def infer_impl(self):
"""Performs inference on examples that require inference."""
indices_to_infer = sorted(self.updated_example_indices)
examples_to_infer = [
self.json_to_proto(self.examples[index])
for index in indices_to_infer
]
infer_objs = []
attribution_objs = []
serving_bundle = inference_utils.ServingBundle(
self.config.get('inference_address'),
self.config.get('model_name'), self.config.get('model_type'),
self.config.get('model_version'),
self.config.get('model_signature'),
self.config.get('uses_predict_api'),
self.config.get('predict_input_tensor'),
self.config.get('predict_output_tensor'),
self.estimator_and_spec.get('estimator'),
self.estimator_and_spec.get('feature_spec'),
self.custom_predict_fn)
(predictions,
attributions) = (inference_utils.run_inference_for_inference_results(
examples_to_infer, serving_bundle))
infer_objs.append(predictions)
attribution_objs.append(attributions)
if ('inference_address_2' in self.config
or self.compare_estimator_and_spec.get('estimator')
or self.compare_custom_predict_fn):
serving_bundle = inference_utils.ServingBundle(
self.config.get('inference_address_2'),
self.config.get('model_name_2'), self.config.get('model_type'),
self.config.get('model_version_2'),
self.config.get('model_signature_2'),
self.config.get('uses_predict_api'),
self.config.get('predict_input_tensor'),
self.config.get('predict_output_tensor'),
self.compare_estimator_and_spec.get('estimator'),
self.compare_estimator_and_spec.get('feature_spec'),
self.compare_custom_predict_fn)
(predictions, attributions) = (
inference_utils.run_inference_for_inference_results(
examples_to_infer, serving_bundle))
infer_objs.append(predictions)
attribution_objs.append(attributions)
self.updated_example_indices = set()
return {
'inferences': {
'indices': indices_to_infer,
'results': infer_objs
},
'label_vocab': self.config.get('label_vocab'),
'attributions': attribution_objs
}
def infer(self):
indices_to_infer = sorted(self.updated_example_indices)
examples_to_infer = [
self.json_to_proto(self.examples[index])
for index in indices_to_infer
]
infer_objs = []
serving_bundle = inference_utils.ServingBundle(
self.config.get('inference_address'),
self.config.get('model_name'), self.config.get('model_type'),
self.config.get('model_version'),
self.config.get('model_signature'),
self.config.get('uses_predict_api'),
self.config.get('predict_input_tensor'),
self.config.get('predict_output_tensor'),
self.estimator_and_spec.get('estimator'),
self.estimator_and_spec.get('feature_spec'))
infer_objs.append(
inference_utils.run_inference_for_inference_results(
examples_to_infer, serving_bundle))
if ('inference_address_2' in self.config
or self.compare_estimator_and_spec.get('estimator')):
serving_bundle = inference_utils.ServingBundle(
self.config.get('inference_address_2'),
self.config.get('model_name_2'), self.config.get('model_type'),
self.config.get('model_version_2'),
self.config.get('model_signature_2'),
self.config.get('uses_predict_api'),
self.config.get('predict_input_tensor'),
self.config.get('predict_output_tensor'),
self.compare_estimator_and_spec.get('estimator'),
self.compare_estimator_and_spec.get('feature_spec'))
infer_objs.append(
inference_utils.run_inference_for_inference_results(
examples_to_infer, serving_bundle))
self.updated_example_indices = set()
inferences = {
'inferences': {
'indices': indices_to_infer,
'results': infer_objs
},
'label_vocab': self.config.get('label_vocab')
}
output.eval_js("""inferenceCallback('{inferences}')""".format(
inferences=json.dumps(inferences)))
def _infer(self, request):
"""Returns JSON for the `vz-line-chart`s for a feature.
Args:
request: A request that should contain 'inference_address', 'model_name',
'model_type, 'model_version', 'model_signature' and 'label_vocab_path'.
Returns:
A list of JSON objects, one for each chart.
"""
label_vocab = inference_utils.get_label_vocab(
request.args.get('label_vocab_path'))
try:
if request.method != 'GET':
logger.error('%s requests are forbidden.', request.method)
return http_util.Respond(request,
{'error': 'invalid non-GET request'},
'application/json',
code=405)
(inference_addresses, model_names, model_versions,
model_signatures) = self._parse_request_arguments(request)
indices_to_infer = sorted(self.updated_example_indices)
examples_to_infer = [
self.examples[index] for index in indices_to_infer
]
infer_objs = []
for model_num in xrange(len(inference_addresses)):
serving_bundle = inference_utils.ServingBundle(
inference_addresses[model_num], model_names[model_num],
request.args.get('model_type'), model_versions[model_num],
model_signatures[model_num],
request.args.get('use_predict') == 'true',
request.args.get('predict_input_tensor'),
request.args.get('predict_output_tensor'))
(predictions,
_) = inference_utils.run_inference_for_inference_results(
examples_to_infer, serving_bundle)
infer_objs.append(predictions)
resp = {'indices': indices_to_infer, 'results': infer_objs}
self.updated_example_indices = set()
return http_util.Respond(request, {
'inferences': json.dumps(resp),
'vocab': json.dumps(label_vocab)
}, 'application/json')
except common_utils.InvalidUserInputError as e:
return http_util.Respond(request, {'error': e.message},
'application/json',
code=400)
except AbortionError as e:
return http_util.Respond(request, {'error': e.details},
'application/json',
code=400)
def _infer(self, change):
indices_to_infer = sorted(self.updated_example_indices)
examples_to_infer = [
self.json_to_proto(self.examples[index]) for index in indices_to_infer]
infer_objs = []
serving_bundle = inference_utils.ServingBundle(
self.config.get('inference_address'),
self.config.get('model_name'),
self.config.get('model_type'),
self.config.get('model_version'),
self.config.get('model_signature'),
self.config.get('uses_predict_api'),
self.config.get('predict_input_tensor'),
self.config.get('predict_output_tensor'),
self.estimator_and_spec.get('estimator'),
self.estimator_and_spec.get('feature_spec'),
self.custom_predict_fn)
infer_objs.append(inference_utils.run_inference_for_inference_results(
examples_to_infer, serving_bundle))
if ('inference_address_2' in self.config or
self.compare_estimator_and_spec.get('estimator') or
self.compare_custom_predict_fn):
serving_bundle = inference_utils.ServingBundle(
self.config.get('inference_address_2'),
self.config.get('model_name_2'),
self.config.get('model_type'),
self.config.get('model_version_2'),
self.config.get('model_signature_2'),
self.config.get('uses_predict_api'),
self.config.get('predict_input_tensor'),
self.config.get('predict_output_tensor'),
self.compare_estimator_and_spec.get('estimator'),
self.compare_estimator_and_spec.get('feature_spec'),
self.compare_custom_predict_fn)
infer_objs.append(inference_utils.run_inference_for_inference_results(
examples_to_infer, serving_bundle))
self.updated_example_indices = set()
self.inferences = {
'inferences': {'indices': indices_to_infer, 'results': infer_objs},
'label_vocab': self.config.get('label_vocab')}
def infer_impl(self):
"""Performs inference on examples that require inference."""
indices_to_infer = sorted(self.updated_example_indices)
examples_to_infer = [
self.json_to_proto(self.examples[index])
for index in indices_to_infer
]
infer_objs = []
extra_output_objs = []
serving_bundle = inference_utils.ServingBundle(
self.config.get("inference_address"),
self.config.get("model_name"),
self.config.get("model_type"),
self.config.get("model_version"),
self.config.get("model_signature"),
self.config.get("uses_predict_api"),
self.config.get("predict_input_tensor"),
self.config.get("predict_output_tensor"),
self.estimator_and_spec.get("estimator"),
self.estimator_and_spec.get("feature_spec"),
self.custom_predict_fn,
)
(
predictions,
extra_output,
) = inference_utils.run_inference_for_inference_results(
examples_to_infer, serving_bundle)
infer_objs.append(predictions)
extra_output_objs.append(extra_output)
if ("inference_address_2" in self.config
or self.compare_estimator_and_spec.get("estimator")
or self.compare_custom_predict_fn):
serving_bundle = inference_utils.ServingBundle(
self.config.get("inference_address_2"),
self.config.get("model_name_2"),
self.config.get("model_type"),
self.config.get("model_version_2"),
self.config.get("model_signature_2"),
self.config.get("uses_predict_api"),
self.config.get("predict_input_tensor"),
self.config.get("predict_output_tensor"),
self.compare_estimator_and_spec.get("estimator"),
self.compare_estimator_and_spec.get("feature_spec"),
self.compare_custom_predict_fn,
)
(
predictions,
extra_output,
) = inference_utils.run_inference_for_inference_results(
examples_to_infer, serving_bundle)
infer_objs.append(predictions)
extra_output_objs.append(extra_output)
self.updated_example_indices = set()
return {
"inferences": {
"indices": indices_to_infer,
"results": infer_objs
},
"label_vocab": self.config.get("label_vocab"),
"extra_outputs": extra_output_objs,
}
