Instance Methods
cancel(name, body, x__xgafv=None)
Cancels a running job.
create(parent, body, x__xgafv=None)
Creates a training or a batch prediction job.
get(name, x__xgafv=None)
Describes a job.
list(parent, pageSize=None, filter=None, pageToken=None, x__xgafv=None)
Lists the jobs in the project.
list_next(previous_request, previous_response)
Retrieves the next page of results.
Method Details
cancel(name, body, x__xgafv=None)
Cancels a running job.
Args:
name: string, Required. The name of the job to cancel.
Authorization: requires `Editor` role on the parent project. (required)
body: object, The request body. (required)
The object takes the form of:
{ # Request message for the CancelJob method.
}
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # A generic empty message that you can re-use to avoid defining duplicated
# empty messages in your APIs. A typical example is to use it as the request
# or the response type of an API method. For instance:
#
# service Foo {
# rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty);
# }
#
# The JSON representation for `Empty` is empty JSON object `{}`.
}
create(parent, body, x__xgafv=None)
Creates a training or a batch prediction job.
Args:
parent: string, Required. The project name.
Authorization: requires `Editor` role on the specified project. (required)
body: object, The request body. (required)
The object takes the form of:
{ # Represents a training or prediction job.
"trainingOutput": { # Represents results of a training job. Output only. # The current training job result.
"completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
# Only set for hyperparameter tuning jobs.
"isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.
"trials": [ # Results for individual Hyperparameter trials.
# Only set for hyperparameter tuning jobs.
{ # Represents the result of a single hyperparameter tuning trial from a
# training job. The TrainingOutput object that is returned on successful
# completion of a training job with hyperparameter tuning includes a list
# of HyperparameterOutput objects, one for each successful trial.
"hyperparameters": { # The hyperparameters given to this trial.
"a_key": "A String",
},
"trialId": "A String", # The trial id for these results.
"allMetrics": [ # All recorded object metrics for this trial.
{ # An observed value of a metric.
"trainingStep": "A String", # The global training step for this metric.
"objectiveValue": 3.14, # The objective value at this training step.
},
],
"finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.
"trainingStep": "A String", # The global training step for this metric.
"objectiveValue": 3.14, # The objective value at this training step.
},
},
],
"consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
},
"trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job.
"workerType": "A 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.
"runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for training. If not
# set, Google Cloud ML will choose the latest stable version.
"scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
# and parameter servers.
"masterType": "A 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`.
"hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune.
"maxTrials": 42, # Optional. How many training trials should be attempted to optimize
# the specified hyperparameters.
#
# Defaults to one.
"hyperparameterMetricTag": "A String", # Optional. The Tensorflow summary tag name to use for optimizing trials. For
# current versions of Tensorflow, this tag name should exactly match what is
# shown in Tensorboard, including all scopes. For versions of Tensorflow
# prior to 0.12, this should be only the tag passed to tf.Summary.
# By default, "training/hptuning/metric" will be used.
"params": [ # Required. The set of parameters to tune.
{ # Represents a single hyperparameter to optimize.
"maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field
# should be unset if type is `CATEGORICAL`. This value should be integers if
# type is `INTEGER`.
"categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
"A String",
],
"discreteValues": [ # Required if type is `DISCRETE`.
# A list of feasible points.
# The list should be in strictly increasing order. For instance, this
# parameter might have possible settings of 1.5, 2.5, and 4.0. This list
# should not contain more than 1,000 values.
3.14,
],
"parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in
# a HyperparameterSpec message. E.g., "learning_rate".
"minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field
# should be unset if type is `CATEGORICAL`. This value should be integers if
# type is INTEGER.
"type": "A String", # Required. The type of the parameter.
"scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube.
# Leave unset for categorical parameters.
# Some kind of scaling is strongly recommended for real or integral
# parameters (e.g., `UNIT_LINEAR_SCALE`).
},
],
"maxParallelTrials": 42, # Optional. The number of training trials to run concurrently.
# You can reduce the time it takes to perform hyperparameter tuning by adding
# trials in parallel. However, each trail only benefits from the information
# gained in completed trials. That means that a trial does not get access to
# the results of trials running at the same time, which could reduce the
# quality of the overall optimization.
#
# Each trial will use the same scale tier and machine types.
#
# Defaults to one.
"goal": "A String", # Required. The type of goal to use for tuning. Available types are
# `MAXIMIZE` and `MINIMIZE`.
#
# Defaults to `MAXIMIZE`.
},
"region": "A String", # Required. The Google Compute Engine region to run the training job in.
"args": [ # Optional. Command line arguments to pass to the program.
"A String",
],
"pythonModule": "A String", # Required. The Python module name to run after installing the packages.
"jobDir": "A 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.
"packageUris": [ # 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.
"A String",
],
"workerCount": "A String", # 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`.
"parameterServerType": "A 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.
"parameterServerCount": "A String", # 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`.
},
"startTime": "A String", # Output only. When the job processing was started.
"errorMessage": "A String", # Output only. The details of a failure or a cancellation.
"jobId": "A String", # Required. The user-specified id of the job.
"state": "A String", # Output only. The detailed state of a job.
"predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
"modelName": "A String", # Use this field if you want to use the default version for the specified
# model. The string must use the following format:
#
# `"projects/
[YOUR_PROJECT]/models/
[YOUR_MODEL]"`
"runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this batch
# prediction. If not set, Google Cloud ML will pick the runtime version used
# during the CreateVersion request for this model version, or choose the
# latest stable version when model version information is not available
# such as when the model is specified by uri.
"inputPaths": [ # Required. The Google Cloud Storage location of the input data files.
# May contain wildcards.
"A String",
],
"maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing.
# Defaults to 10 if not specified.
"uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for
# the model to use.
"outputPath": "A String", # Required. The output Google Cloud Storage location.
"dataFormat": "A String", # Required. The format of the input data files.
"versionName": "A String", # Use this field if you want to specify a version of the model to use. The
# string is formatted the same way as `model_version`, with the addition
# of the version information:
#
# `"projects/
[YOUR_PROJECT]/models/
YOUR_MODEL/versions/[YOUR_VERSION]"`
"region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
},
"endTime": "A String", # Output only. When the job processing was completed.
"predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
"outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
"nodeHours": 3.14, # Node hours used by the batch prediction job.
"predictionCount": "A String", # The number of generated predictions.
"errorCount": "A String", # The number of data instances which resulted in errors.
},
"createTime": "A String", # Output only. When the job was created.
}
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # Represents a training or prediction job.
"trainingOutput": { # Represents results of a training job. Output only. # The current training job result.
"completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
# Only set for hyperparameter tuning jobs.
"isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.
"trials": [ # Results for individual Hyperparameter trials.
# Only set for hyperparameter tuning jobs.
{ # Represents the result of a single hyperparameter tuning trial from a
# training job. The TrainingOutput object that is returned on successful
# completion of a training job with hyperparameter tuning includes a list
# of HyperparameterOutput objects, one for each successful trial.
"hyperparameters": { # The hyperparameters given to this trial.
"a_key": "A String",
},
"trialId": "A String", # The trial id for these results.
"allMetrics": [ # All recorded object metrics for this trial.
{ # An observed value of a metric.
"trainingStep": "A String", # The global training step for this metric.
"objectiveValue": 3.14, # The objective value at this training step.
},
],
"finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.
"trainingStep": "A String", # The global training step for this metric.
"objectiveValue": 3.14, # The objective value at this training step.
},
},
],
"consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
},
"trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job.
"workerType": "A 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.
"runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for training. If not
# set, Google Cloud ML will choose the latest stable version.
"scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
# and parameter servers.
"masterType": "A 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`.
"hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune.
"maxTrials": 42, # Optional. How many training trials should be attempted to optimize
# the specified hyperparameters.
#
# Defaults to one.
"hyperparameterMetricTag": "A String", # Optional. The Tensorflow summary tag name to use for optimizing trials. For
# current versions of Tensorflow, this tag name should exactly match what is
# shown in Tensorboard, including all scopes. For versions of Tensorflow
# prior to 0.12, this should be only the tag passed to tf.Summary.
# By default, "training/hptuning/metric" will be used.
"params": [ # Required. The set of parameters to tune.
{ # Represents a single hyperparameter to optimize.
"maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field
# should be unset if type is `CATEGORICAL`. This value should be integers if
# type is `INTEGER`.
"categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
"A String",
],
"discreteValues": [ # Required if type is `DISCRETE`.
# A list of feasible points.
# The list should be in strictly increasing order. For instance, this
# parameter might have possible settings of 1.5, 2.5, and 4.0. This list
# should not contain more than 1,000 values.
3.14,
],
"parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in
# a HyperparameterSpec message. E.g., "learning_rate".
"minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field
# should be unset if type is `CATEGORICAL`. This value should be integers if
# type is INTEGER.
"type": "A String", # Required. The type of the parameter.
"scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube.
# Leave unset for categorical parameters.
# Some kind of scaling is strongly recommended for real or integral
# parameters (e.g., `UNIT_LINEAR_SCALE`).
},
],
"maxParallelTrials": 42, # Optional. The number of training trials to run concurrently.
# You can reduce the time it takes to perform hyperparameter tuning by adding
# trials in parallel. However, each trail only benefits from the information
# gained in completed trials. That means that a trial does not get access to
# the results of trials running at the same time, which could reduce the
# quality of the overall optimization.
#
# Each trial will use the same scale tier and machine types.
#
# Defaults to one.
"goal": "A String", # Required. The type of goal to use for tuning. Available types are
# `MAXIMIZE` and `MINIMIZE`.
#
# Defaults to `MAXIMIZE`.
},
"region": "A String", # Required. The Google Compute Engine region to run the training job in.
"args": [ # Optional. Command line arguments to pass to the program.
"A String",
],
"pythonModule": "A String", # Required. The Python module name to run after installing the packages.
"jobDir": "A 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.
"packageUris": [ # 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.
"A String",
],
"workerCount": "A String", # 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`.
"parameterServerType": "A 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.
"parameterServerCount": "A String", # 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`.
},
"startTime": "A String", # Output only. When the job processing was started.
"errorMessage": "A String", # Output only. The details of a failure or a cancellation.
"jobId": "A String", # Required. The user-specified id of the job.
"state": "A String", # Output only. The detailed state of a job.
"predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
"modelName": "A String", # Use this field if you want to use the default version for the specified
# model. The string must use the following format:
#
# `"projects/[YOUR_PROJECT]/models/[YOUR_MODEL]"`
"runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this batch
# prediction. If not set, Google Cloud ML will pick the runtime version used
# during the CreateVersion request for this model version, or choose the
# latest stable version when model version information is not available
# such as when the model is specified by uri.
"inputPaths": [ # Required. The Google Cloud Storage location of the input data files.
# May contain wildcards.
"A String",
],
"maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing.
# Defaults to 10 if not specified.
"uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for
# the model to use.
"outputPath": "A String", # Required. The output Google Cloud Storage location.
"dataFormat": "A String", # Required. The format of the input data files.
"versionName": "A String", # Use this field if you want to specify a version of the model to use. The
# string is formatted the same way as `model_version`, with the addition
# of the version information:
#
# `"projects/[YOUR_PROJECT]/models/YOUR_MODEL/versions/[YOUR_VERSION]"`
"region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
},
"endTime": "A String", # Output only. When the job processing was completed.
"predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
"outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
"nodeHours": 3.14, # Node hours used by the batch prediction job.
"predictionCount": "A String", # The number of generated predictions.
"errorCount": "A String", # The number of data instances which resulted in errors.
},
"createTime": "A String", # Output only. When the job was created.
}
get(name, x__xgafv=None)
Describes a job.
Args:
name: string, Required. The name of the job to get the description of.
Authorization: requires `Viewer` role on the parent project. (required)
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # Represents a training or prediction job.
"trainingOutput": { # Represents results of a training job. Output only. # The current training job result.
"completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
# Only set for hyperparameter tuning jobs.
"isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.
"trials": [ # Results for individual Hyperparameter trials.
# Only set for hyperparameter tuning jobs.
{ # Represents the result of a single hyperparameter tuning trial from a
# training job. The TrainingOutput object that is returned on successful
# completion of a training job with hyperparameter tuning includes a list
# of HyperparameterOutput objects, one for each successful trial.
"hyperparameters": { # The hyperparameters given to this trial.
"a_key": "A String",
},
"trialId": "A String", # The trial id for these results.
"allMetrics": [ # All recorded object metrics for this trial.
{ # An observed value of a metric.
"trainingStep": "A String", # The global training step for this metric.
"objectiveValue": 3.14, # The objective value at this training step.
},
],
"finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.
"trainingStep": "A String", # The global training step for this metric.
"objectiveValue": 3.14, # The objective value at this training step.
},
},
],
"consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
},
"trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job.
"workerType": "A 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.
"runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for training. If not
# set, Google Cloud ML will choose the latest stable version.
"scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
# and parameter servers.
"masterType": "A 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`.
"hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune.
"maxTrials": 42, # Optional. How many training trials should be attempted to optimize
# the specified hyperparameters.
#
# Defaults to one.
"hyperparameterMetricTag": "A String", # Optional. The Tensorflow summary tag name to use for optimizing trials. For
# current versions of Tensorflow, this tag name should exactly match what is
# shown in Tensorboard, including all scopes. For versions of Tensorflow
# prior to 0.12, this should be only the tag passed to tf.Summary.
# By default, "training/hptuning/metric" will be used.
"params": [ # Required. The set of parameters to tune.
{ # Represents a single hyperparameter to optimize.
"maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field
# should be unset if type is `CATEGORICAL`. This value should be integers if
# type is `INTEGER`.
"categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
"A String",
],
"discreteValues": [ # Required if type is `DISCRETE`.
# A list of feasible points.
# The list should be in strictly increasing order. For instance, this
# parameter might have possible settings of 1.5, 2.5, and 4.0. This list
# should not contain more than 1,000 values.
3.14,
],
"parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in
# a HyperparameterSpec message. E.g., "learning_rate".
"minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field
# should be unset if type is `CATEGORICAL`. This value should be integers if
# type is INTEGER.
"type": "A String", # Required. The type of the parameter.
"scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube.
# Leave unset for categorical parameters.
# Some kind of scaling is strongly recommended for real or integral
# parameters (e.g., `UNIT_LINEAR_SCALE`).
},
],
"maxParallelTrials": 42, # Optional. The number of training trials to run concurrently.
# You can reduce the time it takes to perform hyperparameter tuning by adding
# trials in parallel. However, each trail only benefits from the information
# gained in completed trials. That means that a trial does not get access to
# the results of trials running at the same time, which could reduce the
# quality of the overall optimization.
#
# Each trial will use the same scale tier and machine types.
#
# Defaults to one.
"goal": "A String", # Required. The type of goal to use for tuning. Available types are
# `MAXIMIZE` and `MINIMIZE`.
#
# Defaults to `MAXIMIZE`.
},
"region": "A String", # Required. The Google Compute Engine region to run the training job in.
"args": [ # Optional. Command line arguments to pass to the program.
"A String",
],
"pythonModule": "A String", # Required. The Python module name to run after installing the packages.
"jobDir": "A 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.
"packageUris": [ # 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.
"A String",
],
"workerCount": "A String", # 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`.
"parameterServerType": "A 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.
"parameterServerCount": "A String", # 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`.
},
"startTime": "A String", # Output only. When the job processing was started.
"errorMessage": "A String", # Output only. The details of a failure or a cancellation.
"jobId": "A String", # Required. The user-specified id of the job.
"state": "A String", # Output only. The detailed state of a job.
"predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
"modelName": "A String", # Use this field if you want to use the default version for the specified
# model. The string must use the following format:
#
# `"projects/
[YOUR_PROJECT]/models/
[YOUR_MODEL]"`
"runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this batch
# prediction. If not set, Google Cloud ML will pick the runtime version used
# during the CreateVersion request for this model version, or choose the
# latest stable version when model version information is not available
# such as when the model is specified by uri.
"inputPaths": [ # Required. The Google Cloud Storage location of the input data files.
# May contain wildcards.
"A String",
],
"maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing.
# Defaults to 10 if not specified.
"uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for
# the model to use.
"outputPath": "A String", # Required. The output Google Cloud Storage location.
"dataFormat": "A String", # Required. The format of the input data files.
"versionName": "A String", # Use this field if you want to specify a version of the model to use. The
# string is formatted the same way as `model_version`, with the addition
# of the version information:
#
# `"projects/
[YOUR_PROJECT]/models/
YOUR_MODEL/versions/[YOUR_VERSION]"`
"region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
},
"endTime": "A String", # Output only. When the job processing was completed.
"predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
"outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
"nodeHours": 3.14, # Node hours used by the batch prediction job.
"predictionCount": "A String", # The number of generated predictions.
"errorCount": "A String", # The number of data instances which resulted in errors.
},
"createTime": "A String", # Output only. When the job was created.
}
list(parent, pageSize=None, filter=None, pageToken=None, x__xgafv=None)
Lists the jobs in the project.
Args:
parent: string, Required. The name of the project for which to list jobs.
Authorization: requires `Viewer` role on the specified project. (required)
pageSize: integer, Optional. The number of jobs to retrieve per "page" of results. If there
are more remaining results than this number, the response message will
contain a valid value in the `next_page_token` field.
The default value is 20, and the maximum page size is 100.
filter: string, Optional. Specifies the subset of jobs to retrieve.
pageToken: string, Optional. A page token to request the next page of results.
You get the token from the `next_page_token` field of the response from
the previous call.
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # Response message for the ListJobs method.
"nextPageToken": "A String", # Optional. Pass this token as the `page_token` field of the request for a
# subsequent call.
"jobs": [ # The list of jobs.
{ # Represents a training or prediction job.
"trainingOutput": { # Represents results of a training job. Output only. # The current training job result.
"completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
# Only set for hyperparameter tuning jobs.
"isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.
"trials": [ # Results for individual Hyperparameter trials.
# Only set for hyperparameter tuning jobs.
{ # Represents the result of a single hyperparameter tuning trial from a
# training job. The TrainingOutput object that is returned on successful
# completion of a training job with hyperparameter tuning includes a list
# of HyperparameterOutput objects, one for each successful trial.
"hyperparameters": { # The hyperparameters given to this trial.
"a_key": "A String",
},
"trialId": "A String", # The trial id for these results.
"allMetrics": [ # All recorded object metrics for this trial.
{ # An observed value of a metric.
"trainingStep": "A String", # The global training step for this metric.
"objectiveValue": 3.14, # The objective value at this training step.
},
],
"finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.
"trainingStep": "A String", # The global training step for this metric.
"objectiveValue": 3.14, # The objective value at this training step.
},
},
],
"consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
},
"trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job.
"workerType": "A 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.
"runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for training. If not
# set, Google Cloud ML will choose the latest stable version.
"scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
# and parameter servers.
"masterType": "A 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`.
"hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune.
"maxTrials": 42, # Optional. How many training trials should be attempted to optimize
# the specified hyperparameters.
#
# Defaults to one.
"hyperparameterMetricTag": "A String", # Optional. The Tensorflow summary tag name to use for optimizing trials. For
# current versions of Tensorflow, this tag name should exactly match what is
# shown in Tensorboard, including all scopes. For versions of Tensorflow
# prior to 0.12, this should be only the tag passed to tf.Summary.
# By default, "training/hptuning/metric" will be used.
"params": [ # Required. The set of parameters to tune.
{ # Represents a single hyperparameter to optimize.
"maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field
# should be unset if type is `CATEGORICAL`. This value should be integers if
# type is `INTEGER`.
"categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
"A String",
],
"discreteValues": [ # Required if type is `DISCRETE`.
# A list of feasible points.
# The list should be in strictly increasing order. For instance, this
# parameter might have possible settings of 1.5, 2.5, and 4.0. This list
# should not contain more than 1,000 values.
3.14,
],
"parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in
# a HyperparameterSpec message. E.g., "learning_rate".
"minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field
# should be unset if type is `CATEGORICAL`. This value should be integers if
# type is INTEGER.
"type": "A String", # Required. The type of the parameter.
"scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube.
# Leave unset for categorical parameters.
# Some kind of scaling is strongly recommended for real or integral
# parameters (e.g., `UNIT_LINEAR_SCALE`).
},
],
"maxParallelTrials": 42, # Optional. The number of training trials to run concurrently.
# You can reduce the time it takes to perform hyperparameter tuning by adding
# trials in parallel. However, each trail only benefits from the information
# gained in completed trials. That means that a trial does not get access to
# the results of trials running at the same time, which could reduce the
# quality of the overall optimization.
#
# Each trial will use the same scale tier and machine types.
#
# Defaults to one.
"goal": "A String", # Required. The type of goal to use for tuning. Available types are
# `MAXIMIZE` and `MINIMIZE`.
#
# Defaults to `MAXIMIZE`.
},
"region": "A String", # Required. The Google Compute Engine region to run the training job in.
"args": [ # Optional. Command line arguments to pass to the program.
"A String",
],
"pythonModule": "A String", # Required. The Python module name to run after installing the packages.
"jobDir": "A 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.
"packageUris": [ # 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.
"A String",
],
"workerCount": "A String", # 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`.
"parameterServerType": "A 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.
"parameterServerCount": "A String", # 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`.
},
"startTime": "A String", # Output only. When the job processing was started.
"errorMessage": "A String", # Output only. The details of a failure or a cancellation.
"jobId": "A String", # Required. The user-specified id of the job.
"state": "A String", # Output only. The detailed state of a job.
"predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
"modelName": "A String", # Use this field if you want to use the default version for the specified
# model. The string must use the following format:
#
# `"projects/
[YOUR_PROJECT]/models/
[YOUR_MODEL]"`
"runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this batch
# prediction. If not set, Google Cloud ML will pick the runtime version used
# during the CreateVersion request for this model version, or choose the
# latest stable version when model version information is not available
# such as when the model is specified by uri.
"inputPaths": [ # Required. The Google Cloud Storage location of the input data files.
# May contain wildcards.
"A String",
],
"maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing.
# Defaults to 10 if not specified.
"uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for
# the model to use.
"outputPath": "A String", # Required. The output Google Cloud Storage location.
"dataFormat": "A String", # Required. The format of the input data files.
"versionName": "A String", # Use this field if you want to specify a version of the model to use. The
# string is formatted the same way as `model_version`, with the addition
# of the version information:
#
# `"projects/
[YOUR_PROJECT]/models/
YOUR_MODEL/versions/[YOUR_VERSION]"`
"region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
},
"endTime": "A String", # Output only. When the job processing was completed.
"predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
"outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
"nodeHours": 3.14, # Node hours used by the batch prediction job.
"predictionCount": "A String", # The number of generated predictions.
"errorCount": "A String", # The number of data instances which resulted in errors.
},
"createTime": "A String", # Output only. When the job was created.
},
],
}
list_next(previous_request, previous_response)
Retrieves the next page of results.
Args:
previous_request: The request for the previous page. (required)
previous_response: The response from the request for the previous page. (required)
Returns:
A request object that you can call 'execute()' on to request the next
page. Returns None if there are no more items in the collection.