GoogleCloudMlV1TrainingInput (AI Platform Training & Prediction API v1-rev20210212-1.31.0)

java.lang.Object
- java.util.AbstractMap<String,Object>
- - com.google.api.client.util.GenericData
  - - com.google.api.client.json.GenericJson
    - - com.google.api.services.ml.v1.model.GoogleCloudMlV1TrainingInput

All Implemented Interfaces:

Cloneable, Map<String,Object>
```
public final class GoogleCloudMlV1TrainingInput
extends GenericJson
```
Represents input parameters for a training job. When using the gcloud command to submit your training job, you can specify the input parameters as command-line arguments and/or in a YAML configuration file referenced from the --config command-line argument. For details, see the guide to [submitting a training job](/ai-platform/training/docs/training-jobs).
This is the Java data model class that specifies how to parse/serialize into the JSON that is transmitted over HTTP when working with the AI Platform Training & Prediction API. For a detailed explanation see: https://developers.google.com/api-client-library/java/google-http-java-client/json

Author:

Google, Inc.

Nested Class Summary
- Nested classes/interfaces inherited from class com.google.api.client.util.GenericData
  GenericData.Flags
- Nested classes/interfaces inherited from class java.util.AbstractMap
  AbstractMap.SimpleEntry<K,V>, AbstractMap.SimpleImmutableEntry<K,V>
- Nested classes/interfaces inherited from interface java.util.Map
  Map.Entry<K,V>

Constructor Summary

Constructors
Constructor and Description

GoogleCloudMlV1TrainingInput()

Constructors
Constructor and Description
`GoogleCloudMlV1TrainingInput()`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`GoogleCloudMlV1TrainingInput`	`clone()`
`List<String>`	`getArgs()` Optional.
`GoogleCloudMlV1EncryptionConfig`	`getEncryptionConfig()` Optional.
`GoogleCloudMlV1ReplicaConfig`	`getEvaluatorConfig()` Optional.
`Long`	`getEvaluatorCount()` Optional.
`String`	`getEvaluatorType()` Optional.
`GoogleCloudMlV1HyperparameterSpec`	`getHyperparameters()` Optional.
`String`	`getJobDir()` Optional.
`GoogleCloudMlV1ReplicaConfig`	`getMasterConfig()` Optional.
`String`	`getMasterType()` Optional.
`String`	`getNetwork()` Optional.
`List<String>`	`getPackageUris()` Required.
`GoogleCloudMlV1ReplicaConfig`	`getParameterServerConfig()` Optional.
`Long`	`getParameterServerCount()` Optional.
`String`	`getParameterServerType()` Optional.
`String`	`getPythonModule()` Required.
`String`	`getPythonVersion()` Optional.
`String`	`getRegion()` Required.
`String`	`getRuntimeVersion()` Optional.
`String`	`getScaleTier()` Required.
`GoogleCloudMlV1Scheduling`	`getScheduling()` Optional.
`String`	`getServiceAccount()` Optional.
`Boolean`	`getUseChiefInTfConfig()` Optional.
`GoogleCloudMlV1ReplicaConfig`	`getWorkerConfig()` Optional.
`Long`	`getWorkerCount()` Optional.
`String`	`getWorkerType()` Optional.
`GoogleCloudMlV1TrainingInput`	`set(String fieldName, Object value)`
`GoogleCloudMlV1TrainingInput`	`setArgs(List<String> args)` Optional.
`GoogleCloudMlV1TrainingInput`	`setEncryptionConfig(GoogleCloudMlV1EncryptionConfig encryptionConfig)` Optional.
`GoogleCloudMlV1TrainingInput`	`setEvaluatorConfig(GoogleCloudMlV1ReplicaConfig evaluatorConfig)` Optional.
`GoogleCloudMlV1TrainingInput`	`setEvaluatorCount(Long evaluatorCount)` Optional.
`GoogleCloudMlV1TrainingInput`	`setEvaluatorType(String evaluatorType)` Optional.
`GoogleCloudMlV1TrainingInput`	`setHyperparameters(GoogleCloudMlV1HyperparameterSpec hyperparameters)` Optional.
`GoogleCloudMlV1TrainingInput`	`setJobDir(String jobDir)` Optional.
`GoogleCloudMlV1TrainingInput`	`setMasterConfig(GoogleCloudMlV1ReplicaConfig masterConfig)` Optional.
`GoogleCloudMlV1TrainingInput`	`setMasterType(String masterType)` Optional.
`GoogleCloudMlV1TrainingInput`	`setNetwork(String network)` Optional.
`GoogleCloudMlV1TrainingInput`	`setPackageUris(List<String> packageUris)` Required.
`GoogleCloudMlV1TrainingInput`	`setParameterServerConfig(GoogleCloudMlV1ReplicaConfig parameterServerConfig)` Optional.
`GoogleCloudMlV1TrainingInput`	`setParameterServerCount(Long parameterServerCount)` Optional.
`GoogleCloudMlV1TrainingInput`	`setParameterServerType(String parameterServerType)` Optional.
`GoogleCloudMlV1TrainingInput`	`setPythonModule(String pythonModule)` Required.
`GoogleCloudMlV1TrainingInput`	`setPythonVersion(String pythonVersion)` Optional.
`GoogleCloudMlV1TrainingInput`	`setRegion(String region)` Required.
`GoogleCloudMlV1TrainingInput`	`setRuntimeVersion(String runtimeVersion)` Optional.
`GoogleCloudMlV1TrainingInput`	`setScaleTier(String scaleTier)` Required.
`GoogleCloudMlV1TrainingInput`	`setScheduling(GoogleCloudMlV1Scheduling scheduling)` Optional.
`GoogleCloudMlV1TrainingInput`	`setServiceAccount(String serviceAccount)` Optional.
`GoogleCloudMlV1TrainingInput`	`setUseChiefInTfConfig(Boolean useChiefInTfConfig)` Optional.
`GoogleCloudMlV1TrainingInput`	`setWorkerConfig(GoogleCloudMlV1ReplicaConfig workerConfig)` Optional.
`GoogleCloudMlV1TrainingInput`	`setWorkerCount(Long workerCount)` Optional.
`GoogleCloudMlV1TrainingInput`	`setWorkerType(String workerType)` Optional.

Methods inherited from class com.google.api.client.json.GenericJson
getFactory, setFactory, toPrettyString, toString

Methods inherited from class com.google.api.client.util.GenericData
entrySet, equals, get, getClassInfo, getUnknownKeys, hashCode, put, putAll, remove, setUnknownKeys

Methods inherited from class java.util.AbstractMap
clear, containsKey, containsValue, isEmpty, keySet, size, values

Methods inherited from class java.lang.Object
finalize, getClass, notify, notifyAll, wait, wait, wait

Methods inherited from interface java.util.Map
compute, computeIfAbsent, computeIfPresent, forEach, getOrDefault, merge, putIfAbsent, remove, replace, replace, replaceAll

- Constructor Detail
  - GoogleCloudMlV1TrainingInput
```
public GoogleCloudMlV1TrainingInput()
```
- Method Detail
  - getArgs
```
public List<String> getArgs()
```
    Optional. Command-line arguments passed to the training application when it starts. If your job uses a custom container, then the arguments are passed to the container's `ENTRYPOINT` command.
    
    Returns:
    
    value or null for none
  - setArgs
```
public GoogleCloudMlV1TrainingInput setArgs(List<String> args)
```
    Optional. Command-line arguments passed to the training application when it starts. If your job uses a custom container, then the arguments are passed to the container's `ENTRYPOINT` command.
    
    Parameters:
    
    args - args or null for none
  - getEncryptionConfig
```
public GoogleCloudMlV1EncryptionConfig getEncryptionConfig()
```
    Optional. Options for using customer-managed encryption keys (CMEK) to protect resources created by a training job, instead of using Google's default encryption. If this is set, then all resources created by the training job will be encrypted with the customer-managed encryption key that you specify. [Learn how and when to use CMEK with AI Platform Training ](/ai-platform/training/docs/cmek).
    
    Returns:
    
    value or null for none
  - setEncryptionConfig
```
public GoogleCloudMlV1TrainingInput setEncryptionConfig(GoogleCloudMlV1EncryptionConfig encryptionConfig)
```
    Optional. Options for using customer-managed encryption keys (CMEK) to protect resources created by a training job, instead of using Google's default encryption. If this is set, then all resources created by the training job will be encrypted with the customer-managed encryption key that you specify. [Learn how and when to use CMEK with AI Platform Training ](/ai-platform/training/docs/cmek).
    
    Parameters:
    
    encryptionConfig - encryptionConfig or null for none
  - getEvaluatorConfig
```
public GoogleCloudMlV1ReplicaConfig getEvaluatorConfig()
```
    Optional. The configuration for evaluators. You should only set `evaluatorConfig.acceleratorConfig` if `evaluatorType` is set to a Compute Engine machine type. [Learn about restrictions on accelerator configurations for training.](/ai- platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu) Set `evaluatorConfig.imageUri` only if you build a custom image for your evaluator. If `evaluatorConfig.imageUri` has not been set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom containers](/ai- platform/training/docs/distributed-training-containers).
    
    Returns:
    
    value or null for none
  - setEvaluatorConfig
```
public GoogleCloudMlV1TrainingInput setEvaluatorConfig(GoogleCloudMlV1ReplicaConfig evaluatorConfig)
```
    Optional. The configuration for evaluators. You should only set `evaluatorConfig.acceleratorConfig` if `evaluatorType` is set to a Compute Engine machine type. [Learn about restrictions on accelerator configurations for training.](/ai- platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu) Set `evaluatorConfig.imageUri` only if you build a custom image for your evaluator. If `evaluatorConfig.imageUri` has not been set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom containers](/ai- platform/training/docs/distributed-training-containers).
    
    Parameters:
    
    evaluatorConfig - evaluatorConfig or null for none
  - getEvaluatorCount
```
public Long getEvaluatorCount()
```
    Optional. The number of evaluator replicas to use for the training job. Each replica in the cluster will be of the type specified in `evaluator_type`. This value can only be used when `scale_tier` is set to `CUSTOM`. If you set this value, you must also set `evaluator_type`. The default value is zero.
    
    Returns:
    
    value or null for none
  - setEvaluatorCount
```
public GoogleCloudMlV1TrainingInput setEvaluatorCount(Long evaluatorCount)
```
    Optional. The number of evaluator replicas to use for the training job. Each replica in the cluster will be of the type specified in `evaluator_type`. This value can only be used when `scale_tier` is set to `CUSTOM`. If you set this value, you must also set `evaluator_type`. The default value is zero.
    
    Parameters:
    
    evaluatorCount - evaluatorCount or null for none
  - getEvaluatorType
```
public String getEvaluatorType()
```
    Optional. Specifies the type of virtual machine to use for your training job's evaluator nodes. The supported values are the same as those described in the entry for `masterType`. This value must be consistent with the category of machine type that `masterType` uses. In other words, both must be Compute Engine machine types or both must be legacy machine types. This value must be present when `scaleTier` is set to `CUSTOM` and `evaluatorCount` is greater than zero.
    
    Returns:
    
    value or null for none
  - setEvaluatorType
```
public GoogleCloudMlV1TrainingInput setEvaluatorType(String evaluatorType)
```
    Optional. Specifies the type of virtual machine to use for your training job's evaluator nodes. The supported values are the same as those described in the entry for `masterType`. This value must be consistent with the category of machine type that `masterType` uses. In other words, both must be Compute Engine machine types or both must be legacy machine types. This value must be present when `scaleTier` is set to `CUSTOM` and `evaluatorCount` is greater than zero.
    
    Parameters:
    
    evaluatorType - evaluatorType or null for none
  - getHyperparameters
```
public GoogleCloudMlV1HyperparameterSpec getHyperparameters()
```
    Optional. The set of Hyperparameters to tune.
    
    Returns:
    
    value or null for none
  - setHyperparameters
```
public GoogleCloudMlV1TrainingInput setHyperparameters(GoogleCloudMlV1HyperparameterSpec hyperparameters)
```
    Optional. The set of Hyperparameters to tune.
    
    Parameters:
    
    hyperparameters - hyperparameters or null for none
  - getJobDir
```
public String getJobDir()
```
    Optional. A Google Cloud Storage path in which to store training outputs and other data needed for training. This path is passed to your TensorFlow program as the '--job-dir' command-line argument. The benefit of specifying this field is that Cloud ML validates the path for use in training.
    
    Returns:
    
    value or null for none
  - setJobDir
```
public GoogleCloudMlV1TrainingInput setJobDir(String jobDir)
```
    Optional. A Google Cloud Storage path in which to store training outputs and other data needed for training. This path is passed to your TensorFlow program as the '--job-dir' command-line argument. The benefit of specifying this field is that Cloud ML validates the path for use in training.
    
    Parameters:
    
    jobDir - jobDir or null for none
  - getMasterConfig
```
public GoogleCloudMlV1ReplicaConfig getMasterConfig()
```
    Optional. The configuration for your master worker. You should only set `masterConfig.acceleratorConfig` if `masterType` is set to a Compute Engine machine type. Learn about [restrictions on accelerator configurations for training.](/ai-platform/training/docs /using-gpus#compute-engine-machine-types-with-gpu) Set `masterConfig.imageUri` only if you build a custom image. Only one of `masterConfig.imageUri` and `runtimeVersion` should be set. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed- training-containers).
    
    Returns:
    
    value or null for none
  - setMasterConfig
```
public GoogleCloudMlV1TrainingInput setMasterConfig(GoogleCloudMlV1ReplicaConfig masterConfig)
```
    Optional. The configuration for your master worker. You should only set `masterConfig.acceleratorConfig` if `masterType` is set to a Compute Engine machine type. Learn about [restrictions on accelerator configurations for training.](/ai-platform/training/docs /using-gpus#compute-engine-machine-types-with-gpu) Set `masterConfig.imageUri` only if you build a custom image. Only one of `masterConfig.imageUri` and `runtimeVersion` should be set. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed- training-containers).
    
    Parameters:
    
    masterConfig - masterConfig or null for none
  - getMasterType
```
public String getMasterType()
```
    Optional. Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when `scaleTier` is set to `CUSTOM`. You can use certain Compute Engine machine types directly in this field. See the [list of compatible Compute Engine machine types](/ai-platform/training/docs/machine-types#compute-engine-machine-types). Alternatively, you can use the certain legacy machine types in this field. See the [list of legacy machine types](/ai-platform/training/docs/machine-types#legacy-machine-types). Finally, if you want to use a TPU for training, specify `cloud_tpu` in this field. Learn more about the [special configuration options for training with TPUs](/ai-platform/training/docs/using- tpus#configuring_a_custom_tpu_machine).
    
    Returns:
    
    value or null for none
  - setMasterType
```
public GoogleCloudMlV1TrainingInput setMasterType(String masterType)
```
    Optional. Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when `scaleTier` is set to `CUSTOM`. You can use certain Compute Engine machine types directly in this field. See the [list of compatible Compute Engine machine types](/ai-platform/training/docs/machine-types#compute-engine-machine-types). Alternatively, you can use the certain legacy machine types in this field. See the [list of legacy machine types](/ai-platform/training/docs/machine-types#legacy-machine-types). Finally, if you want to use a TPU for training, specify `cloud_tpu` in this field. Learn more about the [special configuration options for training with TPUs](/ai-platform/training/docs/using- tpus#configuring_a_custom_tpu_machine).
    
    Parameters:
    
    masterType - masterType or null for none
  - getNetwork
```
public String getNetwork()
```
    Optional. The full name of the [Compute Engine network](/vpc/docs/vpc) to which the Job is peered. For example, `projects/12345/global/networks/myVPC`. The format of this field is `projects/{project}/global/networks/{network}`, where {project} is a project number (like `12345`) and {network} is network name. Private services access must already be configured for the network. If left unspecified, the Job is not peered with any network. [Learn about using VPC Network Peering.](/ai-platform/training/docs/vpc-peering).
    
    Returns:
    
    value or null for none
  - setNetwork
```
public GoogleCloudMlV1TrainingInput setNetwork(String network)
```
    Optional. The full name of the [Compute Engine network](/vpc/docs/vpc) to which the Job is peered. For example, `projects/12345/global/networks/myVPC`. The format of this field is `projects/{project}/global/networks/{network}`, where {project} is a project number (like `12345`) and {network} is network name. Private services access must already be configured for the network. If left unspecified, the Job is not peered with any network. [Learn about using VPC Network Peering.](/ai-platform/training/docs/vpc-peering).
    
    Parameters:
    
    network - network or null for none
  - getPackageUris
```
public List<String> getPackageUris()
```
    Required. The Google Cloud Storage location of the packages with the training program and any additional dependencies. The maximum number of package URIs is 100.
    
    Returns:
    
    value or null for none
  - setPackageUris
```
public GoogleCloudMlV1TrainingInput setPackageUris(List<String> packageUris)
```
    Required. The Google Cloud Storage location of the packages with the training program and any additional dependencies. The maximum number of package URIs is 100.
    
    Parameters:
    
    packageUris - packageUris or null for none
  - getParameterServerConfig
```
public GoogleCloudMlV1ReplicaConfig getParameterServerConfig()
```
    Optional. The configuration for parameter servers. You should only set `parameterServerConfig.acceleratorConfig` if `parameterServerType` is set to a Compute Engine machine type. [Learn about restrictions on accelerator configurations for training.](/ai- platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu) Set `parameterServerConfig.imageUri` only if you build a custom image for your parameter server. If `parameterServerConfig.imageUri` has not been set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom containers](/ai- platform/training/docs/distributed-training-containers).
    
    Returns:
    
    value or null for none
  - setParameterServerConfig
```
public GoogleCloudMlV1TrainingInput setParameterServerConfig(GoogleCloudMlV1ReplicaConfig parameterServerConfig)
```
    Optional. The configuration for parameter servers. You should only set `parameterServerConfig.acceleratorConfig` if `parameterServerType` is set to a Compute Engine machine type. [Learn about restrictions on accelerator configurations for training.](/ai- platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu) Set `parameterServerConfig.imageUri` only if you build a custom image for your parameter server. If `parameterServerConfig.imageUri` has not been set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom containers](/ai- platform/training/docs/distributed-training-containers).
    
    Parameters:
    
    parameterServerConfig - parameterServerConfig or null for none
  - getParameterServerCount
```
public Long getParameterServerCount()
```
    Optional. The number of parameter server replicas to use for the training job. Each replica in the cluster will be of the type specified in `parameter_server_type`. This value can only be used when `scale_tier` is set to `CUSTOM`. If you set this value, you must also set `parameter_server_type`. The default value is zero.
    
    Returns:
    
    value or null for none
  - setParameterServerCount
```
public GoogleCloudMlV1TrainingInput setParameterServerCount(Long parameterServerCount)
```
    Optional. The number of parameter server replicas to use for the training job. Each replica in the cluster will be of the type specified in `parameter_server_type`. This value can only be used when `scale_tier` is set to `CUSTOM`. If you set this value, you must also set `parameter_server_type`. The default value is zero.
    
    Parameters:
    
    parameterServerCount - parameterServerCount or null for none
  - getParameterServerType
```
public String getParameterServerType()
```
    Optional. Specifies the type of virtual machine to use for your training job's parameter server. The supported values are the same as those described in the entry for `master_type`. This value must be consistent with the category of machine type that `masterType` uses. In other words, both must be Compute Engine machine types or both must be legacy machine types. This value must be present when `scaleTier` is set to `CUSTOM` and `parameter_server_count` is greater than zero.
    
    Returns:
    
    value or null for none
  - setParameterServerType
```
public GoogleCloudMlV1TrainingInput setParameterServerType(String parameterServerType)
```
    Optional. Specifies the type of virtual machine to use for your training job's parameter server. The supported values are the same as those described in the entry for `master_type`. This value must be consistent with the category of machine type that `masterType` uses. In other words, both must be Compute Engine machine types or both must be legacy machine types. This value must be present when `scaleTier` is set to `CUSTOM` and `parameter_server_count` is greater than zero.
    
    Parameters:
    
    parameterServerType - parameterServerType or null for none
  - getPythonModule
```
public String getPythonModule()
```
    Required. The Python module name to run after installing the packages.
    
    Returns:
    
    value or null for none
  - setPythonModule
```
public GoogleCloudMlV1TrainingInput setPythonModule(String pythonModule)
```
    Required. The Python module name to run after installing the packages.
    
    Parameters:
    
    pythonModule - pythonModule or null for none
  - getPythonVersion
```
public String getPythonVersion()
```
    Optional. The version of Python used in training. You must either specify this field or specify `masterConfig.imageUri`. The following Python versions are available: * Python '3.7' is available when `runtime_version` is set to '1.15' or later. * Python '3.5' is available when `runtime_version` is set to a version from '1.4' to '1.14'. * Python '2.7' is available when `runtime_version` is set to '1.15' or earlier. Read more about the Python versions available for [each runtime version](/ml-engine/docs/runtime-version-list).
    
    Returns:
    
    value or null for none
  - setPythonVersion
```
public GoogleCloudMlV1TrainingInput setPythonVersion(String pythonVersion)
```
    Optional. The version of Python used in training. You must either specify this field or specify `masterConfig.imageUri`. The following Python versions are available: * Python '3.7' is available when `runtime_version` is set to '1.15' or later. * Python '3.5' is available when `runtime_version` is set to a version from '1.4' to '1.14'. * Python '2.7' is available when `runtime_version` is set to '1.15' or earlier. Read more about the Python versions available for [each runtime version](/ml-engine/docs/runtime-version-list).
    
    Parameters:
    
    pythonVersion - pythonVersion or null for none
  - getRegion
```
public String getRegion()
```
    Required. The region to run the training job in. See the [available regions](/ai- platform/training/docs/regions) for AI Platform Training.
    
    Returns:
    
    value or null for none
  - setRegion
```
public GoogleCloudMlV1TrainingInput setRegion(String region)
```
    Required. The region to run the training job in. See the [available regions](/ai- platform/training/docs/regions) for AI Platform Training.
    
    Parameters:
    
    region - region or null for none
  - getRuntimeVersion
```
public String getRuntimeVersion()
```
    Optional. The AI Platform runtime version to use for training. You must either specify this field or specify `masterConfig.imageUri`. For more information, see the [runtime version list ](/ai-platform/training/docs/runtime-version-list) and learn [how to manage runtime versions ](/ai-platform/training/docs/versioning).
    
    Returns:
    
    value or null for none
  - setRuntimeVersion
```
public GoogleCloudMlV1TrainingInput setRuntimeVersion(String runtimeVersion)
```
    Optional. The AI Platform runtime version to use for training. You must either specify this field or specify `masterConfig.imageUri`. For more information, see the [runtime version list ](/ai-platform/training/docs/runtime-version-list) and learn [how to manage runtime versions ](/ai-platform/training/docs/versioning).
    
    Parameters:
    
    runtimeVersion - runtimeVersion or null for none
  - getScaleTier
```
public String getScaleTier()
```
    Required. Specifies the machine types, the number of replicas for workers and parameter servers.
    
    Returns:
    
    value or null for none
  - setScaleTier
```
public GoogleCloudMlV1TrainingInput setScaleTier(String scaleTier)
```
    Required. Specifies the machine types, the number of replicas for workers and parameter servers.
    
    Parameters:
    
    scaleTier - scaleTier or null for none
  - getScheduling
```
public GoogleCloudMlV1Scheduling getScheduling()
```
    Optional. Scheduling options for a training job.
    
    Returns:
    
    value or null for none
  - setScheduling
```
public GoogleCloudMlV1TrainingInput setScheduling(GoogleCloudMlV1Scheduling scheduling)
```
    Optional. Scheduling options for a training job.
    
    Parameters:
    
    scheduling - scheduling or null for none
  - getServiceAccount
```
public String getServiceAccount()
```
    Optional. The email address of a service account to use when running the training appplication. You must have the `iam.serviceAccounts.actAs` permission for the specified service account. In addition, the AI Platform Training Google-managed service account must have the `roles/iam.serviceAccountAdmin` role for the specified service account. [Learn more about configuring a service account.](/ai-platform/training/docs/custom-service-account) If not specified, the AI Platform Training Google-managed service account is used by default.
    
    Returns:
    
    value or null for none
  - setServiceAccount
```
public GoogleCloudMlV1TrainingInput setServiceAccount(String serviceAccount)
```
    Optional. The email address of a service account to use when running the training appplication. You must have the `iam.serviceAccounts.actAs` permission for the specified service account. In addition, the AI Platform Training Google-managed service account must have the `roles/iam.serviceAccountAdmin` role for the specified service account. [Learn more about configuring a service account.](/ai-platform/training/docs/custom-service-account) If not specified, the AI Platform Training Google-managed service account is used by default.
    
    Parameters:
    
    serviceAccount - serviceAccount or null for none
  - getUseChiefInTfConfig
```
public Boolean getUseChiefInTfConfig()
```
    Optional. Use `chief` instead of `master` in the `TF_CONFIG` environment variable when training with a custom container. Defaults to `false`. [Learn more about this field.](/ai- platform/training/docs/distributed-training-details#chief-versus-master) This field has no effect for training jobs that don't use a custom container.
    
    Returns:
    
    value or null for none
  - setUseChiefInTfConfig
```
public GoogleCloudMlV1TrainingInput setUseChiefInTfConfig(Boolean useChiefInTfConfig)
```
    Optional. Use `chief` instead of `master` in the `TF_CONFIG` environment variable when training with a custom container. Defaults to `false`. [Learn more about this field.](/ai- platform/training/docs/distributed-training-details#chief-versus-master) This field has no effect for training jobs that don't use a custom container.
    
    Parameters:
    
    useChiefInTfConfig - useChiefInTfConfig or null for none
  - getWorkerConfig
```
public GoogleCloudMlV1ReplicaConfig getWorkerConfig()
```
    Optional. The configuration for workers. You should only set `workerConfig.acceleratorConfig` if `workerType` is set to a Compute Engine machine type. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine- machine-types-with-gpu) Set `workerConfig.imageUri` only if you build a custom image for your worker. If `workerConfig.imageUri` has not been set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom containers](/ai- platform/training/docs/distributed-training-containers).
    
    Returns:
    
    value or null for none
  - setWorkerConfig
```
public GoogleCloudMlV1TrainingInput setWorkerConfig(GoogleCloudMlV1ReplicaConfig workerConfig)
```
    Optional. The configuration for workers. You should only set `workerConfig.acceleratorConfig` if `workerType` is set to a Compute Engine machine type. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine- machine-types-with-gpu) Set `workerConfig.imageUri` only if you build a custom image for your worker. If `workerConfig.imageUri` has not been set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom containers](/ai- platform/training/docs/distributed-training-containers).
    
    Parameters:
    
    workerConfig - workerConfig or null for none
  - getWorkerCount
```
public Long getWorkerCount()
```
    Optional. The number of worker replicas to use for the training job. Each replica in the cluster will be of the type specified in `worker_type`. This value can only be used when `scale_tier` is set to `CUSTOM`. If you set this value, you must also set `worker_type`. The default value is zero.
    
    Returns:
    
    value or null for none
  - setWorkerCount
```
public GoogleCloudMlV1TrainingInput setWorkerCount(Long workerCount)
```
    Optional. The number of worker replicas to use for the training job. Each replica in the cluster will be of the type specified in `worker_type`. This value can only be used when `scale_tier` is set to `CUSTOM`. If you set this value, you must also set `worker_type`. The default value is zero.
    
    Parameters:
    
    workerCount - workerCount or null for none
  - getWorkerType
```
public String getWorkerType()
```
    Optional. Specifies the type of virtual machine to use for your training job's worker nodes. The supported values are the same as those described in the entry for `masterType`. This value must be consistent with the category of machine type that `masterType` uses. In other words, both must be Compute Engine machine types or both must be legacy machine types. If you use `cloud_tpu` for this value, see special instructions for [configuring a custom TPU machine ](/ml-engine/docs/tensorflow/using-tpus#configuring_a_custom_tpu_machine). This value must be present when `scaleTier` is set to `CUSTOM` and `workerCount` is greater than zero.
    
    Returns:
    
    value or null for none
  - setWorkerType
```
public GoogleCloudMlV1TrainingInput setWorkerType(String workerType)
```
    Optional. Specifies the type of virtual machine to use for your training job's worker nodes. The supported values are the same as those described in the entry for `masterType`. This value must be consistent with the category of machine type that `masterType` uses. In other words, both must be Compute Engine machine types or both must be legacy machine types. If you use `cloud_tpu` for this value, see special instructions for [configuring a custom TPU machine ](/ml-engine/docs/tensorflow/using-tpus#configuring_a_custom_tpu_machine). This value must be present when `scaleTier` is set to `CUSTOM` and `workerCount` is greater than zero.
    
    Parameters:
    
    workerType - workerType or null for none
  - set
```
public GoogleCloudMlV1TrainingInput set(String fieldName,
                                        Object value)
```
    Overrides:
    
    set in class GenericJson
  - clone
```
public GoogleCloudMlV1TrainingInput clone()
```
    Overrides:
    
    clone in class GenericJson

Class GoogleCloudMlV1TrainingInput

Nested Class Summary

Nested classes/interfaces inherited from class com.google.api.client.util.GenericData

Nested classes/interfaces inherited from class java.util.AbstractMap

Nested classes/interfaces inherited from interface java.util.Map

Constructor Summary

Method Summary

Methods inherited from class com.google.api.client.json.GenericJson

Methods inherited from class com.google.api.client.util.GenericData

Methods inherited from class java.util.AbstractMap

Methods inherited from class java.lang.Object

Methods inherited from interface java.util.Map

Constructor Detail

GoogleCloudMlV1TrainingInput

Method Detail

getArgs

setArgs

getEncryptionConfig

setEncryptionConfig

getEvaluatorConfig

setEvaluatorConfig

getEvaluatorCount

setEvaluatorCount

getEvaluatorType

setEvaluatorType

getHyperparameters

setHyperparameters

getJobDir

setJobDir

getMasterConfig

setMasterConfig

getMasterType

setMasterType

getNetwork

setNetwork

getPackageUris

setPackageUris

getParameterServerConfig

setParameterServerConfig

getParameterServerCount

setParameterServerCount

getParameterServerType

setParameterServerType

getPythonModule

setPythonModule

getPythonVersion

setPythonVersion

getRegion

setRegion

getRuntimeVersion

setRuntimeVersion

getScaleTier

setScaleTier

getScheduling

setScheduling

getServiceAccount

setServiceAccount

getUseChiefInTfConfig

setUseChiefInTfConfig

getWorkerConfig

setWorkerConfig

getWorkerCount

setWorkerCount

getWorkerType

setWorkerType

set

clone