def test_iter_grid(self):
grid = dict(a=[], b=[])
self.assertCountEqual(list(utils_impl.iter_grid(grid)), [])
grid = dict(a=[1])
self.assertCountEqual(list(utils_impl.iter_grid(grid)), [dict(a=1)])
grid = dict(a=[1, 2])
self.assertCountEqual(list(utils_impl.iter_grid(grid)),
[dict(a=1), dict(a=2)])
grid = dict(a=[1, 2], b='b', c=[3.0, 4.0])
self.assertCountEqual(list(utils_impl.iter_grid(grid)), [
dict(a=1, b='b', c=3.0),
dict(a=1, b='b', c=4.0),
dict(a=2, b='b', c=3.0),
dict(a=2, b='b', c=4.0)
])
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
target = '//tensorflow_federated/python/research/gans/experiments/emnist:train'
executable = 'bazel run {} --'.format(target)
grid_iter = utils_impl.iter_grid({
'filtering': ['by_user'],
'invert_imagery_probability': ['0p0', '0p5'],
'accuracy_threshold': ['lt0p882', 'gt0p939'],
'num_client_disc_train_steps': [6],
'num_server_gen_train_steps': [6],
'num_clients_per_round': [10],
'num_rounds': [1000],
'use_dp': [True],
'dp_l2_norm_clip': [0.1],
'dp_noise_multiplier': [0.01],
'num_rounds_per_eval': [10],
'num_rounds_per_save_images': [10]
})
utils_impl.launch_experiment(executable,
grid_iter,
root_output_dir='/tmp/exp',
short_names={
'filtering': 'filt',
'invert_imagery_probability': 'inv_lik',
'accuracy_threshold': 'acc',
'num_client_disc_train_steps': 'n_disc',
'num_server_gen_train_steps': 'n_gen',
'dp_l2_norm_clip': 'dp_clip',
'dp_noise_multiplier': 'dp_noise',
'num_rounds_per_eval': 'n_rds_eval',
'num_rounds_per_save_images':
'n_rds_images'
},
max_workers=1)
print('Experiments launched.')
