| Name | esmpi JSON |
| Version |
1.0
JSON |
| download |
| home_page | None |
| Summary | Collection of Evolution Strategies optimizers with native MPI parallelization. |
| upload_time | 2024-07-25 10:46:01 |
| maintainer | None |
| docs_url | None |
| author | Giacomo Spigler <http://www.spigler.net/giacomo> |
| requires_python | >=3.7 |
| license | MIT License Copyright (c) [year] [fullname] Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
evolution strategies
cma-es
mpi
openai-es
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
## About
Compact implementation of Evolution Strategies using MPI, together with an MPI-based wrapper for the `cmaes' Python library (only parallelizing the function evaluations). This repository uses part of the code from OpenAI's implementation (https://github.com/openai/evolution-strategies-starter).
Example usage:
```python
from esmpi import ES_MPI
def eval_fn(x):
time.sleep(0.01)
return (x[0] - 3) ** 2 + (10 * (x[1] + 2)) ** 2
optimizer = ES_MPI(n_params=2, population_size=16, learning_rate=0.1, sigma=0.02)
for i in range(50):
fit = optimizer.step(eval_fn)
if optimizer.is_master == 0:
print(f"{i}: fitness {np.mean(fit)}, current best params {optimizer.get_parameters()}\n")
```
Raw data
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