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| import pytest |
| import torch |
|
|
| import evotorch as et |
|
|
|
|
| class MyProblem(et.Problem): |
| def __init__(self, num_actors: int): |
| super().__init__( |
| objective_sense="min", |
| initial_bounds=(-10.0, 10.0), |
| solution_length=5, |
| num_actors=int(num_actors), |
| ) |
|
|
| def _evaluate_batch(self, batch: et.SolutionBatch): |
| x = batch.values |
| norms = torch.linalg.norm(x, dim=-1) |
| batch.set_evals(norms) |
|
|
| def get_actor_index(self) -> int: |
| return self.actor_index |
|
|
| def get_env(self): |
| class DummyEnv: |
| def __init__(self, actor_index: int): |
| self.actor_index = actor_index |
|
|
| def get_parent_actor_index(self) -> int: |
| return self.actor_index |
|
|
| result = DummyEnv(self.actor_index) |
| return result |
|
|
|
|
| def test_remote_access(): |
| num_actors = 4 |
| problem = MyProblem(num_actors=num_actors) |
| expected_indices = set(range(num_actors)) |
| all_actor_indices = set(problem.all_remote_problems().get_actor_index()) |
| all_env_actor_indices = set(problem.all_remote_envs().get_parent_actor_index()) |
| assert all_actor_indices == expected_indices |
| assert all_env_actor_indices == expected_indices |
| assert len(problem.actors) == num_actors |
|
|
