def tuner_fn(fn_args: FnArgs) -> TunerFnResult:
train_files = fn_args.train_files
eval_files = fn_args.eval_files
tf_transform_output = tft.TFTransformOutput(fn_args.transform_graph_path)
hparams = _get_hyperparameters()
tuner = kerastuner.Hyperband(hypermodel=_build_keras_model,
hyperparameters=hparams,
objective=kerastuner.Objective(
'binary_accuracy', 'max'),
factor=3,
max_epochs=2,
directory=fn_args.working_dir,
project_name='ftfx:simple_e2e')
train_dataset = _input_fn(train_files, tf_transform_output)
eval_dataset = _input_fn(eval_files, tf_transform_output)
return TunerFnResult(tuner=tuner,
fit_kwargs={
'x': train_dataset,
'validation_data': eval_dataset,
'steps_per_epoch': fn_args.train_steps,
'validation_steps': fn_args.eval_steps
})
def tuner_fn(fn_args: FnArgs) -> TunerFnResult:
"""Build the tuner using the CloudTuner API.
Args:
fn_args: Holds args as name/value pairs. See
https://www.tensorflow.org/tfx/api_docs/python/tfx/components/trainer/fn_args_utils/FnArgs.
- transform_graph_path: optional transform graph produced by TFT.
- custom_config: An optional dictionary passed to the component. In this
example, it contains the dict ai_platform_tuning_args.
- working_dir: working dir for tuning.
- train_files: List of file paths containing training tf.Example data.
- eval_files: List of file paths containing eval tf.Example data.
- train_steps: number of train steps.
- eval_steps: number of eval steps.
Returns:
A namedtuple contains the following:
- tuner: A BaseTuner that will be used for tuning.
- fit_kwargs: Args to pass to tuner's run_trial function for fitting the
model , e.g., the training and validation dataset. Required
args depend on the above tuner's implementation.
"""
transform_graph = tft.TFTransformOutput(fn_args.transform_graph_path)
# CloudTuner is a subclass of kerastuner.Tuner which inherits from
# BaseTuner.
tuner = CloudTuner(
_build_keras_model,
# The project/region configuations for Cloud Vizier service and its trial
# executions. Note: this example uses the same configuration as the
# CAIP Training service for distributed tuning flock management to view
# all of the pipeline's jobs and resources in the same project. It can
# also be configured separately.
project_id=fn_args.custom_config['ai_platform_tuning_args']['project'],
region=fn_args.custom_config['ai_platform_tuning_args']['region'],
objective=kerastuner.Objective('val_sparse_categorical_accuracy',
'max'),
hyperparameters=_get_hyperparameters(),
max_trials=8, # Optional.
directory=fn_args.working_dir)
train_dataset = _input_fn(fn_args.train_files,
fn_args.data_accessor,
transform_graph,
batch_size=_TRAIN_BATCH_SIZE)
eval_dataset = _input_fn(fn_args.eval_files,
fn_args.data_accessor,
transform_graph,
batch_size=_EVAL_BATCH_SIZE)
return TunerFnResult(tuner=tuner,
fit_kwargs={
'x': train_dataset,
'validation_data': eval_dataset,
'steps_per_epoch': fn_args.train_steps,
'validation_steps': fn_args.eval_steps
})
def tuner_fn(fn_args: TrainerFnArgs) -> TunerFnResult:
"""Build the tuner using the KerasTuner API.
Args:
fn_args: Holds args as name/value pairs.
- working_dir: working dir for tuning.
- train_files: List of file paths containing training tf.Example data.
- eval_files: List of file paths containing eval tf.Example data.
- train_steps: number of train steps.
- eval_steps: number of eval steps.
- schema_path: optional schema of the input data.
- transform_graph_path: optional transform graph produced by TFT.
Returns:
A namedtuple contains the following:
- tuner: A BaseTuner that will be used for tuning.
- fit_kwargs: Args to pass to tuner's run_trial function for fitting the
model , e.g., the training and validation dataset. Required
args depend on the above tuner's implementation.
"""
transform_graph = tft.TFTransformOutput(fn_args.transform_graph_path)
# Construct a build_keras_model_fn that just takes hyperparams from get_hyperparameters as input.
build_keras_model_fn = functools.partial(
_build_keras_model, tf_transform_output=transform_graph)
# BayesianOptimization is a subclass of kerastuner.Tuner which inherits from BaseTuner.
tuner = kerastuner.BayesianOptimization(
build_keras_model_fn,
max_trials=10,
hyperparameters=_get_hyperparameters(),
# New entries allowed for n_units hyperparameter construction conditional on n_layers selected.
# allow_new_entries=True,
# tune_new_entries=True,
objective=kerastuner.Objective('val_sparse_categorical_accuracy',
'max'),
directory=fn_args.working_dir,
project_name='covertype_tuning')
train_dataset = _input_fn(fn_args.train_files,
fn_args.data_accessor,
transform_graph,
batch_size=TRAIN_BATCH_SIZE)
eval_dataset = _input_fn(fn_args.eval_files,
fn_args.data_accessor,
transform_graph,
batch_size=EVAL_BATCH_SIZE)
return TunerFnResult(tuner=tuner,
fit_kwargs={
'x': train_dataset,
'validation_data': eval_dataset,
'steps_per_epoch': fn_args.train_steps,
'validation_steps': fn_args.eval_steps
})
def tuner_fn(fn_args: FnArgs) -> TunerFnResult:
"""Build the tuner using the KerasTuner API.
Args:
fn_args: Holds args as name/value pairs.
- working_dir: working dir for tuning.
- train_files: List of file paths containing training tf.Example data.
- eval_files: List of file paths containing eval tf.Example data.
- train_steps: number of train steps.
- eval_steps: number of eval steps.
- schema_path: optional schema of the input data.
- transform_graph_path: optional transform graph produced by TFT.
Returns:
A namedtuple contains the following:
- tuner: A BaseTuner that will be used for tuning.
- fit_kwargs: Args to pass to tuner's run_trial function for fitting the
model , e.g., the training and validation dataset. Required
args depend on the above tuner's implementation.
"""
# RandomSearch is a subclass of kerastuner.Tuner which inherits from
# BaseTuner.
tuner = kerastuner.RandomSearch(_build_keras_model,
max_trials=6,
hyperparameters=_get_hyperparameters(),
allow_new_entries=False,
objective=kerastuner.Objective(
'val_sparse_categorical_accuracy',
'max'),
directory=fn_args.working_dir,
project_name='penguin_tuning')
transform_graph = tft.TFTransformOutput(fn_args.transform_graph_path)
train_dataset = _input_fn(fn_args.train_files,
fn_args.data_accessor,
transform_graph,
batch_size=_TRAIN_BATCH_SIZE)
eval_dataset = _input_fn(fn_args.eval_files,
fn_args.data_accessor,
transform_graph,
batch_size=_EVAL_BATCH_SIZE)
return TunerFnResult(tuner=tuner,
fit_kwargs={
'x': train_dataset,
'validation_data': eval_dataset,
'steps_per_epoch': fn_args.train_steps,
'validation_steps': fn_args.eval_steps
})
def tuner_fn(fn_args: FnArgs) -> TunerFnResult:
"""Build the tuner using the KerasTuner API.
Args:
fn_args: Holds args as name/value pairs.
- working_dir: working dir for tuning.
- train_files: List of file paths containing training tf.Example data.
- eval_files: List of file paths containing eval tf.Example data.
- train_steps: number of train steps.
- eval_steps: number of eval steps.
- schema_path: optional schema of the input data.
- transform_graph_path: optional transform graph produced by TFT.
Returns:
A namedtuple contains the following:
- tuner: A BaseTuner that will be used for tuning.
- fit_kwargs: Args to pass to tuner's run_trial function for fitting the
model , e.g., the training and validation dataset. Required
args depend on the above tuner's implementation.
"""
hp = kerastuner.HyperParameters()
# Defines search space.
hp.Choice('learning_rate', [1e-1, 1e-3])
hp.Int('num_layers', 1, 5)
# RandomSearch is a subclass of Keras model Tuner.
tuner = kerastuner.RandomSearch(
_build_keras_model,
max_trials=5,
hyperparameters=hp,
allow_new_entries=False,
objective='val_sparse_categorical_accuracy',
directory=fn_args.working_dir,
project_name='test')
schema = schema_pb2.Schema()
io_utils.parse_pbtxt_file(fn_args.schema_path, schema)
train_dataset = _input_fn(fn_args.train_files, schema)
eval_dataset = _input_fn(fn_args.eval_files, schema)
return TunerFnResult(
tuner=tuner,
fit_kwargs={
'x': train_dataset,
'validation_data': eval_dataset,
'steps_per_epoch': fn_args.train_steps,
'validation_steps': fn_args.eval_steps
})
def tuner_fn(fn_args: TrainerFnArgs) -> TunerFnResult:
"""Build the tuner using CloudTuner (KerasTuner instance).
Args:
fn_args: Holds args used to train and tune the model as name/value pairs. See
https://www.tensorflow.org/tfx/api_docs/python/tfx/components/trainer/fn_args_utils/FnArgs.
Returns:
A namedtuple contains the following:
- tuner: A BaseTuner that will be used for tuning.
- fit_kwargs: Args to pass to tuner's run_trial function for fitting the
model , e.g., the training and validation dataset. Required
args depend on the above tuner's implementation.
"""
transform_graph = tft.TFTransformOutput(fn_args.transform_graph_path)
# Construct a build_keras_model_fn that just takes hyperparams from get_hyperparameters as input.
build_keras_model_fn = functools.partial(
_build_keras_model, tf_transform_output=transform_graph)
# CloudTuner is a subclass of kerastuner.Tuner which inherits from BaseTuner.
tuner = CloudTuner(
build_keras_model_fn,
project_id=fn_args.custom_config['ai_platform_training_args']['project'],
region=fn_args.custom_config['ai_platform_training_args']['region'],
max_trials=50,
hyperparameters=_get_hyperparameters(),
objective=kerastuner.Objective('val_sparse_categorical_accuracy', 'max'),
directory=fn_args.working_dir)
train_dataset = _input_fn(
fn_args.train_files,
fn_args.data_accessor,
transform_graph,
batch_size=TRAIN_BATCH_SIZE)
eval_dataset = _input_fn(
fn_args.eval_files,
fn_args.data_accessor,
transform_graph,
batch_size=EVAL_BATCH_SIZE)
return TunerFnResult(
tuner=tuner,
fit_kwargs={
'x': train_dataset,
'validation_data': eval_dataset,
'steps_per_epoch': fn_args.train_steps,
'validation_steps': fn_args.eval_steps
})
def tuner_fn(fn_args: FnArgs) -> TunerFnResult:
"""Build the tuner using the CloudTuner API.
Args:
fn_args: Holds args as name/value pairs.
- working_dir: working dir for tuning.
- train_files: List of file paths containing training tf.Example data.
- eval_files: List of file paths containing eval tf.Example data.
- train_steps: number of train steps.
- eval_steps: number of eval steps.
- schema_path: optional schema of the input data.
- transform_graph_path: optional transform graph produced by TFT.
Returns:
A namedtuple contains the following:
- tuner: A BaseTuner that will be used for tuning.
- fit_kwargs: Args to pass to tuner's run_trial function for fitting the
model , e.g., the training and validation dataset. Required
args depend on the above tuner's implementation.
"""
# CloudTuner is a subclass of kerastuner.Tuner which inherits from
# BaseTuner.
tuner = CloudTuner(
_build_keras_model,
project_id=_PROJECT_ID,
region=_REGION,
objective=kerastuner.Objective('val_sparse_categorical_accuracy', 'max'),
hyperparameters=_get_hyperparameters(),
max_trials=8, # Optional.
directory=fn_args.working_dir
)
transform_graph = tft.TFTransformOutput(fn_args.transform_graph_path)
train_dataset = _input_fn(fn_args.train_files, transform_graph)
eval_dataset = _input_fn(fn_args.eval_files, transform_graph)
return TunerFnResult(
tuner=tuner,
fit_kwargs={
'x': train_dataset,
'validation_data': eval_dataset,
'steps_per_epoch': fn_args.train_steps,
'validation_steps': fn_args.eval_steps
})
def tuner_fn(fn_args: FnArgs) -> TunerFnResult:
"""Build the tuner using the CloudTuner API.
Args:
fn_args: Holds args as name/value pairs. See
https://www.tensorflow.org/tfx/api_docs/python/tfx/components/trainer/fn_args_utils/FnArgs.
- transform_graph_path: optional transform graph produced by TFT.
- custom_config: An optional dictionary passed to the component. In this
example, it contains the dict ai_platform_tuning_args.
- working_dir: working dir for tuning.
- train_files: List of file paths containing training tf.Example data.
- eval_files: List of file paths containing eval tf.Example data.
- train_steps: number of train steps.
- eval_steps: number of eval steps.
Returns:
A namedtuple contains the following:
- tuner: A BaseTuner that will be used for tuning.
- fit_kwargs: Args to pass to tuner's run_trial function for fitting the
model , e.g., the training and validation dataset. Required
args depend on the above tuner's implementation. For
DistributingCloudTuner, we generate datasets at the remote
jobs rather than serialize and then deserialize them.
"""
# study_id should be the same across multiple tuner workers which starts
# approximately at the same time.
study_id = 'DistributingCloudTuner_study_{}'.format(
datetime.datetime.now().strftime('%Y%m%d%H'))
if _CLOUD_FIT_IMAGE == 'gcr.io/my-project-id/cloud_fit':
raise ValueError('Build your own cloud_fit image, ' +
'default dummy one is used!')
tuner = cloud_tuner.DistributingCloudTuner(
_build_keras_model,
# The project/region configuations for Cloud Vizier service and its trial
# executions. Note: this example uses the same configuration as the
# CAIP Training service for distributed tuning flock management to view
# all of the pipeline's jobs and resources in the same project. It can
# also be configured separately.
project_id=fn_args.custom_config['ai_platform_tuning_args']['project'],
region=fn_args.custom_config['ai_platform_tuning_args']['region'],
objective=kerastuner.Objective('val_sparse_categorical_accuracy',
'max'),
hyperparameters=_get_hyperparameters(),
max_trials=5, # Optional.
directory=os.path.join(
fn_args.custom_config['remote_trials_working_dir'], study_id),
study_id=study_id,
container_uri=_CLOUD_FIT_IMAGE,
# Optional `MachineConfig` that represents the configuration for the
# general workers in a distribution cluster. More options see:
# https://github.com/tensorflow/cloud/blob/master/src/python/tensorflow_cloud/core/machine_config.py
replica_config=machine_config.COMMON_MACHINE_CONFIGS['K80_1X'],
# Optional total number of workers in a distribution cluster including a
# chief worker.
replica_count=2)
return TunerFnResult(tuner=tuner,
fit_kwargs={
'steps_per_epoch': fn_args.train_steps,
'validation_steps': fn_args.eval_steps,
'train_files': fn_args.train_files,
'eval_files': fn_args.eval_files,
'transform_graph_path':
fn_args.transform_graph_path,
'label_key': _LABEL_KEY,
'train_batch_size': _TRAIN_BATCH_SIZE,
'eval_batch_size': _EVAL_BATCH_SIZE,
})
def tuner_fn(fn_args: fn_args_utils.FnArgs) -> TunerFnResult:
"""Build the tuner using the KerasTuner API.
Args:
fn_args: Holds args as name/value pairs.
- working_dir: working dir for tuning.
- train_files: List of file paths containing training tf.Example data.
- eval_files: List of file paths containing eval tf.Example data.
- train_steps: number of train steps.
- eval_steps: number of eval steps.
- schema_path: optional schema of the input data.
- transform_graph_path: optional transform graph produced by TFT.
- custom_config: A dict with a single 'problem_statement' entry containing
a text-format serialized ProblemStatement proto which defines the task.
Returns:
A namedtuple contains the following:
- tuner: A BaseTuner that will be used for tuning.
- fit_kwargs: Args to pass to tuner's run_trial function for fitting the
model , e.g., the training and validation dataset. Required
args depend on the above tuner's implementation.
"""
problem_statement = text_format.Parse(
fn_args.custom_config['problem_statement'], ps_pb2.ProblemStatement())
autodata_adapter = kma.KerasModelAdapter(
problem_statement=problem_statement,
transform_graph_dir=fn_args.transform_graph_path)
build_keras_model_fn = functools.partial(_build_keras_model,
autodata_adapter=autodata_adapter)
if 'warmup_hyperparameters' in fn_args.custom_config:
hyperparameters = hp_module.HyperParameters.from_config(
fn_args.custom_config['warmup_hyperparameters'])
else:
hyperparameters = _get_hyperparameters()
tuner_cls = get_tuner_cls_with_callbacks(kerastuner.RandomSearch)
tuner = tuner_cls(build_keras_model_fn,
max_trials=fn_args.custom_config.get('max_trials', 10),
hyperparameters=hyperparameters,
allow_new_entries=False,
objective=autodata_adapter.tuner_objective,
directory=fn_args.working_dir,
project_name=f'{problem_statement.tasks[0].name}_tuning')
# TODO(nikhilmehta): Make batch-size tunable hyperparameter.
train_dataset = autodata_adapter.get_dataset(
file_pattern=fn_args.train_files,
batch_size=128,
num_epochs=None,
shuffle=True)
eval_dataset = autodata_adapter.get_dataset(
file_pattern=fn_args.eval_files,
batch_size=128,
num_epochs=1,
shuffle=False)
return TunerFnResult(tuner=tuner,
fit_kwargs={
'x': train_dataset,
'validation_data': eval_dataset,
'steps_per_epoch': fn_args.train_steps,
})
def tuner_fn(fn_args: FnArgs) -> TunerFnResult:
"""Build the tuner using CloudTuner (KerasTuner instance).
Args:
fn_args: Holds args used to train and tune the model as name/value pairs. See
https://www.tensorflow.org/tfx/api_docs/python/tfx/components/trainer/fn_args_utils/FnArgs.
Returns:
A namedtuple contains the following:
- tuner: A BaseTuner that will be used for tuning.
- fit_kwargs: Args to pass to tuner's run_trial function for fitting the
model , e.g., the training and validation dataset. Required
args depend on the above tuner's implementation.
"""
custom_config_dict = _get_custom_config_dict(fn_args)
max_trials = custom_config_dict.get('max_trials', MAX_TRIALS)
transform_graph = tft.TFTransformOutput(fn_args.transform_graph_path)
# Construct a build_keras_model_fn that just takes hyperparams from get_hyperparameters as input.
build_keras_model_fn = functools.partial(
_build_keras_model, tf_transform_output=transform_graph)
# CloudTuner is a subclass of kerastuner.Tuner which inherits from BaseTuner.
#is_local_run = "custom_config" not in fn_args.custom_config
is_local_run = custom_config_dict.get("is_local_run", True)
absl.logging.info('is_local_run : %s' % is_local_run)
if is_local_run:
tuner = kerastuner.RandomSearch(build_keras_model_fn,
max_trials=max_trials,
hyperparameters=_get_hyperparameters(),
allow_new_entries=False,
objective=kerastuner.Objective(
'val_binary_accuracy', 'max'),
directory=fn_args.working_dir,
project_name='titanic_tuning')
else:
tuner = CloudTuner(
build_keras_model_fn,
project_id=fn_args.custom_config['ai_platform_training_args']
['project'],
region=fn_args.custom_config['ai_platform_training_args']
['region'],
max_trials=max_trials,
hyperparameters=_get_hyperparameters(),
objective=kerastuner.Objective('val_binary_accuracy', 'max'),
# objective=kerastuner.Objective('auc', 'min'),
directory=fn_args.working_dir)
train_dataset = _input_fn(fn_args.train_files,
fn_args.data_accessor,
transform_graph,
batch_size=TRAIN_BATCH_SIZE)
eval_dataset = _input_fn(fn_args.eval_files,
fn_args.data_accessor,
transform_graph,
batch_size=EVAL_BATCH_SIZE)
return TunerFnResult(tuner=tuner,
fit_kwargs={
'x': train_dataset,
'validation_data': eval_dataset,
'steps_per_epoch': fn_args.train_steps,
'validation_steps': fn_args.eval_steps
})