def _generate_blessing_result(self, eval_examples_uri: Text,
slice_spec: List[
tfma.slicer.SingleSliceSpec],
current_model_dir: Text,
blessed_model_dir: Text) -> bool:
current_model_eval_result_path = os.path.join(
self._temp_path, constants.CURRENT_MODEL_EVAL_RESULT_PATH)
blessed_model_eval_result_path = os.path.join(
self._temp_path, constants.BLESSED_MODEL_EVAL_RESULT_PATH)
with self._make_beam_pipeline() as pipeline:
eval_data = (pipeline | 'ReadData' >> beam.io.ReadFromTFRecord(
file_pattern=io_utils.all_files_pattern(eval_examples_uri)))
current_model = tfma.default_eval_shared_model(
eval_saved_model_path=path_utils.eval_model_path(
current_model_dir))
(eval_data
| 'EvalCurrentModel' >> tfma.ExtractEvaluateAndWriteResults( # pylint: disable=expression-not-assigned
eval_shared_model=current_model,
slice_spec=slice_spec,
output_path=current_model_eval_result_path))
if blessed_model_dir is not None:
blessed_model = tfma.default_eval_shared_model(
eval_saved_model_path=path_utils.eval_model_path(
blessed_model_dir))
(eval_data
| 'EvalBlessedModel' >> tfma.ExtractEvaluateAndWriteResults( # pylint: disable=expression-not-assigned
eval_shared_model=blessed_model,
slice_spec=slice_spec,
output_path=blessed_model_eval_result_path))
absl.logging.info(
'all files in current_model_eval_result_path: [%s]',
str(tf.io.gfile.listdir(current_model_eval_result_path)))
current_model_eval_result = tfma.load_eval_result(
output_path=current_model_eval_result_path)
if not self._pass_threshold(current_model_eval_result):
absl.logging.info('Current model does not pass threshold.')
return False
absl.logging.info('Current model passes threshold.')
if blessed_model_dir is None:
absl.logging.info('No blessed model yet.')
return True
absl.logging.info(
'all files in blessed_model_eval_result: [%s]',
str(tf.io.gfile.listdir(blessed_model_eval_result_path)))
blessed_model_eval_result = tfma.load_eval_result(
output_path=blessed_model_eval_result_path)
if (self._compare_eval_result(current_model_eval_result,
blessed_model_eval_result)):
absl.logging.info('Current model better than blessed model.')
return True
else:
absl.logging.info('Current model worse than blessed model.')
return False
def _generate_blessing_result(self, eval_examples_uri, slice_spec,
current_model_dir, blessed_model_dir):
current_model_eval_result_path = os.path.join(
self._temp_path, CURRENT_MODEL_EVAL_RESULT_PATH)
blessed_model_eval_result_path = os.path.join(
self._temp_path, BLESSED_MODEL_EVAL_RESULT_PATH)
with beam.Pipeline(argv=self._get_beam_pipeline_args()) as pipeline:
eval_data = (pipeline | 'ReadData' >> beam.io.ReadFromTFRecord(
file_pattern=io_utils.all_files_pattern(eval_examples_uri)))
current_model = tfma.default_eval_shared_model(
eval_saved_model_path=path_utils.eval_model_path(
current_model_dir))
(eval_data
| 'EvalCurrentModel' >> tfma.ExtractEvaluateAndWriteResults( # pylint: disable=expression-not-assigned
eval_shared_model=current_model,
slice_spec=slice_spec,
output_path=current_model_eval_result_path))
if blessed_model_dir is not None:
blessed_model = tfma.default_eval_shared_model(
eval_saved_model_path=path_utils.eval_model_path(
blessed_model_dir))
(eval_data
| 'EvalBlessedModel' >> tfma.ExtractEvaluateAndWriteResults( # pylint: disable=expression-not-assigned
eval_shared_model=blessed_model,
slice_spec=slice_spec,
output_path=blessed_model_eval_result_path))
current_model_eval_result = tfma.load_eval_result(
output_path=current_model_eval_result_path)
if not self._pass_threshold(current_model_eval_result):
tf.logging.info('Current model does not pass threshold.')
return False
tf.logging.info('Current model passes threshold.')
if blessed_model_dir is None:
tf.logging.info('No blessed model yet.')
return True
blessed_model_eval_result = tfma.load_eval_result(
output_path=blessed_model_eval_result_path)
if (self._compare_eval_result(current_model_eval_result,
blessed_model_eval_result)):
tf.logging.info('Current model better than blessed model.')
return True
else:
tf.logging.info('Current model worse than blessed model.')
return False
def Do(self, input_dict: Dict[Text, List[types.Artifact]],
output_dict: Dict[Text, List[types.Artifact]],
exec_properties: Dict[Text, Any]) -> None:
"""Runs a batch job to evaluate the eval_model against the given input.
Args:
input_dict: Input dict from input key to a list of Artifacts.
- model_exports: exported model.
- examples: examples for eval the model.
output_dict: Output dict from output key to a list of Artifacts.
- output: model evaluation results.
exec_properties: A dict of execution properties.
- feature_slicing_spec: JSON string of evaluator_pb2.FeatureSlicingSpec
instance, providing the way to slice the data.
Returns:
None
"""
if 'model_exports' not in input_dict:
raise ValueError('\'model_exports\' is missing in input dict.')
if 'examples' not in input_dict:
raise ValueError('\'examples\' is missing in input dict.')
if 'output' not in output_dict:
raise ValueError('\'output\' is missing in output dict.')
self._log_startup(input_dict, output_dict, exec_properties)
# Extract input artifacts
model_exports_uri = artifact_utils.get_single_uri(
input_dict['model_exports'])
feature_slicing_spec = evaluator_pb2.FeatureSlicingSpec()
json_format.Parse(exec_properties['feature_slicing_spec'],
feature_slicing_spec)
slice_spec = self._get_slice_spec_from_feature_slicing_spec(
feature_slicing_spec)
output_uri = artifact_utils.get_single_uri(output_dict['output'])
eval_model_path = path_utils.eval_model_path(model_exports_uri)
tf.logging.info('Using {} for model eval.'.format(eval_model_path))
eval_shared_model = tfma.default_eval_shared_model(
eval_saved_model_path=eval_model_path)
tf.logging.info('Evaluating model.')
with self._make_beam_pipeline() as pipeline:
# pylint: disable=expression-not-assigned
(pipeline
| 'ReadData' >>
beam.io.ReadFromTFRecord(file_pattern=io_utils.all_files_pattern(
artifact_utils.get_split_uri(input_dict['examples'], 'eval')))
| 'ExtractEvaluateAndWriteResults' >>
tfma.ExtractEvaluateAndWriteResults(
eval_shared_model=eval_shared_model,
slice_spec=slice_spec,
output_path=output_uri))
tf.logging.info(
'Evaluation complete. Results written to {}.'.format(output_uri))
def Do(self, input_dict,
output_dict,
exec_properties):
"""Runs a batch job to evaluate the eval_model against the given input.
Args:
input_dict: Input dict from input key to a list of Artifacts.
- model_exports: exported model.
- examples: examples for eval the model.
output_dict: Output dict from output key to a list of Artifacts.
- output: model evaluation results.
exec_properties: A dict of execution properties.
- feature_slicing_spec: JSON string of evaluator_pb2.FeatureSlicingSpec
instance, providing the way to slice the data.
Returns:
None
"""
if 'model_exports' not in input_dict:
raise ValueError('\'model_exports\' is missing in input dict.')
if 'examples' not in input_dict:
raise ValueError('\'examples\' is missing in input dict.')
if 'output' not in output_dict:
raise ValueError('\'output\' is missing in output dict.')
self._log_startup(input_dict, output_dict, exec_properties)
# Extract input artifacts
model_exports_uri = types.get_single_uri(input_dict['model_exports'])
feature_slicing_spec = evaluator_pb2.FeatureSlicingSpec()
json_format.Parse(exec_properties['feature_slicing_spec'],
feature_slicing_spec)
slice_spec = self._get_slice_spec_from_feature_slicing_spec(
feature_slicing_spec)
output_uri = types.get_single_uri(output_dict['output'])
eval_model_path = path_utils.eval_model_path(model_exports_uri)
tf.logging.info('Using {} for model eval.'.format(eval_model_path))
eval_shared_model = tfma.default_eval_shared_model(
eval_saved_model_path=eval_model_path)
tf.logging.info('Evaluating model.')
with beam.Pipeline(argv=self._get_beam_pipeline_args()) as pipeline:
# pylint: disable=expression-not-assigned
(pipeline
| 'ReadData' >> beam.io.ReadFromTFRecord(
file_pattern=io_utils.all_files_pattern(
types.get_split_uri(input_dict['examples'], 'eval')))
|
'ExtractEvaluateAndWriteResults' >> tfma.ExtractEvaluateAndWriteResults(
eval_shared_model=eval_shared_model,
slice_spec=slice_spec,
output_path=output_uri))
tf.logging.info(
'Evaluation complete. Results written to {}.'.format(output_uri))
def testKerasModelPath(self):
# Create folders based on Keras based Trainer output model directory.
output_uri = os.path.join(self.get_temp_dir(), 'model_dir')
serving_model_path = os.path.join(output_uri, 'serving_model_dir')
tf.io.gfile.makedirs(serving_model_path)
# Test retrieving model folder.
self.assertEqual(serving_model_path,
path_utils.eval_model_path(output_uri))
self.assertEqual(serving_model_path,
path_utils.serving_model_path(output_uri))
def _get_eval_saved_model(artifact: List[types.Artifact],
tags=None) -> tfma.EvalSharedModel:
model_uri = artifact_utils.get_single_uri(artifact)
if tags and tf.saved_model.SERVING in tags:
model_path = path_utils.serving_model_path(model_uri)
else:
model_path = path_utils.eval_model_path(model_uri)
return tfma.default_eval_shared_model(
eval_saved_model_path=model_path,
tags=tags,
add_metrics_callbacks=add_metrics_callbacks)
def testKerasModelPath(self):
# Create folders based on Keras based Trainer output model directory.
output_uri = os.path.join(self.get_temp_dir(), 'model_dir')
serving_model_path = path_utils.serving_model_dir(output_uri)
serving_model = os.path.join(serving_model_path, 'saved_model.pb')
io_utils.write_string_file(serving_model, 'testing')
# Test retrieving model folder.
self.assertEqual(serving_model_path,
path_utils.eval_model_path(output_uri))
self.assertEqual(serving_model_path,
path_utils.serving_model_path(output_uri))
def testEstimatorModelPath(self):
# Create folders based on Estimator based Trainer output model directory.
output_uri = os.path.join(self.get_temp_dir(), 'model_dir')
eval_model_path = os.path.join(output_uri, 'eval_model_dir', '123')
tf.io.gfile.makedirs(eval_model_path)
serving_model_path = os.path.join(output_uri, 'serving_model_dir',
'export', 'taxi', '123')
tf.io.gfile.makedirs(serving_model_path)
# Test retrieving model folder.
self.assertEqual(eval_model_path,
path_utils.eval_model_path(output_uri))
self.assertEqual(serving_model_path,
path_utils.serving_model_path(output_uri))
def testEstimatorModelPath(self, is_old_artifact):
# Create folders based on Estimator based Trainer output model directory,
# after Executor performs cleaning.
output_uri = os.path.join(self.get_temp_dir(), 'model_dir')
eval_model_path = path_utils.eval_model_dir(output_uri,
is_old_artifact)
eval_model = os.path.join(eval_model_path, 'saved_model.pb')
io_utils.write_string_file(eval_model, 'testing')
serving_model_path = path_utils.serving_model_dir(
output_uri, is_old_artifact)
serving_model = os.path.join(eval_model_path, 'saved_model.pb')
io_utils.write_string_file(serving_model, 'testing')
# Test retrieving model folder.
self.assertEqual(
eval_model_path,
path_utils.eval_model_path(output_uri, is_old_artifact))
self.assertEqual(
serving_model_path,
path_utils.serving_model_path(output_uri, is_old_artifact))
self.assertEqual(
eval_model_path,
path_utils.get_model_dir_by_type(output_uri,
path_constants.TFMA_EVAL,
is_old_artifact))
self.assertEqual(
serving_model_path,
path_utils.get_model_dir_by_type(output_uri,
path_constants.TF_KERAS,
is_old_artifact))
self.assertEqual(
serving_model_path,
path_utils.get_model_dir_by_type(output_uri,
path_constants.TF_GENERIC,
is_old_artifact))
self.assertEqual(
serving_model_path,
path_utils.get_model_dir_by_type(output_uri,
path_constants.TF_ESTIMATOR,
is_old_artifact))
self.assertEqual(
serving_model_path,
path_utils.get_model_dir_by_type(output_uri, path_constants.TF_JS,
is_old_artifact))
self.assertEqual(
serving_model_path,
path_utils.get_model_dir_by_type(output_uri,
path_constants.TF_LITE,
is_old_artifact))
def testEstimatorModelPath(self):
# Create folders based on Estimator based Trainer output model directory,
# after Executor performs cleaning.
output_uri = os.path.join(self.get_temp_dir(), 'model_dir')
eval_model_path = path_utils.eval_model_dir(output_uri)
eval_model = os.path.join(eval_model_path, 'saved_model.pb')
io_utils.write_string_file(eval_model, 'testing')
serving_model_path = path_utils.serving_model_dir(output_uri)
serving_model = os.path.join(eval_model_path, 'saved_model.pb')
io_utils.write_string_file(serving_model, 'testing')
# Test retrieving model folder.
self.assertEqual(eval_model_path,
path_utils.eval_model_path(output_uri))
self.assertEqual(serving_model_path,
path_utils.serving_model_path(output_uri))
def run_fn(fn_args: executor.TrainerFnArgs):
"""Train the model based on given args.
Args:
fn_args: Holds args used to train the model as name/value pairs.
"""
schema = io_utils.parse_pbtxt_file(fn_args.schema_file,
schema_pb2.Schema())
training_spec = _trainer_fn(fn_args, schema)
# Train the model
absl.logging.info('Training model.')
tf.estimator.train_and_evaluate(training_spec['estimator'],
training_spec['train_spec'],
training_spec['eval_spec'])
absl.logging.info('Training complete. Model written to %s',
fn_args.serving_model_dir)
# Export an eval savedmodel for TFMA
# NOTE: When trained in distributed training cluster, eval_savedmodel must be
# exported only by the chief worker (check TF_CONFIG).
absl.logging.info('Exporting eval_savedmodel for TFMA.')
eval_export_dir = path_utils.eval_model_dir(fn_args.model_run_dir)
tfma.export.export_eval_savedmodel(
estimator=training_spec['estimator'],
export_dir_base=eval_export_dir,
eval_input_receiver_fn=training_spec['eval_input_receiver_fn'])
absl.logging.info('Exported eval_savedmodel to %s.',
fn_args.eval_model_dir)
# TODO(b/160795287): Deprecate estimator based executor.
# Copy serving and eval model from model_run to model artifact directory.
serving_source = path_utils.serving_model_path(fn_args.model_run_dir)
io_utils.copy_dir(serving_source, fn_args.serving_model_dir)
absl.logging.info('Serving model copied to: %s.',
fn_args.serving_model_dir)
eval_source = path_utils.eval_model_path(fn_args.model_run_dir)
io_utils.copy_dir(eval_source, fn_args.eval_model_dir)
absl.logging.info('Eval model copied to: %s.', fn_args.eval_model_dir)
def Do(self, input_dict: Dict[Text, List[types.Artifact]],
output_dict: Dict[Text, List[types.Artifact]],
exec_properties: Dict[Text, Any]) -> None:
"""Uses a user-supplied tf.estimator to train a TensorFlow model locally.
The Trainer Executor invokes a training_fn callback function provided by
the user via the module_file parameter. With the tf.estimator returned by
this function, the Trainer Executor then builds a TensorFlow model using the
user-provided tf.estimator.
Args:
input_dict: Input dict from input key to a list of ML-Metadata Artifacts.
- examples: Examples used for training, must include 'train' and 'eval'
splits.
- transform_output: Optional input transform graph.
- schema: Schema of the data.
output_dict: Output dict from output key to a list of Artifacts.
- model: Exported model.
- model_run: Model training related outputs (e.g., Tensorboard logs)
exec_properties: A dict of execution properties.
- train_args: JSON string of trainer_pb2.TrainArgs instance, providing
args for training.
- eval_args: JSON string of trainer_pb2.EvalArgs instance, providing
args for eval.
- module_file: Python module file containing UDF model definition.
- warm_starting: Whether or not we need to do warm starting.
- warm_start_from: Optional. If warm_starting is True, this is the
directory to find previous model to warm start on.
- custom_config: Optional. JSON-serialized dict of additional parameters
to pass to trainer function.
Returns:
None
Raises:
ValueError: When neither or both of 'module_file' and 'trainer_fn'
are present in 'exec_properties'.
"""
self._log_startup(input_dict, output_dict, exec_properties)
fn_args = self._GetFnArgs(input_dict, output_dict, exec_properties)
trainer_fn = udf_utils.get_fn(exec_properties, 'trainer_fn')
schema = io_utils.parse_pbtxt_file(fn_args.schema_file, schema_pb2.Schema())
# TODO(b/160795287): Deprecate estimator based executor.
# Provide user with a modified fn_args, with model_run given as
# the working directory. Executor will then copy user models to
# model artifact directory.
serving_dest = fn_args.serving_model_dir
eval_dest = fn_args.eval_model_dir
working_dir = fn_args.model_run_dir
fn_args.serving_model_dir = path_utils.serving_model_dir(working_dir)
fn_args.eval_model_dir = path_utils.eval_model_dir(working_dir)
training_spec = trainer_fn(fn_args, schema)
# Train the model
absl.logging.info('Training model.')
tf.estimator.train_and_evaluate(training_spec['estimator'],
training_spec['train_spec'],
training_spec['eval_spec'])
absl.logging.info(
'Training complete. Model written to %s. ModelRun written to %s',
fn_args.serving_model_dir, fn_args.model_run_dir)
# Export an eval savedmodel for TFMA. If distributed training, it must only
# be written by the chief worker, as would be done for serving savedmodel.
if _is_chief():
absl.logging.info('Exporting eval_savedmodel for TFMA.')
tfma.export.export_eval_savedmodel(
estimator=training_spec['estimator'],
export_dir_base=fn_args.eval_model_dir,
eval_input_receiver_fn=training_spec['eval_input_receiver_fn'])
absl.logging.info('Exported eval_savedmodel to %s.',
fn_args.eval_model_dir)
# TODO(b/160795287): Deprecate estimator based executor.
# Copy serving and eval model from model_run to model artifact directory.
serving_source = path_utils.serving_model_path(fn_args.model_run_dir)
io_utils.copy_dir(serving_source, serving_dest)
absl.logging.info('Serving model copied to: %s.', serving_dest)
eval_source = path_utils.eval_model_path(fn_args.model_run_dir)
io_utils.copy_dir(eval_source, eval_dest)
absl.logging.info('Eval model copied to: %s.', eval_dest)
else:
absl.logging.info(
'Model export is skipped because this is not the chief worker.')
def Do(self, input_dict: Dict[Text, List[types.Artifact]],
output_dict: Dict[Text, List[types.Artifact]],
exec_properties: Dict[Text, Any]) -> None:
"""Runs a batch job to evaluate the eval_model against the given input.
Args:
input_dict: Input dict from input key to a list of Artifacts.
- model_exports: exported model.
- examples: examples for eval the model.
output_dict: Output dict from output key to a list of Artifacts.
- output: model evaluation results.
exec_properties: A dict of execution properties.
- feature_slicing_spec: JSON string of evaluator_pb2.FeatureSlicingSpec
instance, providing the way to slice the data.
Returns:
None
"""
if 'model_exports' not in input_dict:
raise ValueError('\'model_exports\' is missing in input dict.')
if 'examples' not in input_dict:
raise ValueError('\'examples\' is missing in input dict.')
if 'output' not in output_dict:
raise ValueError('\'output\' is missing in output dict.')
self._log_startup(input_dict, output_dict, exec_properties)
# Extract input artifacts
model_exports_uri = artifact_utils.get_single_uri(
input_dict['model_exports'])
feature_slicing_spec = evaluator_pb2.FeatureSlicingSpec()
json_format.Parse(exec_properties['feature_slicing_spec'],
feature_slicing_spec)
slice_spec = self._get_slice_spec_from_feature_slicing_spec(
feature_slicing_spec)
output_uri = artifact_utils.get_single_uri(output_dict['output'])
eval_model_path = path_utils.eval_model_path(model_exports_uri)
# Add fairness indicator metric callback if necessary.
fairness_indicator_thresholds = exec_properties.get(
'fairness_indicator_thresholds', None)
add_metrics_callbacks = None
if fairness_indicator_thresholds:
# Need to import the following module so that the fairness indicator
# post-export metric is registered.
import tensorflow_model_analysis.addons.fairness.post_export_metrics.fairness_indicators # pylint: disable=g-import-not-at-top, unused-variable
add_metrics_callbacks = [
tfma.post_export_metrics.fairness_indicators( # pytype: disable=module-attr
thresholds=fairness_indicator_thresholds),
]
absl.logging.info('Using {} for model eval.'.format(eval_model_path))
eval_shared_model = tfma.default_eval_shared_model(
eval_saved_model_path=eval_model_path,
add_metrics_callbacks=add_metrics_callbacks)
absl.logging.info('Evaluating model.')
with self._make_beam_pipeline() as pipeline:
# pylint: disable=expression-not-assigned
(pipeline
| 'ReadData' >> beam.io.ReadFromTFRecord(
file_pattern=io_utils.all_files_pattern(
artifact_utils.get_split_uri(input_dict['examples'], 'eval')))
|
'ExtractEvaluateAndWriteResults' >> tfma.ExtractEvaluateAndWriteResults(
eval_shared_model=eval_shared_model,
slice_spec=slice_spec,
output_path=output_uri))
absl.logging.info(
'Evaluation complete. Results written to {}.'.format(output_uri))
def Do(self, input_dict: Dict[Text, List[types.Artifact]],
output_dict: Dict[Text, List[types.Artifact]],
exec_properties: Dict[Text, Any]) -> None:
"""Runs a batch job to evaluate the eval_model against the given input.
Args:
input_dict: Input dict from input key to a list of Artifacts.
- model_exports: exported model.
- examples: examples for eval the model.
output_dict: Output dict from output key to a list of Artifacts.
- output: model evaluation results.
exec_properties: A dict of execution properties.
- eval_config: JSON string of tfma.EvalConfig.
- feature_slicing_spec: JSON string of evaluator_pb2.FeatureSlicingSpec
instance, providing the way to slice the data. Deprecated, use
eval_config.slicing_specs instead.
Returns:
None
"""
if constants.EXAMPLES_KEY not in input_dict:
raise ValueError('EXAMPLES_KEY is missing from input dict.')
if constants.MODEL_KEY not in input_dict:
raise ValueError('MODEL_KEY is missing from input dict.')
if constants.EVALUATION_KEY not in output_dict:
raise ValueError('EVALUATION_KEY is missing from output dict.')
if len(input_dict[constants.MODEL_KEY]) > 1:
raise ValueError(
'There can be only one candidate model, there are {}.'.format(
len(input_dict[constants.MODEL_KEY])))
if constants.BASELINE_MODEL_KEY in input_dict and len(
input_dict[constants.BASELINE_MODEL_KEY]) > 1:
raise ValueError(
'There can be only one baseline model, there are {}.'.format(
len(input_dict[constants.BASELINE_MODEL_KEY])))
self._log_startup(input_dict, output_dict, exec_properties)
# Add fairness indicator metric callback if necessary.
fairness_indicator_thresholds = exec_properties.get(
'fairness_indicator_thresholds', None)
add_metrics_callbacks = None
if fairness_indicator_thresholds:
# Need to import the following module so that the fairness indicator
# post-export metric is registered.
import tensorflow_model_analysis.addons.fairness.post_export_metrics.fairness_indicators # pylint: disable=g-import-not-at-top, unused-variable
add_metrics_callbacks = [
tfma.post_export_metrics.fairness_indicators( # pytype: disable=module-attr
thresholds=fairness_indicator_thresholds),
]
output_uri = artifact_utils.get_single_uri(
output_dict[constants.EVALUATION_KEY])
run_validation = False
models = []
if 'eval_config' in exec_properties and exec_properties['eval_config']:
slice_spec = None
has_baseline = bool(input_dict.get(constants.BASELINE_MODEL_KEY))
eval_config = tfma.EvalConfig()
json_format.Parse(exec_properties['eval_config'], eval_config)
eval_config = tfma.update_eval_config_with_defaults(
eval_config,
maybe_add_baseline=has_baseline,
maybe_remove_baseline=not has_baseline)
tfma.verify_eval_config(eval_config)
# Do not validate model when there is no thresholds configured. This is to
# avoid accidentally blessing models when users forget to set thresholds.
run_validation = bool(
tfma.metrics.metric_thresholds_from_metrics_specs(
eval_config.metrics_specs))
if len(eval_config.model_specs) > 2:
raise ValueError(
"""Cannot support more than two models. There are {} models in this
eval_config.""".format(len(eval_config.model_specs)))
# Extract model artifacts.
for model_spec in eval_config.model_specs:
if model_spec.is_baseline:
model_uri = artifact_utils.get_single_uri(
input_dict[constants.BASELINE_MODEL_KEY])
else:
model_uri = artifact_utils.get_single_uri(
input_dict[constants.MODEL_KEY])
if tfma.get_model_type(model_spec) == tfma.TF_ESTIMATOR:
model_path = path_utils.eval_model_path(model_uri)
else:
model_path = path_utils.serving_model_path(model_uri)
absl.logging.info('Using {} as {} model.'.format(
model_path, model_spec.name))
models.append(
tfma.default_eval_shared_model(
model_name=model_spec.name,
eval_saved_model_path=model_path,
add_metrics_callbacks=add_metrics_callbacks,
eval_config=eval_config))
else:
eval_config = None
assert ('feature_slicing_spec' in exec_properties
and exec_properties['feature_slicing_spec']
), 'both eval_config and feature_slicing_spec are unset.'
feature_slicing_spec = evaluator_pb2.FeatureSlicingSpec()
json_format.Parse(exec_properties['feature_slicing_spec'],
feature_slicing_spec)
slice_spec = self._get_slice_spec_from_feature_slicing_spec(
feature_slicing_spec)
model_uri = artifact_utils.get_single_uri(
input_dict[constants.MODEL_KEY])
model_path = path_utils.eval_model_path(model_uri)
absl.logging.info('Using {} for model eval.'.format(model_path))
models.append(
tfma.default_eval_shared_model(
eval_saved_model_path=model_path,
add_metrics_callbacks=add_metrics_callbacks))
file_pattern = io_utils.all_files_pattern(
artifact_utils.get_split_uri(input_dict[constants.EXAMPLES_KEY],
'eval'))
eval_shared_model = models[0] if len(models) == 1 else models
schema = None
if constants.SCHEMA_KEY in input_dict:
schema = io_utils.SchemaReader().read(
io_utils.get_only_uri_in_dir(
artifact_utils.get_single_uri(
input_dict[constants.SCHEMA_KEY])))
absl.logging.info('Evaluating model.')
with self._make_beam_pipeline() as pipeline:
# pylint: disable=expression-not-assigned
if _USE_TFXIO:
tensor_adapter_config = None
if tfma.is_batched_input(eval_shared_model, eval_config):
tfxio = tf_example_record.TFExampleRecord(
file_pattern=file_pattern,
schema=schema,
raw_record_column_name=tfma.BATCHED_INPUT_KEY)
if schema is not None:
tensor_adapter_config = tensor_adapter.TensorAdapterConfig(
arrow_schema=tfxio.ArrowSchema(),
tensor_representations=tfxio.TensorRepresentations(
))
data = pipeline | 'ReadFromTFRecordToArrow' >> tfxio.BeamSource(
)
else:
data = pipeline | 'ReadFromTFRecord' >> beam.io.ReadFromTFRecord(
file_pattern=file_pattern)
(data
| 'ExtractEvaluateAndWriteResults' >>
tfma.ExtractEvaluateAndWriteResults(
eval_shared_model=models[0]
if len(models) == 1 else models,
eval_config=eval_config,
output_path=output_uri,
slice_spec=slice_spec,
tensor_adapter_config=tensor_adapter_config))
else:
data = pipeline | 'ReadFromTFRecord' >> beam.io.ReadFromTFRecord(
file_pattern=file_pattern)
(data
| 'ExtractEvaluateAndWriteResults' >>
tfma.ExtractEvaluateAndWriteResults(
eval_shared_model=models[0]
if len(models) == 1 else models,
eval_config=eval_config,
output_path=output_uri,
slice_spec=slice_spec))
absl.logging.info(
'Evaluation complete. Results written to {}.'.format(output_uri))
if not run_validation:
# TODO(jinhuang): delete the BLESSING_KEY from output_dict when supported.
absl.logging.info(
'No threshold configured, will not validate model.')
return
# Set up blessing artifact
blessing = artifact_utils.get_single_instance(
output_dict[constants.BLESSING_KEY])
blessing.set_string_custom_property(
constants.ARTIFACT_PROPERTY_CURRENT_MODEL_URI_KEY,
artifact_utils.get_single_uri(input_dict[constants.MODEL_KEY]))
blessing.set_int_custom_property(
constants.ARTIFACT_PROPERTY_CURRENT_MODEL_ID_KEY,
input_dict[constants.MODEL_KEY][0].id)
if input_dict.get(constants.BASELINE_MODEL_KEY):
baseline_model = input_dict[constants.BASELINE_MODEL_KEY][0]
blessing.set_string_custom_property(
constants.ARTIFACT_PROPERTY_BASELINE_MODEL_URI_KEY,
baseline_model.uri)
blessing.set_int_custom_property(
constants.ARTIFACT_PROPERTY_BASELINE_MODEL_ID_KEY,
baseline_model.id)
if 'current_component_id' in exec_properties:
blessing.set_string_custom_property(
'component_id', exec_properties['current_component_id'])
# Check validation result and write BLESSED file accordingly.
absl.logging.info('Checking validation results.')
validation_result = tfma.load_validation_result(output_uri)
if validation_result.validation_ok:
io_utils.write_string_file(
os.path.join(blessing.uri, constants.BLESSED_FILE_NAME), '')
blessing.set_int_custom_property(
constants.ARTIFACT_PROPERTY_BLESSED_KEY,
constants.BLESSED_VALUE)
else:
io_utils.write_string_file(
os.path.join(blessing.uri, constants.NOT_BLESSED_FILE_NAME),
'')
blessing.set_int_custom_property(
constants.ARTIFACT_PROPERTY_BLESSED_KEY,
constants.NOT_BLESSED_VALUE)
absl.logging.info('Blessing result {} written to {}.'.format(
validation_result.validation_ok, blessing.uri))
def test_model_path(self):
# Test retrieving model folder.
self.assertEqual(self._eval_model_path,
path_utils.eval_model_path(self._output_uri))
self.assertEqual(self._serving_model_path,
path_utils.serving_model_path(self._output_uri))
def Do(self, input_dict: Dict[Text, List[types.Artifact]],
output_dict: Dict[Text, List[types.Artifact]],
exec_properties: Dict[Text, Any]) -> None:
"""Runs a batch job to evaluate the eval_model against the given input.
Args:
input_dict: Input dict from input key to a list of Artifacts.
- model_exports: exported model.
- examples: examples for eval the model.
output_dict: Output dict from output key to a list of Artifacts.
- output: model evaluation results.
exec_properties: A dict of execution properties.
- eval_config: JSON string of tfma.EvalConfig.
- feature_slicing_spec: JSON string of evaluator_pb2.FeatureSlicingSpec
instance, providing the way to slice the data. Deprecated, use
eval_config.slicing_specs instead.
- example_splits: JSON-serialized list of names of splits on which the
metrics are computed. Default behavior (when example_splits is set to
None) is using the 'eval' split.
Returns:
None
"""
if constants.EXAMPLES_KEY not in input_dict:
raise ValueError('EXAMPLES_KEY is missing from input dict.')
if constants.MODEL_KEY not in input_dict:
raise ValueError('MODEL_KEY is missing from input dict.')
if constants.EVALUATION_KEY not in output_dict:
raise ValueError('EVALUATION_KEY is missing from output dict.')
if len(input_dict[constants.MODEL_KEY]) > 1:
raise ValueError(
'There can be only one candidate model, there are %d.' %
(len(input_dict[constants.MODEL_KEY])))
if constants.BASELINE_MODEL_KEY in input_dict and len(
input_dict[constants.BASELINE_MODEL_KEY]) > 1:
raise ValueError(
'There can be only one baseline model, there are %d.' %
(len(input_dict[constants.BASELINE_MODEL_KEY])))
self._log_startup(input_dict, output_dict, exec_properties)
# Add fairness indicator metric callback if necessary.
fairness_indicator_thresholds = exec_properties.get(
'fairness_indicator_thresholds', None)
add_metrics_callbacks = None
if fairness_indicator_thresholds:
add_metrics_callbacks = [
tfma.post_export_metrics.fairness_indicators( # pytype: disable=module-attr
thresholds=fairness_indicator_thresholds),
]
output_uri = artifact_utils.get_single_uri(
output_dict[constants.EVALUATION_KEY])
eval_shared_model_fn = udf_utils.try_get_fn(
exec_properties=exec_properties,
fn_name='custom_eval_shared_model'
) or tfma.default_eval_shared_model
run_validation = False
models = []
if 'eval_config' in exec_properties and exec_properties['eval_config']:
slice_spec = None
has_baseline = bool(input_dict.get(constants.BASELINE_MODEL_KEY))
eval_config = tfma.EvalConfig()
json_format.Parse(exec_properties['eval_config'], eval_config)
eval_config = tfma.update_eval_config_with_defaults(
eval_config,
maybe_add_baseline=has_baseline,
maybe_remove_baseline=not has_baseline)
tfma.verify_eval_config(eval_config)
# Do not validate model when there is no thresholds configured. This is to
# avoid accidentally blessing models when users forget to set thresholds.
run_validation = bool(
tfma.metrics.metric_thresholds_from_metrics_specs(
eval_config.metrics_specs))
if len(eval_config.model_specs) > 2:
raise ValueError(
"""Cannot support more than two models. There are %d models in this
eval_config.""" % (len(eval_config.model_specs)))
# Extract model artifacts.
for model_spec in eval_config.model_specs:
if model_spec.is_baseline:
model_uri = artifact_utils.get_single_uri(
input_dict[constants.BASELINE_MODEL_KEY])
else:
model_uri = artifact_utils.get_single_uri(
input_dict[constants.MODEL_KEY])
if tfma.get_model_type(model_spec) == tfma.TF_ESTIMATOR:
model_path = path_utils.eval_model_path(model_uri)
else:
model_path = path_utils.serving_model_path(model_uri)
logging.info('Using %s as %s model.', model_path,
model_spec.name)
models.append(
eval_shared_model_fn(
eval_saved_model_path=model_path,
model_name=model_spec.name,
eval_config=eval_config,
add_metrics_callbacks=add_metrics_callbacks))
else:
eval_config = None
assert ('feature_slicing_spec' in exec_properties
and exec_properties['feature_slicing_spec']
), 'both eval_config and feature_slicing_spec are unset.'
feature_slicing_spec = evaluator_pb2.FeatureSlicingSpec()
json_format.Parse(exec_properties['feature_slicing_spec'],
feature_slicing_spec)
slice_spec = self._get_slice_spec_from_feature_slicing_spec(
feature_slicing_spec)
model_uri = artifact_utils.get_single_uri(
input_dict[constants.MODEL_KEY])
model_path = path_utils.eval_model_path(model_uri)
logging.info('Using %s for model eval.', model_path)
models.append(
eval_shared_model_fn(
eval_saved_model_path=model_path,
model_name='',
eval_config=None,
add_metrics_callbacks=add_metrics_callbacks))
eval_shared_model = models[0] if len(models) == 1 else models
schema = None
if constants.SCHEMA_KEY in input_dict:
schema = io_utils.SchemaReader().read(
io_utils.get_only_uri_in_dir(
artifact_utils.get_single_uri(
input_dict[constants.SCHEMA_KEY])))
# Load and deserialize example splits from execution properties.
example_splits = json_utils.loads(
exec_properties.get(constants.EXAMPLE_SPLITS_KEY, 'null'))
if not example_splits:
example_splits = ['eval']
logging.info(
"The 'example_splits' parameter is not set, using 'eval' "
'split.')
logging.info('Evaluating model.')
with self._make_beam_pipeline() as pipeline:
examples_list = []
tensor_adapter_config = None
# pylint: disable=expression-not-assigned
if _USE_TFXIO and tfma.is_batched_input(eval_shared_model,
eval_config):
tfxio_factory = tfxio_utils.get_tfxio_factory_from_artifact(
examples=[
artifact_utils.get_single_instance(
input_dict[constants.EXAMPLES_KEY])
],
telemetry_descriptors=_TELEMETRY_DESCRIPTORS,
schema=schema,
raw_record_column_name=tfma_constants.ARROW_INPUT_COLUMN)
# TODO(b/161935932): refactor after TFXIO supports multiple patterns.
for split in example_splits:
file_pattern = io_utils.all_files_pattern(
artifact_utils.get_split_uri(
input_dict[constants.EXAMPLES_KEY], split))
tfxio = tfxio_factory(file_pattern)
data = (pipeline
| 'ReadFromTFRecordToArrow[%s]' % split >>
tfxio.BeamSource())
examples_list.append(data)
if schema is not None:
# Use last tfxio as TensorRepresentations and ArrowSchema are fixed.
tensor_adapter_config = tensor_adapter.TensorAdapterConfig(
arrow_schema=tfxio.ArrowSchema(),
tensor_representations=tfxio.TensorRepresentations())
else:
for split in example_splits:
file_pattern = io_utils.all_files_pattern(
artifact_utils.get_split_uri(
input_dict[constants.EXAMPLES_KEY], split))
data = (
pipeline
| 'ReadFromTFRecord[%s]' % split >>
beam.io.ReadFromTFRecord(file_pattern=file_pattern))
examples_list.append(data)
custom_extractors = udf_utils.try_get_fn(
exec_properties=exec_properties, fn_name='custom_extractors')
extractors = None
if custom_extractors:
extractors = custom_extractors(
eval_shared_model=eval_shared_model,
eval_config=eval_config,
tensor_adapter_config=tensor_adapter_config)
(examples_list | 'FlattenExamples' >> beam.Flatten()
| 'ExtractEvaluateAndWriteResults' >>
tfma.ExtractEvaluateAndWriteResults(
eval_shared_model=models[0] if len(models) == 1 else models,
eval_config=eval_config,
extractors=extractors,
output_path=output_uri,
slice_spec=slice_spec,
tensor_adapter_config=tensor_adapter_config))
logging.info('Evaluation complete. Results written to %s.', output_uri)
if not run_validation:
# TODO(jinhuang): delete the BLESSING_KEY from output_dict when supported.
logging.info('No threshold configured, will not validate model.')
return
# Set up blessing artifact
blessing = artifact_utils.get_single_instance(
output_dict[constants.BLESSING_KEY])
blessing.set_string_custom_property(
constants.ARTIFACT_PROPERTY_CURRENT_MODEL_URI_KEY,
artifact_utils.get_single_uri(input_dict[constants.MODEL_KEY]))
blessing.set_int_custom_property(
constants.ARTIFACT_PROPERTY_CURRENT_MODEL_ID_KEY,
input_dict[constants.MODEL_KEY][0].id)
if input_dict.get(constants.BASELINE_MODEL_KEY):
baseline_model = input_dict[constants.BASELINE_MODEL_KEY][0]
blessing.set_string_custom_property(
constants.ARTIFACT_PROPERTY_BASELINE_MODEL_URI_KEY,
baseline_model.uri)
blessing.set_int_custom_property(
constants.ARTIFACT_PROPERTY_BASELINE_MODEL_ID_KEY,
baseline_model.id)
if 'current_component_id' in exec_properties:
blessing.set_string_custom_property(
'component_id', exec_properties['current_component_id'])
# Check validation result and write BLESSED file accordingly.
logging.info('Checking validation results.')
validation_result = tfma.load_validation_result(output_uri)
if validation_result.validation_ok:
io_utils.write_string_file(
os.path.join(blessing.uri, constants.BLESSED_FILE_NAME), '')
blessing.set_int_custom_property(
constants.ARTIFACT_PROPERTY_BLESSED_KEY,
constants.BLESSED_VALUE)
else:
io_utils.write_string_file(
os.path.join(blessing.uri, constants.NOT_BLESSED_FILE_NAME),
'')
blessing.set_int_custom_property(
constants.ARTIFACT_PROPERTY_BLESSED_KEY,
constants.NOT_BLESSED_VALUE)
logging.info('Blessing result %s written to %s.',
validation_result.validation_ok, blessing.uri)
def testModelPath(self):
# Test retrieving model folder.
self.assertEqual(self._eval_model_path,
path_utils.eval_model_path(self._output_uri))
self.assertEqual(self._serving_model_path,
path_utils.serving_model_path(self._output_uri))
def Do(self, input_dict: Dict[str, List[types.Artifact]],
output_dict: Dict[str, List[types.Artifact]],
exec_properties: Dict[str, Any]) -> None:
"""Runs a batch job to evaluate the eval_model against the given input.
Args:
input_dict: Input dict from input key to a list of Artifacts.
- model: exported model.
- examples: examples for eval the model.
output_dict: Output dict from output key to a list of Artifacts.
- evaluation: model evaluation results.
exec_properties: A dict of execution properties.
- eval_config: JSON string of tfma.EvalConfig.
- feature_slicing_spec: JSON string of evaluator_pb2.FeatureSlicingSpec
instance, providing the way to slice the data. Deprecated, use
eval_config.slicing_specs instead.
- example_splits: JSON-serialized list of names of splits on which the
metrics are computed. Default behavior (when example_splits is set to
None) is using the 'eval' split.
Returns:
None
"""
if standard_component_specs.EXAMPLES_KEY not in input_dict:
raise ValueError('EXAMPLES_KEY is missing from input dict.')
if standard_component_specs.EVALUATION_KEY not in output_dict:
raise ValueError('EVALUATION_KEY is missing from output dict.')
if standard_component_specs.MODEL_KEY in input_dict and len(
input_dict[standard_component_specs.MODEL_KEY]) > 1:
raise ValueError('There can be only one candidate model, there are %d.' %
(len(input_dict[standard_component_specs.MODEL_KEY])))
if standard_component_specs.BASELINE_MODEL_KEY in input_dict and len(
input_dict[standard_component_specs.BASELINE_MODEL_KEY]) > 1:
raise ValueError(
'There can be only one baseline model, there are %d.' %
(len(input_dict[standard_component_specs.BASELINE_MODEL_KEY])))
self._log_startup(input_dict, output_dict, exec_properties)
# Add fairness indicator metric callback if necessary.
fairness_indicator_thresholds = json_utils.loads(
exec_properties.get(
standard_component_specs.FAIRNESS_INDICATOR_THRESHOLDS_KEY, 'null'))
add_metrics_callbacks = None
if fairness_indicator_thresholds:
add_metrics_callbacks = [
tfma.post_export_metrics.fairness_indicators( # pytype: disable=module-attr
thresholds=fairness_indicator_thresholds),
]
output_uri = artifact_utils.get_single_uri(
output_dict[constants.EVALUATION_KEY])
# Make sure user packages get propagated to the remote Beam worker.
unused_module_path, extra_pip_packages = udf_utils.decode_user_module_key(
exec_properties.get(standard_component_specs.MODULE_PATH_KEY, None))
for pip_package_path in extra_pip_packages:
local_pip_package_path = io_utils.ensure_local(pip_package_path)
self._beam_pipeline_args.append('--extra_package=%s' %
local_pip_package_path)
eval_shared_model_fn = udf_utils.try_get_fn(
exec_properties=exec_properties,
fn_name='custom_eval_shared_model') or tfma.default_eval_shared_model
run_validation = False
models = []
if (standard_component_specs.EVAL_CONFIG_KEY in exec_properties
and exec_properties[standard_component_specs.EVAL_CONFIG_KEY]):
slice_spec = None
has_baseline = bool(
input_dict.get(standard_component_specs.BASELINE_MODEL_KEY))
eval_config = tfma.EvalConfig()
proto_utils.json_to_proto(
exec_properties[standard_component_specs.EVAL_CONFIG_KEY],
eval_config)
# rubber_stamp is always assumed true, i.e., change threshold will always
# be ignored when a baseline model is missing.
if hasattr(tfma, 'utils'):
eval_config = tfma.utils.update_eval_config_with_defaults(
eval_config, has_baseline=has_baseline, rubber_stamp=True)
tfma.utils.verify_eval_config(eval_config)
else:
# TODO(b/171992041): Replaced by tfma.utils.
eval_config = tfma.update_eval_config_with_defaults(
eval_config, has_baseline=has_baseline, rubber_stamp=True)
tfma.verify_eval_config(eval_config)
# Do not validate model when there is no thresholds configured. This is to
# avoid accidentally blessing models when users forget to set thresholds.
run_validation = bool(
tfma.metrics.metric_thresholds_from_metrics_specs(
eval_config.metrics_specs, eval_config=eval_config))
if len(eval_config.model_specs) > 2:
raise ValueError(
"""Cannot support more than two models. There are %d models in this
eval_config.""" % (len(eval_config.model_specs)))
# Extract model artifacts.
for model_spec in eval_config.model_specs:
if standard_component_specs.MODEL_KEY not in input_dict:
if not model_spec.prediction_key:
raise ValueError(
'model_spec.prediction_key required if model not provided')
continue
if model_spec.is_baseline:
model_artifact = artifact_utils.get_single_instance(
input_dict[standard_component_specs.BASELINE_MODEL_KEY])
else:
model_artifact = artifact_utils.get_single_instance(
input_dict[standard_component_specs.MODEL_KEY])
# TODO(b/171992041): tfma.get_model_type replaced by tfma.utils.
if ((hasattr(tfma, 'utils') and
tfma.utils.get_model_type(model_spec) == tfma.TF_ESTIMATOR) or
hasattr(tfma, 'get_model_type') and
tfma.get_model_type(model_spec) == tfma.TF_ESTIMATOR):
model_path = path_utils.eval_model_path(
model_artifact.uri,
path_utils.is_old_model_artifact(model_artifact))
else:
model_path = path_utils.serving_model_path(
model_artifact.uri,
path_utils.is_old_model_artifact(model_artifact))
logging.info('Using %s as %s model.', model_path, model_spec.name)
models.append(
eval_shared_model_fn(
eval_saved_model_path=model_path,
model_name=model_spec.name,
eval_config=eval_config,
add_metrics_callbacks=add_metrics_callbacks))
else:
eval_config = None
assert (standard_component_specs.FEATURE_SLICING_SPEC_KEY
in exec_properties and
exec_properties[standard_component_specs.FEATURE_SLICING_SPEC_KEY]
), 'both eval_config and feature_slicing_spec are unset.'
feature_slicing_spec = evaluator_pb2.FeatureSlicingSpec()
proto_utils.json_to_proto(
exec_properties[standard_component_specs.FEATURE_SLICING_SPEC_KEY],
feature_slicing_spec)
slice_spec = self._get_slice_spec_from_feature_slicing_spec(
feature_slicing_spec)
model_artifact = artifact_utils.get_single_instance(
input_dict[standard_component_specs.MODEL_KEY])
model_path = path_utils.eval_model_path(
model_artifact.uri, path_utils.is_old_model_artifact(model_artifact))
logging.info('Using %s for model eval.', model_path)
models.append(
eval_shared_model_fn(
eval_saved_model_path=model_path,
model_name='',
eval_config=None,
add_metrics_callbacks=add_metrics_callbacks))
eval_shared_model = models[0] if len(models) == 1 else models
schema = None
if standard_component_specs.SCHEMA_KEY in input_dict:
schema = io_utils.SchemaReader().read(
io_utils.get_only_uri_in_dir(
artifact_utils.get_single_uri(
input_dict[standard_component_specs.SCHEMA_KEY])))
# Load and deserialize example splits from execution properties.
example_splits = json_utils.loads(
exec_properties.get(standard_component_specs.EXAMPLE_SPLITS_KEY,
'null'))
if not example_splits:
example_splits = ['eval']
logging.info("The 'example_splits' parameter is not set, using 'eval' "
'split.')
logging.info('Evaluating model.')
# TempPipInstallContext is needed here so that subprocesses (which
# may be created by the Beam multi-process DirectRunner) can find the
# needed dependencies.
# TODO(b/187122662): Move this to the ExecutorOperator or Launcher.
with udf_utils.TempPipInstallContext(extra_pip_packages):
with self._make_beam_pipeline() as pipeline:
examples_list = []
tensor_adapter_config = None
# pylint: disable=expression-not-assigned
if tfma.is_batched_input(eval_shared_model, eval_config):
tfxio_factory = tfxio_utils.get_tfxio_factory_from_artifact(
examples=input_dict[standard_component_specs.EXAMPLES_KEY],
telemetry_descriptors=_TELEMETRY_DESCRIPTORS,
schema=schema,
raw_record_column_name=tfma_constants.ARROW_INPUT_COLUMN)
# TODO(b/161935932): refactor after TFXIO supports multiple patterns.
for split in example_splits:
split_uris = artifact_utils.get_split_uris(
input_dict[standard_component_specs.EXAMPLES_KEY], split)
for index in range(len(split_uris)):
split_uri = split_uris[index]
file_pattern = io_utils.all_files_pattern(split_uri)
tfxio = tfxio_factory(file_pattern)
data = (
pipeline
| f'ReadFromTFRecordToArrow[{split}][{index}]' >>
tfxio.BeamSource())
examples_list.append(data)
if schema is not None:
# Use last tfxio as TensorRepresentations and ArrowSchema are fixed.
tensor_adapter_config = tensor_adapter.TensorAdapterConfig(
arrow_schema=tfxio.ArrowSchema(),
tensor_representations=tfxio.TensorRepresentations())
else:
for split in example_splits:
split_uris = artifact_utils.get_split_uris(
input_dict[standard_component_specs.EXAMPLES_KEY], split)
for index in range(len(split_uris)):
split_uri = split_uris[index]
file_pattern = io_utils.all_files_pattern(split_uri)
data = (
pipeline
| f'ReadFromTFRecord[{split}][{index}]' >>
beam.io.ReadFromTFRecord(file_pattern=file_pattern))
examples_list.append(data)
custom_extractors = udf_utils.try_get_fn(
exec_properties=exec_properties, fn_name='custom_extractors')
extractors = None
if custom_extractors:
extractors = custom_extractors(
eval_shared_model=eval_shared_model,
eval_config=eval_config,
tensor_adapter_config=tensor_adapter_config)
(examples_list | 'FlattenExamples' >> beam.Flatten()
| 'ExtractEvaluateAndWriteResults' >>
(tfma.ExtractEvaluateAndWriteResults(
eval_shared_model=models[0] if len(models) == 1 else models,
eval_config=eval_config,
extractors=extractors,
output_path=output_uri,
slice_spec=slice_spec,
tensor_adapter_config=tensor_adapter_config)))
logging.info('Evaluation complete. Results written to %s.', output_uri)
if not run_validation:
# TODO(jinhuang): delete the BLESSING_KEY from output_dict when supported.
logging.info('No threshold configured, will not validate model.')
return
# Set up blessing artifact
blessing = artifact_utils.get_single_instance(
output_dict[standard_component_specs.BLESSING_KEY])
blessing.set_string_custom_property(
constants.ARTIFACT_PROPERTY_CURRENT_MODEL_URI_KEY,
artifact_utils.get_single_uri(
input_dict[standard_component_specs.MODEL_KEY]))
blessing.set_int_custom_property(
constants.ARTIFACT_PROPERTY_CURRENT_MODEL_ID_KEY,
input_dict[standard_component_specs.MODEL_KEY][0].id)
if input_dict.get(standard_component_specs.BASELINE_MODEL_KEY):
baseline_model = input_dict[
standard_component_specs.BASELINE_MODEL_KEY][0]
blessing.set_string_custom_property(
constants.ARTIFACT_PROPERTY_BASELINE_MODEL_URI_KEY,
baseline_model.uri)
blessing.set_int_custom_property(
constants.ARTIFACT_PROPERTY_BASELINE_MODEL_ID_KEY, baseline_model.id)
if 'current_component_id' in exec_properties:
blessing.set_string_custom_property(
'component_id', exec_properties['current_component_id'])
# Check validation result and write BLESSED file accordingly.
logging.info('Checking validation results.')
validation_result = tfma.load_validation_result(output_uri)
if validation_result.validation_ok:
io_utils.write_string_file(
os.path.join(blessing.uri, constants.BLESSED_FILE_NAME), '')
blessing.set_int_custom_property(constants.ARTIFACT_PROPERTY_BLESSED_KEY,
constants.BLESSED_VALUE)
else:
io_utils.write_string_file(
os.path.join(blessing.uri, constants.NOT_BLESSED_FILE_NAME), '')
blessing.set_int_custom_property(constants.ARTIFACT_PROPERTY_BLESSED_KEY,
constants.NOT_BLESSED_VALUE)
logging.info('Blessing result %s written to %s.',
validation_result.validation_ok, blessing.uri)
