def test_get_best_trials(tmp_dir):
def _test_get_best_trials():
hps = kt.HyperParameters()
hps.Int("a", 0, 100, default=5)
hps.Int("b", 0, 100, default=6)
oracle = randomsearch.RandomSearchOracle(
objective=kt.Objective("score", direction="max"),
max_trials=10,
hyperparameters=hps,
)
oracle._set_project_dir(tmp_dir, "untitled")
tuner_id = os.environ["KERASTUNER_TUNER_ID"]
if "chief" in tuner_id:
oracle_chief.start_server(oracle)
else:
client = oracle_client.OracleClient(oracle)
trial_scores = {}
for score in range(10):
trial = client.create_trial(tuner_id)
assert trial.status == "RUNNING"
assert "a" in trial.hyperparameters.values
assert "b" in trial.hyperparameters.values
trial_id = trial.trial_id
client.update_trial(trial_id, {"score": score})
client.end_trial(trial_id)
trial_scores[trial_id] = score
return
best_trials = client.get_best_trials(3)
best_scores = [t.score for t in best_trials]
assert best_scores == [9, 8, 7]
# Check that trial_ids are correctly mapped to scores.
for t in best_trials:
assert trial_scores[t.trial_id] == t.score
mock_distribute.mock_distribute(_test_get_best_trials, num_workers=1)
def test_base_tuner_distribution(tmp_dir):
num_workers = 3
barrier = threading.Barrier(num_workers)
def _test_base_tuner():
def build_model(hp):
return hp.Int("a", 1, 100)
tuner = SimpleTuner(
oracle=kt.oracles.RandomSearch(objective=kt.Objective(
"score", "max"),
max_trials=10),
hypermodel=build_model,
directory=tmp_dir,
)
tuner.search()
# Only worker makes it to this point, server runs until thread stops.
assert dist_utils.has_chief_oracle()
assert not dist_utils.is_chief_oracle()
assert isinstance(tuner.oracle,
kt.distribute.oracle_client.OracleClient)
barrier.wait(60)
# Model is just a score.
scores = tuner.get_best_models(10)
assert len(scores)
assert scores == sorted(copy.copy(scores), reverse=True)
mock_distribute.mock_distribute(_test_base_tuner, num_workers=num_workers)
def test_update_trial(tmp_dir):
def _test_update_trial():
hps = kt.HyperParameters()
hps.Int("a", 0, 10, default=5)
oracle = randomsearch.RandomSearchOracle(
objective=kt.Objective("score", "max"),
max_trials=10,
hyperparameters=hps,
)
oracle._set_project_dir(tmp_dir, "untitled")
tuner_id = os.environ["KERASTUNER_TUNER_ID"]
if "chief" in tuner_id:
oracle_chief.start_server(oracle)
else:
client = oracle_client.OracleClient(oracle)
trial = client.create_trial(tuner_id)
assert "score" not in trial.metrics.metrics
trial_id = trial.trial_id
client.update_trial(trial_id, {"score": 1}, step=2)
updated_trial = client.get_trial(trial_id)
assert updated_trial.metrics.get_history("score") == [
metrics_tracking.MetricObservation([1], step=2)
]
mock_distribute.mock_distribute(_test_update_trial)
def test_exception_raising():
def worker_error_fn():
if "worker" in os.environ["KERASTUNER_TUNER_ID"]:
raise ValueError("Found a worker error")
with pytest.raises(ValueError, match="Found a worker error"):
mock_distribute.mock_distribute(worker_error_fn, num_workers=2)
def chief_error_fn():
if "chief" in os.environ["KERASTUNER_TUNER_ID"]:
raise ValueError("Found a chief error")
with pytest.raises(ValueError, match="Found a chief error"):
mock_distribute.mock_distribute(chief_error_fn, num_workers=2)
def test_random_search(tmp_dir):
# TensorFlow model building and execution is not thread-safe.
num_workers = 1
def _test_random_search():
def build_model(hp):
model = keras.Sequential()
model.add(keras.layers.Dense(3, input_shape=(5, )))
for i in range(hp.Int("num_layers", 1, 3)):
model.add(
keras.layers.Dense(hp.Int("num_units_%i" % i, 1, 3),
activation="relu"))
model.add(keras.layers.Dense(1, activation="sigmoid"))
model.compile("sgd", "binary_crossentropy")
return model
x = np.random.uniform(-1, 1, size=(2, 5))
y = np.ones((2, 1))
tuner = kt.tuners.RandomSearch(
hypermodel=build_model,
objective="val_loss",
max_trials=10,
directory=tmp_dir,
)
# Only worker makes it to this point, server runs until thread stops.
assert dist_utils.has_chief_oracle()
assert not dist_utils.is_chief_oracle()
assert isinstance(tuner.oracle,
kt.distribute.oracle_client.OracleClient)
tuner.search(x, y, validation_data=(x, y), epochs=1, batch_size=2)
# Suppress warnings about optimizer state not being restored by tf.keras.
tf.get_logger().setLevel(logging.ERROR)
trials = tuner.oracle.get_best_trials(2)
assert trials[0].score <= trials[1].score
models = tuner.get_best_models(2)
assert models[0].evaluate(x, y) <= models[1].evaluate(x, y)
mock_distribute.mock_distribute(_test_random_search, num_workers)
def test_get_space(tmp_dir):
def _test_get_space():
hps = kt.HyperParameters()
hps.Int("a", 0, 10, default=3)
oracle = randomsearch.RandomSearchOracle(
objective=kt.Objective("score", "max"),
max_trials=10,
hyperparameters=hps,
)
oracle._set_project_dir(tmp_dir, "untitled")
tuner_id = os.environ["KERASTUNER_TUNER_ID"]
if "chief" in tuner_id:
oracle_chief.start_server(oracle)
else:
client = oracle_client.OracleClient(oracle)
retrieved_hps = client.get_space()
assert retrieved_hps.values == {"a": 3}
assert len(retrieved_hps.space) == 1
mock_distribute.mock_distribute(_test_get_space)
def test_mock_distribute(tmp_dir):
def process_fn():
assert "KERASTUNER_ORACLE_IP" in os.environ
# Wait, to test that other threads aren't overriding env vars.
time.sleep(1)
assert isinstance(os.environ, mock_distribute.MockEnvVars)
tuner_id = os.environ["KERASTUNER_TUNER_ID"]
if "worker" in tuner_id:
# Give the chief process time to write its value,
# as we do not join on the chief since it will run
# a server.
time.sleep(2)
fname = os.path.join(str(tmp_dir), tuner_id)
with tf.io.gfile.GFile(fname, "w") as f:
f.write(tuner_id)
mock_distribute.mock_distribute(process_fn, num_workers=3)
for tuner_id in {"chief", "worker0", "worker1", "worker2"}:
fname = os.path.join(str(tmp_dir), tuner_id)
with tf.io.gfile.GFile(fname, "r") as f:
assert f.read() == tuner_id
def test_update_space(tmp_dir):
def _test_update_space():
oracle = randomsearch.RandomSearchOracle(objective=kt.Objective(
"score", "max"),
max_trials=10)
oracle._set_project_dir(tmp_dir, "untitled")
tuner_id = os.environ["KERASTUNER_TUNER_ID"]
if "chief" in tuner_id:
oracle_chief.start_server(oracle)
else:
client = oracle_client.OracleClient(oracle)
hps = kt.HyperParameters()
hps.Int("a", 0, 10, default=5)
hps.Choice("b", [1, 2, 3])
client.update_space(hps)
retrieved_hps = client.get_space()
assert len(retrieved_hps.space) == 2
assert retrieved_hps.values["a"] == 5
assert retrieved_hps.values["b"] == 1
mock_distribute.mock_distribute(_test_update_space)
def test_end_trial(tmp_dir):
def _test_end_trial():
hps = kt.HyperParameters()
hps.Int("a", 0, 10, default=5)
oracle = randomsearch.RandomSearchOracle(
objective=kt.Objective("score", "max"),
max_trials=10,
hyperparameters=hps,
)
oracle._set_project_dir(tmp_dir, "untitled")
tuner_id = os.environ["KERASTUNER_TUNER_ID"]
if "chief" in tuner_id:
oracle_chief.start_server(oracle)
else:
client = oracle_client.OracleClient(oracle)
trial = client.create_trial(tuner_id)
trial_id = trial.trial_id
client.update_trial(trial_id, {"score": 1}, step=2)
client.end_trial(trial_id, "INVALID")
updated_trial = client.get_trial(trial_id)
assert updated_trial.status == "INVALID"
mock_distribute.mock_distribute(_test_end_trial)
def test_create_trial(tmp_dir):
def _test_create_trial():
hps = kt.HyperParameters()
hps.Int("a", 0, 10, default=5)
hps.Choice("b", [1, 2, 3])
oracle = randomsearch.RandomSearchOracle(
objective=kt.Objective("score", "max"),
max_trials=10,
hyperparameters=hps,
)
oracle._set_project_dir(tmp_dir, "untitled")
tuner_id = os.environ["KERASTUNER_TUNER_ID"]
if "chief" in tuner_id:
oracle_chief.start_server(oracle)
else:
client = oracle_client.OracleClient(oracle)
trial = client.create_trial(tuner_id)
assert trial.status == "RUNNING"
a = trial.hyperparameters.get("a")
assert a >= 0 and a <= 10
b = trial.hyperparameters.get("b")
assert b in {1, 2, 3}
mock_distribute.mock_distribute(_test_create_trial)