Repository URL to install this package:
|
Version:
2.0.0rc1 ▾
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# isort: skip_file
import logging
import os
logger = logging.getLogger(__name__)
def _configure_system():
import os
import platform
import sys
"""Wraps system configuration to avoid 'leaking' variables into ray."""
# Sanity check pickle5 if it has been installed.
if "pickle5" in sys.modules:
if sys.version_info >= (3, 8):
logger.warning(
"Package pickle5 becomes unnecessary in Python 3.8 and above. "
"Its presence may confuse libraries including Ray. "
"Please uninstall the package."
)
import pkg_resources
try:
version_info = pkg_resources.require("pickle5")
version = tuple(int(n) for n in version_info[0].version.split("."))
if version < (0, 0, 10):
logger.warning(
"Although not used by Ray, a version of pickle5 that leaks memory "
"is found in the environment. Please run 'pip install pickle5 -U' "
"to upgrade."
)
except pkg_resources.DistributionNotFound:
logger.warning(
"You are using the 'pickle5' module, but "
"the exact version is unknown (possibly carried as "
"an internal component by another module). Please "
"make sure you are using pickle5 >= 0.0.10 because "
"previous versions may leak memory."
)
# MUST add pickle5 to the import path because it will be imported by some
# raylet modules.
#
# When running Python version < 3.8, Ray needs to use pickle5 instead of
# Python's built-in pickle. Add the directory containing pickle5 to the
# Python path so that we find the pickle5 version packaged with Ray and
# not a pre-existing pickle5.
if sys.version_info < (3, 8):
pickle5_path = os.path.join(
os.path.abspath(os.path.dirname(__file__)), "pickle5_files"
)
sys.path.insert(0, pickle5_path)
# Check that grpc can actually be imported on Apple Silicon. Some package
# managers (such as `pip`) can't properly install the grpcio library yet,
# so provide a proactive error message if that's the case.
if platform.system() == "Darwin" and platform.machine() == "arm64":
try:
import grpc # noqa: F401
except ImportError:
raise ImportError(
"Failed to import grpc on Apple Silicon. On Apple"
" Silicon machines, try `pip uninstall grpcio; conda "
"install grpcio`. Check out "
"https://docs.ray.io/en/master/ray-overview/installation.html"
"#m1-mac-apple-silicon-support for more details."
)
if "OMP_NUM_THREADS" not in os.environ:
logger.debug(
"[ray] Forcing OMP_NUM_THREADS=1 to avoid performance "
"degradation with many workers (issue #6998). You can "
"override this by explicitly setting OMP_NUM_THREADS."
)
os.environ["OMP_NUM_THREADS"] = "1"
# Importing psutil & setproctitle. Must be before ray._raylet is
# initialized.
thirdparty_files = os.path.join(
os.path.abspath(os.path.dirname(__file__)), "thirdparty_files"
)
sys.path.insert(0, thirdparty_files)
if (
platform.system() == "Linux"
and "Microsoft".lower() in platform.release().lower()
):
import ray._private.compat # noqa: E402
ray._private.compat.patch_psutil()
# Expose ray ABI symbols which may be dependent by other shared
# libraries such as _streaming.so. See BUILD.bazel:_raylet
python_shared_lib_suffix = ".so" if sys.platform != "win32" else ".pyd"
so_path = os.path.join(
os.path.dirname(__file__), "_raylet" + python_shared_lib_suffix
)
if os.path.exists(so_path):
import ctypes
from ctypes import CDLL
CDLL(so_path, ctypes.RTLD_GLOBAL)
_configure_system()
# Delete configuration function.
del _configure_system
# Replaced with the current commit when building the wheels.
__commit__ = "321d8717f73995153d4f9abe98678160831090e1"
__version__ = "2.0.0rc1"
import ray._raylet # noqa: E402
from ray._raylet import ( # noqa: E402
ActorClassID,
ActorID,
NodeID,
Config as _Config,
JobID,
WorkerID,
FunctionID,
ObjectID,
ObjectRef,
TaskID,
UniqueID,
Language,
PlacementGroupID,
)
_config = _Config()
from ray._private.state import ( # noqa: E402
nodes,
timeline,
cluster_resources,
available_resources,
)
from ray._private.worker import ( # noqa: E402,F401
LOCAL_MODE,
SCRIPT_MODE,
WORKER_MODE,
RESTORE_WORKER_MODE,
SPILL_WORKER_MODE,
cancel,
get,
get_actor,
get_gpu_ids,
init,
is_initialized,
put,
kill,
remote,
shutdown,
wait,
)
# We import ray.actor because some code is run in actor.py which initializes
# some functions in the worker.
import ray.actor # noqa: E402,F401
from ray.actor import method # noqa: E402
# TODO(qwang): We should remove this exporting in Ray2.0.
from ray.cross_language import java_function, java_actor_class # noqa: E402
from ray.runtime_context import get_runtime_context # noqa: E402
from ray import autoscaler # noqa:E402
from ray import data # noqa: E402,F401
from ray import internal # noqa: E402,F401
from ray import util # noqa: E402
from ray import _private # noqa: E402,F401
from ray import workflow # noqa: E402,F401
# We import ClientBuilder so that modules can inherit from `ray.ClientBuilder`.
from ray.client_builder import client, ClientBuilder # noqa: E402
class _DeprecationWrapper(object):
def __init__(self, name, real_worker):
self._name = name
self._real_worker = real_worker
self._warned = set()
def __getattr__(self, attr):
value = getattr(self._real_worker, attr)
if attr not in self._warned:
import traceback
self._warned.add(attr)
logger.warning(
f"DeprecationWarning: `ray.{self._name}.{attr}` is a private "
"attribute and access will be removed in a future Ray version."
)
traceback.print_stack()
return value
# TODO(ekl) remove this entirely after 3rd party libraries are all migrated.
worker = _DeprecationWrapper("worker", ray._private.worker)
ray_constants = _DeprecationWrapper("ray_constants", ray._private.ray_constants)
serialization = _DeprecationWrapper("serialization", ray._private.serialization)
state = _DeprecationWrapper("state", ray._private.state)
__all__ = [
"__version__",
"_config",
"get_runtime_context",
"actor",
"available_resources",
"autoscaler",
"cancel",
"client",
"ClientBuilder",
"cluster_resources",
"data",
"get",
"get_actor",
"get_gpu_ids",
"init",
"internal",
"is_initialized",
"java_actor_class",
"java_function",
"cpp_function",
"kill",
"Language",
"method",
"nodes",
"put",
"remote",
"shutdown",
"show_in_dashboard",
"timeline",
"util",
"wait",
"widgets",
"LOCAL_MODE",
"SCRIPT_MODE",
"WORKER_MODE",
]
# ID types
__all__ += [
"ActorClassID",
"ActorID",
"NodeID",
"JobID",
"WorkerID",
"FunctionID",
"ObjectID",
"ObjectRef",
"TaskID",
"UniqueID",
"PlacementGroupID",
]
del os
del logging