def testResourceNumericalError(self):
resource = Resources(cpu=0.99, gpu=0.99, custom_resources={"a": 0.99})
small_resource = Resources(
cpu=0.33, gpu=0.33, custom_resources={"a": 0.33})
for i in range(3):
resource = Resources.subtract(resource, small_resource)
self.assertTrue(resource.is_nonnegative())
def testSubtraction(self):
resource_1 = Resources(
1,
0,
0,
1,
custom_resources={
"a": 1,
"b": 2
},
extra_custom_resources={
"a": 1,
"b": 1
})
resource_2 = Resources(
1,
0,
0,
1,
custom_resources={
"a": 1,
"b": 2
},
extra_custom_resources={
"a": 1,
"b": 1
})
new_res = Resources.subtract(resource_1, resource_2)
self.assertTrue(new_res.cpu == 0)
self.assertTrue(new_res.gpu == 0)
self.assertTrue(new_res.extra_cpu == 0)
self.assertTrue(new_res.extra_gpu == 0)
self.assertTrue(all(k == 0 for k in new_res.custom_resources.values()))
self.assertTrue(
all(k == 0 for k in new_res.extra_custom_resources.values()))
def has_resources(self, resources: Resources) -> bool:
"""Returns whether this runner has at least the specified resources.
This refreshes the Ray cluster resources if the time since last update
has exceeded self._refresh_period. This also assumes that the
cluster is not resizing very frequently.
"""
if resources.has_placement_group:
return self._pg_manager.can_stage()
self._update_avail_resources()
currently_available = Resources.subtract(self._avail_resources,
self._committed_resources)
have_space = (
resources.cpu_total() <= currently_available.cpu
and resources.gpu_total() <= currently_available.gpu
and resources.memory_total() <= currently_available.memory
and resources.object_store_memory_total() <=
currently_available.object_store_memory and all(
resources.get_res_total(res) <= currently_available.get(res)
for res in resources.custom_resources))
if have_space:
# The assumption right now is that we block all trials if one
# trial is queued.
return True
return False
def _fluid(self, meta: TrialGroupMeta):
"""Run fluid on a specific group"""
self._dump_groups()
# set of trials to consider
A = {trial.trial_id for trial in self._trial_group(meta.grp)}
logger.debug(
f"_fluid: meta.perf.trials_missing_info={meta.perf.trials_missing_info} meta.trials={meta.trials}, meta.grp={meta.grp}, trial_groups={self.trial_groups}, A={A}"
)
# assignment of resources
W: Dict[str, Resources] = {}
# compute new idle resources if every trials in this group were stopped
M = resources_add(self.idle_resources,
self._committed_resources_in_group(meta.grp))
if meta.perf.trials_missing_info:
# there are still trials need perf data,
# restrict A to only these trials
others = A.difference(meta.perf.trials_missing_info)
A = meta.perf.trials_missing_info
# set others to use 0 resource
for tid in others:
W[tid] = Resources(cpu=0, gpu=0)
# use 1 gpu per trial to get reference perf
for tid in A:
r = Resources(cpu=1, gpu=1)
Mp = Resources.subtract(M, r)
if not Mp.is_nonnegative():
break
M = Mp
W[tid] = r
else:
# convert A to array for sorting
A = np.array(list(A))
# reference height (1 width)
H1 = np.array([meta.perf.get_height(tid, 1) for tid in A])
# sort by H1 in non-increasing order
ord = np.argsort(H1[::-1])
A = A[ord]
H1 = H1[ord]
# $$w_i= \min(
# \max(
# \floor{
# \frac{h_{i,1}}{\sum_j h_{j,1} } n
# },
# \frac{1}{c}),
# d
# )$$
c = 1 / 2
d = 4
w = np.minimum(
np.maximum(np.floor(H1 * np.size(H1) / np.sum(H1)), 1 / c), d)
# assign resources based on w
w = w / w.sum() * self._avail_resources.gpu_total()
resW = [Resources(cpu=1, gpu=g) for g in w]
# write to W
W = dict(zip(A, resW))
self._ensure_W(W, meta)
def _ensure_W(self, W: Dict[str, Resources], meta: TrialGroupMeta):
"""Adjust group resources given in W"""
logger.debug(f"ensure_W: W={W} meta.trials={meta.trials}")
# stop any trials with 0 res
# this has to be done first to free up resources for others to use
for trial_id, res in W.items():
trial = self.trial_groups[trial_id].trial
if res.cpu_total() + res.gpu_total() == 0:
# add to paused, then ensure_stop, we do not change trial's status which is visible outside
running = self._find_running(trial)
if running is not None:
# don't call pause_trial, which will trigger another fluid reschedule
self.jobs_paused[running.in_flight_future] = running
self._ensure_stop(running.trial)
trial.resources = res
# add to pending
self.start_trial(trial)
# adjust any trials with different res, including any not already running
for trial_id, res in W.items():
# use trial group to map trial_id to trial
trial = self.trial_groups[trial_id].trial
if res.cpu_total() + res.gpu_total() == 0:
# already handled in the loop above
continue
if (
# current_res != res
Resources.subtract(trial.resources, res).is_nonnegative()
!= Resources.subtract(res,
trial.resources).is_nonnegative()):
running = self._find_running(trial)
if running is not None:
# don't call pause_trial, which will trigger another fluid reschedule
self.jobs_paused[running.in_flight_future] = running
self._ensure_stop(trial)
# at this point, the job is always stopped but not in the pending queue,
# because fluid clears the pending queue.
trial.resources = res
self._kickoff(PendingJob(trial, None, True), res)
def testDifferentResources(self):
resource_1 = Resources(1, 0, 0, 1, custom_resources={"a": 1, "b": 2})
resource_2 = Resources(1, 0, 0, 1, custom_resources={"a": 1, "c": 2})
new_res = Resources.subtract(resource_1, resource_2)
assert "c" in new_res.custom_resources
assert "b" in new_res.custom_resources
self.assertTrue(new_res.cpu == 0)
self.assertTrue(new_res.gpu == 0)
self.assertTrue(new_res.extra_cpu == 0)
self.assertTrue(new_res.extra_gpu == 0)
self.assertTrue(new_res.get("a") == 0)
def has_resources(self, resources):
"""Returns whether this runner has at least the specified resources.
This refreshes the Ray cluster resources if the time since last update
has exceeded self._refresh_period. This also assumes that the
cluster is not resizing very frequently.
"""
if time.time() - self._last_resource_refresh > self._refresh_period:
self._update_avail_resources()
currently_available = Resources.subtract(self._avail_resources,
self._committed_resources)
have_space = (
resources.cpu_total() <= currently_available.cpu
and resources.gpu_total() <= currently_available.gpu
and resources.memory_total() <= currently_available.memory
and resources.object_store_memory_total() <=
currently_available.object_store_memory and all(
resources.get_res_total(res) <= currently_available.get(res)
for res in resources.custom_resources))
if have_space:
return True
can_overcommit = self._queue_trials
if ((resources.cpu_total() > 0 and currently_available.cpu <= 0)
or (resources.gpu_total() > 0 and currently_available.gpu <= 0)
or
(resources.memory_total() > 0 and currently_available.memory <= 0)
or (resources.object_store_memory_total() > 0
and currently_available.object_store_memory <= 0) or any(
(resources.get_res_total(res_name) > 0
and currently_available.get(res_name) <= 0)
for res_name in resources.custom_resources)):
can_overcommit = False # requested resource is already saturated
if can_overcommit:
logger.warning(
"Allowing trial to start even though the "
"cluster does not have enough free resources. Trial actors "
"may appear to hang until enough resources are added to the "
"cluster (e.g., via autoscaling). You can disable this "
"behavior by specifying `queue_trials=False` in "
"ray.tune.run().")
return True
return False
def has_resources(self, resources):
"""Returns whether this runner has at least the specified resources.
This refreshes the Ray cluster resources if the time since last update
has exceeded self._refresh_period. This also assumes that the
cluster is not resizing very frequently.
"""
if resources.has_placement_group:
return self._pg_manager.can_stage()
self._update_avail_resources()
currently_available = Resources.subtract(self._avail_resources,
self._committed_resources)
have_space = (
resources.cpu_total() <= currently_available.cpu
and resources.gpu_total() <= currently_available.gpu
and resources.memory_total() <= currently_available.memory
and resources.object_store_memory_total() <=
currently_available.object_store_memory and all(
resources.get_res_total(res) <= currently_available.get(res)
for res in resources.custom_resources))
if have_space:
# The assumption right now is that we block all trials if one
# trial is queued.
self._trial_queued = False
return True
can_overcommit = self._queue_trials and not self._trial_queued
if can_overcommit:
self._trial_queued = True
logger.warning(
"Allowing trial to start even though the "
"cluster does not have enough free resources. Trial actors "
"may appear to hang until enough resources are added to the "
"cluster (e.g., via autoscaling). You can disable this "
"behavior by specifying `queue_trials=False` in "
"ray.tune.run().")
return True
return False
def resources_add(a: Resources, b: Resources) -> Resources:
zero = Resources(cpu=0, gpu=0)
nb = Resources.subtract(zero, b)
return Resources.subtract(a, nb)
def idle_resources(self) -> Resources:
return Resources.subtract(self._avail_resources,
self._committed_resources)
