polaris-py 0.7.3

# Polaris : Hyperparamter optimization library.

[](https://polaris.readthedocs.io/en/latest/?badge=latest)

Polaris is a hyperparamter optimization library.
We plan to support every state-of-art tuning method as follows.

- Random Search
- Bayesian Optimization
- Tree-structured Parzen Estimators (TPE)

## Documentation
Polaris' documentation can be found at [https://polaris.readthedocs.io/](https://polaris.readthedocs.io/)

## Prerequisites
- Python >= 3.6
- RabbitMQ (Only for parallel execution)

## Installation

```shell
$ pip install polaris-py
```

## Examples

### Sequential Execution

```python
from polaris import Polaris, STATUS_SUCCESS, Trials, Bounds


def pseudo_train(params, exp_info):
lr_squared = (params['lr'] - 0.006) ** 2
weight_decay_squared = (params['weight_decay'] - 0.02) ** 2
loss = lr_squared + weight_decay_squared
return {
'loss': loss,
'status': STATUS_SUCCESS,
}


if __name__ == '__main__':
bounds = [
Bounds('lr', 0.001, 0.01),
Bounds('weight_decay', 0.0002, 0.04),
]
trials = Trials()
polaris = Polaris(
pseudo_train, bounds, 'bayesian_opt',
trials, max_evals=100, exp_key='this_is_test')
best_params = polaris.run()
print(best_params)
```

### Parallel Execution

#### Single Process

1. Run `rabbitmq-server`
1. Set `RABBITMQ_URL` (ampq://guest:guest@localhost//)
1. Run `polaris-worker --exp-key this_is_test`
1. Run codes as follows

### Multiple Processes (Use MPI)

1. Run `rabbitmq-server`
1. Set `RABBITMQ_URL` (ampq://guest:guest@localhost//)
1. Run `mpirun -n 4 polaris-worker --mpi --exp-key this_is_test`
1. Run codes as follows


```python

...

if __name__ == '__main__':
bounds = [
Bounds('lr', 0.001, 0.01),
Bounds('weight_decay', 0.0002, 0.04),
]
trials = Trials()
polaris = Polaris(
pseudo_train, bounds, 'bayesian_opt',
trials, max_evals=100, exp_key='this_is_test')
best_params = polaris.run_parallel()
print(best_params)
```