def _dist_search_fn(temp_dir, study_id, tuner_id):
"""Multi-process safe tuner instantiation and tuner.search()."""
# Jitter instantiation so as to avoid contention on study
# creation and dataset download.
time.sleep(int(tuner_id[5:])) # tuner_id is formatted as 'tuner%d'
# Dataset must be loaded independently in sub-process.
(x, y), (val_x, val_y) = _load_data()
tuner = cloudtuner.CloudTuner(_build_model,
project_id=_PROJECT_ID,
region=_REGION,
objective='acc',
hyperparameters=_HPS,
max_trials=5,
study_id=study_id,
directory=os.path.join(
temp_dir, study_id, tuner_id))
tuner.tuner_id = tuner_id
tuner.search(
x=x,
y=y,
epochs=2,
steps_per_epoch=20,
validation_steps=10,
validation_data=(val_x, val_y),
)
return tuner
def testCloudTunerDatasets(self):
"""Test case to configure Tuner with tf.data.Dataset as input data."""
train_dataset = tf.data.Dataset.from_tensor_slices(
(self._x, self._y)).batch(128).cache().prefetch(1000)
eval_dataset = tf.data.Dataset.from_tensor_slices(
(self._val_x, self._val_y)).batch(128).cache().prefetch(1000)
study_id = '{}_dataset'.format(_STUDY_ID_BASE)
tuner = cloudtuner.CloudTuner(_build_model,
project_id=_PROJECT_ID,
region=_REGION,
objective='acc',
hyperparameters=_HPS,
study_id=study_id,
max_trials=5,
directory=os.path.join(
self.get_temp_dir(), study_id))
self._assert_output(
tuner.search_space_summary,
r'.*Search space summary(?=.*learning_rate \(Float\))'
r'(?=.*num_layers \(Int\).*)')
tuner.search(x=train_dataset,
epochs=2,
steps_per_epoch=20,
validation_steps=10,
validation_data=eval_dataset)
self._assert_results_summary(tuner.results_summary)
def testCloudTunerHyperparameters(self):
"""Test case to configure Tuner with HyperParameters object."""
study_id = '{}_hyperparameters'.format(_STUDY_ID_BASE)
tuner = cloudtuner.CloudTuner(_build_model,
project_id=_PROJECT_ID,
region=_REGION,
objective='acc',
hyperparameters=_HPS,
max_trials=5,
study_id=study_id,
directory=os.path.join(
self.get_temp_dir(), study_id))
# "Search space summary" comes first, but the order of
# "learning_rate (Float)" and "num_layers (Int)" is not deterministic,
# hence they are wrapped as look-ahead assertions in the regex.
self._assert_output(
tuner.search_space_summary,
r'.*Search space summary(?=.*learning_rate \(Float\))'
r'(?=.*num_layers \(Int\).*)')
tuner.search(x=self._x,
y=self._y,
epochs=2,
steps_per_epoch=20,
validation_steps=10,
validation_data=(self._val_x, self._val_y))
self._assert_results_summary(tuner.results_summary)
def testCloudTunerStudyConfig(self):
"""Test case to configure Tuner with StudyConfig object."""
# Configure the search space. Specification:
# https://cloud.google.com/ai-platform/optimizer/docs/reference/rest/v1/projects.locations.studies#StudyConfig
study_config = {
'metrics': [{
'goal': 'MAXIMIZE',
'metric': 'acc'
}],
'parameters': [{
'discrete_value_spec': {
'values': [0.0001, 0.001, 0.01]
},
'parameter': 'learning_rate',
'type': 'DISCRETE'
}, {
'integer_value_spec': {
'max_value': 10,
'min_value': 2
},
'parameter': 'num_layers',
'type': 'INTEGER'
}, {
'discrete_value_spec': {
'values': [32, 64, 96, 128]
},
'parameter': 'units',
'type': 'DISCRETE'
}],
'algorithm':
'ALGORITHM_UNSPECIFIED',
'automatedStoppingConfig': {
'decayCurveStoppingConfig': {
'useElapsedTime': True
}
}
}
study_id = '{}_study_config'.format(_STUDY_ID_BASE)
tuner = cloudtuner.CloudTuner(_build_model,
project_id=_PROJECT_ID,
region=_REGION,
study_config=study_config,
study_id=study_id,
max_trials=5,
directory=os.path.join(
self.get_temp_dir(), study_id))
self._assert_output(
tuner.search_space_summary,
r'.*Search space summary(?=.*learning_rate \(Choice\))'
r'(?=.*num_layers \(Int\))(?=.*units \(Choice\))')
tuner.search(x=self._x,
y=self._y,
epochs=2,
steps_per_epoch=20,
validation_steps=10,
validation_data=(self._val_x, self._val_y))
self._assert_results_summary(tuner.results_summary)