Example #1
0
    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
Example #2
0
    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.
            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