def test_multitrial_experiment(pytestconfig):
base_model = Net()
evaluator = get_mnist_evaluator()
search_strategy = strategy.Random()
exp = RetiariiExperiment(base_model, evaluator, strategy=search_strategy)
exp_config = RetiariiExeConfig('local')
exp_config.trial_concurrency = 1
exp_config.max_trial_number = 1
exp_config._trial_command_params = nas_experiment_trial_params(pytestconfig.rootpath)
exp.run(exp_config)
ensure_success(exp)
assert isinstance(exp.export_top_models()[0], dict)
exp.stop()
def test_multi_trial(model, pytestconfig):
evaluator_kwargs = {
'max_epochs': 1
}
base_model, evaluator = _mnist_net(model, evaluator_kwargs)
search_strategy = strategy.Random()
exp = RetiariiExperiment(base_model, evaluator, strategy=search_strategy)
exp_config = RetiariiExeConfig('local')
exp_config.experiment_name = 'mnist_unittest'
exp_config.trial_concurrency = 1
exp_config.max_trial_number = 1
exp_config._trial_command_params = nas_experiment_trial_params(pytestconfig.rootpath)
exp.run(exp_config)
ensure_success(exp)
assert isinstance(exp.export_top_models()[0], dict)
exp.stop()