| """ |
| Example solution for cant-be-late-multi problem. |
| |
| Solution interface: |
| class Solution(MultiRegionStrategy): |
| def solve(self, spec_path: str) -> "Solution": |
| # Read config from spec_path and initialize |
| return self |
| |
| def _step(self, last_cluster_type, has_spot) -> ClusterType: |
| # Decision logic at each simulation step |
| ... |
| """ |
| import json |
| import math |
| from argparse import Namespace |
|
|
| from sky_spot.strategies.multi_strategy import MultiRegionStrategy |
| from sky_spot.utils import ClusterType |
|
|
|
|
| class Solution(MultiRegionStrategy): |
| """Greedy multi-region strategy: use spot when available, switch regions if not.""" |
|
|
| NAME = "greedy_multi_region" |
|
|
| def solve(self, spec_path: str) -> "Solution": |
| """Initialize the solution from spec_path config.""" |
| with open(spec_path) as f: |
| config = json.load(f) |
|
|
| args = Namespace( |
| deadline_hours=float(config["deadline"]), |
| task_duration_hours=[float(config["duration"])], |
| restart_overhead_hours=[float(config["overhead"])], |
| inter_task_overhead=[0.0], |
| ) |
| super().__init__(args) |
| return self |
|
|
| def _step(self, last_cluster_type: ClusterType, has_spot: bool) -> ClusterType: |
| """Make decision at each simulation step.""" |
| env = self.env |
| gap = env.gap_seconds |
|
|
| work_left = self.task_duration - sum(self.task_done_time) |
| if work_left <= 1e-9: |
| return ClusterType.NONE |
|
|
| left_ticks = max(0, math.floor((self.deadline - env.elapsed_seconds) / gap)) |
| need1d = math.ceil((work_left + self.restart_overhead) / gap) |
| need2d = math.ceil((work_left + 2 * self.restart_overhead) / gap) |
|
|
| |
| if need1d >= left_ticks: |
| return ClusterType.ON_DEMAND |
|
|
| |
| if need2d >= left_ticks: |
| if last_cluster_type == ClusterType.SPOT and has_spot: |
| return ClusterType.SPOT |
| return ClusterType.ON_DEMAND |
|
|
| |
| if has_spot: |
| return ClusterType.SPOT |
|
|
| |
| num_regions = env.get_num_regions() |
| current_region = env.get_current_region() |
|
|
| for i in range(num_regions): |
| next_region = (current_region + 1 + i) % num_regions |
| if next_region != current_region: |
| env.switch_region(next_region) |
| return ClusterType.NONE |
|
|
| |
| return ClusterType.NONE |
|
|
