update remote workers' new policy weigh... | DQN training iteration function.
Each training iteration, we:
Sample (MultiAgentBatch) from workers.
Store new samples in replay buffer.
Sample training batch (MultiAgentBatch) from replay buffer.
Learn on training batch.
Update remote workers' new policy weights.
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6727e8cf6693500551fbd509af758b4932cea61b | kisuke95/ray | dashboard/modules/state/state_head.py | [
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9002d0ecaf0196a350fee52fad10cab59aed6e59 | kisuke95/ray | python/ray/ml/utils/remote_storage.py | [
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"""Return a hint how to install required filesystem package"""
if pyarrow is None:
return "Please make sure PyArrow is installed: `pip install pyarrow`."
if fsspec is None:
return "Try installing fsspec: `pip install fsspec`."
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9002d0ecaf0196a350fee52fad10cab59aed6e59 | kisuke95/ray | python/ray/ml/utils/remote_storage.py | [
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"""Check if target URI points to a non-local location"""
parsed = urllib.parse.urlparse(uri)
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return False
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9002d0ecaf0196a350fee52fad10cab59aed6e59 | kisuke95/ray | python/ray/ml/utils/remote_storage.py | [
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90374ece77df704ba7b52bd19bdf62e3a8a56461 | kisuke95/ray | python/ray/serve/client.py | [
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ray.get(self._controller.shutdown.remote())
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90374ece77df704ba7b52bd19bdf62e3a8a56461 | kisuke95/ray | python/ray/serve/client.py | [
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Raises TimeoutError if this doesn't happen before timeout_s.
"""
start = time.time()
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statuses = self.get_de... | Waits for all deployments to be shut down and deleted.
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90374ece77df704ba7b52bd19bdf62e3a8a56461 | kisuke95/ray | python/ray/serve/client.py | [
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90374ece77df704ba7b52bd19bdf62e3a8a56461 | kisuke95/ray | python/ray/serve/client.py | [
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c644f3dc2a721aed08b6bfcf4c896db29d14f509 | kisuke95/ray | rllib/utils/tf_utils.py | [
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max(-1.0, 1.0 - (std(y - pred)^2 / std(y)^2))
Args:
y: The labels.
pred: The predictions.
Returns:
The expla... | Computes the explained variance for a pair of labels and predictions.
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y: The labels.
pred: The predictions.
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c644f3dc2a721aed08b6bfcf4c896db29d14f509 | kisuke95/ray | rllib/utils/tf_utils.py | [
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) -> TensorType:
"""Flattens arbitrary input structs according to the given spaces struct.
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... | Flattens arbitrary input structs according to the given spaces struct.
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c644f3dc2a721aed08b6bfcf4c896db29d14f509 | kisuke95/ray | rllib/utils/tf_utils.py | [
"Apache-2.0"
] | Python | make_tf_callable | Callable | def make_tf_callable(
session_or_none: Optional["tf1.Session"], dynamic_shape: bool = False
) -> Callable:
"""Returns a function that can be executed in either graph or eager mode.
The function must take only positional args.
If eager is enabled, this will act as just a function. Otherwise, it
wil... | Returns a function that can be executed in either graph or eager mode.
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If eager is enabled, this will act as just a function. Otherwise, it
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internally.
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c644f3dc2a721aed08b6bfcf4c896db29d14f509 | kisuke95/ray | rllib/utils/tf_utils.py | [
"Apache-2.0"
] | Python | minimize_and_clip | ModelGradients | def minimize_and_clip(
optimizer: LocalOptimizer,
objective: TensorType,
var_list: List["tf.Variable"],
clip_val: float = 10.0,
) -> ModelGradients:
"""Computes, then clips gradients using objective, optimizer and var list.
Ensures the norm of the gradients for each variable is clipped to
`... | Computes, then clips gradients using objective, optimizer and var list.
Ensures the norm of the gradients for each variable is clipped to
`clip_val`.
Args:
optimizer: Either a shim optimizer (tf eager) containing a
tf.GradientTape under `self.tape` or a tf1 local optimizer
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var_list: List["tf.Variable"],
clip_val: float = 10.0,
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c644f3dc2a721aed08b6bfcf4c896db29d14f509 | kisuke95/ray | rllib/utils/tf_utils.py | [
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] | Python | one_hot | TensorType | def one_hot(x: TensorType, space: gym.Space) -> TensorType:
"""Returns a one-hot tensor, given and int tensor and a space.
Handles the MultiDiscrete case as well.
Args:
x: The input tensor.
space: The space to use for generating the one-hot tensor.
Returns:
The resulting one-h... | Returns a one-hot tensor, given and int tensor and a space.
Handles the MultiDiscrete case as well.
Args:
x: The input tensor.
space: The space to use for generating the one-hot tensor.
Returns:
The resulting one-hot tensor.
Raises:
ValueError: If the given space is n... | Returns a one-hot tensor, given and int tensor and a space.
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c644f3dc2a721aed08b6bfcf4c896db29d14f509 | kisuke95/ray | rllib/utils/tf_utils.py | [
"Apache-2.0"
] | Python | scope_vars | List["tf.Variable"] | def scope_vars(
scope: Union[str, "tf1.VariableScope"], trainable_only: bool = False
) -> List["tf.Variable"]:
"""Get variables inside a given scope.
Args:
scope: Scope in which the variables reside.
trainable_only: Whether or not to return only the variables that were
marked as... | Get variables inside a given scope.
Args:
scope: Scope in which the variables reside.
trainable_only: Whether or not to return only the variables that were
marked as trainable.
Returns:
The list of variables in the given `scope`.
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scope: Union[str, "tf1.VariableScope"], trainable_only: bool = False
) -> List["tf.Variable"]:
return tf1.get_collection(
tf1.GraphKeys.TRAINABLE_VARIABLES
if trainable_only
else tf1.GraphKeys.VARIABLES,
scope=scope if isinstance(scope, str) else scope.name,
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c644f3dc2a721aed08b6bfcf4c896db29d14f509 | kisuke95/ray | rllib/utils/tf_utils.py | [
"Apache-2.0"
] | Python | zero_logps_from_actions | TensorType | def zero_logps_from_actions(actions: TensorStructType) -> TensorType:
"""Helper function useful for returning dummy logp's (0) for some actions.
Args:
actions: The input actions. This can be any struct
of complex action components or a simple tensor of different
dimensions, e.g.... | Helper function useful for returning dummy logp's (0) for some actions.
Args:
actions: The input actions. This can be any struct
of complex action components or a simple tensor of different
dimensions, e.g. [B], [B, 2], or {"a": [B, 4, 5], "b": [B]}.
Returns:
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action_component = tree.flatten(actions)[0]
logp_ = tf.zeros_like(action_component, dtype=tf.float32)
while len(logp_.shape) > 1:
logp_ = logp_[:, 0]
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e5865db357e6cd67a3427854bb1fd24e2781e1a3 | kisuke95/ray | python/ray/workflow/common.py | [
"Apache-2.0"
] | Python | from_output | <not_specific> | def from_output(cls, step_id: str, output: Any):
"""Create static ref from given output."""
if not isinstance(output, cls):
if not isinstance(output, ray.ObjectRef):
output = ray.put(output)
output = cls(step_id=step_id, ref=output)
return output | Create static ref from given output. | Create static ref from given output. | [
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] | def from_output(cls, step_id: str, output: Any):
if not isinstance(output, cls):
if not isinstance(output, ray.ObjectRef):
output = ray.put(output)
output = cls(step_id=step_id, ref=output)
return output | [
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e5865db357e6cd67a3427854bb1fd24e2781e1a3 | kisuke95/ray | python/ray/workflow/common.py | [
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] | Python | run | Any | def run(
self,
workflow_id: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> Any:
"""Run a workflow.
If the workflow with the given id already exists, it will be resumed.
Examples:
>>> from ray import workflow
>>> Flight,... | Run a workflow.
If the workflow with the given id already exists, it will be resumed.
Examples:
>>> from ray import workflow
>>> Flight, Reservation, Trip = ... # doctest: +SKIP
>>> @workflow.step # doctest: +SKIP
... def book_flight(origin: str, dest: s... | Run a workflow.
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workflow_id: Optional[str] = None,
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e5865db357e6cd67a3427854bb1fd24e2781e1a3 | kisuke95/ray | python/ray/workflow/common.py | [
"Apache-2.0"
] | Python | run_async | ObjectRef | def run_async(
self,
workflow_id: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> ObjectRef:
"""Run a workflow asynchronously.
If the workflow with the given id already exists, it will be resumed.
Examples:
>>> from ray import workf... | Run a workflow asynchronously.
If the workflow with the given id already exists, it will be resumed.
Examples:
>>> from ray import workflow
>>> Flight, Reservation, Trip = ... # doctest: +SKIP
>>> @workflow.step # doctest: +SKIP
... def book_flight(origi... | Run a workflow asynchronously.
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self._step_id = None
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8bf6a5c267d25d8d0158ae61fdf26941f5683752 | kisuke95/ray | python/ray/tune/tune.py | [
"Apache-2.0"
] | Python | run_experiments | <not_specific> | def run_experiments(
experiments: Union[Experiment, Mapping, Sequence[Union[Experiment, Mapping]]],
scheduler: Optional[TrialScheduler] = None,
server_port: Optional[int] = None,
verbose: Union[int, Verbosity] = Verbosity.V3_TRIAL_DETAILS,
progress_reporter: Optional[ProgressReporter] = None,
re... | Runs and blocks until all trials finish.
Example:
>>> from ray.tune.experiment import Experiment
>>> from ray.tune.tune import run_experiments
>>> def my_func(config): return {"score": 0}
>>> experiment_spec = Experiment("experiment", my_func) # doctest: +SKIP
>>> run_experi... | Runs and blocks until all trials finish. | [
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experiments: Union[Experiment, Mapping, Sequence[Union[Experiment, Mapping]]],
scheduler: Optional[TrialScheduler] = None,
server_port: Optional[int] = None,
verbose: Union[int, Verbosity] = Verbosity.V3_TRIAL_DETAILS,
progress_reporter: Optional[ProgressReporter] = None,
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8bf6a5c267d25d8d0158ae61fdf26941f5683752 | kisuke95/ray | python/ray/tune/tune.py | [
"Apache-2.0"
] | Python | _ray_auto_init | null | def _ray_auto_init():
"""Initialize Ray unless already configured."""
if os.environ.get("TUNE_DISABLE_AUTO_INIT") == "1":
logger.info("'TUNE_DISABLE_AUTO_INIT=1' detected.")
elif not ray.is_initialized():
logger.info(
"Initializing Ray automatically."
"For cluster usa... | Initialize Ray unless already configured. | Initialize Ray unless already configured. | [
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] | def _ray_auto_init():
if os.environ.get("TUNE_DISABLE_AUTO_INIT") == "1":
logger.info("'TUNE_DISABLE_AUTO_INIT=1' detected.")
elif not ray.is_initialized():
logger.info(
"Initializing Ray automatically."
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} |
183ae73f437c0b8d47dbaefec115faaa7cf9a838 | kisuke95/ray | python/ray/tests/kuberay/utils.py | [
"Apache-2.0"
] | Python | wait_for_crd | <not_specific> | def wait_for_crd(crd_name: str, tries=60, backoff_s=5):
"""CRD creation can take a bit of time after the client request.
This function waits until the crd with the provided name is registered.
"""
for i in range(tries):
get_crd_output = subprocess.check_output(["kubectl", "get", "crd"]).decode()... | CRD creation can take a bit of time after the client request.
This function waits until the crd with the provided name is registered.
| CRD creation can take a bit of time after the client request.
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for i in range(tries):
get_crd_output = subprocess.check_output(["kubectl", "get", "crd"]).decode()
if crd_name in get_crd_output:
logger.info(f"Confirmed existence of CRD {crd_name}.")
return
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183ae73f437c0b8d47dbaefec115faaa7cf9a838 | kisuke95/ray | python/ray/tests/kuberay/utils.py | [
"Apache-2.0"
] | Python | wait_for_pods | None | def wait_for_pods(goal_num_pods: int, namespace: str, tries=60, backoff_s=5) -> None:
"""Wait for the number of pods in the `namespace` to be exactly `num_pods`.
Raise an exception after exceeding `tries` attempts with `backoff_s` second waits.
"""
for i in range(tries):
cur_num_pods = _get_nu... | Wait for the number of pods in the `namespace` to be exactly `num_pods`.
Raise an exception after exceeding `tries` attempts with `backoff_s` second waits.
| Wait for the number of pods in the `namespace` to be exactly `num_pods`.
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cur_num_pods = _get_num_pods(namespace)
if cur_num_pods == goal_num_pods:
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183ae73f437c0b8d47dbaefec115faaa7cf9a838 | kisuke95/ray | python/ray/tests/kuberay/utils.py | [
"Apache-2.0"
] | Python | wait_for_pod_to_start | None | def wait_for_pod_to_start(
pod_name_filter: str, namespace: str, tries=60, backoff_s=5
) -> None:
"""Waits for a pod to have Running status.phase.
More precisely, waits until there is a pod with name containing `pod_name_filter`
and the pod has Running status.phase."""
for i in range(tries):
... | Waits for a pod to have Running status.phase.
More precisely, waits until there is a pod with name containing `pod_name_filter`
and the pod has Running status.phase. | Waits for a pod to have Running status.phase.
More precisely, waits until there is a pod with name containing `pod_name_filter`
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pod = get_pod(pod_name_filter=pod_name_filter, namespace=namespace)
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pod_status = (
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183ae73f437c0b8d47dbaefec115faaa7cf9a838 | kisuke95/ray | python/ray/tests/kuberay/utils.py | [
"Apache-2.0"
] | Python | wait_for_ray_health | None | def wait_for_ray_health(
pod_name_filter: str,
namespace: str,
tries=60,
backoff_s=5,
ray_container="ray-head",
) -> None:
"""Waits until a Ray pod passes `ray health-check`.
More precisely, waits until a Ray pod whose name includes the string
`pod_name_filter` passes `ray health-check`... | Waits until a Ray pod passes `ray health-check`.
More precisely, waits until a Ray pod whose name includes the string
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(Ensures Ray has completely started in the pod.)
Use case: Wait until there is a Ray head pod with Ray running on it.
| Waits until a Ray pod passes `ray health-check`.
More precisely, waits until a Ray pod whose name includes the string
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183ae73f437c0b8d47dbaefec115faaa7cf9a838 | kisuke95/ray | python/ray/tests/kuberay/utils.py | [
"Apache-2.0"
] | Python | kubectl_exec | str | def kubectl_exec(
command: List[str],
pod: str,
namespace: str,
container: Optional[str] = None,
) -> str:
"""kubectl exec the `command` in the given `pod` in the given `namespace`.
If a `container` is specified, will specify that container for kubectl.
Prints and return kubectl's output as... | kubectl exec the `command` in the given `pod` in the given `namespace`.
If a `container` is specified, will specify that container for kubectl.
Prints and return kubectl's output as a string.
|
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command: List[str],
pod: str,
namespace: str,
container: Optional[str] = None,
) -> str:
container_option = ["-c", container] if container else []
kubectl_exec_command = (
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df0d953c7d46beec063613326818e024cd061fe2 | kisuke95/ray | dashboard/state_aggregator.py | [
"Apache-2.0"
] | Python | list_actors | dict | async def list_actors(self, *, option: ListApiOptions) -> dict:
"""List all actor information from the cluster.
Returns:
{actor_id -> actor_data_in_dict}
actor_data_in_dict's schema is in ActorState
"""
reply = await self._client.get_all_actor_info(timeout=option... | List all actor information from the cluster.
Returns:
{actor_id -> actor_data_in_dict}
actor_data_in_dict's schema is in ActorState
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result = []
for message in reply.actor_table_data:
data = self._message_to_dict(message=message, fields_to_decode=["actor_id"])
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df0d953c7d46beec063613326818e024cd061fe2 | kisuke95/ray | dashboard/state_aggregator.py | [
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"""List all placement group information from the cluster.
Returns:
{pg_id -> pg_data_in_dict}
pg_data_in_dict's schema is in PlacementGroupState
"""
reply = await self._client.get_all_place... | List all placement group information from the cluster.
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result = []
for message in reply.placement_group_table_data:
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] | [
{
"param": "self",
"type": null
},
{
"param": "option",
"type": "ListApiOptions"
}
] | {
"returns": [
{
"docstring": "{pg_id -> pg_data_in_dict}\npg_data_in_dict's schema is in PlacementGroupState",
"docstring_tokens": [
"{",
"pg_id",
"-",
">",
"pg_data_in_dict",
"}",
"pg_data_in_dict",
"'",
"s",
"schema",
... |
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