Allocation Functions

Below are example allocation functions available in libEnsemble.

Important

See the API for allocation functions here.

Note

The default allocation function is give_sim_work_first.

give_sim_work_first

give_sim_work_first.give_sim_work_first(W, H, sim_specs, gen_specs, alloc_specs, persis_info, libE_info)

Decide what should be given to workers. This allocation function gives any available simulation work first, and only when all simulations are completed or running does it start (at most alloc_specs["user"]["num_active_gens"]) generator instances.

Allows for a alloc_specs["user"]["batch_mode"] where no generation work is given out unless all entries in H are returned.

Can give points in highest priority, if "priority" is a field in H. If alloc_specs[“user”][“give_all_with_same_priority”] is set to True, then all points with the same priority value are given as a batch to the sim.

Workers performing sims will be assigned resources given in H[“resource_sets”] this field exists, else defaulting to one. Workers performing gens are assigned resource_sets given by persis_info[“gen_resources”] or zero.

This is the default allocation function if one is not defined.

tags: alloc, default, batch, priority

See also

test_uniform_sampling.py # noqa

Parameters:

  • W (ndarray[Any, dtype[_ScalarType_co]])

  • H (ndarray[Any, dtype[_ScalarType_co]])

  • sim_specs (dict)

  • gen_specs (dict)

  • alloc_specs (dict)

  • persis_info (dict)

  • libE_info (dict)

Return type:

Tuple[dict]

give_sim_work_first.py
 1import time
 2from typing import Tuple
 3
 4import numpy as np
 5import numpy.typing as npt
 6
 7from libensemble.tools.alloc_support import AllocSupport, InsufficientFreeResources
 8
 9
10def give_sim_work_first(
11    W: npt.NDArray,
12    H: npt.NDArray,
13    sim_specs: dict,
14    gen_specs: dict,
15    alloc_specs: dict,
16    persis_info: dict,
17    libE_info: dict,
18) -> Tuple[dict]:
19    """
20    Decide what should be given to workers. This allocation function gives any
21    available simulation work first, and only when all simulations are
22    completed or running does it start (at most ``alloc_specs["user"]["num_active_gens"]``)
23    generator instances.
24
25    Allows for a ``alloc_specs["user"]["batch_mode"]`` where no generation
26    work is given out unless all entries in ``H`` are returned.
27
28    Can give points in highest priority, if ``"priority"`` is a field in ``H``.
29    If alloc_specs["user"]["give_all_with_same_priority"] is set to True, then
30    all points with the same priority value are given as a batch to the sim.
31
32    Workers performing sims will be assigned resources given in H["resource_sets"]
33    this field exists, else defaulting to one. Workers performing gens are
34    assigned resource_sets given by persis_info["gen_resources"] or zero.
35
36    This is the default allocation function if one is not defined.
37
38    tags: alloc, default, batch, priority
39
40    .. seealso::
41        `test_uniform_sampling.py <https://github.com/Libensemble/libensemble/blob/develop/libensemble/tests/functionality_tests/test_uniform_sampling.py>`_ # noqa
42    """
43
44    user = alloc_specs.get("user", {})
45
46    if "cancel_sims_time" in user:
47        # Cancel simulations that are taking too long
48        rows = np.where(np.logical_and.reduce((H["sim_started"], ~H["sim_ended"], ~H["cancel_requested"])))[0]
49        inds = time.time() - H["sim_started_time"][rows] > user["cancel_sims_time"]
50        to_request_cancel = rows[inds]
51        for row in to_request_cancel:
52            H[row]["cancel_requested"] = True
53
54    if libE_info["sim_max_given"] or not libE_info["any_idle_workers"]:
55        return {}, persis_info
56
57    # Initialize alloc_specs["user"] as user.
58    batch_give = user.get("give_all_with_same_priority", False)
59    gen_in = gen_specs.get("in", [])
60
61    manage_resources = libE_info["use_resource_sets"]
62    support = AllocSupport(W, manage_resources, persis_info, libE_info)
63    gen_count = support.count_gens()
64    Work = {}
65
66    points_to_evaluate = ~H["sim_started"] & ~H["cancel_requested"]
67
68    if np.any(points_to_evaluate):
69        for wid in support.avail_worker_ids(gen_workers=False):
70            sim_ids_to_send = support.points_by_priority(H, points_avail=points_to_evaluate, batch=batch_give)
71            try:
72                Work[wid] = support.sim_work(wid, H, sim_specs["in"], sim_ids_to_send, persis_info.get(wid))
73            except InsufficientFreeResources:
74                break
75            points_to_evaluate[sim_ids_to_send] = False
76            if not np.any(points_to_evaluate):
77                break
78    else:
79        for wid in support.avail_worker_ids(gen_workers=True):
80            # Allow at most num_active_gens active generator instances
81            if gen_count >= user.get("num_active_gens", gen_count + 1):
82                break
83
84            # Do not start gen instances in batch mode if workers still working
85            if user.get("batch_mode") and not support.all_sim_ended(H):
86                break
87
88            # Give gen work
89            return_rows = range(len(H)) if gen_in else []
90            try:
91                Work[wid] = support.gen_work(wid, gen_in, return_rows, persis_info.get(wid))
92            except InsufficientFreeResources:
93                break
94            gen_count += 1
95
96    return Work, persis_info

fast_alloc

fast_alloc.give_sim_work_first(W, H, sim_specs, gen_specs, alloc_specs, persis_info, libE_info)

This allocation function gives (in order) entries in H to idle workers to evaluate in the simulation function. The fields in sim_specs["in"] are given. If all entries in H have been given a be evaluated, a worker is told to call the generator function, provided this wouldn’t result in more than alloc_specs["user"]["num_active_gen"] active generators.

This fast_alloc variation of give_sim_work_first is useful for cases that simply iterate through H, issuing evaluations in order and, in particular, is likely to be faster if there will be many short simulation evaluations, given that this function contains fewer column length operations.

tags: alloc, simple, fast

See also

test_fast_alloc.py # noqa

fast_alloc.py
 1from libensemble.tools.alloc_support import AllocSupport, InsufficientFreeResources
 2
 3
 4def give_sim_work_first(W, H, sim_specs, gen_specs, alloc_specs, persis_info, libE_info):
 5    """
 6    This allocation function gives (in order) entries in ``H`` to idle workers
 7    to evaluate in the simulation function. The fields in ``sim_specs["in"]``
 8    are given. If all entries in `H` have been given a be evaluated, a worker
 9    is told to call the generator function, provided this wouldn't result in
10    more than ``alloc_specs["user"]["num_active_gen"]`` active generators.
11
12    This fast_alloc variation of give_sim_work_first is useful for cases that
13    simply iterate through H, issuing evaluations in order and, in particular,
14    is likely to be faster if there will be many short simulation evaluations,
15    given that this function contains fewer column length operations.
16
17    tags: alloc, simple, fast
18
19    .. seealso::
20        `test_fast_alloc.py <https://github.com/Libensemble/libensemble/blob/develop/libensemble/tests/functionality_tests/test_fast_alloc.py>`_ # noqa
21    """
22
23    if libE_info["sim_max_given"] or not libE_info["any_idle_workers"]:
24        return {}, persis_info
25
26    user = alloc_specs.get("user", {})
27    manage_resources = libE_info["use_resource_sets"]
28
29    support = AllocSupport(W, manage_resources, persis_info, libE_info)
30
31    gen_count = support.count_gens()
32    Work = {}
33    gen_in = gen_specs.get("in", [])
34
35    # Give sim work if possible
36    for wid in support.avail_worker_ids(gen_workers=False):
37        persis_info = support.skip_canceled_points(H, persis_info)
38        if persis_info["next_to_give"] < len(H):
39            try:
40                Work[wid] = support.sim_work(wid, H, sim_specs["in"], [persis_info["next_to_give"]], [])
41            except InsufficientFreeResources:
42                break
43            persis_info["next_to_give"] += 1
44
45    # Give gen work if possible
46    if persis_info["next_to_give"] >= len(H):
47        for wid in support.avail_worker_ids(gen_workers=True):
48            if wid not in Work and gen_count < user.get("num_active_gens", gen_count + 1):
49                return_rows = range(len(H)) if gen_in else []
50                try:
51                    Work[wid] = support.gen_work(wid, gen_in, return_rows, persis_info.get(wid))
52                except InsufficientFreeResources:
53                    break
54                gen_count += 1
55                persis_info["total_gen_calls"] += 1
56
57    return Work, persis_info

start_only_persistent

start_only_persistent.only_persistent_gens(W, H, sim_specs, gen_specs, alloc_specs, persis_info, libE_info)

This allocation function will give simulation work if possible, but otherwise start up to alloc_specs["user"]["num_active_gens"] persistent generators (defaulting to one).

By default, evaluation results are given back to the generator once all generated points have been returned from the simulation evaluation. If alloc_specs["user"]["async_return"] is set to True, then any returned points are given back to the generator.

If any workers are marked as zero_resource_workers, then these will only be used for generators.

If any of the persistent generators has exited, then ensemble shutdown is triggered.

User options:

To be provided in calling script: E.g., alloc_specs["user"]["async_return"] = True

init_sample_size: int, optional

Initial sample size - always return in batch. Default: 0

num_active_gens: int, optional

Maximum number of persistent generators to start. Default: 1

async_return: Boolean, optional

Return results to gen as they come in (after sample). Default: False (batch return).

active_recv_gen: Boolean, optional

Create gen in active receive mode. If True, the manager does not need to wait for a return from the generator before sending further returned points. Default: False

tags: alloc, batch, async, persistent, priority

See also

test_persistent_uniform_sampling.py # noqa test_persistent_uniform_sampling_async.py # noqa test_persistent_surmise_calib.py # noqa test_persistent_uniform_gen_decides_stop.py # noqa

start_only_persistent.py
  1import numpy as np
  2
  3from libensemble.message_numbers import EVAL_GEN_TAG, EVAL_SIM_TAG
  4from libensemble.tools.alloc_support import AllocSupport, InsufficientFreeResources
  5
  6
  7def only_persistent_gens(W, H, sim_specs, gen_specs, alloc_specs, persis_info, libE_info):
  8    """
  9    This allocation function will give simulation work if possible, but
 10    otherwise start up to ``alloc_specs["user"]["num_active_gens"]``
 11    persistent generators (defaulting to one).
 12
 13    By default, evaluation results are given back to the generator once
 14    all generated points have been returned from the simulation evaluation.
 15    If ``alloc_specs["user"]["async_return"]`` is set to True, then any
 16    returned points are given back to the generator.
 17
 18    If any workers are marked as zero_resource_workers, then these will only
 19    be used for generators.
 20
 21    If any of the persistent generators has exited, then ensemble shutdown
 22    is triggered.
 23
 24    **User options**:
 25
 26    To be provided in calling script: E.g., ``alloc_specs["user"]["async_return"] = True``
 27
 28    init_sample_size: int, optional
 29        Initial sample size - always return in batch. Default: 0
 30
 31    num_active_gens: int, optional
 32        Maximum number of persistent generators to start. Default: 1
 33
 34    async_return: Boolean, optional
 35        Return results to gen as they come in (after sample). Default: False (batch return).
 36
 37    active_recv_gen: Boolean, optional
 38        Create gen in active receive mode. If True, the manager does not need to wait
 39        for a return from the generator before sending further returned points.
 40        Default: False
 41
 42    tags: alloc, batch, async, persistent, priority
 43
 44    .. seealso::
 45        `test_persistent_uniform_sampling.py <https://github.com/Libensemble/libensemble/blob/develop/libensemble/tests/functionality_tests/test_persistent_uniform_sampling.py>`_ # noqa
 46        `test_persistent_uniform_sampling_async.py <https://github.com/Libensemble/libensemble/blob/develop/libensemble/tests/functionality_tests/test_persistent_uniform_sampling_async.py>`_ # noqa
 47        `test_persistent_surmise_calib.py <https://github.com/Libensemble/libensemble/blob/develop/libensemble/tests/regression_tests/test_persistent_surmise_calib.py>`_ # noqa
 48        `test_persistent_uniform_gen_decides_stop.py <https://github.com/Libensemble/libensemble/blob/develop/libensemble/tests/functionality_tests/test_persistent_uniform_gen_decides_stop.py>`_ # noqa
 49    """
 50
 51    if libE_info["sim_max_given"] or not libE_info["any_idle_workers"]:
 52        return {}, persis_info
 53
 54    # Initialize alloc_specs["user"] as user.
 55    user = alloc_specs.get("user", {})
 56    manage_resources = libE_info["use_resource_sets"]
 57
 58    active_recv_gen = user.get("active_recv_gen", False)  # Persistent gen can handle irregular communications
 59    init_sample_size = user.get("init_sample_size", 0)  # Always batch return until this many evals complete
 60    batch_give = user.get("give_all_with_same_priority", False)
 61
 62    support = AllocSupport(W, manage_resources, persis_info, libE_info)
 63    gen_count = support.count_persis_gens()
 64    Work = {}
 65
 66    # Asynchronous return to generator
 67    async_return = user.get("async_return", False) and sum(H["sim_ended"]) >= init_sample_size
 68
 69    if gen_count < persis_info.get("num_gens_started", 0):
 70        # When a persistent worker is done, trigger a shutdown (returning exit condition of 1)
 71        return Work, persis_info, 1
 72
 73    # Give evaluated results back to a running persistent gen
 74    for wid in support.avail_worker_ids(persistent=EVAL_GEN_TAG, active_recv=active_recv_gen):
 75        gen_inds = H["gen_worker"] == wid
 76        returned_but_not_given = np.logical_and.reduce((H["sim_ended"], ~H["gen_informed"], gen_inds))
 77        if np.any(returned_but_not_given):
 78            if async_return or support.all_sim_ended(H, gen_inds):
 79                point_ids = np.where(returned_but_not_given)[0]
 80                Work[wid] = support.gen_work(
 81                    wid,
 82                    gen_specs["persis_in"],
 83                    point_ids,
 84                    persis_info.get(wid),
 85                    persistent=True,
 86                    active_recv=active_recv_gen,
 87                )
 88                returned_but_not_given[point_ids] = False
 89
 90    # Now the give_sim_work_first part
 91    points_to_evaluate = ~H["sim_started"] & ~H["cancel_requested"]
 92    avail_workers = support.avail_worker_ids(persistent=False, zero_resource_workers=False, gen_workers=False)
 93    if user.get("alt_type"):
 94        avail_workers = list(
 95            set(support.avail_worker_ids(persistent=False, zero_resource_workers=False))
 96            | set(support.avail_worker_ids(persistent=EVAL_SIM_TAG, zero_resource_workers=False))
 97        )
 98    for wid in avail_workers:
 99        if not np.any(points_to_evaluate):
100            break
101
102        sim_ids_to_send = support.points_by_priority(H, points_avail=points_to_evaluate, batch=batch_give)
103
104        try:
105            if user.get("alt_type"):
106                Work[wid] = support.sim_work(
107                    wid, H, sim_specs["in"], sim_ids_to_send, persis_info.get(wid), persistent=True
108                )
109            else:
110                Work[wid] = support.sim_work(wid, H, sim_specs["in"], sim_ids_to_send, persis_info.get(wid))
111        except InsufficientFreeResources:
112            break
113
114        points_to_evaluate[sim_ids_to_send] = False
115
116    # Start persistent gens if no worker to give out. Uses zero_resource_workers if defined.
117    if not np.any(points_to_evaluate):
118        avail_workers = support.avail_worker_ids(persistent=False, zero_resource_workers=True, gen_workers=True)
119
120        for wid in avail_workers:
121            if gen_count < user.get("num_active_gens", 1):
122                # Finally, start a persistent generator as there is nothing else to do.
123                try:
124                    Work[wid] = support.gen_work(
125                        wid,
126                        gen_specs.get("in", []),
127                        range(len(H)),
128                        persis_info.get(wid),
129                        persistent=True,
130                        active_recv=active_recv_gen,
131                    )
132                except InsufficientFreeResources:
133                    break
134
135                persis_info["num_gens_started"] = persis_info.get("num_gens_started", 0) + 1
136                gen_count += 1
137
138    return Work, persis_info, 0

start_persistent_local_opt_gens

start_persistent_local_opt_gens.start_persistent_local_opt_gens(W, H, sim_specs, gen_specs, alloc_specs, persis_info, libE_info)

This allocation function will do the following:

  • Start up a persistent generator that is a local opt run at the first point identified by APOSMM’s decide_where_to_start_localopt. Note, it will do this only if at least one worker will be left to perform simulation evaluations.

  • If multiple starting points are available, the one with smallest function value is chosen.

  • If no candidate starting points exist, points from existing runs will be evaluated (oldest first).

  • If no points are left, call the generation function.

tags: alloc, persistent, aposmm

See also

test_uniform_sampling_then_persistent_localopt_runs.py # noqa