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483026f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | # Copyright 2022 NNAISENSE SA
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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
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