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.

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



1218
1219
1220
 
# File 'generated/google/apis/ml_v1/classes.rb', line 1218

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>) 


1068
1069
1070
 
# File 'generated/google/apis/ml_v1/classes.rb', line 1068

def args
  @args
end

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

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

Returns:



1073
1074
1075
 
# File 'generated/google/apis/ml_v1/classes.rb', line 1073

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) 


1081
1082
1083
 
# File 'generated/google/apis/ml_v1/classes.rb', line 1081

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 single NVIDIA Tesla K80 GPU. See more about using GPUs for training 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. The availability of these GPUs is in the Alpha launch stage.
complex_model_m_p100
A machine equivalent to complex_model_m that also includes four NVIDIA Tesla P100 GPUs. The availability of these GPUs is in the Alpha launch stage.

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

Returns:

  • (String) 


1150
1151
1152
 
# File 'generated/google/apis/ml_v1/classes.rb', line 1150

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>) 


1157
1158
1159
 
# File 'generated/google/apis/ml_v1/classes.rb', line 1157

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) 


1166
1167
1168
 
# File 'generated/google/apis/ml_v1/classes.rb', line 1166

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) 


1176
1177
1178
 
# File 'generated/google/apis/ml_v1/classes.rb', line 1176

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) 


1181
1182
1183
 
# File 'generated/google/apis/ml_v1/classes.rb', line 1181

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) 


1186
1187
1188
 
# File 'generated/google/apis/ml_v1/classes.rb', line 1186

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) 


1192
1193
1194
 
# File 'generated/google/apis/ml_v1/classes.rb', line 1192

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) 


1198
1199
1200
 
# File 'generated/google/apis/ml_v1/classes.rb', line 1198

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) 


1206
1207
1208
 
# File 'generated/google/apis/ml_v1/classes.rb', line 1206

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) 


1216
1217
1218
 
# File 'generated/google/apis/ml_v1/classes.rb', line 1216

def worker_type
  @worker_type
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
 
# File 'generated/google/apis/ml_v1/classes.rb', line 1223

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