How Do I Make Ray.tune.run Reproducible?

I'm using Tune class-based Trainable API. See code sample: from ray import tune import numpy as np np.random.seed(42) # first run tune.run(tune.Trainable, ...) # second run, expec

Solution 1:

(This answer focuses on class API and ray version 0.8.7. Function API does not support reproducibility due to implementation specifics)

There are two main sources of undeterministic results.

1. Search algorithm

Every search algorithm supports random seed, although interface to it may vary. This initializes hyperparameter space sampling.

For example, if you're using AxSearch, it looks like this:

from ax.service.ax_client import AxClient
from ray.tune.suggest.ax import AxSearch

client = AxClient(..., random_seed=42)
client.create_experiment(...)
algo = AxSearch(client)

2. Trainable API

This is distributed among worker processes, which requires seeding within tune.Trainable class. Depending on the tune.Trainable.train logic that you implement, you need to manually seed numpy, tf, or whatever other framework you use, inside tune.Trainable.setup by passing seed with config argument of tune.run.

The following code is based on RLLib PR5197 that handled the same issue:

See the example:

from ray import tune
import numpy as np
import random

classTuner(tune.Trainable):
  defsetup(self, config):
    seed = config['seed']
    np.random.seed(seed)
    random.seed(seed)
    ...
  ...

seed = 42

tune.run(Tuner, config={'seed': seed})