All Superinterfaces:

com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder

All Known Implementing Classes:

ImageClassificationModelMetadata, ImageClassificationModelMetadata.Builder
```
public interface ImageClassificationModelMetadataOrBuilder
extends com.google.protobuf.MessageOrBuilder
```

Method Summary

All Methods Instance Methods Abstract Methods
Modifier and Type	Method	Description
`String`	`getBaseModelId()`	Optional.
`com.google.protobuf.ByteString`	`getBaseModelIdBytes()`	Optional.
`String`	`getModelType()`	Optional.
`com.google.protobuf.ByteString`	`getModelTypeBytes()`	Optional.
`long`	`getNodeCount()`	Output only.
`double`	`getNodeQps()`	Output only.
`String`	`getStopReason()`	Output only.
`com.google.protobuf.ByteString`	`getStopReasonBytes()`	Output only.
`long`	`getTrainBudget()`	Required.
`long`	`getTrainCost()`	Output only.

Methods inherited from interface com.google.protobuf.MessageLiteOrBuilder
isInitialized

Methods inherited from interface com.google.protobuf.MessageOrBuilder
findInitializationErrors, getAllFields, getDefaultInstanceForType, getDescriptorForType, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof

Method Detail

getBaseModelId

String getBaseModelId()

 Optional. The ID of the `base` model. If it is specified, the new model
 will be created based on the `base` model. Otherwise, the new model will be
 created from scratch. The `base` model must be in the same
 `project` and `location` as the new model to create, and have the same
 `model_type`.

string base_model_id = 1;

Returns:: The baseModelId.

getBaseModelIdBytes

com.google.protobuf.ByteString getBaseModelIdBytes()

 Optional. The ID of the `base` model. If it is specified, the new model
 will be created based on the `base` model. Otherwise, the new model will be
 created from scratch. The `base` model must be in the same
 `project` and `location` as the new model to create, and have the same
 `model_type`.

string base_model_id = 1;

Returns:: The bytes for baseModelId.

getTrainBudget

long getTrainBudget()

 Required. The train budget of creating this model, expressed in hours. The
 actual `train_cost` will be equal or less than this value.

int64 train_budget = 2;

Returns:: The trainBudget.

getTrainCost

long getTrainCost()

 Output only. The actual train cost of creating this model, expressed in
 hours. If this model is created from a `base` model, the train cost used
 to create the `base` model are not included.

int64 train_cost = 3;

Returns:: The trainCost.

getStopReason

String getStopReason()

 Output only. The reason that this create model operation stopped,
 e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.

string stop_reason = 5;

Returns:: The stopReason.

getStopReasonBytes

com.google.protobuf.ByteString getStopReasonBytes()

 Output only. The reason that this create model operation stopped,
 e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.

string stop_reason = 5;

Returns:: The bytes for stopReason.

getModelType

String getModelType()

Optional. Type of the model. The available values are:
* `cloud` - Model to be used via prediction calls to AutoML API.
This is the default value.
* `mobile-low-latency-1` - A model that, in addition to providing
prediction via AutoML API, can also be exported (see
[AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]) and used on a mobile or edge device
with TensorFlow afterwards. Expected to have low latency, but
may have lower prediction quality than other models.
* `mobile-versatile-1` - A model that, in addition to providing
prediction via AutoML API, can also be exported (see
[AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]) and used on a mobile or edge device
with TensorFlow afterwards.
* `mobile-high-accuracy-1` - A model that, in addition to providing
prediction via AutoML API, can also be exported (see
[AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]) and used on a mobile or edge device
with TensorFlow afterwards. Expected to have a higher
latency, but should also have a higher prediction quality
than other models.
* `mobile-core-ml-low-latency-1` - A model that, in addition to providing
prediction via AutoML API, can also be exported (see
[AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]) and used on a mobile device with Core
ML afterwards. Expected to have low latency, but may have
lower prediction quality than other models.
* `mobile-core-ml-versatile-1` - A model that, in addition to providing
prediction via AutoML API, can also be exported (see
[AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]) and used on a mobile device with Core
ML afterwards.
* `mobile-core-ml-high-accuracy-1` - A model that, in addition to
providing prediction via AutoML API, can also be exported
(see [AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]) and used on a mobile device with
Core ML afterwards. Expected to have a higher latency, but
should also have a higher prediction quality than other
models.

string model_type = 7;

Returns:: The modelType.

getModelTypeBytes

com.google.protobuf.ByteString getModelTypeBytes()

Optional. Type of the model. The available values are:
* `cloud` - Model to be used via prediction calls to AutoML API.
This is the default value.
* `mobile-low-latency-1` - A model that, in addition to providing
prediction via AutoML API, can also be exported (see
[AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]) and used on a mobile or edge device
with TensorFlow afterwards. Expected to have low latency, but
may have lower prediction quality than other models.
* `mobile-versatile-1` - A model that, in addition to providing
prediction via AutoML API, can also be exported (see
[AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]) and used on a mobile or edge device
with TensorFlow afterwards.
* `mobile-high-accuracy-1` - A model that, in addition to providing
prediction via AutoML API, can also be exported (see
[AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]) and used on a mobile or edge device
with TensorFlow afterwards. Expected to have a higher
latency, but should also have a higher prediction quality
than other models.
* `mobile-core-ml-low-latency-1` - A model that, in addition to providing
prediction via AutoML API, can also be exported (see
[AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]) and used on a mobile device with Core
ML afterwards. Expected to have low latency, but may have
lower prediction quality than other models.
* `mobile-core-ml-versatile-1` - A model that, in addition to providing
prediction via AutoML API, can also be exported (see
[AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]) and used on a mobile device with Core
ML afterwards.
* `mobile-core-ml-high-accuracy-1` - A model that, in addition to
providing prediction via AutoML API, can also be exported
(see [AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]) and used on a mobile device with
Core ML afterwards. Expected to have a higher latency, but
should also have a higher prediction quality than other
models.

string model_type = 7;

Returns:: The bytes for modelType.

getNodeQps

double getNodeQps()

 Output only. An approximate number of online prediction QPS that can
 be supported by this model per each node on which it is deployed.

double node_qps = 13;

Returns:: The nodeQps.

getNodeCount

long getNodeCount()

 Output only. The number of nodes this model is deployed on. A node is an
 abstraction of a machine resource, which can handle online prediction QPS
 as given in the node_qps field.

int64 node_count = 14;

Returns:: The nodeCount.

Interface ImageClassificationModelMetadataOrBuilder