Source code for ray.tune.utils.util

import copy
import glob
import inspect
import logging
import os
import threading
import time
from collections import defaultdict
from datetime import datetime
from numbers import Number
from threading import Thread
from typing import Any, Callable, Dict, List, Optional, Sequence, Type, Union

import numpy as np

import ray
from ray._private.dict import (  # noqa: F401
from ray.air._internal.json import SafeFallbackEncoder  # noqa
from ray.air._internal.util import is_nan, is_nan_or_inf  # noqa: F401
from ray.util.annotations import DeveloperAPI, PublicAPI

import psutil

logger = logging.getLogger(__name__)

def _import_gputil():
        import GPUtil
    except ImportError:
        GPUtil = None
    return GPUtil

START_OF_TIME = time.time()

class UtilMonitor(Thread):
    """Class for system usage utilization monitoring.

    It keeps track of CPU, RAM, GPU, VRAM usage (each gpu separately) by
    pinging for information every x seconds in a separate thread.

    Requires psutil and GPUtil to be installed. Can be enabled with
    Tuner(param_space={"log_sys_usage": True}).

    def __init__(self, start=True, delay=0.7):
        self.stopped = True
        GPUtil = _import_gputil()
        self.GPUtil = GPUtil
        if GPUtil is None and start:
            logger.warning("Install gputil for GPU system monitoring.")

        if psutil is None and start:
            logger.warning("Install psutil to monitor system performance.")

        if GPUtil is None and psutil is None:

        super(UtilMonitor, self).__init__()
        self.delay = delay  # Time between calls to GPUtil
        self.values = defaultdict(list)
        self.lock = threading.Lock()
        self.daemon = True
        if start:

    def _read_utilization(self):
        with self.lock:
            if psutil is not None:
                    float(getattr(psutil.virtual_memory(), "percent"))
            if self.GPUtil is not None:
                gpu_list = []
                    gpu_list = self.GPUtil.getGPUs()
                except Exception:
                    logger.debug("GPUtil failed to retrieve GPUs.")
                for gpu in gpu_list:
                    self.values["gpu_util_percent" + str(].append(
                    self.values["vram_util_percent" + str(].append(

    def get_data(self):
        if self.stopped:
            return {}

        with self.lock:
            ret_values = copy.deepcopy(self.values)
            for key, val in self.values.items():
                del val[:]
        return {"perf": {k: np.mean(v) for k, v in ret_values.items() if len(v) > 0}}

    def run(self):
        self.stopped = False
        while not self.stopped:

    def stop(self):
        self.stopped = True

def retry_fn(
    fn: Callable[[], Any],
    exception_type: Union[Type[Exception], Sequence[Type[Exception]]] = Exception,
    num_retries: int = 3,
    sleep_time: int = 1,
    timeout: Optional[Number] = None,
) -> bool:
    errored = threading.Event()

    def _try_fn():
        except exception_type as e:

    for i in range(num_retries):

        proc = threading.Thread(target=_try_fn)
        proc.daemon = True

        if proc.is_alive():
                f"Process timed out (try {i+1}/{num_retries}): "
                f"{getattr(fn, '__name__', None)}"
        elif not errored.is_set():
            return True

        # Timed out, sleep and try again

    # Timed out, so return False
    return False

class warn_if_slow:
    """Prints a warning if a given operation is slower than 500ms.

        >>> from ray.tune.utils.util import warn_if_slow
        >>> something = ... # doctest: +SKIP
        >>> with warn_if_slow("some_operation"): # doctest: +SKIP
        ...    ray.get(something) # doctest: +SKIP

    DEFAULT_THRESHOLD = float(os.environ.get("TUNE_WARN_THRESHOLD_S", 0.5))
        "The `{name}` operation took {duration:.3f} s, "
        "which may be a performance bottleneck."

    def __init__(
        name: str,
        threshold: Optional[float] = None,
        message: Optional[str] = None,
        disable: bool = False,
    ): = name
        self.threshold = threshold or self.DEFAULT_THRESHOLD
        self.message = message or self.DEFAULT_MESSAGE
        self.too_slow = False
        self.disable = disable

    def __enter__(self):
        self.start = time.time()
        return self

    def __exit__(self, type, value, traceback):
        now = time.time()
        if self.disable:
        if now - self.start > self.threshold and now - START_OF_TIME > 60.0:
            self.too_slow = True
            duration = now - self.start
            logger.warning(self.message.format(, duration=duration))

class Tee(object):
    def __init__(self, stream1, stream2):
        self.stream1 = stream1
        self.stream2 = stream2

        # If True, we are currently handling a warning.
        # We use this flag to avoid infinite recursion.
        self._handling_warning = False

    def _warn(self, op, s, args, kwargs):
        # If we are already handling a warning, this is because
        # `logger.warning` below triggered the same object again
        # (e.g. because stderr is redirected to this object).
        # In that case, exit early to avoid recursion.
        if self._handling_warning:

        msg = f"ValueError when calling '{op}' on stream ({s}). "
        msg += f"args: {args} kwargs: {kwargs}"

        self._handling_warning = True
        self._handling_warning = False

    def seek(self, *args, **kwargs):
        for s in [self.stream1, self.stream2]:
      *args, **kwargs)
            except ValueError:
                self._warn("seek", s, args, kwargs)

    def write(self, *args, **kwargs):
        for s in [self.stream1, self.stream2]:
                s.write(*args, **kwargs)
            except ValueError:
                self._warn("write", s, args, kwargs)

    def flush(self, *args, **kwargs):
        for s in [self.stream1, self.stream2]:
                s.flush(*args, **kwargs)
            except ValueError:
                self._warn("flush", s, args, kwargs)

    def encoding(self):
        if hasattr(self.stream1, "encoding"):
            return self.stream1.encoding
        return self.stream2.encoding

    def error(self):
        if hasattr(self.stream1, "error"):
            return self.stream1.error
        return self.stream2.error

    def newlines(self):
        if hasattr(self.stream1, "newlines"):
            return self.stream1.newlines
        return self.stream2.newlines

    def detach(self):
        raise NotImplementedError

    def read(self, *args, **kwargs):
        raise NotImplementedError

    def readline(self, *args, **kwargs):
        raise NotImplementedError

    def tell(self, *args, **kwargs):
        raise NotImplementedError

def date_str():

def _to_pinnable(obj):
    """Converts obj to a form that can be pinned in object store memory.

    Currently only numpy arrays are pinned in memory, if you have a strong
    reference to the array value.

    return (obj, np.zeros(1))

def _from_pinnable(obj):
    """Retrieve from _to_pinnable format."""

    return obj[0]

[docs]@DeveloperAPI def diagnose_serialization(trainable: Callable): """Utility for detecting why your trainable function isn't serializing. Args: trainable: The trainable object passed to tune.Tuner(trainable). Currently only supports Function API. Returns: bool | set of unserializable objects. Example: .. code-block:: python import threading # this is not serializable e = threading.Event() def test(): print(e) diagnose_serialization(test) # should help identify that 'e' should be moved into # the `test` scope. # correct implementation def test(): e = threading.Event() print(e) assert diagnose_serialization(test) is True """ from ray.tune.registry import _check_serializability, register_trainable def check_variables(objects, failure_set, printer): for var_name, variable in objects.items(): msg = None try: _check_serializability(var_name, variable) status = "PASSED" except Exception as e: status = "FAILED" msg = f"{e.__class__.__name__}: {str(e)}" failure_set.add(var_name) printer(f"{str(variable)}[name='{var_name}'']... {status}") if msg: printer(msg) print(f"Trying to serialize {trainable}...") try: register_trainable("__test:" + str(trainable), trainable, warn=False) print("Serialization succeeded!") return True except Exception as e: print(f"Serialization failed: {e}") print( "Inspecting the scope of the trainable by running " f"`inspect.getclosurevars({str(trainable)})`..." ) closure = inspect.getclosurevars(trainable) failure_set = set() if closure.globals: print( f"Detected {len(closure.globals)} global variables. " "Checking serializability..." ) check_variables(closure.globals, failure_set, lambda s: print(" " + s)) if closure.nonlocals: print( f"Detected {len(closure.nonlocals)} nonlocal variables. " "Checking serializability..." ) check_variables(closure.nonlocals, failure_set, lambda s: print(" " + s)) if not failure_set: print( "Nothing was found to have failed the diagnostic test, though " "serialization did not succeed. Feel free to raise an " "issue on github." ) return failure_set else: print( f"Variable(s) {failure_set} was found to be non-serializable. " "Consider either removing the instantiation/imports " "of these objects or moving them into the scope of " "the trainable. " ) return failure_set
def _atomic_save(state: Dict, checkpoint_dir: str, file_name: str, tmp_file_name: str): """Atomically saves the state object to the checkpoint directory. This is automatically used by Tuner().fit during a Tune job. Args: state: Object state to be serialized. checkpoint_dir: Directory location for the checkpoint. file_name: Final name of file. tmp_file_name: Temporary name of file. """ import ray.cloudpickle as cloudpickle tmp_search_ckpt_path = os.path.join(checkpoint_dir, tmp_file_name) with open(tmp_search_ckpt_path, "wb") as f: cloudpickle.dump(state, f) os.replace(tmp_search_ckpt_path, os.path.join(checkpoint_dir, file_name)) def _load_newest_checkpoint(dirpath: str, ckpt_pattern: str) -> Optional[Dict]: """Returns the most recently modified checkpoint. Assumes files are saved with an ordered name, most likely by :obj:atomic_save. Args: dirpath: Directory in which to look for the checkpoint file. ckpt_pattern: File name pattern to match to find checkpoint files. Returns: (dict) Deserialized state dict. """ import ray.cloudpickle as cloudpickle full_paths = glob.glob(os.path.join(dirpath, ckpt_pattern)) if not full_paths: return most_recent_checkpoint = max(full_paths) with open(most_recent_checkpoint, "rb") as f: checkpoint_state = cloudpickle.load(f) return checkpoint_state
[docs]@PublicAPI(stability="beta") def wait_for_gpu( gpu_id: Optional[Union[int, str]] = None, target_util: float = 0.01, retry: int = 20, delay_s: int = 5, gpu_memory_limit: Optional[float] = None, ): """Checks if a given GPU has freed memory. Requires ``gputil`` to be installed: ``pip install gputil``. Args: gpu_id: GPU id or uuid to check. Must be found within GPUtil.getGPUs(). If none, resorts to the first item returned from `ray.get_gpu_ids()`. target_util: The utilization threshold to reach to unblock. Set this to 0 to block until the GPU is completely free. retry: Number of times to check GPU limit. Sleeps `delay_s` seconds between checks. delay_s: Seconds to wait before check. Returns: bool: True if free. Raises: RuntimeError: If GPUtil is not found, if no GPUs are detected or if the check fails. Example: .. code-block:: python def tune_func(config): tune.utils.wait_for_gpu() train() tuner = tune.Tuner( tune.with_resources( tune_func, resources={"gpu": 1} ), tune_config=tune.TuneConfig(num_samples=10) ) """ GPUtil = _import_gputil() if GPUtil is None: raise RuntimeError("GPUtil must be installed if calling `wait_for_gpu`.") if gpu_id is None: gpu_id_list = ray.get_gpu_ids() if not gpu_id_list: raise RuntimeError( "No GPU ids found from `ray.get_gpu_ids()`. " "Did you set Tune resources correctly?" ) gpu_id = gpu_id_list[0] gpu_attr = "id" if isinstance(gpu_id, str): if gpu_id.isdigit(): # GPU ID returned from `ray.get_gpu_ids()` is a str representation # of the int GPU ID gpu_id = int(gpu_id) else: # Could not coerce gpu_id to int, so assume UUID # and compare against `uuid` attribute e.g., # 'GPU-04546190-b68d-65ac-101b-035f8faed77d' gpu_attr = "uuid" elif not isinstance(gpu_id, int): raise ValueError(f"gpu_id ({type(gpu_id)}) must be type str/int.") def gpu_id_fn(g): # Returns either `` or `g.uuid` depending on # the format of the input `gpu_id` return getattr(g, gpu_attr) gpu_ids = {gpu_id_fn(g) for g in GPUtil.getGPUs()} if gpu_id not in gpu_ids: raise ValueError( f"{gpu_id} not found in set of available GPUs: {gpu_ids}. " "`wait_for_gpu` takes either GPU ordinal ID (e.g., '0') or " "UUID (e.g., 'GPU-04546190-b68d-65ac-101b-035f8faed77d')." ) for i in range(int(retry)): gpu_object = next(g for g in GPUtil.getGPUs() if gpu_id_fn(g) == gpu_id) if gpu_object.memoryUtil > target_util: f"Waiting for GPU util to reach {target_util}. " f"Util: {gpu_object.memoryUtil:0.3f}" ) time.sleep(delay_s) else: return True raise RuntimeError("GPU memory was not freed.")
[docs]@DeveloperAPI def validate_save_restore( trainable_cls: Type, config: Optional[Dict] = None, num_gpus: int = 0, ): """Helper method to check if your Trainable class will resume correctly. Args: trainable_cls: Trainable class for evaluation. config: Config to pass to Trainable when testing. num_gpus: GPU resources to allocate when testing. use_object_store: Whether to save and restore to Ray's object store. Recommended to set this to True if planning to use algorithms that pause training (i.e., PBT, HyperBand). """ assert ray.is_initialized(), "Need Ray to be initialized." remote_cls = ray.remote(num_gpus=num_gpus)(trainable_cls) trainable_1 = remote_cls.remote(config=config) trainable_2 = remote_cls.remote(config=config) from ray.air.constants import TRAINING_ITERATION for _ in range(3): res = ray.get(trainable_1.train.remote()) assert res.get(TRAINING_ITERATION), ( "Validation will not pass because it requires `training_iteration` " "to be returned." ) ray.get(trainable_2.restore.remote( res = ray.get(trainable_2.train.remote()) assert res[TRAINING_ITERATION] == 4 res = ray.get(trainable_2.train.remote()) assert res[TRAINING_ITERATION] == 5 return True
def _detect_config_single(func): """Check if func({}) works.""" func_sig = inspect.signature(func) use_config_single = True try: func_sig.bind({}) except Exception as e: logger.debug(str(e)) use_config_single = False return use_config_single @PublicAPI() def validate_warmstart( parameter_names: List[str], points_to_evaluate: List[Union[List, Dict]], evaluated_rewards: List, validate_point_name_lengths: bool = True, ): """Generic validation of a Searcher's warm start functionality. Raises exceptions in case of type and length mismatches between parameters. If ``validate_point_name_lengths`` is False, the equality of lengths between ``points_to_evaluate`` and ``parameter_names`` will not be validated. """ if points_to_evaluate: if not isinstance(points_to_evaluate, list): raise TypeError( "points_to_evaluate expected to be a list, got {}.".format( type(points_to_evaluate) ) ) for point in points_to_evaluate: if not isinstance(point, (dict, list)): raise TypeError( f"points_to_evaluate expected to include list or dict, " f"got {point}." ) if validate_point_name_lengths and (not len(point) == len(parameter_names)): raise ValueError( "Dim of point {}".format(point) + " and parameter_names {}".format(parameter_names) + " do not match." ) if points_to_evaluate and evaluated_rewards: if not isinstance(evaluated_rewards, list): raise TypeError( "evaluated_rewards expected to be a list, got {}.".format( type(evaluated_rewards) ) ) if not len(evaluated_rewards) == len(points_to_evaluate): raise ValueError( "Dim of evaluated_rewards {}".format(evaluated_rewards) + " and points_to_evaluate {}".format(points_to_evaluate) + " do not match." )