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How to use num_queued method in autotest

Best Python code snippet using autotest_python

mm1_queue.py

Source:mm1_queue.py

1"""Implements a discrete-time M/M/1 Queue environment.2Converted from continuous time and simplified to fixed set of states as per:3https://www.aaai.org/Papers/AAAI/1996/AAAI96-130.pdf4This implementation is a bit messy. We intended to clean it up, but did not.5State Representation6---------------------7In paper, state has 2 components: (jobs_in_queue: int, job_available: bool)8We have packed these into a single integer:9  state = jobs_in_queue*2 + job_available.10Invalid Actions11---------------------12In the paper, the algorithm does not have the option to admit a job when none13is available. Since our MDP transition and rewards are stored as matrices, we14we modified the reward/transition function such that the ADMIT action when no15job has arrived. The transitions are applied as if CONTINUE was chose, but an16extra penalty is added to the reward. Essentially, it's always worse to ADMIT17than to CONTINUE when no jobs have arrived.18Finite Implementation19---------------------20In the paper, the algorithm can (in theory) store an unbounded number of jobs.21Our implementation has a limit on the number of jobs that can be stored so that22we can represent transition and reward dynamics with a fixed-size matrix.23This limit should be set such that no reasonable algorithm would admit a new job24when the limit of jobs has been reached.25If the algorithm chooses to ADMIT a new job when the maximum is reached, there26is no reward for admitting the job. Further, there is no probability that an27existing job will be completed on the transition.28Essentially, ADMIT at capacity gives same reward as CONTINUE, but expected next29 state is worse than CONTINUE.30"""31import functools32import itertools33from typing import Callable34import numpy as np35from differential_value_iteration.environments import structure36COST_FN = Callable[[int], float]37CONTINUE = 038ADMIT = 139def linear_cost_fn(cost_constant: float, jobs_waiting: int) -> float:40  if cost_constant <= 0.:41    raise ValueError(f'cost_constant must be positive, got: {cost_constant}')42  if jobs_waiting < 0:43    raise ValueError(f'jobs_waiting must be non-negative, got: {jobs_waiting}')44  return jobs_waiting * cost_constant45def global_state_to_paper_state(global_state: int):46  jobs_in_queue = global_state // 247  new_job = True if global_state % 2 == 1 else False48  return jobs_in_queue, new_job49def to_global_state(jobs_in_queue: int, new_job: bool):50  return jobs_in_queue * 2 + new_job51def create(arrival_rate: float, service_rate: float, admit_reward: float,52    cost_fn: COST_FN, max_stored_jobs: int,53    dtype: np.dtype) -> structure.MarkovDecisionProcess:54  """Creates a new MM1 Queue MDP.55  Args:56    arrival_rate: Rate of new jobs arriving. Should be positive.57    service_rate: Rate that accepted jobs are processed. Should be positive.58    admit_reward: Reward for accepting a job for processing.59    cost_fn: Function that returns cost (expressed as a positive number) for60      holding some number of jobs.61    max_stored_jobs: Limit on number of stored jobs transitions and rewards can62      be stored as fixed-size matrices.63    dtype: NumPy dtype of MDP, should be a float type, probably np.float64.64  Returns: An MDP.65  """66  arrive_prob = arrival_rate / (arrival_rate + service_rate)67  complete_prob = service_rate / (arrival_rate + service_rate)68  joint_rate = service_rate + arrival_rate69  num_states = max_stored_jobs * 270  num_actions = 271  transitions = np.zeros((num_actions, num_states, num_states), dtype=dtype)72  rewards = np.zeros((num_actions, num_states), dtype=dtype)73  transition_possibilities = itertools.product(74      range(max_stored_jobs),75      [False, True],76      [ADMIT, CONTINUE])77  for num_queued, new_job, action in transition_possibilities:78    s = to_global_state(jobs_in_queue=num_queued,79                        new_job=new_job)80    # Base Case.81    if num_queued == 0:82      if not new_job:83        no_new_job_next = s84        new_job_next = to_global_state(jobs_in_queue=num_queued,85                                       new_job=True)86        transitions[action, s, no_new_job_next] = complete_prob87        transitions[action, s, new_job_next] = arrive_prob88        if action == CONTINUE:89          rewards[action, s] = 0.90        elif action == ADMIT:91          rewards[action, s] = -1.92      elif new_job:93        if action == CONTINUE:94          no_new_job_next = to_global_state(jobs_in_queue=num_queued,95                                            new_job=False)96          new_job_next = s97          transitions[action, s, no_new_job_next] = complete_prob98          transitions[action, s, new_job_next] = arrive_prob99          rewards[action, s] = 0.100        elif action == ADMIT:101          no_new_job_next = to_global_state(jobs_in_queue=num_queued,102                                            new_job=False)103          new_job_next = to_global_state(jobs_in_queue=num_queued + 1,104                                         new_job=True)105          transitions[action, s, no_new_job_next] = complete_prob106          transitions[action, s, new_job_next] = arrive_prob107          rewards[action, s] = (admit_reward - cost_fn(108            jobs_waiting=num_queued + 1)) * joint_rate109    # General Case.110    elif num_queued > 0 and (num_queued < max_stored_jobs - 1):111      if not new_job:112        no_new_job_next = to_global_state(jobs_in_queue=num_queued - 1,113                                          new_job=False)114        new_job_next = to_global_state(jobs_in_queue=num_queued,115                                       new_job=True)116        transitions[action, s, no_new_job_next] = complete_prob117        transitions[action, s, new_job_next] = arrive_prob118        if action == CONTINUE:119          rewards[action, s] = (-cost_fn(jobs_waiting=num_queued)) * joint_rate120        elif action == ADMIT:121          # Small penalty for invalid action.122          rewards[action, s] = (-cost_fn(123            jobs_waiting=num_queued + 1)) * joint_rate124      elif new_job:125        if action == CONTINUE:126          no_new_job_next = to_global_state(jobs_in_queue=num_queued - 1,127                                            new_job=False)128          new_job_next = to_global_state(jobs_in_queue=num_queued,129                                         new_job=True)130          transitions[action, s, no_new_job_next] = complete_prob131          transitions[action, s, new_job_next] = arrive_prob132          rewards[action, s] = (-cost_fn(jobs_waiting=num_queued)) * joint_rate133        elif action == ADMIT:134          no_new_job_next = to_global_state(jobs_in_queue=num_queued,135                                            new_job=False)136          new_job_next = to_global_state(jobs_in_queue=num_queued + 1,137                                         new_job=True)138          transitions[action, s, no_new_job_next] = complete_prob139          transitions[action, s, new_job_next] = arrive_prob140          rewards[action, s] = (admit_reward - cost_fn(141            jobs_waiting=num_queued + 1)) * joint_rate142    # In our finite model, we cannot add more jobs in this state.143    elif num_queued == max_stored_jobs - 1:144      # Same as general case.145      if not new_job:146        new_job_next = to_global_state(jobs_in_queue=num_queued,147                                       new_job=True)148        no_new_job_next = to_global_state(jobs_in_queue=num_queued - 1,149                                          new_job=False)150        transitions[action, s, new_job_next] = arrive_prob151        transitions[action, s, no_new_job_next] = complete_prob152        if action == CONTINUE:153          rewards[action, s] = (-cost_fn(jobs_waiting=num_queued)) * joint_rate154        elif action == ADMIT:155          # Small penalty for invalid action.156          rewards[action, s] = (-cost_fn(157            jobs_waiting=num_queued + 1)) * joint_rate158      elif new_job:159        # Same as general case.160        if action == CONTINUE:161          new_job_next = to_global_state(jobs_in_queue=num_queued,162                                         new_job=True)163          no_new_job_next = to_global_state(jobs_in_queue=num_queued - 1,164                                            new_job=False)165          transitions[action, s, new_job_next] = arrive_prob166          transitions[action, s, no_new_job_next] = complete_prob167          rewards[action, s] = -cost_fn(jobs_waiting=num_queued) * joint_rate168        elif action == ADMIT:169          # Stuck here, cannot add another job to the queue.170          new_job_next = to_global_state(jobs_in_queue=num_queued,171                                         new_job=True)172          no_new_job_next = to_global_state(jobs_in_queue=num_queued,173                                            new_job=False)174          transitions[action, s, new_job_next] = arrive_prob175          transitions[action, s, no_new_job_next] = complete_prob176          # Same as passing b/c could not admit job.177          rewards[action, s] = -cost_fn(jobs_waiting=num_queued) * joint_rate178  name = f'MM1 {arrival_rate}:{service_rate}:{admit_reward}:{max_stored_jobs}:{dtype}:{cost_fn.func.__name__}'179  return structure.MarkovDecisionProcess(transitions=transitions,180                                         rewards=rewards,181                                         name=name)182MM1_QUEUE_1 = functools.partial(create,183                                arrival_rate=1.,184                                service_rate=1.,185                                admit_reward=10.,186                                cost_fn=functools.partial(linear_cost_fn,187                                                          cost_constant=1.),188                                max_stored_jobs=20)189MM1_QUEUE_2 = functools.partial(create,190                                arrival_rate=1.5,191                                service_rate=1.,192                                admit_reward=4.,193                                cost_fn=functools.partial(linear_cost_fn,194                                                          cost_constant=1.),195                                max_stored_jobs=20)196MM1_QUEUE_3 = functools.partial(create,197                                arrival_rate=1.,198                                service_rate=1.5,199                                admit_reward=4.,200                                cost_fn=functools.partial(linear_cost_fn,201                                                          cost_constant=1.),...

test_clients.py

Source:test_clients.py

1from collections import defaultdict2import json3import pytest4from pyglidein_server import clients, resources5from pyglidein_server.condor import JobCounts6from pyglidein_server.util import Error7def test_clients_init():8    clients.Clients()9def test_clients_update_single():10    queues = {11        'foo': {12            'resources': {},13            'num_processing': 10,14            'num_queued': 0,15        }16    }17    cl = clients.Clients()18    cl.update('foo', queues)19    data = cl.get('foo')20    assert len(data) == 121def test_clients_update_multi():22    queues = {23        'bar': {24            'resources': {'memory': 2},25            'num_processing': 10,26            'num_queued': 11,27        },28        'baz': {29            'resources': {'memory': 4},30            'num_processing': 12,31            'num_queued': 13,32        }33    }34    cl = clients.Clients()35    cl.update('foo', queues)36    data = cl.get('foo')37    assert len(data) == 238def test_clients_bad_resource():39    queues = {40        'foo': {41            'resources': {'foo': 1},42            'num_processing': 10,43            'num_queued': 0,44        }45    }46    cl = clients.Clients()47    with pytest.raises(Error) as exc_info:48        cl.update('foo', queues)49    assert 'resources' in exc_info.value.reason50def test_get_json():51    queues = {52        'foo': {53            'resources': {},54            'num_processing': 10,55            'num_queued': 0,56        }57    }58    cl = clients.Clients()59    cl.update('foo', queues)60    data = cl.get_json()61    assert len(data) == 162    values = list(data.values())63    assert len(values) == 164    json.dumps(data)65#######  Matching tests  #######66testdata = [67    # single site, single resource68    ({'site': {'q1': {'resources': {}, 'num_processing': 0, 'num_queued': 0}}},69     [{'resources': {}, 'processing': 0, 'queued': 1}],70     'site',71     {'q1': 1}),72    ({'site': {'q1': {'resources': {}, 'num_processing': 5, 'num_queued': 0}}},73     [{'resources': {}, 'processing': 5, 'queued': 10}],74     'site',75     {'q1': 8}),76    ({'site': {'q1': {'resources': {}, 'num_processing': 5, 'num_queued': 1}}},77     [{'resources': {}, 'processing': 5, 'queued': 10}],78     'site',79     {'q1': 4}),80    ({'site': {'q1': {'resources': {}, 'num_processing': 5, 'num_queued': 2}}},81     [{'resources': {}, 'processing': 5, 'queued': 10}],82     'site',83     {'q1': 2}),84    ({'site': {'q1': {'resources': {}, 'num_processing': 5, 'num_queued': 3}}},85     [{'resources': {}, 'processing': 5, 'queued': 10}],86     'site',87     {}),88    ({'site': {'q1': {'resources': {}, 'num_processing': 50, 'num_queued': 20}}},89     [{'resources': {}, 'processing': 50, 'queued': 100}],90     'site',91     {'q1': 12}),92    # single site, jobs of different sizes93    ({'site': {'q1': {'resources': {'memory':2}, 'num_processing': 50, 'num_queued': 20}}},94     [{'resources': {}, 'processing': 25, 'queued': 50},95      {'resources': {'memory':2}, 'processing': 25, 'queued': 50},96     ],97     'site',98     {'q1': 2}),99    ({'site': {'q1': {'resources': {'memory':2}, 'num_processing': 50, 'num_queued': 10}}},100     [{'resources': {}, 'processing': 45, 'queued': 90},101      {'resources': {'memory':2}, 'processing': 5, 'queued': 10},102     ],103     'site',104     {'q1': 14}),105    # multi-site106    ({'site': {'q1': {'resources': {'memory':2}, 'num_processing': 20, 'num_queued': 10}},107      'site2': {'q2': {'resources': {}, 'num_processing': 30, 'num_queued': 20}},108     },109     [{'resources': {}, 'processing': 45, 'queued': 90},110      {'resources': {'memory':2}, 'processing': 5, 'queued': 10},111     ],112     'site',113     {'q1': 1}),114    ({'site': {'q1': {'resources': {'memory':2}, 'num_processing': 20, 'num_queued': 10}},115      'site2': {'q2': {'resources': {}, 'num_processing': 30, 'num_queued': 20}},116     },117     [{'resources': {}, 'processing': 45, 'queued': 90},118      {'resources': {'memory':2}, 'processing': 5, 'queued': 10},119     ],120     'site2',121     {}),122    ({'site': {'q1': {'resources': {'memory':2}, 'num_processing': 20, 'num_queued': 10}},123      'site2': {'q2': {'resources': {}, 'num_processing': 30, 'num_queued': 20}},124     },125     [{'resources': {}, 'processing': 45, 'queued': 500},126      {'resources': {'memory':2}, 'processing': 5, 'queued': 10},127     ],128     'site',129     {'q1': 45}),130    ({'site': {'q1': {'resources': {'memory':2}, 'num_processing': 20, 'num_queued': 10}},131      'site2': {'q2': {'resources': {}, 'num_processing': 30, 'num_queued': 20}},132     },133     [{'resources': {}, 'processing': 45, 'queued': 500},134      {'resources': {'memory':2}, 'processing': 5, 'queued': 10},135     ],136     'site2',137     {'q2': 73}),138    # larger resources139    ({'site': {'q1': {'resources': {'memory':1}, 'num_processing': 20, 'num_queued': 10}}},140     [{'resources': {}, 'processing': 50, 'queued': 10},141      {'resources': {'memory':2}, 'processing': 0, 'queued': 1000},142     ],143     'site',144     {}),145]146class FakeCondor:147    def __init__(self, data):148        self.data = defaultdict(JobCounts)149        for d in data:150            res = resources.Resources(d['resources'])151            self.data[res]['_sum']['processing'] += d['processing']152            self.data[res]['_sum']['queued'] += d['queued']153    def get(self):154        return self.data155@pytest.mark.parametrize('glideins,condor,name,expected', testdata)156def test_clients_match(glideins, condor, name, expected):157    cl = clients.Clients()158    for site in glideins:159        cl.update(site, glideins[site])160    ret = cl.match(name, FakeCondor(condor))...

clients.py

Source:clients.py

1from copy import deepcopy2import math3import logging4from .resources import Resources5from .util import Error6logger = logging.getLogger(__name__)7class Clients:8    """9    Stores client (site) information.10    Each client may have N resource queues. Lookups are by `Resources`,11    specifically separating queued and processing resources.12    """13    def __init__(self):14        self.data = {}15    def update(self, name, queues):16        """17        Update a client.18        Valid queues are a dict of queue information, including19        `resources`, `num_queued`, and `num_processing` for each client queue.20        The keys are used as references, since the resources may be binned21        and alter slightly.22        Args:23            name (str): name of client24            queues (dict): queue information25        """26        if not isinstance(queues, dict):27            raise Error('client data must be a dict of queue statuses')28        ret = {}29        for ref in queues:30            queue = queues[ref]31            # validate32            if set(queue.keys()) != {'resources', 'num_queued', 'num_processing'}:33                raise Error('client data must have keys: resources, num_queued, num_processing')34            if set(queue['resources']) - set(Resources.RESOURCE_DEFAULTS):35                raise Error(f'client data resources must be: {set(Resources.RESOURCE_DEFAULTS)}')36            # set resources37            ret[Resources(queue['resources'], tolerance=1)] = {38                'ref': ref,39                'num_queued': queue['num_queued'],40                'num_processing': queue['num_processing'],41            }42        # do update43        self.data[name] = ret44    def get(self, name):45        """Get client data"""46        return self.data[name]47    def get_all(self):48        """Get all client data"""49        return self.data50    def get_json(self):51        """Get client data in json format"""52        ret = {}53        for k in self.data:54            r = {}55            for res in self.data[k]:56                key = hash(res)57                r[key] = deepcopy(self.data[k][res])58                r[key]['_resources'] = res.resources59            ret[k] = r60        return ret61    def match(self, name, condor_queue):62        """63        Perform matching for a client.64        This matches a client against the condor queue and other clients,65        to determine if it should submit more glideins on any of its queues.66        Args:67            name (str): name of client68            condor_queue (CondorCache): condor queue69        Returns:70            dict: name of queue and number of jobs to submit71        """72        condor_jobs = condor_queue.get()73        ret = {}74        for res in self.data[name]:75            queue = self.data[name][res]76            jobs_queued = 0.77            jobs_processing = 0.78            for r in condor_jobs:79                if r <= res:80                    mismatch = res.mismatch(r)81                    jobs_queued += mismatch * condor_jobs[r]['_sum']['queued']82                    jobs_processing += mismatch * condor_jobs[r]['_sum']['processing']83            if jobs_processing > 0:84                job_ratio = jobs_processing / (jobs_processing + jobs_queued)85            else:86                job_ratio = 1.87            logger.debug(f'jobs_queued: {jobs_queued}')88            logger.debug(f'jobs_processing: {jobs_processing}')89            logger.debug(f'job_ratio: {job_ratio}')90            glideins_queued = 0.91            glideins_processing = 0.92            for site in self.data:93                for r in self.data[site]:94                    if r <= res:95                        mismatch = res.mismatch(r)96                        glideins_queued += mismatch * self.data[site][r]['num_queued']97                        glideins_processing += mismatch * self.data[site][r]['num_processing']98            if glideins_processing > 0:99                glidein_util = glideins_processing / (glideins_processing + glideins_queued)100            else:101                glidein_util = 1.102            logger.debug(f'glideins_queued: {glideins_queued}')103            logger.debug(f'glideins_processing: {glideins_processing}')104            logger.debug(f'glidein_util: {glidein_util}')105            global_queue = (jobs_queued - glideins_queued) * math.pow(job_ratio, 1/4) * math.pow(glidein_util, 2)106            logger.debug(f'global_queue: {global_queue}')107            local_queue = max(global_queue - queue['num_queued'], 0)108            logger.debug(f'local_queue: {local_queue}')109            if local_queue > 0:110                ret[queue['ref']] = math.ceil(local_queue)...

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