Source code for ray.tune.utils.util

import copy
import logging
import threading
import time
from collections import defaultdict
from threading import Thread

import numpy as np
import ray
import psutil

logger = logging.getLogger(__name__)

    import GPUtil
except ImportError:
    GPUtil = None

_pinned_objects = []
PINNED_OBJECT_PREFIX = "ray.tune.PinnedObject:"
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{"log_sys_usage": True}).

    def __init__(self, start=True, delay=0.7):
        self.stopped = True
        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 GPUtil is not None:
                gpu_list = []
                    gpu_list = 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 pin_in_object_store(obj):
    """Deprecated, use ray.put(value, weakref=False) instead."""

    obj_id = ray.put(obj, weakref=False)
    return obj_id

def get_pinned_object(pinned_id):

    return ray.get(pinned_id)

class warn_if_slow:
    """Prints a warning if a given operation is slower than 100ms.

        >>> with warn_if_slow("some_operation"):
        ...    ray.get(something)


    def __init__(self, name, threshold=None): = name
        self.threshold = threshold or self.DEFAULT_THRESHOLD
        self.too_slow = False

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

    def __exit__(self, type, value, traceback):
        now = time.time()
        if now - self.start > self.threshold and now - START_OF_TIME > 60.0:
            self.too_slow = True
                "The `%s` operation took %s seconds to complete, "
                "which may be a performance bottleneck.",,
                now - self.start)

[docs]def merge_dicts(d1, d2): """ Args: d1 (dict): Dict 1. d2 (dict): Dict 2. Returns: dict: A new dict that is d1 and d2 deep merged. """ merged = copy.deepcopy(d1) deep_update(merged, d2, True, []) return merged
[docs]def deep_update(original, new_dict, new_keys_allowed=False, whitelist=None, override_all_if_type_changes=None): """Updates original dict with values from new_dict recursively. If new key is introduced in new_dict, then if new_keys_allowed is not True, an error will be thrown. Further, for sub-dicts, if the key is in the whitelist, then new subkeys can be introduced. Args: original (dict): Dictionary with default values. new_dict (dict): Dictionary with values to be updated new_keys_allowed (bool): Whether new keys are allowed. whitelist (Optional[List[str]]): List of keys that correspond to dict values where new subkeys can be introduced. This is only at the top level. override_all_if_type_changes(Optional[List[str]]): List of top level keys with value=dict, for which we always simply override the entire value (dict), iff the "type" key in that value dict changes. """ whitelist = whitelist or [] override_all_if_type_changes = override_all_if_type_changes or [] for k, value in new_dict.items(): if k not in original and not new_keys_allowed: raise Exception("Unknown config parameter `{}` ".format(k)) # Both orginal value and new one are dicts. if isinstance(original.get(k), dict) and isinstance(value, dict): # Check old type vs old one. If different, override entire value. if k in override_all_if_type_changes and \ "type" in value and "type" in original[k] and \ value["type"] != original[k]["type"]: original[k] = value # Whitelisted key -> ok to add new subkeys. elif k in whitelist: deep_update(original[k], value, True) # Non-whitelisted key. else: deep_update(original[k], value, new_keys_allowed) # Original value not a dict OR new value not a dict: # Override entire value. else: original[k] = value return original
def flatten_dict(dt, delimiter="/"): dt = copy.deepcopy(dt) while any(isinstance(v, dict) for v in dt.values()): remove = [] add = {} for key, value in dt.items(): if isinstance(value, dict): for subkey, v in value.items(): add[delimiter.join([key, subkey])] = v remove.append(key) dt.update(add) for k in remove: del dt[k] return dt 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] def validate_save_restore(trainable_cls, config=None, num_gpus=0, use_object_store=False): """Helper method to check if your Trainable class will resume correctly. Args: trainable_cls: Trainable class for evaluation. config (dict): Config to pass to Trainable when testing. num_gpus (int): GPU resources to allocate when testing. use_object_store (bool): 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.tune.result 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.") if use_object_store: restore_check = trainable_2.restore_from_object.remote( trainable_1.save_to_object.remote()) ray.get(restore_check) else: restore_check = 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 if __name__ == "__main__": ray.init() X = pin_in_object_store("hello") print(X) result = get_pinned_object(X) print(result)