GoogleCloudMlV1TrainingInput (AI Platform Training & Prediction API v1 (Rev. 120) 1.25.0)

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

All Implemented Interfaces:

java.lang.Cloneable, java.util.Map<java.lang.String,java.lang.Object>
```
public final class GoogleCloudMlV1TrainingInput
extends com.google.api.client.json.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
  com.google.api.client.util.GenericData.Flags
- Nested classes/interfaces inherited from class java.util.AbstractMap
  java.util.AbstractMap.SimpleEntry<K,V>, java.util.AbstractMap.SimpleImmutableEntry<K,V>
- Nested classes/interfaces inherited from interface java.util.Map
  java.util.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()`
`java.util.List<java.lang.String>`	`getArgs()` Optional.
`GoogleCloudMlV1EncryptionConfig`	`getEncryptionConfig()` Custom encryption key options for a training job.
`GoogleCloudMlV1HyperparameterSpec`	`getHyperparameters()` Optional.
`java.lang.String`	`getJobDir()` Optional.
`GoogleCloudMlV1ReplicaConfig`	`getMasterConfig()` Optional.
`java.lang.String`	`getMasterType()` Optional.
`java.util.List<java.lang.String>`	`getPackageUris()` Required.
`GoogleCloudMlV1ReplicaConfig`	`getParameterServerConfig()` Optional.
`java.lang.Long`	`getParameterServerCount()` Optional.
`java.lang.String`	`getParameterServerType()` Optional.
`java.lang.String`	`getPythonModule()` Required.
`java.lang.String`	`getPythonVersion()` Optional.
`java.lang.String`	`getRegion()` Required.
`java.lang.String`	`getRuntimeVersion()` Optional.
`java.lang.String`	`getScaleTier()` Required.
`GoogleCloudMlV1Scheduling`	`getScheduling()` Optional.
`java.lang.Boolean`	`getUseChiefInTfConfig()` Optional.
`GoogleCloudMlV1ReplicaConfig`	`getWorkerConfig()` Optional.
`java.lang.Long`	`getWorkerCount()` Optional.
`java.lang.String`	`getWorkerType()` Optional.
`GoogleCloudMlV1TrainingInput`	`set(java.lang.String fieldName, java.lang.Object value)`
`GoogleCloudMlV1TrainingInput`	`setArgs(java.util.List<java.lang.String> args)` Optional.
`GoogleCloudMlV1TrainingInput`	`setEncryptionConfig(GoogleCloudMlV1EncryptionConfig encryptionConfig)` Custom encryption key options for a training job.
`GoogleCloudMlV1TrainingInput`	`setHyperparameters(GoogleCloudMlV1HyperparameterSpec hyperparameters)` Optional.
`GoogleCloudMlV1TrainingInput`	`setJobDir(java.lang.String jobDir)` Optional.
`GoogleCloudMlV1TrainingInput`	`setMasterConfig(GoogleCloudMlV1ReplicaConfig masterConfig)` Optional.
`GoogleCloudMlV1TrainingInput`	`setMasterType(java.lang.String masterType)` Optional.
`GoogleCloudMlV1TrainingInput`	`setPackageUris(java.util.List<java.lang.String> packageUris)` Required.
`GoogleCloudMlV1TrainingInput`	`setParameterServerConfig(GoogleCloudMlV1ReplicaConfig parameterServerConfig)` Optional.
`GoogleCloudMlV1TrainingInput`	`setParameterServerCount(java.lang.Long parameterServerCount)` Optional.
`GoogleCloudMlV1TrainingInput`	`setParameterServerType(java.lang.String parameterServerType)` Optional.
`GoogleCloudMlV1TrainingInput`	`setPythonModule(java.lang.String pythonModule)` Required.
`GoogleCloudMlV1TrainingInput`	`setPythonVersion(java.lang.String pythonVersion)` Optional.
`GoogleCloudMlV1TrainingInput`	`setRegion(java.lang.String region)` Required.
`GoogleCloudMlV1TrainingInput`	`setRuntimeVersion(java.lang.String runtimeVersion)` Optional.
`GoogleCloudMlV1TrainingInput`	`setScaleTier(java.lang.String scaleTier)` Required.
`GoogleCloudMlV1TrainingInput`	`setScheduling(GoogleCloudMlV1Scheduling scheduling)` Optional.
`GoogleCloudMlV1TrainingInput`	`setUseChiefInTfConfig(java.lang.Boolean useChiefInTfConfig)` Optional.
`GoogleCloudMlV1TrainingInput`	`setWorkerConfig(GoogleCloudMlV1ReplicaConfig workerConfig)` Optional.
`GoogleCloudMlV1TrainingInput`	`setWorkerCount(java.lang.Long workerCount)` Optional.
`GoogleCloudMlV1TrainingInput`	`setWorkerType(java.lang.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, get, getClassInfo, getUnknownKeys, put, putAll, remove, setUnknownKeys

Methods inherited from class java.util.AbstractMap
clear, containsKey, containsValue, equals, hashCode, 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 java.util.List<java.lang.String> getArgs()
```
    Optional. Command line arguments to pass to the program.
    
    Returns:
    
    value or null for none
  - setArgs
```
public GoogleCloudMlV1TrainingInput setArgs(java.util.List<java.lang.String> args)
```
    Optional. Command line arguments to pass to the program.
    
    Parameters:
    
    args - args or null for none
  - getEncryptionConfig
```
public GoogleCloudMlV1EncryptionConfig getEncryptionConfig()
```
    Custom encryption key options for a training job. If this is set, then all resources created by the training job will be encrypted with the provided encryption key.
    
    Returns:
    
    value or null for none
  - setEncryptionConfig
```
public GoogleCloudMlV1TrainingInput setEncryptionConfig(GoogleCloudMlV1EncryptionConfig encryptionConfig)
```
    Custom encryption key options for a training job. If this is set, then all resources created by the training job will be encrypted with the provided encryption key.
    
    Parameters:
    
    encryptionConfig - encryptionConfig 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 java.lang.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(java.lang.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.](/ml- engine/docs/tensorflow/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](/ml-engine/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.](/ml- engine/docs/tensorflow/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](/ml-engine/docs/distributed-training-containers).
    
    Parameters:
    
    masterConfig - masterConfig or null for none
  - getMasterType
```
public java.lang.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. The following types are supported: - `n1-standard-4` - `n1-standard-8` - `n1-standard-16` - `n1-standard-32` - `n1-standard-64` - `n1-standard-96` - `n1-highmem-2` - `n1-highmem-4` - `n1-highmem-8` - `n1-highmem-16` - `n1-highmem-32` - `n1-highmem-64` - `n1-highmem-96` - `n1-highcpu-16` - `n1-highcpu-32` - `n1-highcpu-64` - `n1-highcpu-96` Learn more about [using Compute Engine machine types](/ml-engine/docs/machine-types#compute- engine-machine-types). Alternatively, you can use the following legacy machine types: - `standard` - `large_model` - `complex_model_s` - `complex_model_m` - `complex_model_l` - `standard_gpu` - `complex_model_m_gpu` - `complex_model_l_gpu` - `standard_p100` - `complex_model_m_p100` - `standard_v100` - `large_model_v100` - `complex_model_m_v100` - `complex_model_l_v100` Learn more about [using legacy machine types](/ml-engine/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](/ml-engine/docs/tensorflow /using-tpus#configuring_a_custom_tpu_machine).
    
    Returns:
    
    value or null for none
  - setMasterType
```
public GoogleCloudMlV1TrainingInput setMasterType(java.lang.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. The following types are supported: - `n1-standard-4` - `n1-standard-8` - `n1-standard-16` - `n1-standard-32` - `n1-standard-64` - `n1-standard-96` - `n1-highmem-2` - `n1-highmem-4` - `n1-highmem-8` - `n1-highmem-16` - `n1-highmem-32` - `n1-highmem-64` - `n1-highmem-96` - `n1-highcpu-16` - `n1-highcpu-32` - `n1-highcpu-64` - `n1-highcpu-96` Learn more about [using Compute Engine machine types](/ml-engine/docs/machine-types#compute- engine-machine-types). Alternatively, you can use the following legacy machine types: - `standard` - `large_model` - `complex_model_s` - `complex_model_m` - `complex_model_l` - `standard_gpu` - `complex_model_m_gpu` - `complex_model_l_gpu` - `standard_p100` - `complex_model_m_p100` - `standard_v100` - `large_model_v100` - `complex_model_m_v100` - `complex_model_l_v100` Learn more about [using legacy machine types](/ml-engine/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](/ml-engine/docs/tensorflow /using-tpus#configuring_a_custom_tpu_machine).
    
    Parameters:
    
    masterType - masterType or null for none
  - getPackageUris
```
public java.util.List<java.lang.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(java.util.List<java.lang.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 `parameterServerConfigType` is set to a Compute Engine machine type. [Learn about restrictions on accelerator configurations for training.](/ml-engine/docs/tensorflow/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](/ml-engine/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 `parameterServerConfigType` is set to a Compute Engine machine type. [Learn about restrictions on accelerator configurations for training.](/ml-engine/docs/tensorflow/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](/ml-engine/docs /distributed-training-containers).
    
    Parameters:
    
    parameterServerConfig - parameterServerConfig or null for none
  - getParameterServerCount
```
public java.lang.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(java.lang.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 java.lang.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(java.lang.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 java.lang.String getPythonModule()
```
    Required. The Python module name to run after installing the packages.
    
    Returns:
    
    value or null for none
  - setPythonModule
```
public GoogleCloudMlV1TrainingInput setPythonModule(java.lang.String pythonModule)
```
    Required. The Python module name to run after installing the packages.
    
    Parameters:
    
    pythonModule - pythonModule or null for none
  - getPythonVersion
```
public java.lang.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(java.lang.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 java.lang.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(java.lang.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 java.lang.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(java.lang.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 java.lang.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(java.lang.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
  - getUseChiefInTfConfig
```
public java.lang.Boolean getUseChiefInTfConfig()
```
    Optional. Use 'chief' instead of 'master' in TF_CONFIG when Custom Container is used and evaluator is not specified. Defaults to false.
    
    Returns:
    
    value or null for none
  - setUseChiefInTfConfig
```
public GoogleCloudMlV1TrainingInput setUseChiefInTfConfig(java.lang.Boolean useChiefInTfConfig)
```
    Optional. Use 'chief' instead of 'master' in TF_CONFIG when Custom Container is used and evaluator is not specified. Defaults to false.
    
    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.](/ml- engine/docs/tensorflow/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](/ml-engine/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.](/ml- engine/docs/tensorflow/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](/ml-engine/docs /distributed-training-containers).
    
    Parameters:
    
    workerConfig - workerConfig or null for none
  - getWorkerCount
```
public java.lang.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(java.lang.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 java.lang.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(java.lang.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(java.lang.String fieldName,
                                        java.lang.Object value)
```
    Overrides:
    
    set in class com.google.api.client.json.GenericJson
  - clone
```
public GoogleCloudMlV1TrainingInput clone()
```
    Overrides:
    
    clone in class com.google.api.client.json.GenericJson

AI Platform Training & Prediction API v1 (revision 120)

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

getHyperparameters

setHyperparameters

getJobDir

setJobDir

getMasterConfig

setMasterConfig

getMasterType

setMasterType

getPackageUris

setPackageUris

getParameterServerConfig

setParameterServerConfig

getParameterServerCount

setParameterServerCount

getParameterServerType

setParameterServerType

getPythonModule

setPythonModule

getPythonVersion

setPythonVersion

getRegion

setRegion

getRuntimeVersion

setRuntimeVersion

getScaleTier

setScaleTier

getScheduling

setScheduling

getUseChiefInTfConfig

setUseChiefInTfConfig

getWorkerConfig

setWorkerConfig

getWorkerCount

setWorkerCount

getWorkerType

setWorkerType

set

clone