Class: Google::Apis::MlV1::GoogleCloudMlV1TrainingInput

Inherits:
Object
  • Object
show all
Includes:
Core::Hashable, Core::JsonObjectSupport
Defined in:
lib/google/apis/ml_v1/classes.rb,
lib/google/apis/ml_v1/representations.rb,
lib/google/apis/ml_v1/representations.rb

Overview

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.

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(**args) ⇒ GoogleCloudMlV1TrainingInput

Returns a new instance of GoogleCloudMlV1TrainingInput.



2804
2805
2806
 
# File 'lib/google/apis/ml_v1/classes.rb', line 2804

def initialize(**args)
   update!(**args)
end

Instance Attribute Details

#args ⇒ Array<String>

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. Corresponds to the JSON property args

Returns:

  • (Array<String>) 


2606
2607
2608
 
# File 'lib/google/apis/ml_v1/classes.rb', line 2606

def args
  @args
end

#encryption_config ⇒ Google::Apis::MlV1::GoogleCloudMlV1EncryptionConfig

Represents a custom encryption key configuration that can be applied to a resource. Corresponds to the JSON property encryptionConfig

Returns:



2612
2613
2614
 
# File 'lib/google/apis/ml_v1/classes.rb', line 2612

def encryption_config
  @encryption_config
end

#evaluator_config ⇒ Google::Apis::MlV1::GoogleCloudMlV1ReplicaConfig

Represents the configuration for a replica in a cluster. Corresponds to the JSON property evaluatorConfig

Returns:



2617
2618
2619
 
# File 'lib/google/apis/ml_v1/classes.rb', line 2617

def evaluator_config
  @evaluator_config
end

#evaluator_count ⇒ Fixnum

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. Corresponds to the JSON property evaluatorCount

Returns:

  • (Fixnum) 


2625
2626
2627
 
# File 'lib/google/apis/ml_v1/classes.rb', line 2625

def evaluator_count
  @evaluator_count
end

#evaluator_type ⇒ String

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. Corresponds to the JSON property evaluatorType

Returns:

  • (String) 


2636
2637
2638
 
# File 'lib/google/apis/ml_v1/classes.rb', line 2636

def evaluator_type
  @evaluator_type
end

#hyperparameters ⇒ Google::Apis::MlV1::GoogleCloudMlV1HyperparameterSpec

Represents a set of hyperparameters to optimize. Corresponds to the JSON property hyperparameters

Returns:



2641
2642
2643
 
# File 'lib/google/apis/ml_v1/classes.rb', line 2641

def hyperparameters
  @hyperparameters
end

#job_dir ⇒ String

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. Corresponds to the JSON property jobDir

Returns:

  • (String) 


2649
2650
2651
 
# File 'lib/google/apis/ml_v1/classes.rb', line 2649

def job_dir
  @job_dir
end

#master_config ⇒ Google::Apis::MlV1::GoogleCloudMlV1ReplicaConfig

Represents the configuration for a replica in a cluster. Corresponds to the JSON property masterConfig

Returns:



2654
2655
2656
 
# File 'lib/google/apis/ml_v1/classes.rb', line 2654

def master_config
  @master_config
end

#master_type ⇒ String

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. Alternatively, you can use the certain legacy machine types in this field. See the list of 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. Corresponds to the JSON property masterType

Returns:

  • (String) 


2668
2669
2670
 
# File 'lib/google/apis/ml_v1/classes.rb', line 2668

def master_type
  @master_type
end

#network ⇒ String

Optional. The full name of the Compute Engine network to which the Job is peered. For example, projects/12345/global/networks/myVPC. The format of this field is projects/project/global/networks/network`, whereprojectis a project number (like12345) andnetworkis 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). Corresponds to the JSON propertynetwork`

Returns:

  • (String) 


2679
2680
2681
 
# File 'lib/google/apis/ml_v1/classes.rb', line 2679

def network
  @network
end

#package_uris ⇒ Array<String>

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. Corresponds to the JSON property packageUris

Returns:

  • (Array<String>) 


2686
2687
2688
 
# File 'lib/google/apis/ml_v1/classes.rb', line 2686

def package_uris
  @package_uris
end

#parameter_server_config ⇒ Google::Apis::MlV1::GoogleCloudMlV1ReplicaConfig

Represents the configuration for a replica in a cluster. Corresponds to the JSON property parameterServerConfig

Returns:



2691
2692
2693
 
# File 'lib/google/apis/ml_v1/classes.rb', line 2691

def parameter_server_config
  @parameter_server_config
end

#parameter_server_count ⇒ Fixnum

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. Corresponds to the JSON property parameterServerCount

Returns:

  • (Fixnum) 


2700
2701
2702
 
# File 'lib/google/apis/ml_v1/classes.rb', line 2700

def parameter_server_count
  @parameter_server_count
end

#parameter_server_type ⇒ String

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. Corresponds to the JSON property parameterServerType

Returns:

  • (String) 


2711
2712
2713
 
# File 'lib/google/apis/ml_v1/classes.rb', line 2711

def parameter_server_type
  @parameter_server_type
end

#python_module ⇒ String

Required. The Python module name to run after installing the packages. Corresponds to the JSON property pythonModule

Returns:

  • (String) 


2716
2717
2718
 
# File 'lib/google/apis/ml_v1/classes.rb', line 2716

def python_module
  @python_module
end

#python_version ⇒ String

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. Corresponds to the JSON property pythonVersion

Returns:

  • (String) 


2728
2729
2730
 
# File 'lib/google/apis/ml_v1/classes.rb', line 2728

def python_version
  @python_version
end

#region ⇒ String

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

Returns:

  • (String) 


2734
2735
2736
 
# File 'lib/google/apis/ml_v1/classes.rb', line 2734

def region
  @region
end

#runtime_version ⇒ String

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. Corresponds to the JSON property runtimeVersion

Returns:

  • (String) 


2743
2744
2745
 
# File 'lib/google/apis/ml_v1/classes.rb', line 2743

def runtime_version
  @runtime_version
end

#scale_tier ⇒ String

Required. Specifies the machine types, the number of replicas for workers and parameter servers. Corresponds to the JSON property scaleTier

Returns:

  • (String) 


2749
2750
2751
 
# File 'lib/google/apis/ml_v1/classes.rb', line 2749

def scale_tier
  @scale_tier
end

#scheduling ⇒ Google::Apis::MlV1::GoogleCloudMlV1Scheduling

All parameters related to scheduling of training jobs. Corresponds to the JSON property scheduling

Returns:



2754
2755
2756
 
# File 'lib/google/apis/ml_v1/classes.rb', line 2754

def scheduling
  @scheduling
end

#service_account ⇒ String

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. If not specified, the AI Platform Training Google-managed service account is used by default. Corresponds to the JSON property serviceAccount

Returns:

  • (String) 


2766
2767
2768
 
# File 'lib/google/apis/ml_v1/classes.rb', line 2766

def 
  @service_account
end

#use_chief_in_tf_config ⇒ Boolean Also known as: use_chief_in_tf_config?

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. This field has no effect for training jobs that don't use a custom container. Corresponds to the JSON property useChiefInTfConfig

Returns:

  • (Boolean) 


2775
2776
2777
 
# File 'lib/google/apis/ml_v1/classes.rb', line 2775

def use_chief_in_tf_config
  @use_chief_in_tf_config
end

#worker_config ⇒ Google::Apis::MlV1::GoogleCloudMlV1ReplicaConfig

Represents the configuration for a replica in a cluster. Corresponds to the JSON property workerConfig

Returns:



2781
2782
2783
 
# File 'lib/google/apis/ml_v1/classes.rb', line 2781

def worker_config
  @worker_config
end

#worker_count ⇒ Fixnum

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. Corresponds to the JSON property workerCount

Returns:

  • (Fixnum) 


2789
2790
2791
 
# File 'lib/google/apis/ml_v1/classes.rb', line 2789

def worker_count
  @worker_count
end

#worker_type ⇒ String

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. Corresponds to the JSON property workerType

Returns:

  • (String) 


2802
2803
2804
 
# File 'lib/google/apis/ml_v1/classes.rb', line 2802

def worker_type
  @worker_type
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



2809
2810
2811
2812
2813
2814
2815
2816
2817
2818
2819
2820
2821
2822
2823
2824
2825
2826
2827
2828
2829
2830
2831
2832
2833
2834
2835
 
# File 'lib/google/apis/ml_v1/classes.rb', line 2809

def update!(**args)
  @args = args[:args] if args.key?(:args)
  @encryption_config = args[:encryption_config] if args.key?(:encryption_config)
  @evaluator_config = args[:evaluator_config] if args.key?(:evaluator_config)
  @evaluator_count = args[:evaluator_count] if args.key?(:evaluator_count)
  @evaluator_type = args[:evaluator_type] if args.key?(:evaluator_type)
  @hyperparameters = args[:hyperparameters] if args.key?(:hyperparameters)
  @job_dir = args[:job_dir] if args.key?(:job_dir)
  @master_config = args[:master_config] if args.key?(:master_config)
  @master_type = args[:master_type] if args.key?(:master_type)
  @network = args[:network] if args.key?(:network)
  @package_uris = args[:package_uris] if args.key?(:package_uris)
  @parameter_server_config = args[:parameter_server_config] if args.key?(:parameter_server_config)
  @parameter_server_count = args[:parameter_server_count] if args.key?(:parameter_server_count)
  @parameter_server_type = args[:parameter_server_type] if args.key?(:parameter_server_type)
  @python_module = args[:python_module] if args.key?(:python_module)
  @python_version = args[:python_version] if args.key?(:python_version)
  @region = args[:region] if args.key?(:region)
  @runtime_version = args[:runtime_version] if args.key?(:runtime_version)
  @scale_tier = args[:scale_tier] if args.key?(:scale_tier)
  @scheduling = args[:scheduling] if args.key?(:scheduling)
  @service_account = args[:service_account] if args.key?(:service_account)
  @use_chief_in_tf_config = args[:use_chief_in_tf_config] if args.key?(:use_chief_in_tf_config)
  @worker_config = args[:worker_config] if args.key?(:worker_config)
  @worker_count = args[:worker_count] if args.key?(:worker_count)
  @worker_type = args[:worker_type] if args.key?(:worker_type)
end