| # 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 torch | |
| from evotorch import Problem | |
| from evotorch.algorithms import SNES | |
| from evotorch.logging import StdOutLogger | |
| # Define the cost function that receives a batch of solution vectors (2D Tensor) and returns the costs (1D Tensor) | |
| def cost(x: torch.Tensor) -> torch.Tensor: | |
| return torch.linalg.vector_norm(x, dim=-1) | |
| # The problem is to minimize the cost, for solution vectors of default dtype (float32) and length 10, on the first GPU | |
| problem = Problem( | |
| "min", | |
| cost, | |
| solution_length=10, | |
| initial_bounds=(-10, 10), | |
| vectorized=True, | |
| device="cuda:0" if torch.cuda.is_available() else "cpu", # Change to "cpu" if a GPU is not available | |
| ) | |
| # Run the distribution-based search algorithm SNES for 500 steps and log results to standard output every 100 steps | |
| searcher = SNES(problem, popsize=100, stdev_init=0.01) | |
| logger = StdOutLogger(searcher, interval=100) | |
| searcher.run(500) | |
| print("Final status:\n", searcher.status) | |