def test_launch_experiment(self, mock_multiprocessing):
pool = mock_multiprocessing.Pool(processes=10)
grid_dict = [
collections.OrderedDict([('a_long', 1), ('b', 4.0)]),
collections.OrderedDict([('a_long', 1), ('b', 5.0)])
]
utils.launch_experiment(
'run_exp.py', grid_dict, '/tmp_dir', short_names={'a_long': 'a'})
expected = [
'python run_exp.py --a_long=1 --b=4.0 --root_output_dir=/tmp_dir '
'--exp_name=0-a=1,b=4.0',
'python run_exp.py --a_long=1 --b=5.0 --root_output_dir=/tmp_dir '
'--exp_name=1-a=1,b=5.0'
]
result = pool.apply_async.call_args_list
result = [args[0][1][0] for args in result]
self.assertCountEqual(result, expected)
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.')