def _handle_failure(self, error):
logger.exception("Error in monitor loop")
if self.autoscaler is not None and \
os.environ.get("RAY_AUTOSCALER_FATESHARE_WORKERS", "") == "1":
self.autoscaler.kill_workers()
# Take down autoscaler workers if necessary.
self.destroy_autoscaler_workers()
# Something went wrong, so push an error to all current and future
# drivers.
message = f"The autoscaler failed with the following error:\n{error}"
if _internal_kv_initialized():
_internal_kv_put(DEBUG_AUTOSCALING_ERROR, message, overwrite=True)
redis_client = ray._private.services.create_redis_client(
self.redis_address, password=self.redis_password)
gcs_publisher = None
if args.gcs_address:
gcs_publisher = GcsPublisher(address=args.gcs_address)
elif gcs_pubsub_enabled():
gcs_publisher = GcsPublisher(
address=get_gcs_address_from_redis(redis_client))
from ray._private.utils import publish_error_to_driver
publish_error_to_driver(
ray_constants.MONITOR_DIED_ERROR,
message,
redis_client=redis_client,
gcs_publisher=gcs_publisher)
def upload_runtime_env_package_if_needed(job_config: JobConfig) -> None:
"""Upload runtime env if it's not there.
It'll check whether the runtime environment exists in the cluster or not.
If it doesn't exist, a package will be created based on the working
directory and modules defined in job config. The package will be
uploaded to the cluster after this.
Args:
job_config (JobConfig): The job config of driver.
"""
assert _internal_kv_initialized()
pkg_uris = job_config.get_runtime_env_uris()
for pkg_uri in pkg_uris:
if not package_exists(pkg_uri):
file_path = _get_local_path(pkg_uri)
pkg_file = Path(file_path)
working_dir = job_config.runtime_env.get("working_dir")
py_modules = job_config.runtime_env.get("py_modules")
excludes = job_config.runtime_env.get("excludes") or []
logger.info(f"{pkg_uri} doesn't exist. Create new package with"
f" {working_dir} and {py_modules}")
if not pkg_file.exists():
create_project_package(working_dir, py_modules, excludes,
file_path)
# Push the data to remote storage
pkg_size = push_package(pkg_uri, pkg_file)
logger.info(f"{pkg_uri} has been pushed with {pkg_size} bytes")
def __init__(self, dashboard_agent):
super().__init__(dashboard_agent)
self._runtime_env_dir = dashboard_agent.runtime_env_dir
self._logging_params = dashboard_agent.logging_params
self._per_job_logger_cache = dict()
# Cache the results of creating envs to avoid repeatedly calling into
# conda and other slow calls.
self._env_cache: Dict[str, CreatedEnvResult] = dict()
# Maps a serialized runtime env to a lock that is used
# to prevent multiple concurrent installs of the same env.
self._env_locks: Dict[str, asyncio.Lock] = dict()
# Keeps track of the URIs contained within each env so we can
# invalidate the env cache when a URI is deleted.
# This is a temporary mechanism until we have per-URI caching.
self._uris_to_envs: Dict[str, Set[str]] = defaultdict(set)
# Initialize internal KV to be used by the working_dir setup code.
_initialize_internal_kv(self._dashboard_agent.gcs_client)
assert _internal_kv_initialized()
self._pip_manager = PipManager(self._runtime_env_dir)
self._conda_manager = CondaManager(self._runtime_env_dir)
self._py_modules_manager = PyModulesManager(self._runtime_env_dir)
self._working_dir_manager = WorkingDirManager(self._runtime_env_dir)
self._container_manager = ContainerManager(dashboard_agent.temp_dir)
self._working_dir_uri_cache = URICache(
self._working_dir_manager.delete_uri, WORKING_DIR_CACHE_SIZE_BYTES)
self._py_modules_uri_cache = URICache(
self._py_modules_manager.delete_uri, PY_MODULES_CACHE_SIZE_BYTES)
self._conda_uri_cache = URICache(self._conda_manager.delete_uri,
CONDA_CACHE_SIZE_BYTES)
self._pip_uri_cache = URICache(self._pip_manager.delete_uri,
PIP_CACHE_SIZE_BYTES)
self._logger = default_logger
def _run(self):
"""Run the monitor loop."""
while True:
if self.stop_event and self.stop_event.is_set():
break
self.update_load_metrics()
self.update_resource_requests()
self.update_event_summary()
status = {
"load_metrics_report": self.load_metrics.summary()._asdict(),
"time": time.time(),
"monitor_pid": os.getpid()
}
# Process autoscaling actions
if self.autoscaler:
# Only used to update the load metrics for the autoscaler.
self.autoscaler.update()
status[
"autoscaler_report"] = self.autoscaler.summary()._asdict()
for msg in self.event_summarizer.summary():
logger.info("{}{}".format(
ray_constants.LOG_PREFIX_EVENT_SUMMARY, msg))
self.event_summarizer.clear()
as_json = json.dumps(status)
if _internal_kv_initialized():
_internal_kv_put(
DEBUG_AUTOSCALING_STATUS, as_json, overwrite=True)
# Wait for a autoscaler update interval before processing the next
# round of messages.
time.sleep(AUTOSCALER_UPDATE_INTERVAL_S)
def _handle_failure(self, error):
logger.exception("Error in monitor loop")
if (
self.autoscaler is not None
and os.environ.get("RAY_AUTOSCALER_FATESHARE_WORKERS", "") == "1"
):
self.autoscaler.kill_workers()
# Take down autoscaler workers if necessary.
self.destroy_autoscaler_workers()
# Something went wrong, so push an error to all current and future
# drivers.
message = f"The autoscaler failed with the following error:\n{error}"
if _internal_kv_initialized():
_internal_kv_put(
ray_constants.DEBUG_AUTOSCALING_ERROR, message, overwrite=True
)
gcs_publisher = GcsPublisher(address=args.gcs_address)
from ray._private.utils import publish_error_to_driver
publish_error_to_driver(
ray_constants.MONITOR_DIED_ERROR,
message,
gcs_publisher=gcs_publisher,
)
def _run(self):
"""Run the monitor loop."""
while True:
self.update_raylet_map()
self.update_load_metrics()
self.update_resource_requests()
status = {
"load_metrics_report": self.load_metrics.summary()._asdict()
}
# Process autoscaling actions
if self.autoscaler:
# Only used to update the load metrics for the autoscaler.
self.autoscaler.update()
status["autoscaler_report"] = self.autoscaler.summary(
)._asdict()
as_json = json.dumps(status)
if _internal_kv_initialized():
_internal_kv_put(DEBUG_AUTOSCALING_STATUS,
as_json,
overwrite=True)
# Wait for a autoscaler update interval before processing the next
# round of messages.
time.sleep(AUTOSCALER_UPDATE_INTERVAL_S)
def __init__(self, namespace: str = None):
assert ray_kv._internal_kv_initialized()
if namespace is not None and not isinstance(namespace, str):
raise TypeError("namespace must a string, got: {}.".format(
type(namespace)))
self.namespace = namespace or ""
def ensure_runtime_env_setup(pkg_uris: List[str]) -> Optional[str]:
"""Make sure all required packages are downloaded it local.
Necessary packages required to run the job will be downloaded
into local file system if it doesn't exist.
Args:
pkg_uri list(str): Package of the working dir for the runtime env.
Return:
Working directory is returned if the pkg_uris is not empty,
otherwise, None is returned.
"""
pkg_dir = None
assert _internal_kv_initialized()
for pkg_uri in pkg_uris:
# For each node, the package will only be downloaded one time
# Locking to avoid multiple process download concurrently
pkg_file = Path(_get_local_path(pkg_uri))
with FileLock(str(pkg_file) + ".lock"):
pkg_dir = fetch_package(pkg_uri)
sys.path.insert(0, str(pkg_dir))
# Right now, multiple pkg_uris are not supported correctly.
# We return the last one as working directory
return str(pkg_dir) if pkg_dir else None
def register(self, category, key, value):
if category not in KNOWN_CATEGORIES:
from ray.tune import TuneError
raise TuneError("Unknown category {} not among {}".format(
category, KNOWN_CATEGORIES))
self._to_flush[(category, key)] = pickle.dumps(value)
if _internal_kv_initialized():
self.flush_values()
def register(self, category, key, value):
if category not in KNOWN_CATEGORIES:
from ray.tune import TuneError
raise TuneError("Unknown category {} not among {}".format(
category, KNOWN_CATEGORIES))
self._to_flush[(category, key)] = pickle.dumps(value)
if _internal_kv_initialized():
self.flush_values()
def __init__(self, dashboard_head):
super().__init__(dashboard_head)
self._gcs_job_info_stub = None
self._gcs_actor_info_stub = None
self._dashboard_head = dashboard_head
assert _internal_kv_initialized()
self._job_status_client = JobStatusStorageClient()
def _run(self):
"""Run the monitor loop."""
while True:
try:
if self.stop_event and self.stop_event.is_set():
break
self.update_load_metrics()
self.update_resource_requests()
self.update_event_summary()
status = {
"load_metrics_report": asdict(self.load_metrics.summary()),
"time": time.time(),
"monitor_pid": os.getpid(),
}
if self.autoscaler and not self.load_metrics:
# load_metrics is Falsey iff we haven't collected any
# resource messages from the GCS, which can happen at startup if
# the GCS hasn't yet received data from the Raylets.
# In this case, do not do an autoscaler update.
# Wait to get load metrics.
logger.info(
"Autoscaler has not yet received load metrics. Waiting."
)
elif self.autoscaler:
# Process autoscaling actions
self.autoscaler.update()
autoscaler_summary = self.autoscaler.summary()
if autoscaler_summary:
status["autoscaler_report"] = asdict(autoscaler_summary)
for msg in self.event_summarizer.summary():
# Need to prefix each line of the message for the lines to
# get pushed to the driver logs.
for line in msg.split("\n"):
logger.info(
"{}{}".format(
ray_constants.LOG_PREFIX_EVENT_SUMMARY, line
)
)
self.event_summarizer.clear()
as_json = json.dumps(status)
if _internal_kv_initialized():
_internal_kv_put(
ray_constants.DEBUG_AUTOSCALING_STATUS, as_json, overwrite=True
)
except Exception:
# By default, do not exit the monitor on failure.
if self.retry_on_failure:
logger.exception("Monitor: Execution exception. Trying again...")
else:
raise
# Wait for a autoscaler update interval before processing the next
# round of messages.
time.sleep(AUTOSCALER_UPDATE_INTERVAL_S)
def _put_pre_init_library_usages():
assert _internal_kv_initialized()
# NOTE: When the lib is imported from a worker, ray should
# always be initialized, so there's no need to register the
# pre init hook.
if ray._private.worker.global_worker.mode != ray.SCRIPT_MODE:
return
for library_usage in _recorded_library_usages:
_put_library_usage(library_usage)
def __init__(self, dashboard_head):
super().__init__(dashboard_head)
self._gcs_job_info_stub = None
self._gcs_actor_info_stub = None
self._dashboard_head = dashboard_head
# Initialize internal KV to be used by the working_dir setup code.
_initialize_internal_kv(dashboard_head.gcs_client)
assert _internal_kv_initialized()
def get(self, category, key):
if _internal_kv_initialized():
value = _internal_kv_get(_make_key(category, key))
if value is None:
raise ValueError(
"Registry value for {}/{} doesn't exist.".format(
category, key))
return pickle.loads(value)
else:
return pickle.loads(self._to_flush[(category, key)])
def __init__(self, dashboard_head):
super().__init__(dashboard_head)
self._gcs_job_info_stub = None
self._gcs_actor_info_stub = None
self._dashboard_head = dashboard_head
assert _internal_kv_initialized()
self._job_info_client = JobInfoStorageClient()
# For offloading CPU intensive work.
self._thread_pool = concurrent.futures.ThreadPoolExecutor(
max_workers=2, thread_name_prefix="api_head")
def get(self, category, key):
if _internal_kv_initialized():
value = _internal_kv_get(_make_key(category, key))
if value is None:
raise ValueError(
"Registry value for {}/{} doesn't exist.".format(
category, key))
return pickle.loads(value)
else:
return pickle.loads(self._to_flush[(category, key)])
def _put_library_usage(library_usage: str):
assert _internal_kv_initialized()
try:
_internal_kv_put(
f"{usage_constant.LIBRARY_USAGE_PREFIX}{library_usage}",
"",
namespace=usage_constant.USAGE_STATS_NAMESPACE,
)
except Exception as e:
logger.debug(f"Failed to put library usage, {e}")
def update_resource_requests(self):
"""Fetches resource requests from the internal KV and updates load."""
if not _internal_kv_initialized():
return
data = _internal_kv_get(ray.ray_constants.AUTOSCALER_RESOURCE_REQUEST_CHANNEL)
if data:
try:
resource_request = json.loads(data)
self.load_metrics.set_resource_requests(resource_request)
except Exception:
logger.exception("Error parsing resource requests")
def log_info_string(self, nodes):
tmp = "Cluster status: "
tmp += self.info_string(nodes)
tmp += "\n"
tmp += self.load_metrics.info_string()
tmp += "\n"
tmp += self.resource_demand_scheduler.debug_string(
nodes, self.pending_launches.breakdown(),
self.load_metrics.get_resource_utilization())
if _internal_kv_initialized():
_internal_kv_put(DEBUG_AUTOSCALING_STATUS, tmp, overwrite=True)
logger.debug(tmp)
def register(self, category, key, value):
"""Registers the value with the global registry.
Raises:
PicklingError if unable to pickle to provided file.
"""
if category not in KNOWN_CATEGORIES:
from ray.tune import TuneError
raise TuneError("Unknown category {} not among {}".format(
category, KNOWN_CATEGORIES))
self._to_flush[(category, key)] = pickle.dumps_debug(value)
if _internal_kv_initialized():
self.flush_values()
def run(self):
# Register signal handlers for autoscaler termination.
signal.signal(signal.SIGINT, self._signal_handler)
signal.signal(signal.SIGTERM, self._signal_handler)
try:
if _internal_kv_initialized():
# Delete any previous autoscaling errors.
_internal_kv_del(DEBUG_AUTOSCALING_ERROR)
self._initialize_autoscaler()
self._run()
except Exception:
self._handle_failure(traceback.format_exc())
raise
def record_library_usage(library_usage: str):
"""Record library usage (e.g. which library is used)"""
if library_usage in _recorded_library_usages:
return
_recorded_library_usages.add(library_usage)
if not _internal_kv_initialized():
# This happens if the library is imported before ray.init
return
# Only report library usage from driver to reduce
# the load to kv store.
if ray.worker.global_worker.mode == ray.SCRIPT_MODE:
_put_library_usage(library_usage)
def legacy_log_info_string(autoscaler, nodes):
tmp = "Cluster status: "
tmp += info_string(autoscaler, nodes)
tmp += "\n"
tmp += autoscaler.load_metrics.info_string()
tmp += "\n"
tmp += autoscaler.resource_demand_scheduler.debug_string(
nodes,
autoscaler.pending_launches.breakdown(),
autoscaler.load_metrics.get_resource_utilization(),
)
if _internal_kv_initialized():
_internal_kv_put(DEBUG_AUTOSCALING_STATUS_LEGACY, tmp, overwrite=True)
logger.debug(tmp)
def __init__(self, dashboard_agent):
super().__init__(dashboard_agent)
self._runtime_env_dir = dashboard_agent.runtime_env_dir
self._logging_params = dashboard_agent.logging_params
self._per_job_logger_cache = dict()
# Cache the results of creating envs to avoid repeatedly calling into
# conda and other slow calls.
self._env_cache: Dict[str, CreatedEnvResult] = dict()
# Maps a serialized runtime env to a lock that is used
# to prevent multiple concurrent installs of the same env.
self._env_locks: Dict[str, asyncio.Lock] = dict()
# Initialize internal KV to be used by the working_dir setup code.
_initialize_internal_kv(self._dashboard_agent.gcs_client)
assert _internal_kv_initialized()
def update(self):
try:
self.reset(errors_fatal=False)
self._update()
except Exception as e:
logger.exception("StandardAutoscaler: "
"Error during autoscaling.")
if _internal_kv_initialized():
_internal_kv_put(
DEBUG_AUTOSCALING_ERROR, str(e), overwrite=True)
self.num_failures += 1
if self.num_failures > self.max_failures:
logger.critical("StandardAutoscaler: "
"Too many errors, abort.")
raise e
def package_exists(pkg_uri: str) -> bool:
"""Check whether the package with given uri exists or not.
Args:
pkg_uri (str): The uri of the package
Return:
True for package existing and False for not.
"""
assert _internal_kv_initialized()
(protocol, pkg_name) = _parse_uri(pkg_uri)
if protocol in (Protocol.GCS, Protocol.PIN_GCS):
return _internal_kv_exists(pkg_uri)
else:
raise NotImplementedError(f"Protocol {protocol} is not supported")
def record_library_usage(library_usage: str):
"""Record library usage (e.g. which library is used)"""
if library_usage in _recorded_library_usages:
return
_recorded_library_usages.add(library_usage)
if not _internal_kv_initialized():
# This happens if the library is imported before ray.init
return
# Only report lib usage for driver / workers. Otherwise,
# it can be reported if the library is imported from
# e.g., API server.
if (ray._private.worker.global_worker.mode == ray.SCRIPT_MODE
or ray._private.worker.global_worker.mode == ray.WORKER_MODE):
_put_library_usage(library_usage)
def _handle_failure(self, error):
logger.exception("Error in monitor loop")
if self.autoscaler is not None:
self.autoscaler.kill_workers()
# Take down autoscaler workers if necessary.
self.destroy_autoscaler_workers()
# Something went wrong, so push an error to all current and future
# drivers.
message = f"The autoscaler failed with the following error:\n{error}"
if _internal_kv_initialized():
_internal_kv_put(DEBUG_AUTOSCALING_ERROR, message, overwrite=True)
redis_client = ray._private.services.create_redis_client(
args.redis_address, password=args.redis_password)
from ray.utils import push_error_to_driver_through_redis
push_error_to_driver_through_redis(redis_client,
ray_constants.MONITOR_DIED_ERROR,
message)
def _run(self):
"""Run the monitor loop."""
while True:
try:
if self.stop_event and self.stop_event.is_set():
break
self.update_load_metrics()
self.update_resource_requests()
self.update_event_summary()
status = {
"load_metrics_report": asdict(self.load_metrics.summary()),
"time": time.time(),
"monitor_pid": os.getpid(),
}
# Process autoscaling actions
if self.autoscaler:
# Only used to update the load metrics for the autoscaler.
self.autoscaler.update()
status["autoscaler_report"] = asdict(self.autoscaler.summary())
for msg in self.event_summarizer.summary():
# Need to prefix each line of the message for the lines to
# get pushed to the driver logs.
for line in msg.split("\n"):
logger.info(
"{}{}".format(
ray_constants.LOG_PREFIX_EVENT_SUMMARY, line
)
)
self.event_summarizer.clear()
as_json = json.dumps(status)
if _internal_kv_initialized():
_internal_kv_put(
ray_constants.DEBUG_AUTOSCALING_STATUS, as_json, overwrite=True
)
except Exception:
logger.exception("Monitor: Execution exception. Trying again...")
# Wait for a autoscaler update interval before processing the next
# round of messages.
time.sleep(AUTOSCALER_UPDATE_INTERVAL_S)
def update(self):
try:
self.reset(errors_fatal=False)
self._update()
except Exception as e:
logger.exception("StandardAutoscaler: "
"Error during autoscaling.")
if _internal_kv_initialized():
_internal_kv_put(
DEBUG_AUTOSCALING_ERROR, str(e), overwrite=True)
# Don't abort the autoscaler if the K8s API server is down.
# https://github.com/ray-project/ray/issues/12255
is_k8s_connection_error = (
self.config["provider"]["type"] == "kubernetes"
and isinstance(e, MaxRetryError))
if not is_k8s_connection_error:
self.num_failures += 1
if self.num_failures > self.max_failures:
logger.critical("StandardAutoscaler: "
"Too many errors, abort.")
raise e
def record_library_usage(library_usage: str):
"""Record library usage (e.g. which library is used)"""
if library_usage in _recorded_library_usages:
return
if "-" in library_usage:
# - is not permitted since it should be used as a separator
# of the lib usage file name. See LibUsageRecorder.
raise ValueError(
"The library name contains a char - which is not permitted.")
_recorded_library_usages.add(library_usage)
if not _internal_kv_initialized():
# This happens if the library is imported before ray.init
return
# Only report lib usage for driver / workers. Otherwise,
# it can be reported if the library is imported from
# e.g., API server.
if (ray._private.worker.global_worker.mode == ray.SCRIPT_MODE
or ray._private.worker.global_worker.mode == ray.WORKER_MODE):
_put_library_usage(library_usage)
def contains(self, category, key):
if _internal_kv_initialized():
value = _internal_kv_get(_make_key(category, key))
return value is not None
else:
return (category, key) in self._to_flush