Class: Google::Apis::MlV1beta1::GoogleCloudMlV1beta1TrainingInput

Inherits:
Object
  • Object
show all
Includes:
Core::Hashable, Core::JsonObjectSupport
Defined in:
generated/google/apis/ml_v1beta1/classes.rb,
generated/google/apis/ml_v1beta1/representations.rb,
generated/google/apis/ml_v1beta1/representations.rb

Overview

Represents input parameters for a training job.

Instance Attribute Summary collapse

Instance Method Summary collapse

Methods included from Core::JsonObjectSupport

#to_json

Methods included from Core::Hashable

process_value, #to_h

Constructor Details

#initialize(**args) ⇒ GoogleCloudMlV1beta1TrainingInput

Returns a new instance of GoogleCloudMlV1beta1TrainingInput



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# File 'generated/google/apis/ml_v1beta1/classes.rb', line 1370

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

Instance Attribute Details

#args ⇒ Array<String>

Optional. Command line arguments to pass to the program. Corresponds to the JSON property args

Returns:

  • (Array<String>) 


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# File 'generated/google/apis/ml_v1beta1/classes.rb', line 1239

def args
  @args
end

#hyperparameters ⇒ Google::Apis::MlV1beta1::GoogleCloudMlV1beta1HyperparameterSpec

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

Returns:



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# File 'generated/google/apis/ml_v1beta1/classes.rb', line 1244

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) 


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# File 'generated/google/apis/ml_v1beta1/classes.rb', line 1252

def job_dir
  @job_dir
end

#master_type ⇒ String

Optional. Specifies the type of virtual machine to use for your training job's master worker. The following types are supported:

standard
A basic machine configuration suitable for training simple models with small to moderate datasets.
large_model
A machine with a lot of memory, specially suited for parameter servers when your model is large (having many hidden layers or layers with very large numbers of nodes).
complex_model_s
A machine suitable for the master and workers of the cluster when your model requires more computation than the standard machine can handle satisfactorily.
complex_model_m
A machine with roughly twice the number of cores and roughly double the memory of complex_model_s.
complex_model_l
A machine with roughly twice the number of cores and roughly double the memory of complex_model_m.
standard_gpu
A machine equivalent to standard that also includes a GPU that you can use in your trainer.
complex_model_m_gpu
A machine equivalent to complex_model_m that also includes four GPUs.

You must set this value when scaleTier is set to CUSTOM. Corresponds to the JSON property masterType

Returns:

  • (String) 


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# File 'generated/google/apis/ml_v1beta1/classes.rb', line 1302

def master_type
  @master_type
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>) 


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# File 'generated/google/apis/ml_v1beta1/classes.rb', line 1309

def package_uris
  @package_uris
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. Corresponds to the JSON property parameterServerCount

Returns:

  • (Fixnum) 


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# File 'generated/google/apis/ml_v1beta1/classes.rb', line 1318

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 present when scaleTier is set to CUSTOM and parameter_server_count is greater than zero. Corresponds to the JSON property parameterServerType

Returns:

  • (String) 


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# File 'generated/google/apis/ml_v1beta1/classes.rb', line 1328

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) 


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# File 'generated/google/apis/ml_v1beta1/classes.rb', line 1333

def python_module
  @python_module
end

#region ⇒ String

Required. The Google Compute Engine region to run the training job in. Corresponds to the JSON property region

Returns:

  • (String) 


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# File 'generated/google/apis/ml_v1beta1/classes.rb', line 1338

def region
  @region
end

#runtime_version ⇒ String

Optional. The Google Cloud ML runtime version to use for training. If not set, Google Cloud ML will choose the latest stable version. Corresponds to the JSON property runtimeVersion

Returns:

  • (String) 


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# File 'generated/google/apis/ml_v1beta1/classes.rb', line 1344

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) 


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# File 'generated/google/apis/ml_v1beta1/classes.rb', line 1350

def scale_tier
  @scale_tier
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. Corresponds to the JSON property workerCount

Returns:

  • (Fixnum) 


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# File 'generated/google/apis/ml_v1beta1/classes.rb', line 1358

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 present when scaleTier is set to CUSTOM and workerCount is greater than zero. Corresponds to the JSON property workerType

Returns:

  • (String) 


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# File 'generated/google/apis/ml_v1beta1/classes.rb', line 1368

def worker_type
  @worker_type
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



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# File 'generated/google/apis/ml_v1beta1/classes.rb', line 1375

def update!(**args)
  @args = args[:args] if args.key?(:args)
  @hyperparameters = args[:hyperparameters] if args.key?(:hyperparameters)
  @job_dir = args[:job_dir] if args.key?(:job_dir)
  @master_type = args[:master_type] if args.key?(:master_type)
  @package_uris = args[:package_uris] if args.key?(:package_uris)
  @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)
  @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)
  @worker_count = args[:worker_count] if args.key?(:worker_count)
  @worker_type = args[:worker_type] if args.key?(:worker_type)
end