/
automl_image_classification.py
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/
automl_image_classification.py
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# -*- coding: utf-8 -*-
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import proto # type: ignore
__protobuf__ = proto.module(
package="google.cloud.aiplatform.v1.schema.trainingjob.definition",
manifest={
"AutoMlImageClassification",
"AutoMlImageClassificationInputs",
"AutoMlImageClassificationMetadata",
},
)
class AutoMlImageClassification(proto.Message):
r"""A TrainingJob that trains and uploads an AutoML Image
Classification Model.
Attributes:
inputs (google.cloud.aiplatform.v1.schema.trainingjob.definition_v1.types.AutoMlImageClassificationInputs):
The input parameters of this TrainingJob.
metadata (google.cloud.aiplatform.v1.schema.trainingjob.definition_v1.types.AutoMlImageClassificationMetadata):
The metadata information.
"""
inputs = proto.Field(
proto.MESSAGE, number=1, message="AutoMlImageClassificationInputs",
)
metadata = proto.Field(
proto.MESSAGE, number=2, message="AutoMlImageClassificationMetadata",
)
class AutoMlImageClassificationInputs(proto.Message):
r"""
Attributes:
model_type (google.cloud.aiplatform.v1.schema.trainingjob.definition_v1.types.AutoMlImageClassificationInputs.ModelType):
base_model_id (str):
The ID of the ``base`` model. If it is specified, the new
model will be trained based on the ``base`` model.
Otherwise, the new model will be trained from scratch. The
``base`` model must be in the same Project and Location as
the new Model to train, and have the same modelType.
budget_milli_node_hours (int):
The training budget of creating this model, expressed in
milli node hours i.e. 1,000 value in this field means 1 node
hour. The actual metadata.costMilliNodeHours will be equal
or less than this value. If further model training ceases to
provide any improvements, it will stop without using the
full budget and the metadata.successfulStopReason will be
``model-converged``. Note, node_hour = actual_hour \*
number_of_nodes_involved. For modelType
``cloud``\ (default), the budget must be between 8,000 and
800,000 milli node hours, inclusive. The default value is
192,000 which represents one day in wall time, considering 8
nodes are used. For model types ``mobile-tf-low-latency-1``,
``mobile-tf-versatile-1``, ``mobile-tf-high-accuracy-1``,
the training budget must be between 1,000 and 100,000 milli
node hours, inclusive. The default value is 24,000 which
represents one day in wall time on a single node that is
used.
disable_early_stopping (bool):
Use the entire training budget. This disables
the early stopping feature. When false the early
stopping feature is enabled, which means that
AutoML Image Classification might stop training
before the entire training budget has been used.
multi_label (bool):
If false, a single-label (multi-class) Model
will be trained (i.e. assuming that for each
image just up to one annotation may be
applicable). If true, a multi-label Model will
be trained (i.e. assuming that for each image
multiple annotations may be applicable).
"""
class ModelType(proto.Enum):
r""""""
MODEL_TYPE_UNSPECIFIED = 0
CLOUD = 1
MOBILE_TF_LOW_LATENCY_1 = 2
MOBILE_TF_VERSATILE_1 = 3
MOBILE_TF_HIGH_ACCURACY_1 = 4
model_type = proto.Field(proto.ENUM, number=1, enum=ModelType,)
base_model_id = proto.Field(proto.STRING, number=2,)
budget_milli_node_hours = proto.Field(proto.INT64, number=3,)
disable_early_stopping = proto.Field(proto.BOOL, number=4,)
multi_label = proto.Field(proto.BOOL, number=5,)
class AutoMlImageClassificationMetadata(proto.Message):
r"""
Attributes:
cost_milli_node_hours (int):
The actual training cost of creating this
model, expressed in milli node hours, i.e. 1,000
value in this field means 1 node hour.
Guaranteed to not exceed
inputs.budgetMilliNodeHours.
successful_stop_reason (google.cloud.aiplatform.v1.schema.trainingjob.definition_v1.types.AutoMlImageClassificationMetadata.SuccessfulStopReason):
For successful job completions, this is the
reason why the job has finished.
"""
class SuccessfulStopReason(proto.Enum):
r""""""
SUCCESSFUL_STOP_REASON_UNSPECIFIED = 0
BUDGET_REACHED = 1
MODEL_CONVERGED = 2
cost_milli_node_hours = proto.Field(proto.INT64, number=1,)
successful_stop_reason = proto.Field(
proto.ENUM, number=2, enum=SuccessfulStopReason,
)
__all__ = tuple(sorted(__protobuf__.manifest))