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train | Reporter.run | Run the reporter. | python/ray/reporter.py | def run(self):
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pass
time.sleep(ray_constants.REPORTER_UPDATE_INTERVAL_MS / 1000) | def run(self):
"""Run the reporter."""
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train | is_named_tuple | Return True if cls is a namedtuple and False otherwise. | python/ray/serialization.py | def is_named_tuple(cls):
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train | register_trainable | Register a trainable function or class.
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train | register_env | Register a custom environment for use with RLlib.
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train | get_learner_stats | Return optimization stats reported from the policy graph.
Example:
>>> grad_info = evaluator.learn_on_batch(samples)
>>> print(get_stats(grad_info))
{"vf_loss": ..., "policy_loss": ...} | python/ray/rllib/evaluation/metrics.py | def get_learner_stats(grad_info):
"""Return optimization stats reported from the policy graph.
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>>> print(get_stats(grad_info))
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train | collect_metrics | Gathers episode metrics from PolicyEvaluator instances. | python/ray/rllib/evaluation/metrics.py | def collect_metrics(local_evaluator=None,
remote_evaluators=[],
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"""Gathers episode metrics from PolicyEvaluator instances."""
episodes, num_dropped = collect_episodes(
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"""Gathers episode metrics from PolicyEvaluator instances."""
episodes, num_dropped = collect_episodes(
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train | collect_episodes | Gathers new episodes metrics tuples from the given evaluators. | python/ray/rllib/evaluation/metrics.py | def collect_episodes(local_evaluator=None,
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train | summarize_episodes | Summarizes a set of episode metrics tuples.
Arguments:
episodes: smoothed set of episodes including historical ones
new_episodes: just the new episodes in this iteration
num_dropped: number of workers haven't returned their metrics | python/ray/rllib/evaluation/metrics.py | def summarize_episodes(episodes, new_episodes, num_dropped):
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Arguments:
episodes: smoothed set of episodes including historical ones
new_episodes: just the new episodes in this iteration
num_dropped: number of workers haven't returned their... | def summarize_episodes(episodes, new_episodes, num_dropped):
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train | _partition | Divides metrics data into true rollouts vs off-policy estimates. | python/ray/rllib/evaluation/metrics.py | def _partition(episodes):
"""Divides metrics data into true rollouts vs off-policy estimates."""
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train | TrialExecutor.set_status | Sets status and checkpoints metadata if needed.
Only checkpoints metadata if trial status is a terminal condition.
PENDING, PAUSED, and RUNNING switches have checkpoints taken care of
in the TrialRunner.
Args:
trial (Trial): Trial to checkpoint.
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Only checkpoints metadata if trial status is a terminal condition.
PENDING, PAUSED, and RUNNING switches have checkpoints taken care of
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train | TrialExecutor.try_checkpoint_metadata | Checkpoints metadata.
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trial (Trial): Trial to checkpoint. | python/ray/tune/trial_executor.py | def try_checkpoint_metadata(self, trial):
"""Checkpoints metadata.
Args:
trial (Trial): Trial to checkpoint.
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"""Checkpoints metadata.
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trial (Trial): Trial to checkpoint.
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train | TrialExecutor.pause_trial | Pauses the trial.
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experiment. This results in PAUSED state that similar to TERMINATED. | python/ray/tune/trial_executor.py | def pause_trial(self, trial):
"""Pauses the trial.
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train | TrialExecutor.unpause_trial | Sets PAUSED trial to pending to allow scheduler to start. | python/ray/tune/trial_executor.py | def unpause_trial(self, trial):
"""Sets PAUSED trial to pending to allow scheduler to start."""
assert trial.status == Trial.PAUSED, trial.status
self.set_status(trial, Trial.PENDING) | def unpause_trial(self, trial):
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train | TrialExecutor.resume_trial | Resumes PAUSED trials. This is a blocking call. | python/ray/tune/trial_executor.py | def resume_trial(self, trial):
"""Resumes PAUSED trials. This is a blocking call."""
assert trial.status == Trial.PAUSED, trial.status
self.start_trial(trial) | def resume_trial(self, trial):
"""Resumes PAUSED trials. This is a blocking call."""
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train | NevergradSearch.on_trial_complete | Passes the result to Nevergrad unless early terminated or errored.
The result is internally negated when interacting with Nevergrad
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trial_id,
result=None,
error=False,
early_terminated=False):
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train | ImportThread.start | Start the import thread. | python/ray/import_thread.py | def start(self):
"""Start the import thread."""
self.t = threading.Thread(target=self._run, name="ray_import_thread")
# Making the thread a daemon causes it to exit
# when the main thread exits.
self.t.daemon = True
self.t.start() | def start(self):
"""Start the import thread."""
self.t = threading.Thread(target=self._run, name="ray_import_thread")
# Making the thread a daemon causes it to exit
# when the main thread exits.
self.t.daemon = True
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train | ImportThread._process_key | Process the given export key from redis. | python/ray/import_thread.py | def _process_key(self, key):
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if self.mode != ray.WORKER_MODE:
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train | ImportThread.fetch_and_execute_function_to_run | Run on arbitrary function on the worker. | python/ray/import_thread.py | def fetch_and_execute_function_to_run(self, key):
"""Run on arbitrary function on the worker."""
(driver_id, serialized_function,
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train | clip_action | Called to clip actions to the specified range of this policy.
Arguments:
action: Single action.
space: Action space the actions should be present in.
Returns:
Clipped batch of actions. | python/ray/rllib/evaluation/policy_graph.py | def clip_action(action, space):
"""Called to clip actions to the specified range of this policy.
Arguments:
action: Single action.
space: Action space the actions should be present in.
Returns:
Clipped batch of actions.
"""
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"""Called to clip actions to the specified range of this policy.
Arguments:
action: Single action.
space: Action space the actions should be present in.
Returns:
Clipped batch of actions.
"""
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train | SkOptSearch.on_trial_complete | Passes the result to skopt unless early terminated or errored.
The result is internally negated when interacting with Skopt
so that Skopt Optimizers can "maximize" this value,
as it minimizes on default. | python/ray/tune/suggest/skopt.py | def on_trial_complete(self,
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result=None,
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early_terminated=False):
"""Passes the result to skopt unless early terminated or errored.
The result is internally negated when intera... | def on_trial_complete(self,
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train | address_to_ip | Convert a hostname to a numerical IP addresses in an address.
This should be a no-op if address already contains an actual numerical IP
address.
Args:
address: This can be either a string containing a hostname (or an IP
address) and a port or it can be just an IP address.
Returns:... | python/ray/services.py | def address_to_ip(address):
"""Convert a hostname to a numerical IP addresses in an address.
This should be a no-op if address already contains an actual numerical IP
address.
Args:
address: This can be either a string containing a hostname (or an IP
address) and a port or it can b... | def address_to_ip(address):
"""Convert a hostname to a numerical IP addresses in an address.
This should be a no-op if address already contains an actual numerical IP
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Args:
address: This can be either a string containing a hostname (or an IP
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train | get_node_ip_address | Determine the IP address of the local node.
Args:
address (str): The IP address and port of any known live service on the
network you care about.
Returns:
The IP address of the current node. | python/ray/services.py | def get_node_ip_address(address="8.8.8.8:53"):
"""Determine the IP address of the local node.
Args:
address (str): The IP address and port of any known live service on the
network you care about.
Returns:
The IP address of the current node.
"""
ip_address, port = addres... | def get_node_ip_address(address="8.8.8.8:53"):
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Args:
address (str): The IP address and port of any known live service on the
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Returns:
The IP address of the current node.
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train | create_redis_client | Create a Redis client.
Args:
The IP address, port, and password of the Redis server.
Returns:
A Redis client. | python/ray/services.py | def create_redis_client(redis_address, password=None):
"""Create a Redis client.
Args:
The IP address, port, and password of the Redis server.
Returns:
A Redis client.
"""
redis_ip_address, redis_port = redis_address.split(":")
# For this command to work, some other client (on ... | def create_redis_client(redis_address, password=None):
"""Create a Redis client.
Args:
The IP address, port, and password of the Redis server.
Returns:
A Redis client.
"""
redis_ip_address, redis_port = redis_address.split(":")
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train | start_ray_process | Start one of the Ray processes.
TODO(rkn): We need to figure out how these commands interact. For example,
it may only make sense to start a process in gdb if we also start it in
tmux. Similarly, certain combinations probably don't make sense, like
simultaneously running the process in valgrind and the... | python/ray/services.py | def start_ray_process(command,
process_type,
env_updates=None,
cwd=None,
use_valgrind=False,
use_gdb=False,
use_valgrind_profiler=False,
use_perftools_profiler=False,... | def start_ray_process(command,
process_type,
env_updates=None,
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use_valgrind_profiler=False,
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train | wait_for_redis_to_start | Wait for a Redis server to be available.
This is accomplished by creating a Redis client and sending a random
command to the server until the command gets through.
Args:
redis_ip_address (str): The IP address of the redis server.
redis_port (int): The port of the redis server.
pass... | python/ray/services.py | def wait_for_redis_to_start(redis_ip_address,
redis_port,
password=None,
num_retries=5):
"""Wait for a Redis server to be available.
This is accomplished by creating a Redis client and sending a random
command to the server... | def wait_for_redis_to_start(redis_ip_address,
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password=None,
num_retries=5):
"""Wait for a Redis server to be available.
This is accomplished by creating a Redis client and sending a random
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train | _autodetect_num_gpus | Attempt to detect the number of GPUs on this machine.
TODO(rkn): This currently assumes Nvidia GPUs and Linux.
Returns:
The number of GPUs if any were detected, otherwise 0. | python/ray/services.py | def _autodetect_num_gpus():
"""Attempt to detect the number of GPUs on this machine.
TODO(rkn): This currently assumes Nvidia GPUs and Linux.
Returns:
The number of GPUs if any were detected, otherwise 0.
"""
proc_gpus_path = "/proc/driver/nvidia/gpus"
if os.path.isdir(proc_gpus_path):... | def _autodetect_num_gpus():
"""Attempt to detect the number of GPUs on this machine.
TODO(rkn): This currently assumes Nvidia GPUs and Linux.
Returns:
The number of GPUs if any were detected, otherwise 0.
"""
proc_gpus_path = "/proc/driver/nvidia/gpus"
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train | _compute_version_info | Compute the versions of Python, pyarrow, and Ray.
Returns:
A tuple containing the version information. | python/ray/services.py | def _compute_version_info():
"""Compute the versions of Python, pyarrow, and Ray.
Returns:
A tuple containing the version information.
"""
ray_version = ray.__version__
python_version = ".".join(map(str, sys.version_info[:3]))
pyarrow_version = pyarrow.__version__
return ray_version... | def _compute_version_info():
"""Compute the versions of Python, pyarrow, and Ray.
Returns:
A tuple containing the version information.
"""
ray_version = ray.__version__
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train | check_version_info | Check if various version info of this process is correct.
This will be used to detect if workers or drivers are started using
different versions of Python, pyarrow, or Ray. If the version
information is not present in Redis, then no check is done.
Args:
redis_client: A client for the primary R... | python/ray/services.py | def check_version_info(redis_client):
"""Check if various version info of this process is correct.
This will be used to detect if workers or drivers are started using
different versions of Python, pyarrow, or Ray. If the version
information is not present in Redis, then no check is done.
Args:
... | def check_version_info(redis_client):
"""Check if various version info of this process is correct.
This will be used to detect if workers or drivers are started using
different versions of Python, pyarrow, or Ray. If the version
information is not present in Redis, then no check is done.
Args:
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train | start_redis | Start the Redis global state store.
Args:
node_ip_address: The IP address of the current node. This is only used
for recording the log filenames in Redis.
redirect_files: The list of (stdout, stderr) file pairs.
port (int): If provided, the primary Redis shard will be started on... | python/ray/services.py | def start_redis(node_ip_address,
redirect_files,
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redis_shard_ports=None,
num_redis_shards=1,
redis_max_clients=None,
redirect_worker_output=False,
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train | _start_redis_instance | Start a single Redis server.
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only once. Otherwise, random ports will be used and the maximum
retries count is "num_retries".
Args:
executable (str): Full path of the redis-server executable.
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modules,
port=None,
redis_max_clients=None,
num_retries=20,
stdout_file=None,
stderr_file=None,
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train | start_log_monitor | Start a log monitor process.
Args:
redis_address (str): The address of the Redis instance.
logs_dir (str): The directory of logging files.
stdout_file: A file handle opened for writing to redirect stdout to. If
no redirection should happen, then this should be None.
stde... | python/ray/services.py | def start_log_monitor(redis_address,
logs_dir,
stdout_file=None,
stderr_file=None,
redis_password=None):
"""Start a log monitor process.
Args:
redis_address (str): The address of the Redis instance.
logs_dir... | def start_log_monitor(redis_address,
logs_dir,
stdout_file=None,
stderr_file=None,
redis_password=None):
"""Start a log monitor process.
Args:
redis_address (str): The address of the Redis instance.
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train | start_reporter | Start a reporter process.
Args:
redis_address (str): The address of the Redis instance.
stdout_file: A file handle opened for writing to redirect stdout to. If
no redirection should happen, then this should be None.
stderr_file: A file handle opened for writing to redirect stder... | python/ray/services.py | def start_reporter(redis_address,
stdout_file=None,
stderr_file=None,
redis_password=None):
"""Start a reporter process.
Args:
redis_address (str): The address of the Redis instance.
stdout_file: A file handle opened for writing to redire... | def start_reporter(redis_address,
stdout_file=None,
stderr_file=None,
redis_password=None):
"""Start a reporter process.
Args:
redis_address (str): The address of the Redis instance.
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train | start_dashboard | Start a dashboard process.
Args:
redis_address (str): The address of the Redis instance.
temp_dir (str): The temporary directory used for log files and
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stdout_file: A file handle opened for writing to redirect stdout to. If
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temp_dir,
stdout_file=None,
stderr_file=None,
redis_password=None):
"""Start a dashboard process.
Args:
redis_address (str): The address of the Redis instance.
temp_dir (str): The ... | def start_dashboard(redis_address,
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stdout_file=None,
stderr_file=None,
redis_password=None):
"""Start a dashboard process.
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redis_address (str): The address of the Redis instance.
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train | check_and_update_resources | Sanity check a resource dictionary and add sensible defaults.
Args:
num_cpus: The number of CPUs.
num_gpus: The number of GPUs.
resources: A dictionary mapping resource names to resource quantities.
Returns:
A new resource dictionary. | python/ray/services.py | def check_and_update_resources(num_cpus, num_gpus, resources):
"""Sanity check a resource dictionary and add sensible defaults.
Args:
num_cpus: The number of CPUs.
num_gpus: The number of GPUs.
resources: A dictionary mapping resource names to resource quantities.
Returns:
... | def check_and_update_resources(num_cpus, num_gpus, resources):
"""Sanity check a resource dictionary and add sensible defaults.
Args:
num_cpus: The number of CPUs.
num_gpus: The number of GPUs.
resources: A dictionary mapping resource names to resource quantities.
Returns:
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train | start_raylet | Start a raylet, which is a combined local scheduler and object manager.
Args:
redis_address (str): The address of the primary Redis server.
node_ip_address (str): The IP address of this node.
raylet_name (str): The name of the raylet socket to create.
plasma_store_name (str): The na... | python/ray/services.py | def start_raylet(redis_address,
node_ip_address,
raylet_name,
plasma_store_name,
worker_path,
temp_dir,
num_cpus=None,
num_gpus=None,
resources=None,
object_manager_po... | def start_raylet(redis_address,
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num_cpus=None,
num_gpus=None,
resources=None,
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train | build_java_worker_command | This method assembles the command used to start a Java worker.
Args:
java_worker_options (str): The command options for Java worker.
redis_address (str): Redis address of GCS.
plasma_store_name (str): The name of the plasma store socket to connect
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raylet_name (str): T... | python/ray/services.py | def build_java_worker_command(
java_worker_options,
redis_address,
plasma_store_name,
raylet_name,
redis_password,
temp_dir,
):
"""This method assembles the command used to start a Java worker.
Args:
java_worker_options (str): The command options for Java... | def build_java_worker_command(
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redis_address,
plasma_store_name,
raylet_name,
redis_password,
temp_dir,
):
"""This method assembles the command used to start a Java worker.
Args:
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train | determine_plasma_store_config | Figure out how to configure the plasma object store.
This will determine which directory to use for the plasma store (e.g.,
/tmp or /dev/shm) and how much memory to start the store with. On Linux,
we will try to use /dev/shm unless the shared memory file system is too
small, in which case we will fall ... | python/ray/services.py | def determine_plasma_store_config(object_store_memory=None,
plasma_directory=None,
huge_pages=False):
"""Figure out how to configure the plasma object store.
This will determine which directory to use for the plasma store (e.g.,
/tmp or /d... | def determine_plasma_store_config(object_store_memory=None,
plasma_directory=None,
huge_pages=False):
"""Figure out how to configure the plasma object store.
This will determine which directory to use for the plasma store (e.g.,
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train | _start_plasma_store | Start a plasma store process.
Args:
plasma_store_memory (int): The amount of memory in bytes to start the
plasma store with.
use_valgrind (bool): True if the plasma store should be started inside
of valgrind. If this is True, use_profiler must be False.
use_profiler ... | python/ray/services.py | def _start_plasma_store(plasma_store_memory,
use_valgrind=False,
use_profiler=False,
stdout_file=None,
stderr_file=None,
plasma_directory=None,
huge_pages=False,
... | def _start_plasma_store(plasma_store_memory,
use_valgrind=False,
use_profiler=False,
stdout_file=None,
stderr_file=None,
plasma_directory=None,
huge_pages=False,
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train | start_plasma_store | This method starts an object store process.
Args:
stdout_file: A file handle opened for writing to redirect stdout
to. If no redirection should happen, then this should be None.
stderr_file: A file handle opened for writing to redirect stderr
to. If no redirection should hap... | python/ray/services.py | def start_plasma_store(stdout_file=None,
stderr_file=None,
object_store_memory=None,
plasma_directory=None,
huge_pages=False,
plasma_store_socket_name=None):
"""This method starts an object store proce... | def start_plasma_store(stdout_file=None,
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train | start_worker | This method starts a worker process.
Args:
node_ip_address (str): The IP address of the node that this worker is
running on.
object_store_name (str): The socket name of the object store.
raylet_name (str): The socket name of the raylet server.
redis_address (str): The ad... | python/ray/services.py | def start_worker(node_ip_address,
object_store_name,
raylet_name,
redis_address,
worker_path,
temp_dir,
stdout_file=None,
stderr_file=None):
"""This method starts a worker process.
Args:
... | def start_worker(node_ip_address,
object_store_name,
raylet_name,
redis_address,
worker_path,
temp_dir,
stdout_file=None,
stderr_file=None):
"""This method starts a worker process.
Args:
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train | start_monitor | Run a process to monitor the other processes.
Args:
redis_address (str): The address that the Redis server is listening on.
stdout_file: A file handle opened for writing to redirect stdout to. If
no redirection should happen, then this should be None.
stderr_file: A file handle ... | python/ray/services.py | def start_monitor(redis_address,
stdout_file=None,
stderr_file=None,
autoscaling_config=None,
redis_password=None):
"""Run a process to monitor the other processes.
Args:
redis_address (str): The address that the Redis server is li... | def start_monitor(redis_address,
stdout_file=None,
stderr_file=None,
autoscaling_config=None,
redis_password=None):
"""Run a process to monitor the other processes.
Args:
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train | start_raylet_monitor | Run a process to monitor the other processes.
Args:
redis_address (str): The address that the Redis server is listening on.
stdout_file: A file handle opened for writing to redirect stdout to. If
no redirection should happen, then this should be None.
stderr_file: A file handle ... | python/ray/services.py | def start_raylet_monitor(redis_address,
stdout_file=None,
stderr_file=None,
redis_password=None,
config=None):
"""Run a process to monitor the other processes.
Args:
redis_address (str): The address that... | def start_raylet_monitor(redis_address,
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train | restore_original_dimensions | Unpacks Dict and Tuple space observations into their original form.
This is needed since we flatten Dict and Tuple observations in transit.
Before sending them to the model though, we should unflatten them into
Dicts or Tuples of tensors.
Arguments:
obs: The flattened observation tensor.
... | python/ray/rllib/models/model.py | def restore_original_dimensions(obs, obs_space, tensorlib=tf):
"""Unpacks Dict and Tuple space observations into their original form.
This is needed since we flatten Dict and Tuple observations in transit.
Before sending them to the model though, we should unflatten them into
Dicts or Tuples of tensors... | def restore_original_dimensions(obs, obs_space, tensorlib=tf):
"""Unpacks Dict and Tuple space observations into their original form.
This is needed since we flatten Dict and Tuple observations in transit.
Before sending them to the model though, we should unflatten them into
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train | _unpack_obs | Unpack a flattened Dict or Tuple observation array/tensor.
Arguments:
obs: The flattened observation tensor
space: The original space prior to flattening
tensorlib: The library used to unflatten (reshape) the array/tensor | python/ray/rllib/models/model.py | def _unpack_obs(obs, space, tensorlib=tf):
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Arguments:
obs: The flattened observation tensor
space: The original space prior to flattening
tensorlib: The library used to unflatten (reshape) the array/tensor
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Arguments:
obs: The flattened observation tensor
space: The original space prior to flattening
tensorlib: The library used to unflatten (reshape) the array/tensor
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train | to_aws_format | Convert the Ray node name tag to the AWS-specific 'Name' tag. | python/ray/autoscaler/aws/node_provider.py | def to_aws_format(tags):
"""Convert the Ray node name tag to the AWS-specific 'Name' tag."""
if TAG_RAY_NODE_NAME in tags:
tags["Name"] = tags[TAG_RAY_NODE_NAME]
del tags[TAG_RAY_NODE_NAME]
return tags | def to_aws_format(tags):
"""Convert the Ray node name tag to the AWS-specific 'Name' tag."""
if TAG_RAY_NODE_NAME in tags:
tags["Name"] = tags[TAG_RAY_NODE_NAME]
del tags[TAG_RAY_NODE_NAME]
return tags | [
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train | AWSNodeProvider._node_tag_update_loop | Update the AWS tags for a cluster periodically.
The purpose of this loop is to avoid excessive EC2 calls when a large
number of nodes are being launched simultaneously. | python/ray/autoscaler/aws/node_provider.py | def _node_tag_update_loop(self):
""" Update the AWS tags for a cluster periodically.
The purpose of this loop is to avoid excessive EC2 calls when a large
number of nodes are being launched simultaneously.
"""
while True:
self.tag_cache_update_event.wait()
... | def _node_tag_update_loop(self):
""" Update the AWS tags for a cluster periodically.
The purpose of this loop is to avoid excessive EC2 calls when a large
number of nodes are being launched simultaneously.
"""
while True:
self.tag_cache_update_event.wait()
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train | AWSNodeProvider._get_node | Refresh and get info for this node, updating the cache. | python/ray/autoscaler/aws/node_provider.py | def _get_node(self, node_id):
"""Refresh and get info for this node, updating the cache."""
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train | ExportFormat.validate | Validates export_formats.
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"""Validates export_formats.
Raises:
ValueError if the format is unknown.
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train | Trial.init_logger | Init logger. | python/ray/tune/trial.py | def init_logger(self):
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train | Trial.update_resources | EXPERIMENTAL: Updates the resource requirements.
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Raises:
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train | Trial.should_stop | Whether the given result meets this trial's stopping criteria. | python/ray/tune/trial.py | def should_stop(self, result):
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train | Trial.should_checkpoint | Whether this trial is due for checkpointing. | python/ray/tune/trial.py | def should_checkpoint(self):
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result = self.last_result or {}
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train | Trial.progress_string | Returns a progress message for printing out to the console. | python/ray/tune/trial.py | def progress_string(self):
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if not self.last_result:
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def location_string(hostname, pid):
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train | Trial.should_recover | Returns whether the trial qualifies for restoring.
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max_failures. This may return true even when there may not yet
be a checkpoint. | python/ray/tune/trial.py | def should_recover(self):
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This is if a checkpoint frequency is set and has not failed more than
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"""
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train | Trial.compare_checkpoints | Compares two checkpoints based on the attribute attr_mean param.
Greater than is used by default. If command-line parameter
checkpoint_score_attr starts with "min-" less than is used.
Arguments:
attr_mean: mean of attribute value for the current checkpoint
Returns:
... | python/ray/tune/trial.py | def compare_checkpoints(self, attr_mean):
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Arguments:
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train | preprocess | Preprocess 210x160x3 uint8 frame into 6400 (80x80) 1D float vector. | examples/rl_pong/driver.py | def preprocess(img):
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train | discount_rewards | take 1D float array of rewards and compute discounted reward | examples/rl_pong/driver.py | def discount_rewards(r):
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train | policy_backward | backward pass. (eph is array of intermediate hidden states) | examples/rl_pong/driver.py | def policy_backward(eph, epx, epdlogp, model):
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dh[eph <= 0] = 0
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train | load_class | Load a class at runtime given a full path.
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train | NodeProvider.terminate_nodes | Terminates a set of nodes. May be overridden with a batch method. | python/ray/autoscaler/node_provider.py | def terminate_nodes(self, node_ids):
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for node_id in node_ids:
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train | get_wrapper_by_cls | Returns the gym env wrapper of the given class, or None. | python/ray/rllib/env/atari_wrappers.py | def get_wrapper_by_cls(env, cls):
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train | wrap_deepmind | Configure environment for DeepMind-style Atari.
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train | valid_padding | Note: Padding is added to match TF conv2d `same` padding. See
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train | aligned_array | Returns an array of a given size that is 64-byte aligned.
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train | Queue.put | Adds an item to the queue.
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Returns:
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train | override | Annotation for documenting method overrides.
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train | HyperBandScheduler._cur_band_filled | Checks if the current band is filled.
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This scheduler will not start trials but will stop trials.
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This scheduler will not start trials but will stop trials... | def on_trial_result(self, trial_runner, trial, result):
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train | HyperBandScheduler.on_trial_remove | Notification when trial terminates.
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train | Bracket.add_trial | Add trial to bracket assuming bracket is not filled.
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train | Bracket.cur_iter_done | Checks if all iterations have completed.
TODO(rliaw): also check that `t.iterations == self._r` | python/ray/tune/schedulers/hyperband.py | def cur_iter_done(self):
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in and make sure they're not set as pending later. | python/ray/tune/schedulers/hyperband.py | def update_trial_stats(self, trial, result):
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TODO(rliaw): The other alternative is to keep the trials
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assert trial in... | def update_trial_stats(self, trial, result):
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train | Bracket.cleanup_full | Cleans up bracket after bracket is completely finished.
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train | parse_client_table | Read the client table.
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redis_client: A client to the primary Redis shard.
Returns:
A list of information about the nodes in the cluster. | python/ray/experimental/state.py | def parse_client_table(redis_client):
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Args:
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Returns:
A list of information about the nodes in the cluster.
"""
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redis_client: A client to the primary Redis shard.
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A list of information about the nodes in the cluster.
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key: The object ID or the task ID that the query is about.
args: The command to run.
Returns:
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key: The object ID or the task ID that the query is about.
args: The command to run.
Returns:
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... | def _execute_command(self, key, *args):
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args: The command to run.
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train | GlobalState._keys | Execute the KEYS command on all Redis shards.
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pattern: The KEYS pattern to query.
Returns:
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result = []
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train | GlobalState._object_table | Fetch and parse the object table information for a single object ID.
Args:
object_id: An object ID to get information about.
Returns:
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Args:
object_id: An object ID to get information about.
Returns:
A dictionary with information about the object ID in question.
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train | GlobalState.object_table | Fetch and parse the object table info for one or more object IDs.
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object_id: An object ID to fetch information about. If this is
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Returns:
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train | GlobalState._task_table | Fetch and parse the task table information for a single task ID.
Args:
task_id: A task ID to get information about.
Returns:
A dictionary with information about the task ID in question. | python/ray/experimental/state.py | def _task_table(self, task_id):
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Args:
task_id: A task ID to get information about.
Returns:
A dictionary with information about the task ID in question.
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train | GlobalState.task_table | Fetch and parse the task table information for one or more task IDs.
Args:
task_id: A hex string of the task ID to fetch information about. If
this is None, then the task object table is fetched.
Returns:
Information from the task table. | python/ray/experimental/state.py | def task_table(self, task_id=None):
"""Fetch and parse the task table information for one or more task IDs.
Args:
task_id: A hex string of the task ID to fetch information about. If
this is None, then the task object table is fetched.
Returns:
Informatio... | def task_table(self, task_id=None):
"""Fetch and parse the task table information for one or more task IDs.
Args:
task_id: A hex string of the task ID to fetch information about. If
this is None, then the task object table is fetched.
Returns:
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train | GlobalState.function_table | Fetch and parse the function table.
Returns:
A dictionary that maps function IDs to information about the
function. | python/ray/experimental/state.py | def function_table(self, function_id=None):
"""Fetch and parse the function table.
Returns:
A dictionary that maps function IDs to information about the
function.
"""
self._check_connected()
function_table_keys = self.redis_client.keys(
ra... | def function_table(self, function_id=None):
"""Fetch and parse the function table.
Returns:
A dictionary that maps function IDs to information about the
function.
"""
self._check_connected()
function_table_keys = self.redis_client.keys(
ra... | [
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] | ray-project/ray | python | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/experimental/state.py#L367-L386 | [
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train | GlobalState._profile_table | Get the profile events for a given batch of profile events.
Args:
batch_id: An identifier for a batch of profile events.
Returns:
A list of the profile events for the specified batch. | python/ray/experimental/state.py | def _profile_table(self, batch_id):
"""Get the profile events for a given batch of profile events.
Args:
batch_id: An identifier for a batch of profile events.
Returns:
A list of the profile events for the specified batch.
"""
# TODO(rkn): This method sh... | def _profile_table(self, batch_id):
"""Get the profile events for a given batch of profile events.
Args:
batch_id: An identifier for a batch of profile events.
Returns:
A list of the profile events for the specified batch.
"""
# TODO(rkn): This method sh... | [
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train | GlobalState.chrome_tracing_dump | 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... | python/ray/experimental/state.py | 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... | 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... | [
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] | ray-project/ray | python | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/experimental/state.py#L528-L596 | [
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... | 4eade036a0505e244c976f36aaa2d64386b5129b |
train | GlobalState.chrome_tracing_object_transfer_dump | 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 in the Chrome web browser and load the dumped file.
Make sure to ... | python/ray/experimental/state.py | 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 ... | 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 ... | [
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] | ray-project/ray | python | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/experimental/state.py#L598-L687 | [
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train | GlobalState.workers | Get a dictionary mapping worker ID to worker information. | python/ray/experimental/state.py | def workers(self):
"""Get a dictionary mapping worker ID to worker information."""
worker_keys = self.redis_client.keys("Worker*")
workers_data = {}
for worker_key in worker_keys:
worker_info = self.redis_client.hgetall(worker_key)
worker_id = binary_to_hex(worke... | def workers(self):
"""Get a dictionary mapping worker ID to worker information."""
worker_keys = self.redis_client.keys("Worker*")
workers_data = {}
for worker_key in worker_keys:
worker_info = self.redis_client.hgetall(worker_key)
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] | ray-project/ray | python | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/experimental/state.py#L689-L709 | [
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"... | 4eade036a0505e244c976f36aaa2d64386b5129b |
train | GlobalState.cluster_resources | Get the current total cluster resources.
Note that this information can grow stale as nodes are added to or
removed from the cluster.
Returns:
A dictionary mapping resource name to the total quantity of that
resource in the cluster. | python/ray/experimental/state.py | def cluster_resources(self):
"""Get the current total cluster resources.
Note that this information can grow stale as nodes are added to or
removed from the cluster.
Returns:
A dictionary mapping resource name to the total quantity of that
resource in the cl... | def cluster_resources(self):
"""Get the current total cluster resources.
Note that this information can grow stale as nodes are added to or
removed from the cluster.
Returns:
A dictionary mapping resource name to the total quantity of that
resource in the cl... | [
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] | ray-project/ray | python | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/experimental/state.py#L747-L765 | [
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... | 4eade036a0505e244c976f36aaa2d64386b5129b |
train | GlobalState.available_resources | Get the current available cluster resources.
This is different from `cluster_resources` in that this will return
idle (available) resources rather than total resources.
Note that this information can grow stale as tasks start and finish.
Returns:
A dictionary mapping resou... | python/ray/experimental/state.py | def available_resources(self):
"""Get the current available cluster resources.
This is different from `cluster_resources` in that this will return
idle (available) resources rather than total resources.
Note that this information can grow stale as tasks start and finish.
Retur... | def available_resources(self):
"""Get the current available cluster resources.
This is different from `cluster_resources` in that this will return
idle (available) resources rather than total resources.
Note that this information can grow stale as tasks start and finish.
Retur... | [
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train | GlobalState._error_messages | Get the error messages for a specific driver.
Args:
driver_id: The ID of the driver to get the errors for.
Returns:
A list of the error messages for this driver. | python/ray/experimental/state.py | def _error_messages(self, driver_id):
"""Get the error messages for a specific driver.
Args:
driver_id: The ID of the driver to get the errors for.
Returns:
A list of the error messages for this driver.
"""
assert isinstance(driver_id, ray.DriverID)
... | def _error_messages(self, driver_id):
"""Get the error messages for a specific driver.
Args:
driver_id: The ID of the driver to get the errors for.
Returns:
A list of the error messages for this driver.
"""
assert isinstance(driver_id, ray.DriverID)
... | [
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] | ray-project/ray | python | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/experimental/state.py#L843-L874 | [
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... | 4eade036a0505e244c976f36aaa2d64386b5129b |
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