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<?php
/*
* Copyright 2014 Google Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License"); you may not
* use this file except in compliance with the License. You may obtain a copy of
* the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations under
* the License.
*/
namespace Google\Service\CloudMachineLearningEngine;
class GoogleCloudMlV1TrainingInput extends \Google\Collection
{
/**
* A single worker instance. This tier is suitable for learning how to use
* Cloud ML, and for experimenting with new models using small datasets.
*/
public const SCALE_TIER_BASIC = 'BASIC';
/**
* Many workers and a few parameter servers.
*/
public const SCALE_TIER_STANDARD_1 = 'STANDARD_1';
/**
* A large number of workers with many parameter servers.
*/
public const SCALE_TIER_PREMIUM_1 = 'PREMIUM_1';
/**
* A single worker instance [with a GPU](/ai-platform/training/docs/using-
* gpus).
*/
public const SCALE_TIER_BASIC_GPU = 'BASIC_GPU';
/**
* A single worker instance with a [Cloud TPU](/ml-
* engine/docs/tensorflow/using-tpus).
*/
public const SCALE_TIER_BASIC_TPU = 'BASIC_TPU';
/**
* The CUSTOM tier is not a set tier, but rather enables you to use your own
* cluster specification. When you use this tier, set values to configure your
* processing cluster according to these guidelines: * You _must_ set
* `TrainingInput.masterType` to specify the type of machine to use for your
* master node. This is the only required setting. * You _may_ set
* `TrainingInput.workerCount` to specify the number of workers to use. If you
* specify one or more workers, you _must_ also set `TrainingInput.workerType`
* to specify the type of machine to use for your worker nodes. * You _may_
* set `TrainingInput.parameterServerCount` to specify the number of parameter
* servers to use. If you specify one or more parameter servers, you _must_
* also set `TrainingInput.parameterServerType` to specify the type of machine
* to use for your parameter servers. Note that all of your workers must use
* the same machine type, which can be different from your parameter server
* type and master type. Your parameter servers must likewise use the same
* machine type, which can be different from your worker type and master type.
*/
public const SCALE_TIER_CUSTOM = 'CUSTOM';
protected $collection_key = 'packageUris';
/**
* 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.
*
* @var string[]
*/
public $args;
/**
* Optional. Whether you want AI Platform Training to enable [interactive
* shell access](https://cloud.google.com/ai-platform/training/docs/monitor-
* debug-interactive-shell) to training containers. If set to `true`, you can
* access interactive shells at the URIs given by
* TrainingOutput.web_access_uris or HyperparameterOutput.web_access_uris
* (within TrainingOutput.trials).
*
* @var bool
*/
public $enableWebAccess;
protected $encryptionConfigType = GoogleCloudMlV1EncryptionConfig::class;
protected $encryptionConfigDataType = '';
protected $evaluatorConfigType = GoogleCloudMlV1ReplicaConfig::class;
protected $evaluatorConfigDataType = '';
/**
* 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.
*
* @var string
*/
public $evaluatorCount;
/**
* 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.
*
* @var string
*/
public $evaluatorType;
protected $hyperparametersType = GoogleCloudMlV1HyperparameterSpec::class;
protected $hyperparametersDataType = '';
/**
* 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.
*
* @var string
*/
public $jobDir;
protected $masterConfigType = GoogleCloudMlV1ReplicaConfig::class;
protected $masterConfigDataType = '';
/**
* 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).
*
* @var string
*/
public $masterType;
/**
* 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).
*
* @var string
*/
public $network;
/**
* 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.
*
* @var string[]
*/
public $packageUris;
protected $parameterServerConfigType = GoogleCloudMlV1ReplicaConfig::class;
protected $parameterServerConfigDataType = '';
/**
* 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.
*
* @var string
*/
public $parameterServerCount;
/**
* 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.
*
* @var string
*/
public $parameterServerType;
/**
* Required. The Python module name to run after installing the packages.
*
* @var string
*/
public $pythonModule;
/**
* 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).
*
* @var string
*/
public $pythonVersion;
/**
* Required. The region to run the training job in. See the [available
* regions](/ai-platform/training/docs/regions) for AI Platform Training.
*
* @var string
*/
public $region;
/**
* 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).
*
* @var string
*/
public $runtimeVersion;
/**
* Required. Specifies the machine types, the number of replicas for workers
* and parameter servers.
*
* @var string
*/
public $scaleTier;
protected $schedulingType = GoogleCloudMlV1Scheduling::class;
protected $schedulingDataType = '';
/**
* 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.
*
* @var string
*/
public $serviceAccount;
/**
* 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.
*
* @var bool
*/
public $useChiefInTfConfig;
protected $workerConfigType = GoogleCloudMlV1ReplicaConfig::class;
protected $workerConfigDataType = '';
/**
* 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.
*
* @var string
*/
public $workerCount;
/**
* 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.
*
* @var string
*/
public $workerType;
/**
* 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.
*
* @param string[] $args
*/
public function setArgs($args)
{
$this->args = $args;
}
/**
* @return string[]
*/
public function getArgs()
{
return $this->args;
}
/**
* Optional. Whether you want AI Platform Training to enable [interactive
* shell access](https://cloud.google.com/ai-platform/training/docs/monitor-
* debug-interactive-shell) to training containers. If set to `true`, you can
* access interactive shells at the URIs given by
* TrainingOutput.web_access_uris or HyperparameterOutput.web_access_uris
* (within TrainingOutput.trials).
*
* @param bool $enableWebAccess
*/
public function setEnableWebAccess($enableWebAccess)
{
$this->enableWebAccess = $enableWebAccess;
}
/**
* @return bool
*/
public function getEnableWebAccess()
{
return $this->enableWebAccess;
}
/**
* 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).
*
* @param GoogleCloudMlV1EncryptionConfig $encryptionConfig
*/
public function setEncryptionConfig(GoogleCloudMlV1EncryptionConfig $encryptionConfig)
{
$this->encryptionConfig = $encryptionConfig;
}
/**
* @return GoogleCloudMlV1EncryptionConfig
*/
public function getEncryptionConfig()
{
return $this->encryptionConfig;
}
/**
* 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).
*
* @param GoogleCloudMlV1ReplicaConfig $evaluatorConfig
*/
public function setEvaluatorConfig(GoogleCloudMlV1ReplicaConfig $evaluatorConfig)
{
$this->evaluatorConfig = $evaluatorConfig;
}
/**
* @return GoogleCloudMlV1ReplicaConfig
*/
public function getEvaluatorConfig()
{
return $this->evaluatorConfig;
}
/**
* 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.
*
* @param string $evaluatorCount
*/
public function setEvaluatorCount($evaluatorCount)
{
$this->evaluatorCount = $evaluatorCount;
}
/**
* @return string
*/
public function getEvaluatorCount()
{
return $this->evaluatorCount;
}
/**
* 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.
*
* @param string $evaluatorType
*/
public function setEvaluatorType($evaluatorType)
{
$this->evaluatorType = $evaluatorType;
}
/**
* @return string
*/
public function getEvaluatorType()
{
return $this->evaluatorType;
}
/**
* Optional. The set of Hyperparameters to tune.
*
* @param GoogleCloudMlV1HyperparameterSpec $hyperparameters
*/
public function setHyperparameters(GoogleCloudMlV1HyperparameterSpec $hyperparameters)
{
$this->hyperparameters = $hyperparameters;
}
/**
* @return GoogleCloudMlV1HyperparameterSpec
*/
public function getHyperparameters()
{
return $this->hyperparameters;
}
/**
* 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.
*
* @param string $jobDir
*/
public function setJobDir($jobDir)
{
$this->jobDir = $jobDir;
}
/**
* @return string
*/
public function getJobDir()
{
return $this->jobDir;
}
/**
* 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).
*
* @param GoogleCloudMlV1ReplicaConfig $masterConfig
*/
public function setMasterConfig(GoogleCloudMlV1ReplicaConfig $masterConfig)
{
$this->masterConfig = $masterConfig;
}
/**
* @return GoogleCloudMlV1ReplicaConfig
*/
public function getMasterConfig()
{
return $this->masterConfig;
}
/**
* 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).
*
* @param string $masterType
*/
public function setMasterType($masterType)
{
$this->masterType = $masterType;
}
/**
* @return string
*/
public function getMasterType()
{
return $this->masterType;
}
/**
* 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).
*
* @param string $network
*/
public function setNetwork($network)
{
$this->network = $network;
}
/**
* @return string
*/
public function getNetwork()
{
return $this->network;
}
/**
* 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.
*
* @param string[] $packageUris
*/
public function setPackageUris($packageUris)
{
$this->packageUris = $packageUris;
}
/**
* @return string[]
*/
public function getPackageUris()
{
return $this->packageUris;
}
/**
* 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).
*
* @param GoogleCloudMlV1ReplicaConfig $parameterServerConfig
*/
public function setParameterServerConfig(GoogleCloudMlV1ReplicaConfig $parameterServerConfig)
{
$this->parameterServerConfig = $parameterServerConfig;
}
/**
* @return GoogleCloudMlV1ReplicaConfig
*/
public function getParameterServerConfig()
{
return $this->parameterServerConfig;
}
/**
* 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.
*
* @param string $parameterServerCount
*/
public function setParameterServerCount($parameterServerCount)
{
$this->parameterServerCount = $parameterServerCount;
}
/**
* @return string
*/
public function getParameterServerCount()
{
return $this->parameterServerCount;
}
/**
* 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.
*
* @param string $parameterServerType
*/
public function setParameterServerType($parameterServerType)
{
$this->parameterServerType = $parameterServerType;
}
/**
* @return string
*/
public function getParameterServerType()
{
return $this->parameterServerType;
}
/**
* Required. The Python module name to run after installing the packages.
*
* @param string $pythonModule
*/
public function setPythonModule($pythonModule)
{
$this->pythonModule = $pythonModule;
}
/**
* @return string
*/
public function getPythonModule()
{
return $this->pythonModule;
}
/**
* 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).
*
* @param string $pythonVersion
*/
public function setPythonVersion($pythonVersion)
{
$this->pythonVersion = $pythonVersion;
}
/**
* @return string
*/
public function getPythonVersion()
{
return $this->pythonVersion;
}
/**
* Required. The region to run the training job in. See the [available
* regions](/ai-platform/training/docs/regions) for AI Platform Training.
*
* @param string $region
*/
public function setRegion($region)
{
$this->region = $region;
}
/**
* @return string
*/
public function getRegion()
{
return $this->region;
}
/**
* 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).
*
* @param string $runtimeVersion
*/
public function setRuntimeVersion($runtimeVersion)
{
$this->runtimeVersion = $runtimeVersion;
}
/**
* @return string
*/
public function getRuntimeVersion()
{
return $this->runtimeVersion;
}
/**
* Required. Specifies the machine types, the number of replicas for workers
* and parameter servers.
*
* Accepted values: BASIC, STANDARD_1, PREMIUM_1, BASIC_GPU, BASIC_TPU, CUSTOM
*
* @param self::SCALE_TIER_* $scaleTier
*/
public function setScaleTier($scaleTier)
{
$this->scaleTier = $scaleTier;
}
/**
* @return self::SCALE_TIER_*
*/
public function getScaleTier()
{
return $this->scaleTier;
}
/**
* Optional. Scheduling options for a training job.
*
* @param GoogleCloudMlV1Scheduling $scheduling
*/
public function setScheduling(GoogleCloudMlV1Scheduling $scheduling)
{
$this->scheduling = $scheduling;
}
/**
* @return GoogleCloudMlV1Scheduling
*/
public function getScheduling()
{
return $this->scheduling;
}
/**
* 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.
*
* @param string $serviceAccount
*/
public function setServiceAccount($serviceAccount)
{
$this->serviceAccount = $serviceAccount;
}
/**
* @return string
*/
public function getServiceAccount()
{
return $this->serviceAccount;
}
/**
* 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.
*
* @param bool $useChiefInTfConfig
*/
public function setUseChiefInTfConfig($useChiefInTfConfig)
{
$this->useChiefInTfConfig = $useChiefInTfConfig;
}
/**
* @return bool
*/
public function getUseChiefInTfConfig()
{
return $this->useChiefInTfConfig;
}
/**
* 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).
*
* @param GoogleCloudMlV1ReplicaConfig $workerConfig
*/
public function setWorkerConfig(GoogleCloudMlV1ReplicaConfig $workerConfig)
{
$this->workerConfig = $workerConfig;
}
/**
* @return GoogleCloudMlV1ReplicaConfig
*/
public function getWorkerConfig()
{
return $this->workerConfig;
}
/**
* 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.
*
* @param string $workerCount
*/
public function setWorkerCount($workerCount)
{
$this->workerCount = $workerCount;
}
/**
* @return string
*/
public function getWorkerCount()
{
return $this->workerCount;
}
/**
* 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.
*
* @param string $workerType
*/
public function setWorkerType($workerType)
{
$this->workerType = $workerType;
}
/**
* @return string
*/
public function getWorkerType()
{
return $this->workerType;
}
}
// Adding a class alias for backwards compatibility with the previous class name.
class_alias(GoogleCloudMlV1TrainingInput::class, 'Google_Service_CloudMachineLearningEngine_GoogleCloudMlV1TrainingInput');