def run_fn(fn_args: TrainerFnArgs):
"""Train the model based on given args.
Args:
fn_args: Holds args used to train the model as name/value pairs.
"""
tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)
train_dataset = base.input_fn(fn_args.train_files, tf_transform_output, 40)
eval_dataset = base.input_fn(fn_args.eval_files, tf_transform_output, 40)
mirrored_strategy = tf.distribute.MirroredStrategy()
with mirrored_strategy.scope():
model = base.build_keras_model()
model.fit(train_dataset,
steps_per_epoch=fn_args.train_steps,
validation_data=eval_dataset,
validation_steps=fn_args.eval_steps)
signatures = {
'serving_default':
_get_serve_tf_examples_fn(model,
tf_transform_output).get_concrete_function(
tf.TensorSpec(shape=[None],
dtype=tf.string,
name='examples'))
}
model.save(fn_args.serving_model_dir,
save_format='tf',
signatures=signatures)
def run_fn(fn_args: TrainerFnArgs):
"""Train the model based on given args.
Args:
fn_args: Holds args used to train the model as name/value pairs.
"""
tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)
train_dataset = base.input_fn(fn_args.train_files, tf_transform_output, 40)
eval_dataset = base.input_fn(fn_args.eval_files, tf_transform_output, 40)
mirrored_strategy = tf.distribute.MirroredStrategy()
with mirrored_strategy.scope():
model = base.build_keras_model()
try:
log_dir = fn_args.model_run_dir
except KeyError:
# TODO(b/158106209): use ModelRun instead of Model artifact for logging.
log_dir = os.path.join(os.path.dirname(fn_args.serving_model_dir),
'logs')
# Write logs to path
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir,
update_freq='batch')
model.fit(train_dataset,
steps_per_epoch=fn_args.train_steps,
validation_data=eval_dataset,
validation_steps=fn_args.eval_steps,
callbacks=[tensorboard_callback])
signatures = {
'serving_default':
_get_serve_tf_examples_fn(model,
tf_transform_output).get_concrete_function(
tf.TensorSpec(shape=[None, 784],
dtype=tf.float32,
name='image_floats'))
}
temp_saving_model_dir = os.path.join(fn_args.serving_model_dir, 'temp')
model.save(temp_saving_model_dir, save_format='tf', signatures=signatures)
tfrw = rewriter_factory.create_rewriter(
rewriter_factory.TFLITE_REWRITER,
name='tflite_rewriter',
enable_experimental_new_converter=True)
converters.rewrite_saved_model(temp_saving_model_dir,
fn_args.serving_model_dir, tfrw,
rewriter.ModelType.TFLITE_MODEL)
tf.io.gfile.rmtree(temp_saving_model_dir)
def run_fn(fn_args: tfx.components.FnArgs):
"""Train the model based on given args.
Args:
fn_args: Holds args used to train the model as name/value pairs.
"""
tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)
train_dataset = base.input_fn(fn_args.train_files, fn_args.data_accessor,
tf_transform_output, 40)
eval_dataset = base.input_fn(fn_args.eval_files, fn_args.data_accessor,
tf_transform_output, 40)
mirrored_strategy = tf.distribute.MirroredStrategy()
with mirrored_strategy.scope():
model = base.build_keras_model()
# Write logs to path
tensorboard_callback = tf.keras.callbacks.TensorBoard(
log_dir=fn_args.model_run_dir, update_freq='batch')
model.fit(
train_dataset,
steps_per_epoch=fn_args.train_steps,
validation_data=eval_dataset,
validation_steps=fn_args.eval_steps,
callbacks=[tensorboard_callback])
signatures = {
'serving_default':
_get_serve_tf_examples_fn(
model, tf_transform_output).get_concrete_function(
tf.TensorSpec(
shape=[None, 784],
dtype=tf.float32,
name='image_floats'))
}
temp_saving_model_dir = os.path.join(fn_args.serving_model_dir, 'temp')
model.save(temp_saving_model_dir, save_format='tf', signatures=signatures)
tfrw = rewriter_factory.create_rewriter(
rewriter_factory.TFLITE_REWRITER, name='tflite_rewriter')
converters.rewrite_saved_model(temp_saving_model_dir,
fn_args.serving_model_dir,
tfrw,
rewriter.ModelType.TFLITE_MODEL)
tfx.dsl.io.fileio.rmtree(temp_saving_model_dir)
def run_fn(fn_args: TrainerFnArgs):
"""Train the model based on given args.
Args:
fn_args: Holds args used to train the model as name/value pairs.
"""
tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)
train_dataset = base.input_fn(fn_args.train_files, tf_transform_output, 40)
eval_dataset = base.input_fn(fn_args.eval_files, tf_transform_output, 40)
mirrored_strategy = tf.distribute.MirroredStrategy()
with mirrored_strategy.scope():
model = base.build_keras_model()
try:
log_dir = fn_args.model_run_dir
except KeyError:
# TODO(b/158106209): use ModelRun instead of Model artifact for logging.
log_dir = os.path.join(os.path.dirname(fn_args.serving_model_dir),
'logs')
# Write logs to path
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir,
update_freq='batch')
model.fit(train_dataset,
steps_per_epoch=fn_args.train_steps,
validation_data=eval_dataset,
validation_steps=fn_args.eval_steps,
callbacks=[tensorboard_callback])
signatures = {
'serving_default':
_get_serve_tf_examples_fn(model,
tf_transform_output).get_concrete_function(
tf.TensorSpec(shape=[None],
dtype=tf.string,
name='examples'))
}
model.save(fn_args.serving_model_dir,
save_format='tf',
signatures=signatures)
def run_fn(fn_args: TrainerFnArgs):
"""Train the model based on given args.
Args:
fn_args: Holds args used to train the model as name/value pairs.
"""
tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)
train_dataset = base.input_fn(fn_args.train_files, tf_transform_output, 40)
eval_dataset = base.input_fn(fn_args.eval_files, tf_transform_output, 40)
mirrored_strategy = tf.distribute.MirroredStrategy()
with mirrored_strategy.scope():
model = base.build_keras_model()
model.fit(train_dataset,
steps_per_epoch=fn_args.train_steps,
validation_data=eval_dataset,
validation_steps=fn_args.eval_steps)
signatures = {
'serving_default':
_get_serve_tf_examples_fn(model,
tf_transform_output).get_concrete_function(
tf.TensorSpec(shape=[None, 784],
dtype=tf.float32,
name='image_floats'))
}
temp_saving_model_dir = os.path.join(fn_args.serving_model_dir, 'temp')
model.save(temp_saving_model_dir, save_format='tf', signatures=signatures)
tfrw = rewriter_factory.create_rewriter(
rewriter_factory.TFLITE_REWRITER,
name='tflite_rewriter',
enable_experimental_new_converter=True)
converters.rewrite_saved_model(temp_saving_model_dir,
fn_args.serving_model_dir, tfrw,
rewriter.ModelType.TFLITE_MODEL)
tf.io.gfile.rmtree(temp_saving_model_dir)
