def test_nonscalar_metrics_are_written(self):
summary_dir = os.path.join(self.get_temp_dir(), 'logdir')
tb_mngr = tensorboard_manager.TensorBoardManager(
summary_dir=summary_dir)
tb_mngr.update_metrics(0, _create_nonscalar_metrics())
self.assertTrue(tf.io.gfile.exists(summary_dir))
self.assertLen(tf.io.gfile.listdir(summary_dir), 1)
def test_update_hparams_returns_flat_dict(self):
tb_mngr = tensorboard_manager.TensorBoardManager(
summary_dir=self.get_temp_dir())
input_data_dict = _create_scalar_metrics()
appended_data_dict = tb_mngr.update_hparams(input_data_dict)
self.assertEqual({
'a/b': 1.0,
'a/c': 2.0,
}, appended_data_dict)
def test_update_metrics_raises_value_error_if_round_num_is_out_of_order(
self):
tb_mngr = tensorboard_manager.TensorBoardManager(
summary_dir=self.get_temp_dir())
tb_mngr.update_metrics(1, _create_scalar_metrics())
with self.assertRaises(ValueError):
tb_mngr.update_metrics(0, _create_scalar_metrics())
def _setup_outputs(root_output_dir,
experiment_name,
hparam_dict,
rounds_per_profile=0):
"""Set up directories for experiment loops, write hyperparameters to disk."""
if not experiment_name:
raise ValueError('experiment_name must be specified.')
create_if_not_exists(root_output_dir)
checkpoint_dir = os.path.join(root_output_dir, 'checkpoints',
experiment_name)
create_if_not_exists(checkpoint_dir)
checkpoint_mngr = tff.simulation.FileCheckpointManager(checkpoint_dir)
results_dir = os.path.join(root_output_dir, 'results', experiment_name)
create_if_not_exists(results_dir)
csv_file = os.path.join(results_dir, 'experiment.metrics.csv')
metrics_mngr = tff.simulation.CSVMetricsManager(csv_file)
summary_logdir = os.path.join(root_output_dir, 'logdir', experiment_name)
tb_mngr = tensorboard_manager.TensorBoardManager(
summary_dir=summary_logdir)
if hparam_dict:
hparam_dict['metrics_file'] = metrics_mngr.metrics_filename
hparams_file = os.path.join(results_dir, 'hparams.csv')
utils_impl.atomic_write_to_csv(pd.Series(hparam_dict), hparams_file)
tb_mngr.update_hparams(
{k: v
for k, v in hparam_dict.items() if v is not None})
logging.info('Writing...')
logging.info(' checkpoints to: %s', checkpoint_dir)
logging.info(' metrics csv to: %s', metrics_mngr.metrics_filename)
logging.info(' summaries to: %s', summary_logdir)
@contextlib.contextmanager
def profiler(round_num):
if (rounds_per_profile > 0 and round_num % rounds_per_profile == 0):
with tf.profiler.experimental.Profile(summary_logdir):
yield
else:
yield
return checkpoint_mngr, metrics_mngr, tb_mngr, profiler