def __init__(
self,
examples: types.Channel = None,
transformed_examples: Optional[types.Channel] = None,
transform_graph: Optional[types.Channel] = None,
schema: Optional[types.Channel] = None,
base_model: Optional[types.Channel] = None,
hyperparameters: Optional[types.Channel] = None,
module_file: Optional[Union[Text, data_types.RuntimeParameter]] = None,
run_fn: Optional[Union[Text, data_types.RuntimeParameter]] = None,
# TODO(b/147702778): deprecate trainer_fn.
trainer_fn: Optional[Union[Text, data_types.RuntimeParameter]] = None,
train_args: Union[trainer_pb2.TrainArgs, Dict[Text, Any]] = None,
eval_args: Union[trainer_pb2.EvalArgs, Dict[Text, Any]] = None,
custom_config: Optional[Dict[Text, Any]] = None,
custom_executor_spec: Optional[executor_spec.ExecutorSpec] = None,
model: Optional[types.Channel] = None,
model_run: Optional[types.Channel] = None,
instance_name: Optional[Text] = None):
"""Construct a Trainer component.
Args:
examples: A Channel of type `standard_artifacts.Examples`, serving as
the source of examples used in training (required). May be raw or
transformed.
transformed_examples: Deprecated field. Please set 'examples' instead.
transform_graph: An optional Channel of type
`standard_artifacts.TransformGraph`, serving as the input transform
graph if present.
schema: An optional Channel of type `standard_artifacts.Schema`, serving
as the schema of training and eval data. Schema is optional when
1) transform_graph is provided which contains schema.
2) user module bypasses the usage of schema, e.g., hardcoded.
base_model: A Channel of type `Model`, containing model that will be used
for training. This can be used for warmstart, transfer learning or
model ensembling.
hyperparameters: A Channel of type `standard_artifacts.HyperParameters`,
serving as the hyperparameters for training module. Tuner's output best
hyperparameters can be feed into this.
module_file: A path to python module file containing UDF model definition.
The module_file must implement a function named `run_fn` at its top
level with function signature:
`def run_fn(trainer.fn_args_utils.FnArgs)`,
and the trained model must be saved to FnArgs.serving_model_dir when
this function is executed.
For Estimator based Executor, The module_file must implement a function
named `trainer_fn` at its top level. The function must have the
following signature.
def trainer_fn(trainer.fn_args_utils.FnArgs,
tensorflow_metadata.proto.v0.schema_pb2) -> Dict:
...
where the returned Dict has the following key-values.
'estimator': an instance of tf.estimator.Estimator
'train_spec': an instance of tf.estimator.TrainSpec
'eval_spec': an instance of tf.estimator.EvalSpec
'eval_input_receiver_fn': an instance of tfma EvalInputReceiver.
run_fn: A python path to UDF model definition function for generic
trainer. See 'module_file' for details. Exactly one of 'module_file' or
'run_fn' must be supplied if Trainer uses GenericExecutor (default).
trainer_fn: A python path to UDF model definition function for estimator
based trainer. See 'module_file' for the required signature of the UDF.
Exactly one of 'module_file' or 'trainer_fn' must be supplied if Trainer
uses Estimator based Executor
train_args: A trainer_pb2.TrainArgs instance or a dict, containing args
used for training. Currently only splits and num_steps are available. If
it's provided as a dict and any field is a RuntimeParameter, it should
have the same field names as a TrainArgs proto message. Default
behavior (when splits is empty) is train on `train` split.
eval_args: A trainer_pb2.EvalArgs instance or a dict, containing args
used for evaluation. Currently only splits and num_steps are available.
If it's provided as a dict and any field is a RuntimeParameter, it
should have the same field names as a EvalArgs proto message. Default
behavior (when splits is empty) is evaluate on `eval` split.
custom_config: A dict which contains addtional training job parameters
that will be passed into user module.
custom_executor_spec: Optional custom executor spec.
model: Optional `Model` channel for result of exported models.
model_run: Optional `ModelRun` channel, as the working dir of models,
can be used to output non-model related output (e.g., TensorBoard logs).
instance_name: Optional unique instance name. Necessary iff multiple
Trainer components are declared in the same pipeline.
Raises:
ValueError:
- When both or neither of 'module_file' and user function
(e.g., trainer_fn and run_fn) is supplied.
- When both or neither of 'examples' and 'transformed_examples'
is supplied.
- When 'transformed_examples' is supplied but 'transform_graph'
is not supplied.
"""
if [bool(module_file), bool(run_fn), bool(trainer_fn)].count(True) != 1:
raise ValueError(
"Exactly one of 'module_file', 'trainer_fn', or 'run_fn' must be "
"supplied.")
if bool(examples) == bool(transformed_examples):
raise ValueError(
"Exactly one of 'example' or 'transformed_example' must be supplied.")
if transformed_examples and not transform_graph:
raise ValueError("If 'transformed_examples' is supplied, "
"'transform_graph' must be supplied too.")
examples = examples or transformed_examples
model = model or types.Channel(type=standard_artifacts.Model)
model_run = model_run or types.Channel(type=standard_artifacts.ModelRun)
spec = TrainerSpec(
examples=examples,
transform_graph=transform_graph,
schema=schema,
base_model=base_model,
hyperparameters=hyperparameters,
train_args=train_args,
eval_args=eval_args,
module_file=module_file,
run_fn=run_fn,
trainer_fn=trainer_fn,
custom_config=json_utils.dumps(custom_config),
model=model,
model_run=model_run)
super(Trainer, self).__init__(
spec=spec,
custom_executor_spec=custom_executor_spec,
instance_name=instance_name)
def __init__(
self,
examples: types.Channel = None,
transformed_examples: Optional[types.Channel] = None,
transform_graph: Optional[types.Channel] = None,
schema: types.Channel = None,
base_model: Optional[types.Channel] = None,
hyperparameters: Optional[types.Channel] = None,
module_file: Optional[Union[Text,
data_types.RuntimeParameter]] = None,
trainer_fn: Optional[Union[Text,
data_types.RuntimeParameter]] = None,
train_args: Union[trainer_pb2.TrainArgs, Dict[Text, Any]] = None,
eval_args: Union[trainer_pb2.EvalArgs, Dict[Text, Any]] = None,
custom_config: Optional[Dict[Text, Any]] = None,
custom_executor_spec: Optional[executor_spec.ExecutorSpec] = None,
output: Optional[types.Channel] = None,
transform_output: Optional[types.Channel] = None,
instance_name: Optional[Text] = None):
"""Construct a Trainer component.
Args:
examples: A Channel of type `standard_artifacts.Examples`, serving as
the source of examples used in training (required). May be raw or
transformed.
transformed_examples: Deprecated field. Please set 'examples' instead.
transform_graph: An optional Channel of type
`standard_artifacts.TransformGraph`, serving as the input transform
graph if present.
schema: A Channel of type `standard_artifacts.Schema`, serving as the
schema of training and eval data.
base_model: A Channel of type `Model`, containing model that will be used
for training. This can be used for warmstart, transfer learning or
model ensembling.
hyperparameters: A Channel of type `standard_artifacts.HyperParameters`,
serving as the hyperparameters for training module. Tuner's output best
hyperparameters can be feed into this.
module_file: A path to python module file containing UDF model
definition. The module_file must implement a function named
`trainer_fn` at its top level. The function must have the following
signature.
def trainer_fn(trainer.executor._TrainerFnArgs,
tensorflow_metadata.proto.v0.schema_pb2) -> Dict:
...
where the returned Dict has the following key-values.
'estimator': an instance of tf.estimator.Estimator
'train_spec': an instance of tf.estimator.TrainSpec
'eval_spec': an instance of tf.estimator.EvalSpec
'eval_input_receiver_fn': an instance of
tfma.export.EvalInputReceiver. Exactly one of 'module_file' or
'trainer_fn' must be supplied.
trainer_fn: A python path to UDF model definition function. See
'module_file' for the required signature of the UDF. Exactly one of
'module_file' or 'trainer_fn' must be supplied.
train_args: A trainer_pb2.TrainArgs instance, containing args used for
training. Current only num_steps is available.
eval_args: A trainer_pb2.EvalArgs instance, containing args used for eval.
Current only num_steps is available.
custom_config: A dict which contains addtional training job parameters
that will be passed into user module.
custom_executor_spec: Optional custom executor spec.
output: Optional `Model` channel for result of exported models.
transform_output: Backwards compatibility alias for the 'transform_graph'
argument.
instance_name: Optional unique instance name. Necessary iff multiple
Trainer components are declared in the same pipeline.
Raises:
ValueError:
- When both or neither of 'module_file' and 'trainer_fn' is supplied.
- When both or neither of 'examples' and 'transformed_examples'
is supplied.
- When 'transformed_examples' is supplied but 'transform_graph'
is not supplied.
"""
transform_graph = transform_graph or transform_output
if bool(module_file) == bool(trainer_fn):
raise ValueError(
"Exactly one of 'module_file' or 'trainer_fn' must be supplied"
)
if bool(examples) == bool(transformed_examples):
raise ValueError(
"Exactly one of 'example' or 'transformed_example' must be supplied."
)
if transformed_examples and not transform_graph:
raise ValueError("If 'transformed_examples' is supplied, "
"'transform_graph' must be supplied too.")
examples = examples or transformed_examples
output = output or types.Channel(
type=standard_artifacts.Model,
artifacts=[standard_artifacts.Model()])
spec = TrainerSpec(examples=examples,
transform_output=transform_graph,
schema=schema,
base_model=base_model,
hyperparameters=hyperparameters,
train_args=train_args,
eval_args=eval_args,
module_file=module_file,
trainer_fn=trainer_fn,
custom_config=custom_config,
output=output)
super(Trainer,
self).__init__(spec=spec,
custom_executor_spec=custom_executor_spec,
instance_name=instance_name)
def __init__(
self,
examples: types.Channel = None,
transformed_examples: Optional[types.Channel] = None,
transform_output: Optional[types.Channel] = None,
schema: types.Channel = None,
module_file: Optional[Text] = None,
trainer_fn: Optional[Text] = None,
train_args: trainer_pb2.TrainArgs = None,
eval_args: trainer_pb2.EvalArgs = None,
custom_config: Optional[Dict[Text, Any]] = None,
executor_class: Optional[Type[base_executor.BaseExecutor]] = None,
output: Optional[types.Channel] = None,
transform_graph: Optional[types.Channel] = None,
name: Optional[Text] = None):
"""Construct a Trainer component.
Args:
examples: A Channel of 'ExamplesPath' type, serving as the source of
examples that are used in training (required). May be raw or
transformed.
transformed_examples: Deprecated field. Please set 'examples' instead.
transform_output: An optional Channel of 'TransformPath' type, serving as
the input transform graph if present.
schema: A Channel of 'SchemaPath' type, serving as the schema of training
and eval data.
module_file: A path to python module file containing UDF model definition.
The module_file must implement a function named `trainer_fn` at its
top level. The function must have the following signature.
def trainer_fn(tf.contrib.training.HParams,
tensorflow_metadata.proto.v0.schema_pb2) -> Dict:
...
where the returned Dict has the following key-values.
'estimator': an instance of tf.estimator.Estimator
'train_spec': an instance of tf.estimator.TrainSpec
'eval_spec': an instance of tf.estimator.EvalSpec
'eval_input_receiver_fn': an instance of tfma.export.EvalInputReceiver
Exactly one of 'module_file' or 'trainer_fn' must be supplied.
trainer_fn: A python path to UDF model definition function. See
'module_file' for the required signature of the UDF.
Exactly one of 'module_file' or 'trainer_fn' must be supplied.
train_args: A trainer_pb2.TrainArgs instance, containing args used for
training. Current only num_steps is available.
eval_args: A trainer_pb2.EvalArgs instance, containing args used for eval.
Current only num_steps is available.
custom_config: A dict which contains the training job parameters to be
passed to Google Cloud ML Engine. For the full set of parameters
supported by Google Cloud ML Engine, refer to
https://cloud.google.com/ml-engine/reference/rest/v1/projects.jobs#Job
executor_class: Optional custom executor class.
output: Optional 'ModelExportPath' channel for result of exported models.
transform_graph: Forwards compatibility alias for the 'transform_output'
argument.
name: Optional unique name. Necessary iff multiple Trainer components are
declared in the same pipeline.
Raises:
ValueError:
- When both or neither of 'module_file' and 'trainer_fn' is supplied.
- When both or neither of 'examples' and 'transformed_examples'
is supplied.
- When 'transformed_examples' is supplied but 'transform_output'
is not supplied.
"""
transform_output = transform_output or transform_graph
if bool(module_file) == bool(trainer_fn):
raise ValueError(
"Exactly one of 'module_file' or 'trainer_fn' must be supplied")
if bool(examples) == bool(transformed_examples):
raise ValueError(
"Exactly one of 'example' or 'transformed_example' must be supplied.")
if transformed_examples and not transform_output:
raise ValueError("If 'transformed_examples' is supplied, "
"'transform_output' must be supplied too.")
examples = examples or transformed_examples
output = output or types.Channel(
type=standard_artifacts.Model, artifacts=[standard_artifacts.Model()])
spec = TrainerSpec(
examples=examples,
transform_output=transform_output,
schema=schema,
train_args=train_args,
eval_args=eval_args,
module_file=module_file,
trainer_fn=trainer_fn,
custom_config=custom_config,
output=output)
super(Trainer, self).__init__(
spec=spec, custom_executor_class=executor_class, name=name)
