REST Resource: projects.locations.models

Resource: Model

API proto representing a trained machine learning model.

JSON representation
{
  "name": string,
  "displayName": string,
  "datasetId": string,
  "createTime": string,
  "updateTime": string,
  "deploymentState": enum (DeploymentState),

  // Union field model_metadata can be only one of the following:
  "translationModelMetadata": {
    object (TranslationModelMetadata)
  },
  "imageClassificationModelMetadata": {
    object (ImageClassificationModelMetadata)
  },
  "textClassificationModelMetadata": {
    object (TextClassificationModelMetadata)
  },
  "imageObjectDetectionModelMetadata": {
    object (ImageObjectDetectionModelMetadata)
  },
  "videoClassificationModelMetadata": {
    object (VideoClassificationModelMetadata)
  },
  "videoObjectTrackingModelMetadata": {
    object (VideoObjectTrackingModelMetadata)
  },
  "textExtractionModelMetadata": {
    object (TextExtractionModelMetadata)
  },
  "tablesModelMetadata": {
    object (TablesModelMetadata)
  },
  "textSentimentModelMetadata": {
    object (TextSentimentModelMetadata)
  }
  // End of list of possible types for union field model_metadata.
}
Fields
name

string

Output only. Resource name of the model. Format: projects/{project_id}/locations/{locationId}/models/{modelId}

displayName

string

Required. The name of the model to show in the interface. The name can be up to 32 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscores (_), and ASCII digits 0-9. It must start with a letter.

datasetId

string

Required. The resource ID of the dataset used to create the model. The dataset must come from the same ancestor project and location.

createTime

string (Timestamp format)

Output only. Timestamp when the model training finished and can be used for prediction.

A timestamp in RFC3339 UTC "Zulu" format, accurate to nanoseconds. Example: "2014-10-02T15:01:23.045123456Z".

updateTime

string (Timestamp format)

Output only. Timestamp when this model was last updated.

A timestamp in RFC3339 UTC "Zulu" format, accurate to nanoseconds. Example: "2014-10-02T15:01:23.045123456Z".

deploymentState

enum (DeploymentState)

Output only. Deployment state of the model. A model can only serve prediction requests after it gets deployed.

Union field model_metadata. Required. The model metadata that is specific to the problem type. Must match the metadata type of the dataset used to train the model. model_metadata can be only one of the following:
translationModelMetadata

object (TranslationModelMetadata)

Metadata for translation models.

imageClassificationModelMetadata

object (ImageClassificationModelMetadata)

Metadata for image classification models.

textClassificationModelMetadata

object (TextClassificationModelMetadata)

Metadata for text classification models.

imageObjectDetectionModelMetadata

object (ImageObjectDetectionModelMetadata)

Metadata for image object detection models.

videoClassificationModelMetadata

object (VideoClassificationModelMetadata)

Metadata for video classification models.

videoObjectTrackingModelMetadata

object (VideoObjectTrackingModelMetadata)

Metadata for video object tracking models.

textExtractionModelMetadata

object (TextExtractionModelMetadata)

Metadata for text extraction models.

tablesModelMetadata

object (TablesModelMetadata)

Metadata for Tables models.

textSentimentModelMetadata

object (TextSentimentModelMetadata)

Metadata for text sentiment models.

TranslationModelMetadata

Model metadata that is specific to translation.

JSON representation
{
  "baseModel": string,
  "sourceLanguageCode": string,
  "targetLanguageCode": string
}
Fields
baseModel

string

The resource name of the model to use as a baseline to train the custom model. If unset, we use the default base model provided by Google Translate. Format: projects/{project_id}/locations/{locationId}/models/{modelId}

sourceLanguageCode

string

Output only. Inferred from the dataset. The source languge (The BCP-47 language code) that is used for training.

targetLanguageCode

string

Output only. The target languge (The BCP-47 language code) that is used for training.

ImageClassificationModelMetadata

Model metadata for image classification.

JSON representation
{
  "baseModelId": string,
  "trainBudget": string,
  "trainCost": string,
  "stopReason": string,
  "modelType": string,
  "nodeQps": number,
  "nodeCount": string,
  "disableEarlyStopping": boolean
}
Fields
baseModelId

string

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 modelType.

trainBudget

string (int64 format)

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

trainCost

string (int64 format)

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.

stopReason

string

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

modelType

string

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) 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) 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) 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) 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) 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) 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.

nodeQps

number

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

nodeCount

string (int64 format)

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 nodeQps field.

disableEarlyStopping

boolean

Use the entire training budget. This disables the early stopping feature. By default, the early stopping feature is enabled, which means that AutoML Image Classification might stop training before the entire training budget has been used.

TextClassificationModelMetadata

Model metadata that is specific to text classification.

ImageObjectDetectionModelMetadata

Model metadata specific to image object detection.

JSON representation
{
  "modelType": string,
  "nodeCount": string,
  "nodeQps": number,
  "stopReason": string,
  "trainBudgetMilliNodeHours": string,
  "trainCostMilliNodeHours": string,
  "disableEarlyStopping": boolean
}
Fields
modelType

string

Optional. Type of the model. The available values are: * cloud-high-accuracy-1 - (default) A model to be used via prediction calls to AutoML API. Expected to have a higher latency, but should also have a higher prediction quality than other models. * cloud-low-latency-1 - A model to be used via prediction calls to AutoML API. Expected to have low latency, but may have lower prediction quality than other models.

nodeCount

string (int64 format)

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 qps_per_node field.

nodeQps

number

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

stopReason

string

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

trainBudgetMilliNodeHours

string (int64 format)

The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The actual trainCost will be equal or less than this value. If further model training ceases to provide any improvements, it will stop without using full budget and the stopReason will be MODEL_CONVERGED. Note, node_hour = actual_hour * number_of_nodes_invovled. For model type cloud-high-accuracy-1(default) and cloud-low-latency-1, the train budget must be between 20,000 and 2,000,000 milli node hours, inclusive. The default value is 216, 000 which represents one day in wall time. For model type mobile-low-latency-1, mobile-versatile-1, mobile-high-accuracy-1, mobile-core-ml-low-latency-1, mobile-core-ml-versatile-1, mobile-core-ml-high-accuracy-1, the train 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.

trainCostMilliNodeHours

string (int64 format)

Output only. The actual train 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 the train budget.

disableEarlyStopping

boolean

Use the entire training budget. This disables the early stopping feature. By default, the early stopping feature is enabled, which means that AutoML Image Object Detection might stop training before the entire training budget has been used.

VideoClassificationModelMetadata

Model metadata specific to video classification.

VideoObjectTrackingModelMetadata

Model metadata specific to video object tracking.

TextExtractionModelMetadata

Model metadata that is specific to text extraction.

TablesModelMetadata

Model metadata specific to AutoML Tables.

JSON representation
{
  "targetColumnSpec": {
    object (ColumnSpec)
  },
  "inputFeatureColumnSpecs": [
    {
      object (ColumnSpec)
    }
  ],
  "optimizationObjective": string,
  "tablesModelColumnInfo": [
    {
      object (TablesModelColumnInfo)
    }
  ],
  "trainBudgetMilliNodeHours": string,
  "trainCostMilliNodeHours": string,
  "disableEarlyStopping": boolean
}
Fields
targetColumnSpec

object (ColumnSpec)

Column spec of the dataset's primary table's column the model is predicting. Snapshotted when model creation started. Only 3 fields are used: name - May be set on models.create, if it's not then the ColumnSpec corresponding to the current targetColumnSpecId of the dataset the model is trained from is used. If neither is set, models.create will error. displayName - Output only. dataType - Output only.

inputFeatureColumnSpecs[]

object (ColumnSpec)

Column specs of the dataset's primary table's columns, on which the model is trained and which are used as the input for predictions. The

target_column as well as, according to dataset's state upon model creation,

weight_column, and

ml_use_column must never be included here.

Only 3 fields are used:

  • name - May be set on models.create, if set only the columns specified are used, otherwise all primary table's columns (except the ones listed above) are used for the training and prediction input.

  • displayName - Output only.

  • dataType - Output only.

optimizationObjective

string

Objective function the model is optimizing towards. The training process creates a model that maximizes/minimizes the value of the objective function over the validation set.

The supported optimization objectives depend on the prediction type. If the field is not set, a default objective function is used.

CLASSIFICATION_BINARY: "MAXIMIZE_AU_ROC" (default) - Maximize the area under the receiver operating characteristic (ROC) curve. "MINIMIZE_LOG_LOSS" - Minimize log loss. "MAXIMIZE_AU_PRC" - Maximize the area under the precision-recall curve. "MAXIMIZE_PRECISION_AT_RECALL" - Maximize precision for a specified recall value. "MAXIMIZE_RECALL_AT_PRECISION" - Maximize recall for a specified precision value.

CLASSIFICATION_MULTI_CLASS : "MINIMIZE_LOG_LOSS" (default) - Minimize log loss.

REGRESSION: "MINIMIZE_RMSE" (default) - Minimize root-mean-squared error (RMSE). "MINIMIZE_MAE" - Minimize mean-absolute error (MAE). "MINIMIZE_RMSLE" - Minimize root-mean-squared log error (RMSLE).

tablesModelColumnInfo[]

object (TablesModelColumnInfo)

Output only. Auxiliary information for each of the inputFeatureColumnSpecs with respect to this particular model.

trainBudgetMilliNodeHours

string (int64 format)

Required. The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour.

The training cost of the model will not exceed this budget. The final cost will be attempted to be close to the budget, though may end up being (even) noticeably smaller - at the backend's discretion. This especially may happen when further model training ceases to provide any improvements.

If the budget is set to a value known to be insufficient to train a model for the given dataset, the training won't be attempted and will error.

The train budget must be between 1,000 and 72,000 milli node hours, inclusive.

trainCostMilliNodeHours

string (int64 format)

Output only. The actual training cost of the model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed the train budget.

disableEarlyStopping

boolean

Use the entire training budget. This disables the early stopping feature. By default, the early stopping feature is enabled, which means that AutoML Tables might stop training before the entire training budget has been used.

TablesModelColumnInfo

An information specific to given column and Tables Model, in context of the Model and the predictions created by it.

JSON representation
{
  "columnSpecName": string,
  "columnDisplayName": string,
  "featureImportance": number
}
Fields
columnSpecName

string

Output only. The name of the ColumnSpec describing the column. Not populated when this proto is outputted to BigQuery.

columnDisplayName

string

Output only. The display name of the column (same as the displayName of its ColumnSpec).

featureImportance

number

Output only. When given as part of a Model (always populated): Measurement of how much model predictions correctness on the TEST data depend on values in this column. A value between 0 and 1, higher means higher influence. These values are normalized - for all input feature columns of a given model they add to 1.

When given back by models.predict (populated iff featureImportance param is set) or Batch models.predict (populated iff featureImportance param is set): Measurement of how impactful for the prediction returned for the given row the value in this column was. A value between 0 and 1, higher means larger impact. These values are normalized - for all input feature columns of a single predicted row they add to 1.

TextSentimentModelMetadata

Model metadata that is specific to text sentiment.

DeploymentState

Deployment state of the model.

Enums
DEPLOYMENT_STATE_UNSPECIFIED Should not be used, an un-set enum has this value by default.
DEPLOYED Model is deployed.
UNDEPLOYED Model is not deployed.

Methods

batchPredict

Perform a batch prediction.

create

Creates a model.

delete

Deletes a model.

deploy

Deploys a model.

export

Exports a trained, "export-able", model to a user specified Google Cloud Storage location.

exportEvaluatedExamples

Exports examples on which the model was evaluated (i.e.

get

Gets a model.

getIamPolicy

Gets the access control policy for a resource.

list

Lists models.

predict

Perform an online prediction.

setIamPolicy

Sets the access control policy on the specified resource.

undeploy

Undeploys a model.
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