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

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

Methods included from Core::JsonObjectSupport

#to_json

Methods included from Core::Hashable

process_value, #to_h

Constructor Details

#initialize(**args) ⇒ GoogleCloudMlV1TrainingInput

Returns a new instance of GoogleCloudMlV1TrainingInput



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# File 'generated/google/apis/ml_v1/classes.rb', line 1513

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_v1/classes.rb', line 1280

def args
  @args
end

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

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

Returns:



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# File 'generated/google/apis/ml_v1/classes.rb', line 1285

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_v1/classes.rb', line 1293

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:



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# File 'generated/google/apis/ml_v1/classes.rb', line 1298

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. 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 single NVIDIA Tesla K80 GPU. See more about using GPUs to train your model.
complex_model_m_gpu
A machine equivalent to complex_model_m that also includes four NVIDIA Tesla K80 GPUs.
complex_model_l_gpu
A machine equivalent to complex_model_l that also includes eight NVIDIA Tesla K80 GPUs.
standard_p100
A machine equivalent to standard that also includes a single NVIDIA Tesla P100 GPU.
complex_model_m_p100
A machine equivalent to complex_model_m that also includes four NVIDIA Tesla P100 GPUs.
standard_v100
A machine equivalent to standard that also includes a single NVIDIA Tesla V100 GPU.
large_model_v100
A machine equivalent to large_model that also includes a single NVIDIA Tesla V100 GPU.
complex_model_m_v100
A machine equivalent to complex_model_m that also includes four NVIDIA Tesla V100 GPUs.
complex_model_l_v100
A machine equivalent to complex_model_l that also includes eight NVIDIA Tesla V100 GPUs.
cloud_tpu
A TPU VM including one Cloud TPU. See more about using TPUs to train your model.

You may also 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 See more about using Compute Engine machine types. 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_v1/classes.rb', line 1409

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_v1/classes.rb', line 1416

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:



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# File 'generated/google/apis/ml_v1/classes.rb', line 1421

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) 


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# File 'generated/google/apis/ml_v1/classes.rb', line 1431

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 AI Platform machine types or both must be Compute Engine 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) 


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# File 'generated/google/apis/ml_v1/classes.rb', line 1444

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_v1/classes.rb', line 1449

def python_module
  @python_module
end

#python_version ⇒ String

Optional. The version of Python used in training. If not set, the default version is '2.7'. Python '3.5' is available when runtime_version is set to '1.4' and above. Python '2.7' works with all supported runtime versions. Corresponds to the JSON property pythonVersion

Returns:

  • (String) 


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# File 'generated/google/apis/ml_v1/classes.rb', line 1457

def python_version
  @python_version
end

#region ⇒ String

Required. The Google Compute Engine region to run the training job in. See the available regions for AI Platform services. Corresponds to the JSON property region

Returns:

  • (String) 


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# File 'generated/google/apis/ml_v1/classes.rb', line 1464

def region
  @region
end

#runtime_version ⇒ String

Optional. The AI Platform runtime version to use for training. If not set, AI Platform uses the default stable version, 1.0. For more information, see the runtime version list and how to manage runtime versions. Corresponds to the JSON property runtimeVersion

Returns:

  • (String) 


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# File 'generated/google/apis/ml_v1/classes.rb', line 1474

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_v1/classes.rb', line 1480

def scale_tier
  @scale_tier
end

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

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

Returns:



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# File 'generated/google/apis/ml_v1/classes.rb', line 1485

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) 


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# File 'generated/google/apis/ml_v1/classes.rb', line 1494

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 AI Platform machine types or both must be Compute Engine 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) 


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# File 'generated/google/apis/ml_v1/classes.rb', line 1511

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_v1/classes.rb', line 1518

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_config = args[:master_config] if args.key?(:master_config)
  @master_type = args[:master_type] if args.key?(:master_type)
  @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)
  @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