class RepeatedMessage(messages.Message):
"""Contains all message types as repeated fields."""
class SimpleEnum(messages.Enum):
"""Simple enumeration type."""
VAL1 = 1
VAL2 = 2
double_value = messages.FloatField(1,
variant=messages.Variant.DOUBLE,
repeated=True)
float_value = messages.FloatField(2,
variant=messages.Variant.FLOAT,
repeated=True)
int64_value = messages.IntegerField(3,
variant=messages.Variant.INT64,
repeated=True)
uint64_value = messages.IntegerField(4,
variant=messages.Variant.UINT64,
repeated=True)
int32_value = messages.IntegerField(5,
variant=messages.Variant.INT32,
repeated=True)
bool_value = messages.BooleanField(6,
variant=messages.Variant.BOOL,
repeated=True)
string_value = messages.StringField(7,
variant=messages.Variant.STRING,
repeated=True)
bytes_value = messages.BytesField(8,
variant=messages.Variant.BYTES,
repeated=True)
enum_value = messages.EnumField(SimpleEnum, 10, repeated=True)
class OptionalMessage(messages.Message):
"""Contains all message types."""
class SimpleEnum(messages.Enum):
"""Simple enumeration type."""
VAL1 = 1
VAL2 = 2
double_value = messages.FloatField(1, variant=messages.Variant.DOUBLE)
float_value = messages.FloatField(2, variant=messages.Variant.FLOAT)
int64_value = messages.IntegerField(3, variant=messages.Variant.INT64)
uint64_value = messages.IntegerField(4, variant=messages.Variant.UINT64)
int32_value = messages.IntegerField(5, variant=messages.Variant.INT32)
bool_value = messages.BooleanField(6, variant=messages.Variant.BOOL)
string_value = messages.StringField(7, variant=messages.Variant.STRING)
bytes_value = messages.BytesField(8, variant=messages.Variant.BYTES)
enum_value = messages.EnumField(SimpleEnum, 10)
class RegressionAnalysis(_messages.Message):
"""Represents the analysis of a trained regression model.
Fields:
error: The root mean squared error of a regression model.
"""
error = _messages.FloatField(1)
class ClassificationAnalysis(_messages.Message):
"""Represents the analysis of a trained classification model.
Fields:
error: The error rate of a classification model.
"""
error = _messages.FloatField(1)
class RegressionPrediction(_messages.Message):
"""Represents the regression output or prediction generated from a a
regression model.
Fields:
value: The regression value produced by the model.
"""
value = _messages.FloatField(1)
class AutoscalingPolicyCpuUtilization(_messages.Message):
"""CPU utilization policy.
Fields:
utilizationTarget: The target utilization that the Autoscaler should
maintain. It is represented as a fraction of used cores. For example: 6
cores used in 8-core VM are represented here as 0.75. Must be a float
value between (0, 1]. If not defined, the default is 0.8.
"""
utilizationTarget = _messages.FloatField(1)
class ClassificationPredictionLabel(_messages.Message):
"""Represents a label produced by the model, along with its associated
confidence score.
Fields:
name: The name of the label.
score: The associated confidence score.
"""
name = _messages.StringField(1)
score = _messages.FloatField(2)
class AutoscalingPolicyLoadBalancingUtilization(_messages.Message):
"""Load balancing utilization policy.
Fields:
utilizationTarget: Fraction of backend capacity utilization (set in HTTP
load balancing configuration) that Autoscaler should maintain. Must be a
positive float value. If not defined, the default is 0.8. For example if
your maxRatePerInstance capacity (in HTTP Load Balancing configuration)
is set at 10 and you would like to keep number of instances such that
each instance receives 7 QPS on average, set this to 0.7.
"""
utilizationTarget = _messages.FloatField(1)
class JsonValue(messages.Message):
"""Any valid JSON value."""
# Is this JSON object `null`?
is_null = messages.BooleanField(1, default=False)
# Exactly one of the following is provided if is_null is False; none
# should be provided if is_null is True.
boolean_value = messages.BooleanField(2)
string_value = messages.StringField(3)
# We keep two numeric fields to keep int64 round-trips exact.
double_value = messages.FloatField(4, variant=messages.Variant.DOUBLE)
integer_value = messages.IntegerField(5, variant=messages.Variant.INT64)
# Compound types
object_value = messages.MessageField('JsonObject', 6)
array_value = messages.MessageField('JsonArray', 7)
class AutoscalingPolicyCustomMetricUtilization(_messages.Message):
"""Custom utilization metric policy.
Fields:
metric: Identifier of the metric. It should be a Cloud Monitoring metric.
The metric can not have negative values. The metric should be an
utilization metric (increasing number of VMs handling requests x times
should reduce average value of the metric roughly x times). For example
you could use:
compute.googleapis.com/instance/network/received_bytes_count.
utilizationTarget: Target value of the metric which Autoscaler should
maintain. Must be a positive value.
utilizationTargetType: Defines type in which utilization_target is
expressed.
"""
metric = _messages.StringField(1)
utilizationTarget = _messages.FloatField(2)
utilizationTargetType = _messages.StringField(3)
class PipelineResources(_messages.Message):
"""The system resources for the pipeline run.
Fields:
bootDiskSizeGb: The size of the boot disk. Defaults to 10 (GB).
disks: Disks to attach.
minimumCpuCores: The minimum number of cores to use. Defaults to 1.
minimumRamGb: The minimum amount of RAM to use. Defaults to 3.75 (GB)
preemptible: At create time means that preemptible machines may be used
for the run. At run time, means they should be used. Cannot be true at
run time if false at create time. Defaults to `false`.
zones: List of Google Compute Engine availability zones to which resource
creation will restricted. If empty, any zone may be chosen.
"""
bootDiskSizeGb = _messages.IntegerField(1, variant=_messages.Variant.INT32)
disks = _messages.MessageField('Disk', 2, repeated=True)
minimumCpuCores = _messages.IntegerField(3, variant=_messages.Variant.INT32)
minimumRamGb = _messages.FloatField(4)
preemptible = _messages.BooleanField(5)
zones = _messages.StringField(6, repeated=True)
class AutoscalingModule(_messages.Message):
"""A AutoscalingModule object.
Fields:
coolDownPeriodSec: A integer attribute.
description: A string attribute.
maxNumReplicas: A integer attribute.
minNumReplicas: A integer attribute.
signalType: A string attribute.
targetModule: A string attribute.
targetUtilization: target_utilization should be in range [0,1].
"""
coolDownPeriodSec = _messages.IntegerField(1,
variant=_messages.Variant.INT32)
description = _messages.StringField(2)
maxNumReplicas = _messages.IntegerField(3, variant=_messages.Variant.INT32)
minNumReplicas = _messages.IntegerField(4, variant=_messages.Variant.INT32)
signalType = _messages.StringField(5)
targetModule = _messages.StringField(6)
targetUtilization = _messages.FloatField(7)
class PipelineResources(_messages.Message):
"""The system resources for the pipeline run.
Fields:
disks: Disks to attach.
minimumCpuCores: Required at create time; optional at run time. The
minimum number of cores to use.
minimumRamGb: Required at create time; optional at run time. The minimum
amount of RAM to use.
preemptible: Optional. At create time means that preemptible machines may
be used for the run. At run time, means they should be used. Cannot be
true at run time if false at create time.
zones: List of Google Compute Engine availability zones to which resource
creation will restricted. If empty, any zone may be chosen.
"""
disks = _messages.MessageField('Disk', 1, repeated=True)
minimumCpuCores = _messages.IntegerField(2,
variant=_messages.Variant.INT32)
minimumRamGb = _messages.FloatField(3)
preemptible = _messages.BooleanField(4)
zones = _messages.StringField(5, repeated=True)
