"""
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import google.protobuf.descriptor
import google.protobuf.internal.containers
import google.protobuf.internal.enum_type_wrapper
import google.protobuf.message
import sys
import typing
if sys.version_info >= (3, 10):
import typing as typing_extensions
else:
import typing_extensions
[docs]
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
class _Component:
ValueType = typing.NewType("ValueType", builtins.int)
V: typing_extensions.TypeAlias = ValueType
class _ComponentEnumTypeWrapper(google.protobuf.internal.enum_type_wrapper._EnumTypeWrapper[_Component.ValueType], builtins.type): # noqa: F821
DESCRIPTOR: google.protobuf.descriptor.EnumDescriptor
Component_Unknown: _Component.ValueType # 0
Component_Config_Validator: _Component.ValueType # 1
Component_Config_Populator: _Component.ValueType # 2
Component_Data_Preprocessor: _Component.ValueType # 3
Component_Subgraph_Sampler: _Component.ValueType # 4
Component_Split_Generator: _Component.ValueType # 5
Component_Trainer: _Component.ValueType # 6
Component_Inferencer: _Component.ValueType # 7
[docs]
class Component(_Component, metaclass=_ComponentEnumTypeWrapper):
"""Enum for pipeline components"""
[docs]
Component_Unknown: Component.ValueType # 0
[docs]
Component_Config_Validator: Component.ValueType # 1
[docs]
Component_Config_Populator: Component.ValueType # 2
[docs]
Component_Data_Preprocessor: Component.ValueType # 3
[docs]
Component_Subgraph_Sampler: Component.ValueType # 4
[docs]
Component_Split_Generator: Component.ValueType # 5
[docs]
Component_Trainer: Component.ValueType # 6
[docs]
Component_Inferencer: Component.ValueType # 7
[docs]
global___Component = Component
[docs]
class SparkResourceConfig(google.protobuf.message.Message):
"""Configuration for Spark Components"""
[docs]
DESCRIPTOR: google.protobuf.descriptor.Descriptor
[docs]
MACHINE_TYPE_FIELD_NUMBER: builtins.int
[docs]
NUM_LOCAL_SSDS_FIELD_NUMBER: builtins.int
[docs]
NUM_REPLICAS_FIELD_NUMBER: builtins.int
[docs]
machine_type: builtins.str
"""Machine type for Spark Resource"""
[docs]
num_local_ssds: builtins.int
"""Number of local SSDs"""
[docs]
num_replicas: builtins.int
"""Num workers for Spark Resource"""
def __init__(
self,
*,
machine_type: builtins.str = ...,
num_local_ssds: builtins.int = ...,
num_replicas: builtins.int = ...,
) -> None: ...
[docs]
def ClearField(self, field_name: typing_extensions.Literal["machine_type", b"machine_type", "num_local_ssds", b"num_local_ssds", "num_replicas", b"num_replicas"]) -> None: ...
[docs]
global___SparkResourceConfig = SparkResourceConfig
[docs]
class DataflowResourceConfig(google.protobuf.message.Message):
"""Configuration for Dataflow Components"""
[docs]
DESCRIPTOR: google.protobuf.descriptor.Descriptor
[docs]
NUM_WORKERS_FIELD_NUMBER: builtins.int
[docs]
MAX_NUM_WORKERS_FIELD_NUMBER: builtins.int
[docs]
MACHINE_TYPE_FIELD_NUMBER: builtins.int
[docs]
DISK_SIZE_GB_FIELD_NUMBER: builtins.int
[docs]
num_workers: builtins.int
"""Number of workers for Dataflow resources"""
[docs]
max_num_workers: builtins.int
"""Maximum number of workers for Dataflow resources"""
[docs]
machine_type: builtins.str
"""Machine type for Dataflow resources"""
[docs]
disk_size_gb: builtins.int
"""Disk size in GB for Dataflow resources"""
def __init__(
self,
*,
num_workers: builtins.int = ...,
max_num_workers: builtins.int = ...,
machine_type: builtins.str = ...,
disk_size_gb: builtins.int = ...,
) -> None: ...
[docs]
def ClearField(self, field_name: typing_extensions.Literal["disk_size_gb", b"disk_size_gb", "machine_type", b"machine_type", "max_num_workers", b"max_num_workers", "num_workers", b"num_workers"]) -> None: ...
[docs]
global___DataflowResourceConfig = DataflowResourceConfig
[docs]
class DataPreprocessorConfig(google.protobuf.message.Message):
"""Configuration for Data Preprocessor"""
[docs]
DESCRIPTOR: google.protobuf.descriptor.Descriptor
[docs]
EDGE_PREPROCESSOR_CONFIG_FIELD_NUMBER: builtins.int
[docs]
NODE_PREPROCESSOR_CONFIG_FIELD_NUMBER: builtins.int
@property
[docs]
def edge_preprocessor_config(self) -> global___DataflowResourceConfig: ...
@property
[docs]
def node_preprocessor_config(self) -> global___DataflowResourceConfig: ...
def __init__(
self,
*,
edge_preprocessor_config: global___DataflowResourceConfig | None = ...,
node_preprocessor_config: global___DataflowResourceConfig | None = ...,
) -> None: ...
[docs]
def HasField(self, field_name: typing_extensions.Literal["edge_preprocessor_config", b"edge_preprocessor_config", "node_preprocessor_config", b"node_preprocessor_config"]) -> builtins.bool: ...
[docs]
def ClearField(self, field_name: typing_extensions.Literal["edge_preprocessor_config", b"edge_preprocessor_config", "node_preprocessor_config", b"node_preprocessor_config"]) -> None: ...
[docs]
global___DataPreprocessorConfig = DataPreprocessorConfig
[docs]
class VertexAiTrainerConfig(google.protobuf.message.Message):
"""(deprecated)
Configuration for Vertex AI training resources
"""
[docs]
DESCRIPTOR: google.protobuf.descriptor.Descriptor
[docs]
MACHINE_TYPE_FIELD_NUMBER: builtins.int
[docs]
GPU_TYPE_FIELD_NUMBER: builtins.int
[docs]
GPU_LIMIT_FIELD_NUMBER: builtins.int
[docs]
NUM_REPLICAS_FIELD_NUMBER: builtins.int
[docs]
machine_type: builtins.str
"""Machine type for training job"""
"""GPU type for training job. Must be set to 'ACCELERATOR_TYPE_UNSPECIFIED' for cpu training."""
[docs]
gpu_limit: builtins.int
"""GPU limit for training job. Must be set to 0 for cpu training."""
[docs]
num_replicas: builtins.int
"""Num workers for training job"""
def __init__(
self,
*,
machine_type: builtins.str = ...,
gpu_type: builtins.str = ...,
gpu_limit: builtins.int = ...,
num_replicas: builtins.int = ...,
) -> None: ...
[docs]
def ClearField(self, field_name: typing_extensions.Literal["gpu_limit", b"gpu_limit", "gpu_type", b"gpu_type", "machine_type", b"machine_type", "num_replicas", b"num_replicas"]) -> None: ...
[docs]
global___VertexAiTrainerConfig = VertexAiTrainerConfig
[docs]
class KFPTrainerConfig(google.protobuf.message.Message):
"""(deprecated)
Configuration for KFP training resources
"""
[docs]
DESCRIPTOR: google.protobuf.descriptor.Descriptor
[docs]
CPU_REQUEST_FIELD_NUMBER: builtins.int
[docs]
MEMORY_REQUEST_FIELD_NUMBER: builtins.int
[docs]
GPU_TYPE_FIELD_NUMBER: builtins.int
[docs]
GPU_LIMIT_FIELD_NUMBER: builtins.int
[docs]
NUM_REPLICAS_FIELD_NUMBER: builtins.int
[docs]
cpu_request: builtins.str
"""Num CPU requested for training job (str) which can be a number or a number followed by "m", which means 1/1000"""
[docs]
memory_request: builtins.str
"""Amount of Memory requested for training job (str) can either be a number or a number followed by one of "Ei", "Pi", "Ti", "Gi", "Mi", "Ki"."""
"""GPU type for training job. Must be set to 'ACCELERATOR_TYPE_UNSPECIFIED' for cpu training."""
[docs]
gpu_limit: builtins.int
"""GPU limit for training job. Must be set to 0 for cpu training."""
[docs]
num_replicas: builtins.int
"""Number of replicas for training job"""
def __init__(
self,
*,
cpu_request: builtins.str = ...,
memory_request: builtins.str = ...,
gpu_type: builtins.str = ...,
gpu_limit: builtins.int = ...,
num_replicas: builtins.int = ...,
) -> None: ...
[docs]
def ClearField(self, field_name: typing_extensions.Literal["cpu_request", b"cpu_request", "gpu_limit", b"gpu_limit", "gpu_type", b"gpu_type", "memory_request", b"memory_request", "num_replicas", b"num_replicas"]) -> None: ...
[docs]
global___KFPTrainerConfig = KFPTrainerConfig
[docs]
class LocalTrainerConfig(google.protobuf.message.Message):
"""(deprecated)
Configuration for Local Training
"""
[docs]
DESCRIPTOR: google.protobuf.descriptor.Descriptor
[docs]
NUM_WORKERS_FIELD_NUMBER: builtins.int
[docs]
num_workers: builtins.int
def __init__(
self,
*,
num_workers: builtins.int = ...,
) -> None: ...
[docs]
def ClearField(self, field_name: typing_extensions.Literal["num_workers", b"num_workers"]) -> None: ...
[docs]
global___LocalTrainerConfig = LocalTrainerConfig
[docs]
class VertexAiResourceConfig(google.protobuf.message.Message):
"""Configuration for Vertex AI resources"""
[docs]
DESCRIPTOR: google.protobuf.descriptor.Descriptor
[docs]
MACHINE_TYPE_FIELD_NUMBER: builtins.int
[docs]
GPU_TYPE_FIELD_NUMBER: builtins.int
[docs]
GPU_LIMIT_FIELD_NUMBER: builtins.int
[docs]
NUM_REPLICAS_FIELD_NUMBER: builtins.int
[docs]
TIMEOUT_FIELD_NUMBER: builtins.int
[docs]
machine_type: builtins.str
"""Machine type for job"""
"""GPU type for job. Must be set to 'ACCELERATOR_TYPE_UNSPECIFIED' for cpu."""
[docs]
gpu_limit: builtins.int
"""GPU limit for job. Must be set to 0 for cpu."""
[docs]
num_replicas: builtins.int
"""Num workers for job"""
"""Timeout in seconds for the job. If unset or zero, will use the default @ google.cloud.aiplatform.CustomJob, which is 7 days:
https://github.com/googleapis/python-aiplatform/blob/58fbabdeeefd1ccf1a9d0c22eeb5606aeb9c2266/google/cloud/aiplatform/jobs.py#L2252-L2253
"""
def __init__(
self,
*,
machine_type: builtins.str = ...,
gpu_type: builtins.str = ...,
gpu_limit: builtins.int = ...,
num_replicas: builtins.int = ...,
timeout: builtins.int = ...,
) -> None: ...
[docs]
def ClearField(self, field_name: typing_extensions.Literal["gpu_limit", b"gpu_limit", "gpu_type", b"gpu_type", "machine_type", b"machine_type", "num_replicas", b"num_replicas", "timeout", b"timeout"]) -> None: ...
[docs]
global___VertexAiResourceConfig = VertexAiResourceConfig
[docs]
class KFPResourceConfig(google.protobuf.message.Message):
"""Configuration for KFP job resources"""
[docs]
DESCRIPTOR: google.protobuf.descriptor.Descriptor
[docs]
CPU_REQUEST_FIELD_NUMBER: builtins.int
[docs]
MEMORY_REQUEST_FIELD_NUMBER: builtins.int
[docs]
GPU_TYPE_FIELD_NUMBER: builtins.int
[docs]
GPU_LIMIT_FIELD_NUMBER: builtins.int
[docs]
NUM_REPLICAS_FIELD_NUMBER: builtins.int
[docs]
cpu_request: builtins.str
"""Num CPU requested for job (str) which can be a number or a number followed by "m", which means 1/1000"""
[docs]
memory_request: builtins.str
"""Amount of Memory requested for job (str) can either be a number or a number followed by one of "Ei", "Pi", "Ti", "Gi", "Mi", "Ki"."""
"""GPU type for job. Must be set to 'ACCELERATOR_TYPE_UNSPECIFIED' for cpu."""
[docs]
gpu_limit: builtins.int
"""GPU limit for job. Must be set to 0 for cpu."""
[docs]
num_replicas: builtins.int
"""Number of replicas for job"""
def __init__(
self,
*,
cpu_request: builtins.str = ...,
memory_request: builtins.str = ...,
gpu_type: builtins.str = ...,
gpu_limit: builtins.int = ...,
num_replicas: builtins.int = ...,
) -> None: ...
[docs]
def ClearField(self, field_name: typing_extensions.Literal["cpu_request", b"cpu_request", "gpu_limit", b"gpu_limit", "gpu_type", b"gpu_type", "memory_request", b"memory_request", "num_replicas", b"num_replicas"]) -> None: ...
[docs]
global___KFPResourceConfig = KFPResourceConfig
[docs]
class LocalResourceConfig(google.protobuf.message.Message):
"""Configuration for Local Jobs"""
[docs]
DESCRIPTOR: google.protobuf.descriptor.Descriptor
[docs]
NUM_WORKERS_FIELD_NUMBER: builtins.int
[docs]
num_workers: builtins.int
def __init__(
self,
*,
num_workers: builtins.int = ...,
) -> None: ...
[docs]
def ClearField(self, field_name: typing_extensions.Literal["num_workers", b"num_workers"]) -> None: ...
[docs]
global___LocalResourceConfig = LocalResourceConfig
[docs]
class DistributedTrainerConfig(google.protobuf.message.Message):
"""(deprecated)
Configuration for distributed training resources
"""
[docs]
DESCRIPTOR: google.protobuf.descriptor.Descriptor
[docs]
VERTEX_AI_TRAINER_CONFIG_FIELD_NUMBER: builtins.int
[docs]
KFP_TRAINER_CONFIG_FIELD_NUMBER: builtins.int
[docs]
LOCAL_TRAINER_CONFIG_FIELD_NUMBER: builtins.int
@property
[docs]
def vertex_ai_trainer_config(self) -> global___VertexAiTrainerConfig: ...
@property
[docs]
def kfp_trainer_config(self) -> global___KFPTrainerConfig: ...
@property
[docs]
def local_trainer_config(self) -> global___LocalTrainerConfig: ...
def __init__(
self,
*,
vertex_ai_trainer_config: global___VertexAiTrainerConfig | None = ...,
kfp_trainer_config: global___KFPTrainerConfig | None = ...,
local_trainer_config: global___LocalTrainerConfig | None = ...,
) -> None: ...
[docs]
def HasField(self, field_name: typing_extensions.Literal["kfp_trainer_config", b"kfp_trainer_config", "local_trainer_config", b"local_trainer_config", "trainer_config", b"trainer_config", "vertex_ai_trainer_config", b"vertex_ai_trainer_config"]) -> builtins.bool: ...
[docs]
def ClearField(self, field_name: typing_extensions.Literal["kfp_trainer_config", b"kfp_trainer_config", "local_trainer_config", b"local_trainer_config", "trainer_config", b"trainer_config", "vertex_ai_trainer_config", b"vertex_ai_trainer_config"]) -> None: ...
[docs]
def WhichOneof(self, oneof_group: typing_extensions.Literal["trainer_config", b"trainer_config"]) -> typing_extensions.Literal["vertex_ai_trainer_config", "kfp_trainer_config", "local_trainer_config"] | None: ...
[docs]
global___DistributedTrainerConfig = DistributedTrainerConfig
[docs]
class TrainerResourceConfig(google.protobuf.message.Message):
"""Configuration for training resources"""
[docs]
DESCRIPTOR: google.protobuf.descriptor.Descriptor
[docs]
VERTEX_AI_TRAINER_CONFIG_FIELD_NUMBER: builtins.int
[docs]
KFP_TRAINER_CONFIG_FIELD_NUMBER: builtins.int
[docs]
LOCAL_TRAINER_CONFIG_FIELD_NUMBER: builtins.int
@property
[docs]
def vertex_ai_trainer_config(self) -> global___VertexAiResourceConfig: ...
@property
[docs]
def kfp_trainer_config(self) -> global___KFPResourceConfig: ...
@property
[docs]
def local_trainer_config(self) -> global___LocalResourceConfig: ...
def __init__(
self,
*,
vertex_ai_trainer_config: global___VertexAiResourceConfig | None = ...,
kfp_trainer_config: global___KFPResourceConfig | None = ...,
local_trainer_config: global___LocalResourceConfig | None = ...,
) -> None: ...
[docs]
def HasField(self, field_name: typing_extensions.Literal["kfp_trainer_config", b"kfp_trainer_config", "local_trainer_config", b"local_trainer_config", "trainer_config", b"trainer_config", "vertex_ai_trainer_config", b"vertex_ai_trainer_config"]) -> builtins.bool: ...
[docs]
def ClearField(self, field_name: typing_extensions.Literal["kfp_trainer_config", b"kfp_trainer_config", "local_trainer_config", b"local_trainer_config", "trainer_config", b"trainer_config", "vertex_ai_trainer_config", b"vertex_ai_trainer_config"]) -> None: ...
[docs]
def WhichOneof(self, oneof_group: typing_extensions.Literal["trainer_config", b"trainer_config"]) -> typing_extensions.Literal["vertex_ai_trainer_config", "kfp_trainer_config", "local_trainer_config"] | None: ...
[docs]
global___TrainerResourceConfig = TrainerResourceConfig
[docs]
class InferencerResourceConfig(google.protobuf.message.Message):
"""Configuration for distributed inference resources"""
[docs]
DESCRIPTOR: google.protobuf.descriptor.Descriptor
[docs]
VERTEX_AI_INFERENCER_CONFIG_FIELD_NUMBER: builtins.int
[docs]
DATAFLOW_INFERENCER_CONFIG_FIELD_NUMBER: builtins.int
[docs]
LOCAL_INFERENCER_CONFIG_FIELD_NUMBER: builtins.int
@property
[docs]
def vertex_ai_inferencer_config(self) -> global___VertexAiResourceConfig: ...
@property
[docs]
def dataflow_inferencer_config(self) -> global___DataflowResourceConfig: ...
@property
[docs]
def local_inferencer_config(self) -> global___LocalResourceConfig: ...
def __init__(
self,
*,
vertex_ai_inferencer_config: global___VertexAiResourceConfig | None = ...,
dataflow_inferencer_config: global___DataflowResourceConfig | None = ...,
local_inferencer_config: global___LocalResourceConfig | None = ...,
) -> None: ...
[docs]
def HasField(self, field_name: typing_extensions.Literal["dataflow_inferencer_config", b"dataflow_inferencer_config", "inferencer_config", b"inferencer_config", "local_inferencer_config", b"local_inferencer_config", "vertex_ai_inferencer_config", b"vertex_ai_inferencer_config"]) -> builtins.bool: ...
[docs]
def ClearField(self, field_name: typing_extensions.Literal["dataflow_inferencer_config", b"dataflow_inferencer_config", "inferencer_config", b"inferencer_config", "local_inferencer_config", b"local_inferencer_config", "vertex_ai_inferencer_config", b"vertex_ai_inferencer_config"]) -> None: ...
[docs]
def WhichOneof(self, oneof_group: typing_extensions.Literal["inferencer_config", b"inferencer_config"]) -> typing_extensions.Literal["vertex_ai_inferencer_config", "dataflow_inferencer_config", "local_inferencer_config"] | None: ...
[docs]
global___InferencerResourceConfig = InferencerResourceConfig
[docs]
class SharedResourceConfig(google.protobuf.message.Message):
"""Shared resources configuration"""
[docs]
DESCRIPTOR: google.protobuf.descriptor.Descriptor
[docs]
class CommonComputeConfig(google.protobuf.message.Message):
[docs]
DESCRIPTOR: google.protobuf.descriptor.Descriptor
[docs]
PROJECT_FIELD_NUMBER: builtins.int
[docs]
REGION_FIELD_NUMBER: builtins.int
[docs]
TEMP_ASSETS_BUCKET_FIELD_NUMBER: builtins.int
[docs]
TEMP_REGIONAL_ASSETS_BUCKET_FIELD_NUMBER: builtins.int
[docs]
PERM_ASSETS_BUCKET_FIELD_NUMBER: builtins.int
[docs]
TEMP_ASSETS_BQ_DATASET_NAME_FIELD_NUMBER: builtins.int
[docs]
EMBEDDING_BQ_DATASET_NAME_FIELD_NUMBER: builtins.int
[docs]
GCP_SERVICE_ACCOUNT_EMAIL_FIELD_NUMBER: builtins.int
[docs]
DATAFLOW_RUNNER_FIELD_NUMBER: builtins.int
"""GCP Project"""
"""GCP Region where compute is to be scheduled"""
[docs]
temp_assets_bucket: builtins.str
"""GCS Bucket for where temporary assets are to be stored"""
[docs]
temp_regional_assets_bucket: builtins.str
"""Regional GCS Bucket used to store temporary assets"""
[docs]
perm_assets_bucket: builtins.str
"""Regional GCS Bucket that will store permanent assets like Trained Model"""
[docs]
temp_assets_bq_dataset_name: builtins.str
"""Path to BQ dataset used to store temporary assets"""
[docs]
embedding_bq_dataset_name: builtins.str
"""Path to BQ Dataset used to persist generated embeddings and predictions"""
[docs]
gcp_service_account_email: builtins.str
"""The GCP service account email being used to schedule compute on GCP"""
[docs]
dataflow_runner: builtins.str
"""The runner to use for Dataflow i.e DirectRunner or DataflowRunner"""
def __init__(
self,
*,
project: builtins.str = ...,
region: builtins.str = ...,
temp_assets_bucket: builtins.str = ...,
temp_regional_assets_bucket: builtins.str = ...,
perm_assets_bucket: builtins.str = ...,
temp_assets_bq_dataset_name: builtins.str = ...,
embedding_bq_dataset_name: builtins.str = ...,
gcp_service_account_email: builtins.str = ...,
dataflow_runner: builtins.str = ...,
) -> None: ...
[docs]
def ClearField(self, field_name: typing_extensions.Literal["dataflow_runner", b"dataflow_runner", "embedding_bq_dataset_name", b"embedding_bq_dataset_name", "gcp_service_account_email", b"gcp_service_account_email", "perm_assets_bucket", b"perm_assets_bucket", "project", b"project", "region", b"region", "temp_assets_bq_dataset_name", b"temp_assets_bq_dataset_name", "temp_assets_bucket", b"temp_assets_bucket", "temp_regional_assets_bucket", b"temp_regional_assets_bucket"]) -> None: ...
[docs]
class ResourceLabelsEntry(google.protobuf.message.Message):
[docs]
DESCRIPTOR: google.protobuf.descriptor.Descriptor
[docs]
KEY_FIELD_NUMBER: builtins.int
[docs]
VALUE_FIELD_NUMBER: builtins.int
[docs]
value: builtins.str
def __init__(
self,
*,
key: builtins.str = ...,
value: builtins.str = ...,
) -> None: ...
[docs]
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
[docs]
RESOURCE_LABELS_FIELD_NUMBER: builtins.int
[docs]
COMMON_COMPUTE_CONFIG_FIELD_NUMBER: builtins.int
@property
[docs]
def resource_labels(self) -> google.protobuf.internal.containers.ScalarMap[builtins.str, builtins.str]: ...
@property
[docs]
def common_compute_config(self) -> global___SharedResourceConfig.CommonComputeConfig: ...
def __init__(
self,
*,
resource_labels: collections.abc.Mapping[builtins.str, builtins.str] | None = ...,
common_compute_config: global___SharedResourceConfig.CommonComputeConfig | None = ...,
) -> None: ...
[docs]
def HasField(self, field_name: typing_extensions.Literal["common_compute_config", b"common_compute_config"]) -> builtins.bool: ...
[docs]
def ClearField(self, field_name: typing_extensions.Literal["common_compute_config", b"common_compute_config", "resource_labels", b"resource_labels"]) -> None: ...
[docs]
global___SharedResourceConfig = SharedResourceConfig
[docs]
class GiglResourceConfig(google.protobuf.message.Message):
"""GiGL resources configuration"""
[docs]
DESCRIPTOR: google.protobuf.descriptor.Descriptor
[docs]
SHARED_RESOURCE_CONFIG_URI_FIELD_NUMBER: builtins.int
[docs]
SHARED_RESOURCE_CONFIG_FIELD_NUMBER: builtins.int
[docs]
PREPROCESSOR_CONFIG_FIELD_NUMBER: builtins.int
[docs]
SUBGRAPH_SAMPLER_CONFIG_FIELD_NUMBER: builtins.int
[docs]
SPLIT_GENERATOR_CONFIG_FIELD_NUMBER: builtins.int
[docs]
TRAINER_CONFIG_FIELD_NUMBER: builtins.int
[docs]
INFERENCER_CONFIG_FIELD_NUMBER: builtins.int
[docs]
TRAINER_RESOURCE_CONFIG_FIELD_NUMBER: builtins.int
[docs]
INFERENCER_RESOURCE_CONFIG_FIELD_NUMBER: builtins.int
[docs]
shared_resource_config_uri: builtins.str
@property
[docs]
def shared_resource_config(self) -> global___SharedResourceConfig: ...
@property
[docs]
def preprocessor_config(self) -> global___DataPreprocessorConfig:
"""Configuration for Data Preprocessor"""
@property
[docs]
def subgraph_sampler_config(self) -> global___SparkResourceConfig:
"""Configuration for Spark subgraph sampler"""
@property
[docs]
def split_generator_config(self) -> global___SparkResourceConfig:
"""Configuration for Spark split generator"""
@property
[docs]
def trainer_config(self) -> global___DistributedTrainerConfig:
"""(deprecated)
Configuration for trainer
"""
@property
[docs]
def inferencer_config(self) -> global___DataflowResourceConfig:
"""(deprecated)
Configuration for inferencer
"""
@property
[docs]
def trainer_resource_config(self) -> global___TrainerResourceConfig:
"""Configuration for distributed trainer"""
@property
[docs]
def inferencer_resource_config(self) -> global___InferencerResourceConfig:
"""Configuration for distributed inferencer"""
def __init__(
self,
*,
shared_resource_config_uri: builtins.str = ...,
shared_resource_config: global___SharedResourceConfig | None = ...,
preprocessor_config: global___DataPreprocessorConfig | None = ...,
subgraph_sampler_config: global___SparkResourceConfig | None = ...,
split_generator_config: global___SparkResourceConfig | None = ...,
trainer_config: global___DistributedTrainerConfig | None = ...,
inferencer_config: global___DataflowResourceConfig | None = ...,
trainer_resource_config: global___TrainerResourceConfig | None = ...,
inferencer_resource_config: global___InferencerResourceConfig | None = ...,
) -> None: ...
[docs]
def HasField(self, field_name: typing_extensions.Literal["inferencer_config", b"inferencer_config", "inferencer_resource_config", b"inferencer_resource_config", "preprocessor_config", b"preprocessor_config", "shared_resource", b"shared_resource", "shared_resource_config", b"shared_resource_config", "shared_resource_config_uri", b"shared_resource_config_uri", "split_generator_config", b"split_generator_config", "subgraph_sampler_config", b"subgraph_sampler_config", "trainer_config", b"trainer_config", "trainer_resource_config", b"trainer_resource_config"]) -> builtins.bool: ...
[docs]
def ClearField(self, field_name: typing_extensions.Literal["inferencer_config", b"inferencer_config", "inferencer_resource_config", b"inferencer_resource_config", "preprocessor_config", b"preprocessor_config", "shared_resource", b"shared_resource", "shared_resource_config", b"shared_resource_config", "shared_resource_config_uri", b"shared_resource_config_uri", "split_generator_config", b"split_generator_config", "subgraph_sampler_config", b"subgraph_sampler_config", "trainer_config", b"trainer_config", "trainer_resource_config", b"trainer_resource_config"]) -> None: ...
[docs]
def WhichOneof(self, oneof_group: typing_extensions.Literal["shared_resource", b"shared_resource"]) -> typing_extensions.Literal["shared_resource_config_uri", "shared_resource_config"] | None: ...
[docs]
global___GiglResourceConfig = GiglResourceConfig
