Seeding the Random Number Generators?

[dreavjr]

Currently, in the documentation, it's unclear whether it's possible to control the RNGs' seeding, and if that's possible, where (server, client, problem, study, sampling?) and how to perform that seeding.

[sagipe]

Hi,

Currently, it's only possible to control the RNG seeding at a Designer level, which can happen on the server code itself (e.g. in the policy factory code).

Part of what makes Vizier a good global optimizer is that it's stochastic, and we build a new Designer for every batch of trials. We typically only supply the seed when we want to replicate a specific trial that e.g. a user complained about, based on the seed that was used to generate the suggestion (and it stored in the trial metadata).

Do you have a specific use case where you'd want to control the seed from the client side?

Best,
Sagi

[dreavjr]

Seeding at the server level would work be totally fine for my use case. Do you have an example (or documentation) on how to use the Designer class to accomplish that?

For illustration, this is my current usage:

problem = vz.ProblemStatement()
problem.search_space.root.add_float_param('lr_exp', search_space['lr_exp_min'], search_space['lr_exp_max'])
problem.search_space.root.add_float_param('wd_exp', search_space['wd_exp_min'], search_space['wd_exp_max'])
problem.metric_information.append(vz.MetricInformation(name='dummy_metric', goal=vz.ObjectiveMetricGoal.MAXIMIZE))

study_config = vz.StudyConfig.from_problem(problem)
study_config.algorithm = 'QUASI_RANDOM_SEARCH'

server = servers.DefaultVizierServer(host='localhost', database_url=None)
clients.environment_variables.server_endpoint = server.endpoint
study_client = clients.Study.from_study_config(study_config, owner='owner', study_id = 'example_study_id')

suggestions = study_client.suggest(count=samples_n)