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public interface AutoMlImageClassificationInputsOrBuilder extends MessageOrBuilder
Implements
MessageOrBuilderMethods
getBaseModelId()
public abstract String getBaseModelId()
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.
string base_model_id = 2;
Type | Description |
String | The baseModelId. |
getBaseModelIdBytes()
public abstract ByteString getBaseModelIdBytes()
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.
string base_model_id = 2;
Type | Description |
ByteString | The bytes for baseModelId. |
getBudgetMilliNodeHours()
public abstract long getBudgetMilliNodeHours()
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.
int64 budget_milli_node_hours = 3;
Type | Description |
long | The budgetMilliNodeHours. |
getDisableEarlyStopping()
public abstract boolean getDisableEarlyStopping()
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.
bool disable_early_stopping = 4;
Type | Description |
boolean | The disableEarlyStopping. |
getModelType()
public abstract AutoMlImageClassificationInputs.ModelType getModelType()
.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlImageClassificationInputs.ModelType model_type = 1;
Type | Description |
AutoMlImageClassificationInputs.ModelType | The modelType. |
getModelTypeValue()
public abstract int getModelTypeValue()
.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlImageClassificationInputs.ModelType model_type = 1;
Type | Description |
int | The enum numeric value on the wire for modelType. |
getMultiLabel()
public abstract boolean getMultiLabel()
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).
bool multi_label = 5;
Type | Description |
boolean | The multiLabel. |