ml  v1
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Google.Apis.CloudMachineLearningEngine.v1.Data.GoogleCloudMlV1TrainingInput Class Reference

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. More...

Inheritance diagram for Google.Apis.CloudMachineLearningEngine.v1.Data.GoogleCloudMlV1TrainingInput:
Google::Apis::Requests::IDirectResponseSchema

Properties

virtual System.Collections.Generic.IList< string > Args [get, set]
 Optional. Command line arguments to pass to the program. More...
 
virtual GoogleCloudMlV1EncryptionConfig EncryptionConfig [get, set]
 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. More...
 
virtual GoogleCloudMlV1HyperparameterSpec Hyperparameters [get, set]
 Optional. The set of Hyperparameters to tune. More...
 
virtual string JobDir [get, set]
 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. More...
 
virtual GoogleCloudMlV1ReplicaConfig MasterConfig [get, set]
 Optional. The configuration for your master worker. More...
 
virtual string MasterType [get, set]
 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. More...
 
virtual System.Collections.Generic.IList< string > PackageUris [get, set]
 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. More...
 
virtual GoogleCloudMlV1ReplicaConfig ParameterServerConfig [get, set]
 Optional. The configuration for parameter servers. More...
 
virtual System.Nullable< long > ParameterServerCount [get, set]
 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. More...
 
virtual string ParameterServerType [get, set]
 Optional. Specifies the type of virtual machine to use for your training job's parameter server. More...
 
virtual string PythonModule [get, set]
 Required. The Python module name to run after installing the packages. More...
 
virtual string PythonVersion [get, set]
 Optional. The version of Python used in training. You must either specify this field or specify masterConfig.imageUri. More...
 
virtual string Region [get, set]
 Required. The region to run the training job in. See the available regions for AI Platform Training. More...
 
virtual string RuntimeVersion [get, set]
 Optional. The AI Platform runtime version to use for training. You must either specify this field or specify masterConfig.imageUri. More...
 
virtual string ScaleTier [get, set]
 Required. Specifies the machine types, the number of replicas for workers and parameter servers. More...
 
virtual GoogleCloudMlV1Scheduling Scheduling [get, set]
 Optional. Scheduling options for a training job. More...
 
virtual System.Nullable< bool > UseChiefInTfConfig [get, set]
 Optional. Use 'chief' instead of 'master' in TF_CONFIG when Custom Container is used and evaluator is not specified. More...
 
virtual GoogleCloudMlV1ReplicaConfig WorkerConfig [get, set]
 Optional. The configuration for workers. More...
 
virtual System.Nullable< long > WorkerCount [get, set]
 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. More...
 
virtual string WorkerType [get, set]
 Optional. Specifies the type of virtual machine to use for your training job's worker nodes. More...
 
virtual string ETag [get, set]
 The ETag of the item. More...
 
- Properties inherited from Google::Apis::Requests::IDirectResponseSchema
string ETag
 

Detailed Description

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.

Property Documentation

◆ Args

virtual System.Collections.Generic.IList<string> Google.Apis.CloudMachineLearningEngine.v1.Data.GoogleCloudMlV1TrainingInput.Args
getset

Optional. Command line arguments to pass to the program.

◆ EncryptionConfig

virtual GoogleCloudMlV1EncryptionConfig Google.Apis.CloudMachineLearningEngine.v1.Data.GoogleCloudMlV1TrainingInput.EncryptionConfig
getset

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.

◆ ETag

virtual string Google.Apis.CloudMachineLearningEngine.v1.Data.GoogleCloudMlV1TrainingInput.ETag
getset

The ETag of the item.

◆ Hyperparameters

virtual GoogleCloudMlV1HyperparameterSpec Google.Apis.CloudMachineLearningEngine.v1.Data.GoogleCloudMlV1TrainingInput.Hyperparameters
getset

Optional. The set of Hyperparameters to tune.

◆ JobDir

virtual string Google.Apis.CloudMachineLearningEngine.v1.Data.GoogleCloudMlV1TrainingInput.JobDir
getset

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.

◆ MasterConfig

virtual GoogleCloudMlV1ReplicaConfig Google.Apis.CloudMachineLearningEngine.v1.Data.GoogleCloudMlV1TrainingInput.MasterConfig
getset

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.

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.

◆ MasterType

virtual string Google.Apis.CloudMachineLearningEngine.v1.Data.GoogleCloudMlV1TrainingInput.MasterType
getset

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.

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.

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.

◆ PackageUris

virtual System.Collections.Generic.IList<string> Google.Apis.CloudMachineLearningEngine.v1.Data.GoogleCloudMlV1TrainingInput.PackageUris
getset

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.

◆ ParameterServerConfig

virtual GoogleCloudMlV1ReplicaConfig Google.Apis.CloudMachineLearningEngine.v1.Data.GoogleCloudMlV1TrainingInput.ParameterServerConfig
getset

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.

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.

◆ ParameterServerCount

virtual System.Nullable<long> Google.Apis.CloudMachineLearningEngine.v1.Data.GoogleCloudMlV1TrainingInput.ParameterServerCount
getset

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.

◆ ParameterServerType

virtual string Google.Apis.CloudMachineLearningEngine.v1.Data.GoogleCloudMlV1TrainingInput.ParameterServerType
getset

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.

◆ PythonModule

virtual string Google.Apis.CloudMachineLearningEngine.v1.Data.GoogleCloudMlV1TrainingInput.PythonModule
getset

Required. The Python module name to run after installing the packages.

◆ PythonVersion

virtual string Google.Apis.CloudMachineLearningEngine.v1.Data.GoogleCloudMlV1TrainingInput.PythonVersion
getset

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.

◆ Region

virtual string Google.Apis.CloudMachineLearningEngine.v1.Data.GoogleCloudMlV1TrainingInput.Region
getset

Required. The region to run the training job in. See the available regions for AI Platform Training.

◆ RuntimeVersion

virtual string Google.Apis.CloudMachineLearningEngine.v1.Data.GoogleCloudMlV1TrainingInput.RuntimeVersion
getset

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 and learn how to manage runtime versions.

◆ ScaleTier

virtual string Google.Apis.CloudMachineLearningEngine.v1.Data.GoogleCloudMlV1TrainingInput.ScaleTier
getset

Required. Specifies the machine types, the number of replicas for workers and parameter servers.

◆ Scheduling

virtual GoogleCloudMlV1Scheduling Google.Apis.CloudMachineLearningEngine.v1.Data.GoogleCloudMlV1TrainingInput.Scheduling
getset

Optional. Scheduling options for a training job.

◆ UseChiefInTfConfig

virtual System.Nullable<bool> Google.Apis.CloudMachineLearningEngine.v1.Data.GoogleCloudMlV1TrainingInput.UseChiefInTfConfig
getset

Optional. Use 'chief' instead of 'master' in TF_CONFIG when Custom Container is used and evaluator is not specified.

Defaults to false.

◆ WorkerConfig

virtual GoogleCloudMlV1ReplicaConfig Google.Apis.CloudMachineLearningEngine.v1.Data.GoogleCloudMlV1TrainingInput.WorkerConfig
getset

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.

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.

◆ WorkerCount

virtual System.Nullable<long> Google.Apis.CloudMachineLearningEngine.v1.Data.GoogleCloudMlV1TrainingInput.WorkerCount
getset

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.

◆ WorkerType

virtual string Google.Apis.CloudMachineLearningEngine.v1.Data.GoogleCloudMlV1TrainingInput.WorkerType
getset

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.

This value must be present when scaleTier is set to CUSTOM and workerCount is greater than zero.


The documentation for this class was generated from the following file: