snapchat.research.gbml.gigl_resource_config_pb2#

@generated by mypy-protobuf. Do not edit manually! isort:skip_file

Classes#

`Component`	Enum for pipeline components
`DataPreprocessorConfig`	Configuration for Data Preprocessor
`DataflowResourceConfig`	Configuration for Dataflow Components
`DistributedTrainerConfig`	(deprecated)
`GiglResourceConfig`	GiGL resources configuration
`InferencerResourceConfig`	Configuration for distributed inference resources
`KFPResourceConfig`	Configuration for KFP job resources
`KFPTrainerConfig`	(deprecated)
`LocalResourceConfig`	Configuration for Local Jobs
`LocalTrainerConfig`	(deprecated)
`SharedResourceConfig`	Shared resources configuration
`SparkResourceConfig`	Configuration for Spark Components
`TrainerResourceConfig`	Configuration for training resources
`VertexAiResourceConfig`	Configuration for Vertex AI resources
`VertexAiTrainerConfig`	(deprecated)

Module Contents#

class snapchat.research.gbml.gigl_resource_config_pb2.Component[source]#

Bases: _Component

Enum for pipeline components

class snapchat.research.gbml.gigl_resource_config_pb2.DataPreprocessorConfig(*, edge_preprocessor_config=..., node_preprocessor_config=...)[source]#

Bases: google.protobuf.message.Message

Configuration for Data Preprocessor

Parameters:

edge_preprocessor_config (global___DataflowResourceConfig | None)
node_preprocessor_config (global___DataflowResourceConfig | None)

ClearField(field_name)[source]#

Clears the contents of a given field.

Inside a oneof group, clears the field set. If the name neither refers to a defined field or oneof group, ValueError is raised.

Parameters:: field_name (str) – The name of the field to check for presence.
Raises:: ValueError – if the field_name is not a member of this message.
Return type:: None

HasField(field_name)[source]#

Checks if a certain field is set for the message.

For a oneof group, checks if any field inside is set. Note that if the field_name is not defined in the message descriptor, ValueError will be raised.

Parameters:: field_name (str) – The name of the field to check for presence.
Returns:: Whether a value has been set for the named field.
Return type:: bool
Raises:: ValueError – if the field_name is not a member of this message.

DESCRIPTOR: google.protobuf.descriptor.Descriptor[source]#

EDGE_PREPROCESSOR_CONFIG_FIELD_NUMBER: int[source]#

NODE_PREPROCESSOR_CONFIG_FIELD_NUMBER: int[source]#

property edge_preprocessor_config: global___DataflowResourceConfig[source]#

Return type:: global___DataflowResourceConfig

property node_preprocessor_config: global___DataflowResourceConfig[source]#

Return type:: global___DataflowResourceConfig

class snapchat.research.gbml.gigl_resource_config_pb2.DataflowResourceConfig(*, num_workers=..., max_num_workers=..., machine_type=..., disk_size_gb=...)[source]#

Bases: google.protobuf.message.Message

Configuration for Dataflow Components

Parameters:

num_workers (int)
max_num_workers (int)
machine_type (str)
disk_size_gb (int)

ClearField(field_name)[source]#

Clears the contents of a given field.

Inside a oneof group, clears the field set. If the name neither refers to a defined field or oneof group, ValueError is raised.

Parameters:: field_name (str) – The name of the field to check for presence.
Raises:: ValueError – if the field_name is not a member of this message.
Return type:: None

DESCRIPTOR: google.protobuf.descriptor.Descriptor[source]#

DISK_SIZE_GB_FIELD_NUMBER: int[source]#

MACHINE_TYPE_FIELD_NUMBER: int[source]#

MAX_NUM_WORKERS_FIELD_NUMBER: int[source]#

NUM_WORKERS_FIELD_NUMBER: int[source]#

disk_size_gb: int[source]#: Disk size in GB for Dataflow resources

machine_type: str[source]#: Machine type for Dataflow resources

max_num_workers: int[source]#: Maximum number of workers for Dataflow resources

num_workers: int[source]#: Number of workers for Dataflow resources

class snapchat.research.gbml.gigl_resource_config_pb2.DistributedTrainerConfig(*, vertex_ai_trainer_config=..., kfp_trainer_config=..., local_trainer_config=...)[source]#

Bases: google.protobuf.message.Message

(deprecated) Configuration for distributed training resources

Parameters:

vertex_ai_trainer_config (global___VertexAiTrainerConfig | None)
kfp_trainer_config (global___KFPTrainerConfig | None)
local_trainer_config (global___LocalTrainerConfig | None)

ClearField(field_name)[source]#

Clears the contents of a given field.

Inside a oneof group, clears the field set. If the name neither refers to a defined field or oneof group, ValueError is raised.

Parameters:: field_name (str) – The name of the field to check for presence.
Raises:: ValueError – if the field_name is not a member of this message.
Return type:: None

HasField(field_name)[source]#

Checks if a certain field is set for the message.

For a oneof group, checks if any field inside is set. Note that if the field_name is not defined in the message descriptor, ValueError will be raised.

Parameters:: field_name (str) – The name of the field to check for presence.
Returns:: Whether a value has been set for the named field.
Return type:: bool
Raises:: ValueError – if the field_name is not a member of this message.

WhichOneof(oneof_group)[source]#

Returns the name of the field that is set inside a oneof group.

If no field is set, returns None.

Parameters:: oneof_group (str) – the name of the oneof group to check.
Returns:: The name of the group that is set, or None.
Return type:: str or None
Raises:: ValueError – no group with the given name exists

DESCRIPTOR: google.protobuf.descriptor.Descriptor[source]#

KFP_TRAINER_CONFIG_FIELD_NUMBER: int[source]#

LOCAL_TRAINER_CONFIG_FIELD_NUMBER: int[source]#

VERTEX_AI_TRAINER_CONFIG_FIELD_NUMBER: int[source]#

property kfp_trainer_config: global___KFPTrainerConfig[source]#

Return type:: global___KFPTrainerConfig

property local_trainer_config: global___LocalTrainerConfig[source]#

Return type:: global___LocalTrainerConfig

property vertex_ai_trainer_config: global___VertexAiTrainerConfig[source]#

Return type:: global___VertexAiTrainerConfig

class snapchat.research.gbml.gigl_resource_config_pb2.GiglResourceConfig(*, shared_resource_config_uri=..., shared_resource_config=..., preprocessor_config=..., subgraph_sampler_config=..., split_generator_config=..., trainer_config=..., inferencer_config=..., trainer_resource_config=..., inferencer_resource_config=...)[source]#

Bases: google.protobuf.message.Message

GiGL resources configuration

Parameters:

shared_resource_config_uri (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)

ClearField(field_name)[source]#

Clears the contents of a given field.

Inside a oneof group, clears the field set. If the name neither refers to a defined field or oneof group, ValueError is raised.

Parameters:: field_name (str) – The name of the field to check for presence.
Raises:: ValueError – if the field_name is not a member of this message.
Return type:: None

HasField(field_name)[source]#

Checks if a certain field is set for the message.

For a oneof group, checks if any field inside is set. Note that if the field_name is not defined in the message descriptor, ValueError will be raised.

Parameters:: field_name (str) – The name of the field to check for presence.
Returns:: Whether a value has been set for the named field.
Return type:: bool
Raises:: ValueError – if the field_name is not a member of this message.

WhichOneof(oneof_group)[source]#

Returns the name of the field that is set inside a oneof group.

If no field is set, returns None.

Parameters:: oneof_group (str) – the name of the oneof group to check.
Returns:: The name of the group that is set, or None.
Return type:: str or None
Raises:: ValueError – no group with the given name exists

DESCRIPTOR: google.protobuf.descriptor.Descriptor[source]#

INFERENCER_CONFIG_FIELD_NUMBER: int[source]#

INFERENCER_RESOURCE_CONFIG_FIELD_NUMBER: int[source]#

PREPROCESSOR_CONFIG_FIELD_NUMBER: int[source]#

SHARED_RESOURCE_CONFIG_FIELD_NUMBER: int[source]#

SHARED_RESOURCE_CONFIG_URI_FIELD_NUMBER: int[source]#

SPLIT_GENERATOR_CONFIG_FIELD_NUMBER: int[source]#

SUBGRAPH_SAMPLER_CONFIG_FIELD_NUMBER: int[source]#

TRAINER_CONFIG_FIELD_NUMBER: int[source]#

TRAINER_RESOURCE_CONFIG_FIELD_NUMBER: int[source]#

property inferencer_config: global___DataflowResourceConfig[source]#

(deprecated) Configuration for inferencer

Return type:: global___DataflowResourceConfig

property inferencer_resource_config: global___InferencerResourceConfig[source]#

Configuration for distributed inferencer

Return type:: global___InferencerResourceConfig

property preprocessor_config: global___DataPreprocessorConfig[source]#

Configuration for Data Preprocessor

Return type:: global___DataPreprocessorConfig

property shared_resource_config: global___SharedResourceConfig[source]#

Return type:: global___SharedResourceConfig

shared_resource_config_uri: str[source]#

property split_generator_config: global___SparkResourceConfig[source]#

Configuration for Spark split generator

Return type:: global___SparkResourceConfig

property subgraph_sampler_config: global___SparkResourceConfig[source]#

Configuration for Spark subgraph sampler

Return type:: global___SparkResourceConfig

property trainer_config: global___DistributedTrainerConfig[source]#

(deprecated) Configuration for trainer

Return type:: global___DistributedTrainerConfig

property trainer_resource_config: global___TrainerResourceConfig[source]#

Configuration for distributed trainer

Return type:: global___TrainerResourceConfig

class snapchat.research.gbml.gigl_resource_config_pb2.InferencerResourceConfig(*, vertex_ai_inferencer_config=..., dataflow_inferencer_config=..., local_inferencer_config=...)[source]#

Bases: google.protobuf.message.Message

Configuration for distributed inference resources

Parameters:

vertex_ai_inferencer_config (global___VertexAiResourceConfig | None)
dataflow_inferencer_config (global___DataflowResourceConfig | None)
local_inferencer_config (global___LocalResourceConfig | None)

ClearField(field_name)[source]#

Clears the contents of a given field.

Inside a oneof group, clears the field set. If the name neither refers to a defined field or oneof group, ValueError is raised.

Parameters:: field_name (str) – The name of the field to check for presence.
Raises:: ValueError – if the field_name is not a member of this message.
Return type:: None

HasField(field_name)[source]#

Checks if a certain field is set for the message.

For a oneof group, checks if any field inside is set. Note that if the field_name is not defined in the message descriptor, ValueError will be raised.

Parameters:: field_name (str) – The name of the field to check for presence.
Returns:: Whether a value has been set for the named field.
Return type:: bool
Raises:: ValueError – if the field_name is not a member of this message.

WhichOneof(oneof_group)[source]#

Returns the name of the field that is set inside a oneof group.

If no field is set, returns None.

Parameters:: oneof_group (str) – the name of the oneof group to check.
Returns:: The name of the group that is set, or None.
Return type:: str or None
Raises:: ValueError – no group with the given name exists

DATAFLOW_INFERENCER_CONFIG_FIELD_NUMBER: int[source]#

DESCRIPTOR: google.protobuf.descriptor.Descriptor[source]#

LOCAL_INFERENCER_CONFIG_FIELD_NUMBER: int[source]#

VERTEX_AI_INFERENCER_CONFIG_FIELD_NUMBER: int[source]#

property dataflow_inferencer_config: global___DataflowResourceConfig[source]#

Return type:: global___DataflowResourceConfig

property local_inferencer_config: global___LocalResourceConfig[source]#

Return type:: global___LocalResourceConfig

property vertex_ai_inferencer_config: global___VertexAiResourceConfig[source]#

Return type:: global___VertexAiResourceConfig

class snapchat.research.gbml.gigl_resource_config_pb2.KFPResourceConfig(*, cpu_request=..., memory_request=..., gpu_type=..., gpu_limit=..., num_replicas=...)[source]#

Bases: google.protobuf.message.Message

Configuration for KFP job resources

Parameters:

cpu_request (str)
memory_request (str)
gpu_type (str)
gpu_limit (int)
num_replicas (int)

ClearField(field_name)[source]#

Clears the contents of a given field.

Inside a oneof group, clears the field set. If the name neither refers to a defined field or oneof group, ValueError is raised.

Parameters:: field_name (str) – The name of the field to check for presence.
Raises:: ValueError – if the field_name is not a member of this message.
Return type:: None

CPU_REQUEST_FIELD_NUMBER: int[source]#

DESCRIPTOR: google.protobuf.descriptor.Descriptor[source]#

GPU_LIMIT_FIELD_NUMBER: int[source]#

GPU_TYPE_FIELD_NUMBER: int[source]#

MEMORY_REQUEST_FIELD_NUMBER: int[source]#

NUM_REPLICAS_FIELD_NUMBER: int[source]#

cpu_request: str[source]#: Num CPU requested for job (str) which can be a number or a number followed by “m”, which means 1/1000

gpu_limit: int[source]#: GPU limit for job. Must be set to 0 for cpu.

gpu_type: str[source]#: GPU type for job. Must be set to ‘ACCELERATOR_TYPE_UNSPECIFIED’ for cpu.

memory_request: str[source]#: 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”.

num_replicas: int[source]#: Number of replicas for job

class snapchat.research.gbml.gigl_resource_config_pb2.KFPTrainerConfig(*, cpu_request=..., memory_request=..., gpu_type=..., gpu_limit=..., num_replicas=...)[source]#

Bases: google.protobuf.message.Message

(deprecated) Configuration for KFP training resources

Parameters:

cpu_request (str)
memory_request (str)
gpu_type (str)
gpu_limit (int)
num_replicas (int)

ClearField(field_name)[source]#

Clears the contents of a given field.

Inside a oneof group, clears the field set. If the name neither refers to a defined field or oneof group, ValueError is raised.

Parameters:: field_name (str) – The name of the field to check for presence.
Raises:: ValueError – if the field_name is not a member of this message.
Return type:: None

CPU_REQUEST_FIELD_NUMBER: int[source]#

DESCRIPTOR: google.protobuf.descriptor.Descriptor[source]#

GPU_LIMIT_FIELD_NUMBER: int[source]#

GPU_TYPE_FIELD_NUMBER: int[source]#

MEMORY_REQUEST_FIELD_NUMBER: int[source]#

NUM_REPLICAS_FIELD_NUMBER: int[source]#

cpu_request: str[source]#: Num CPU requested for training job (str) which can be a number or a number followed by “m”, which means 1/1000

gpu_limit: int[source]#: GPU limit for training job. Must be set to 0 for cpu training.

gpu_type: str[source]#: GPU type for training job. Must be set to ‘ACCELERATOR_TYPE_UNSPECIFIED’ for cpu training.

memory_request: str[source]#: 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”.

num_replicas: int[source]#: Number of replicas for training job

class snapchat.research.gbml.gigl_resource_config_pb2.LocalResourceConfig(*, num_workers=...)[source]#

Bases: google.protobuf.message.Message

Configuration for Local Jobs

Parameters:: num_workers (int)

ClearField(field_name)[source]#

Clears the contents of a given field.

Inside a oneof group, clears the field set. If the name neither refers to a defined field or oneof group, ValueError is raised.

Parameters:: field_name (str) – The name of the field to check for presence.
Raises:: ValueError – if the field_name is not a member of this message.
Return type:: None

DESCRIPTOR: google.protobuf.descriptor.Descriptor[source]#

NUM_WORKERS_FIELD_NUMBER: int[source]#

num_workers: int[source]#

class snapchat.research.gbml.gigl_resource_config_pb2.LocalTrainerConfig(*, num_workers=...)[source]#

Bases: google.protobuf.message.Message

(deprecated) Configuration for Local Training

Parameters:: num_workers (int)

ClearField(field_name)[source]#

Clears the contents of a given field.

Inside a oneof group, clears the field set. If the name neither refers to a defined field or oneof group, ValueError is raised.

Parameters:: field_name (str) – The name of the field to check for presence.
Raises:: ValueError – if the field_name is not a member of this message.
Return type:: None

DESCRIPTOR: google.protobuf.descriptor.Descriptor[source]#

NUM_WORKERS_FIELD_NUMBER: int[source]#

num_workers: int[source]#

class snapchat.research.gbml.gigl_resource_config_pb2.SharedResourceConfig(*, resource_labels=..., common_compute_config=...)[source]#

Bases: google.protobuf.message.Message

Shared resources configuration

Parameters:

resource_labels (collections.abc.Mapping[str, str] | None)
common_compute_config (global___SharedResourceConfig | None)

class CommonComputeConfig(*, project=..., region=..., temp_assets_bucket=..., temp_regional_assets_bucket=..., perm_assets_bucket=..., temp_assets_bq_dataset_name=..., embedding_bq_dataset_name=..., gcp_service_account_email=..., dataflow_runner=...)[source]#

Bases: google.protobuf.message.Message

Abstract base class for protocol messages.

Protocol message classes are almost always generated by the protocol compiler. These generated types subclass Message and implement the methods shown below.

Parameters:

project (str)
region (str)
temp_assets_bucket (str)
temp_regional_assets_bucket (str)
perm_assets_bucket (str)
temp_assets_bq_dataset_name (str)
embedding_bq_dataset_name (str)
gcp_service_account_email (str)
dataflow_runner (str)

ClearField(field_name)[source]#

Clears the contents of a given field.

Inside a oneof group, clears the field set. If the name neither refers to a defined field or oneof group, ValueError is raised.

Parameters:: field_name (str) – The name of the field to check for presence.
Raises:: ValueError – if the field_name is not a member of this message.
Return type:: None

DATAFLOW_RUNNER_FIELD_NUMBER: int[source]#

DESCRIPTOR: google.protobuf.descriptor.Descriptor[source]#

EMBEDDING_BQ_DATASET_NAME_FIELD_NUMBER: int[source]#

GCP_SERVICE_ACCOUNT_EMAIL_FIELD_NUMBER: int[source]#

PERM_ASSETS_BUCKET_FIELD_NUMBER: int[source]#

PROJECT_FIELD_NUMBER: int[source]#

REGION_FIELD_NUMBER: int[source]#

TEMP_ASSETS_BQ_DATASET_NAME_FIELD_NUMBER: int[source]#

TEMP_ASSETS_BUCKET_FIELD_NUMBER: int[source]#

TEMP_REGIONAL_ASSETS_BUCKET_FIELD_NUMBER: int[source]#

dataflow_runner: str[source]#: The runner to use for Dataflow i.e DirectRunner or DataflowRunner

embedding_bq_dataset_name: str[source]#: Path to BQ Dataset used to persist generated embeddings and predictions

gcp_service_account_email: str[source]#: The GCP service account email being used to schedule compute on GCP

perm_assets_bucket: str[source]#: Regional GCS Bucket that will store permanent assets like Trained Model

project: str[source]#: GCP Project

region: str[source]#: GCP Region where compute is to be scheduled

temp_assets_bq_dataset_name: str[source]#: Path to BQ dataset used to store temporary assets

temp_assets_bucket: str[source]#: GCS Bucket for where temporary assets are to be stored

temp_regional_assets_bucket: str[source]#: Regional GCS Bucket used to store temporary assets

class ResourceLabelsEntry(*, key=..., value=...)[source]#

Bases: google.protobuf.message.Message

Abstract base class for protocol messages.

Protocol message classes are almost always generated by the protocol compiler. These generated types subclass Message and implement the methods shown below.

Parameters:

key (str)
value (str)

ClearField(field_name)[source]#

Clears the contents of a given field.

Inside a oneof group, clears the field set. If the name neither refers to a defined field or oneof group, ValueError is raised.

Parameters:: field_name (str) – The name of the field to check for presence.
Raises:: ValueError – if the field_name is not a member of this message.
Return type:: None

DESCRIPTOR: google.protobuf.descriptor.Descriptor[source]#

KEY_FIELD_NUMBER: int[source]#

VALUE_FIELD_NUMBER: int[source]#

key: str[source]#

value: str[source]#

ClearField(field_name)[source]#

Clears the contents of a given field.

Inside a oneof group, clears the field set. If the name neither refers to a defined field or oneof group, ValueError is raised.

Parameters:: field_name (str) – The name of the field to check for presence.
Raises:: ValueError – if the field_name is not a member of this message.
Return type:: None

HasField(field_name)[source]#

Checks if a certain field is set for the message.

For a oneof group, checks if any field inside is set. Note that if the field_name is not defined in the message descriptor, ValueError will be raised.

Parameters:: field_name (str) – The name of the field to check for presence.
Returns:: Whether a value has been set for the named field.
Return type:: bool
Raises:: ValueError – if the field_name is not a member of this message.

COMMON_COMPUTE_CONFIG_FIELD_NUMBER: int[source]#

DESCRIPTOR: google.protobuf.descriptor.Descriptor[source]#

RESOURCE_LABELS_FIELD_NUMBER: int[source]#

property common_compute_config: global___SharedResourceConfig[source]#

Return type:: global___SharedResourceConfig

property resource_labels: google.protobuf.internal.containers.ScalarMap[str, str][source]#

Return type:: google.protobuf.internal.containers.ScalarMap[str, str]

class snapchat.research.gbml.gigl_resource_config_pb2.SparkResourceConfig(*, machine_type=..., num_local_ssds=..., num_replicas=...)[source]#

Bases: google.protobuf.message.Message

Configuration for Spark Components

Parameters:

machine_type (str)
num_local_ssds (int)
num_replicas (int)

ClearField(field_name)[source]#

Clears the contents of a given field.

Inside a oneof group, clears the field set. If the name neither refers to a defined field or oneof group, ValueError is raised.

Parameters:: field_name (str) – The name of the field to check for presence.
Raises:: ValueError – if the field_name is not a member of this message.
Return type:: None

DESCRIPTOR: google.protobuf.descriptor.Descriptor[source]#

MACHINE_TYPE_FIELD_NUMBER: int[source]#

NUM_LOCAL_SSDS_FIELD_NUMBER: int[source]#

NUM_REPLICAS_FIELD_NUMBER: int[source]#

machine_type: str[source]#: Machine type for Spark Resource

num_local_ssds: int[source]#: Number of local SSDs

num_replicas: int[source]#: Num workers for Spark Resource

class snapchat.research.gbml.gigl_resource_config_pb2.TrainerResourceConfig(*, vertex_ai_trainer_config=..., kfp_trainer_config=..., local_trainer_config=...)[source]#

Bases: google.protobuf.message.Message

Configuration for training resources

Parameters:

vertex_ai_trainer_config (global___VertexAiResourceConfig | None)
kfp_trainer_config (global___KFPResourceConfig | None)
local_trainer_config (global___LocalResourceConfig | None)

ClearField(field_name)[source]#

Clears the contents of a given field.

Inside a oneof group, clears the field set. If the name neither refers to a defined field or oneof group, ValueError is raised.

Parameters:: field_name (str) – The name of the field to check for presence.
Raises:: ValueError – if the field_name is not a member of this message.
Return type:: None

HasField(field_name)[source]#

Checks if a certain field is set for the message.

For a oneof group, checks if any field inside is set. Note that if the field_name is not defined in the message descriptor, ValueError will be raised.

Parameters:: field_name (str) – The name of the field to check for presence.
Returns:: Whether a value has been set for the named field.
Return type:: bool
Raises:: ValueError – if the field_name is not a member of this message.

WhichOneof(oneof_group)[source]#

Returns the name of the field that is set inside a oneof group.

If no field is set, returns None.

Parameters:: oneof_group (str) – the name of the oneof group to check.
Returns:: The name of the group that is set, or None.
Return type:: str or None
Raises:: ValueError – no group with the given name exists

DESCRIPTOR: google.protobuf.descriptor.Descriptor[source]#

KFP_TRAINER_CONFIG_FIELD_NUMBER: int[source]#

LOCAL_TRAINER_CONFIG_FIELD_NUMBER: int[source]#

VERTEX_AI_TRAINER_CONFIG_FIELD_NUMBER: int[source]#

property kfp_trainer_config: global___KFPResourceConfig[source]#

Return type:: global___KFPResourceConfig

property local_trainer_config: global___LocalResourceConfig[source]#

Return type:: global___LocalResourceConfig

property vertex_ai_trainer_config: global___VertexAiResourceConfig[source]#

Return type:: global___VertexAiResourceConfig

class snapchat.research.gbml.gigl_resource_config_pb2.VertexAiResourceConfig(*, machine_type=..., gpu_type=..., gpu_limit=..., num_replicas=..., timeout=...)[source]#

Bases: google.protobuf.message.Message

Configuration for Vertex AI resources

Parameters:

machine_type (str)
gpu_type (str)
gpu_limit (int)
num_replicas (int)
timeout (int)

ClearField(field_name)[source]#

Clears the contents of a given field.

Inside a oneof group, clears the field set. If the name neither refers to a defined field or oneof group, ValueError is raised.

Parameters:: field_name (str) – The name of the field to check for presence.
Raises:: ValueError – if the field_name is not a member of this message.
Return type:: None

DESCRIPTOR: google.protobuf.descriptor.Descriptor[source]#

GPU_LIMIT_FIELD_NUMBER: int[source]#

GPU_TYPE_FIELD_NUMBER: int[source]#

MACHINE_TYPE_FIELD_NUMBER: int[source]#

NUM_REPLICAS_FIELD_NUMBER: int[source]#

TIMEOUT_FIELD_NUMBER: int[source]#

gpu_limit: int[source]#: GPU limit for job. Must be set to 0 for cpu.

gpu_type: str[source]#: GPU type for job. Must be set to ‘ACCELERATOR_TYPE_UNSPECIFIED’ for cpu.

machine_type: str[source]#: Machine type for job

num_replicas: int[source]#: Num workers for job

timeout: int[source]#: Timeout in seconds for the job. If unset or zero, will use the default @ google.cloud.aiplatform.CustomJob, which is 7 days: googleapis/python-aiplatform

class snapchat.research.gbml.gigl_resource_config_pb2.VertexAiTrainerConfig(*, machine_type=..., gpu_type=..., gpu_limit=..., num_replicas=...)[source]#

Bases: google.protobuf.message.Message

(deprecated) Configuration for Vertex AI training resources

Parameters:

machine_type (str)
gpu_type (str)
gpu_limit (int)
num_replicas (int)

ClearField(field_name)[source]#

Clears the contents of a given field.

Inside a oneof group, clears the field set. If the name neither refers to a defined field or oneof group, ValueError is raised.

Parameters:: field_name (str) – The name of the field to check for presence.
Raises:: ValueError – if the field_name is not a member of this message.
Return type:: None

DESCRIPTOR: google.protobuf.descriptor.Descriptor[source]#

GPU_LIMIT_FIELD_NUMBER: int[source]#

GPU_TYPE_FIELD_NUMBER: int[source]#

MACHINE_TYPE_FIELD_NUMBER: int[source]#

NUM_REPLICAS_FIELD_NUMBER: int[source]#

gpu_limit: int[source]#: GPU limit for training job. Must be set to 0 for cpu training.

gpu_type: str[source]#: GPU type for training job. Must be set to ‘ACCELERATOR_TYPE_UNSPECIFIED’ for cpu training.

machine_type: str[source]#: Machine type for training job

num_replicas: int[source]#: Num workers for training job

snapchat.research.gbml.gigl_resource_config_pb2.Component_Config_Populator: Component[source]#

snapchat.research.gbml.gigl_resource_config_pb2.Component_Config_Validator: Component[source]#

snapchat.research.gbml.gigl_resource_config_pb2.Component_Data_Preprocessor: Component[source]#

snapchat.research.gbml.gigl_resource_config_pb2.Component_Inferencer: Component[source]#

snapchat.research.gbml.gigl_resource_config_pb2.Component_Split_Generator: Component[source]#

snapchat.research.gbml.gigl_resource_config_pb2.Component_Subgraph_Sampler: Component[source]#

snapchat.research.gbml.gigl_resource_config_pb2.Component_Trainer: Component[source]#

snapchat.research.gbml.gigl_resource_config_pb2.Component_Unknown: Component[source]#

snapchat.research.gbml.gigl_resource_config_pb2.DESCRIPTOR: google.protobuf.descriptor.FileDescriptor[source]#

snapchat.research.gbml.gigl_resource_config_pb2.global___Component[source]#

snapchat.research.gbml.gigl_resource_config_pb2.global___DataPreprocessorConfig[source]#

snapchat.research.gbml.gigl_resource_config_pb2.global___DataflowResourceConfig[source]#

snapchat.research.gbml.gigl_resource_config_pb2.global___DistributedTrainerConfig[source]#

snapchat.research.gbml.gigl_resource_config_pb2.global___GiglResourceConfig[source]#

snapchat.research.gbml.gigl_resource_config_pb2.global___InferencerResourceConfig[source]#

snapchat.research.gbml.gigl_resource_config_pb2.global___KFPResourceConfig[source]#

snapchat.research.gbml.gigl_resource_config_pb2.global___KFPTrainerConfig[source]#

snapchat.research.gbml.gigl_resource_config_pb2.global___LocalResourceConfig[source]#

snapchat.research.gbml.gigl_resource_config_pb2.global___LocalTrainerConfig[source]#

snapchat.research.gbml.gigl_resource_config_pb2.global___SharedResourceConfig[source]#

snapchat.research.gbml.gigl_resource_config_pb2.global___SparkResourceConfig[source]#

snapchat.research.gbml.gigl_resource_config_pb2.global___TrainerResourceConfig[source]#

snapchat.research.gbml.gigl_resource_config_pb2.global___VertexAiResourceConfig[source]#

snapchat.research.gbml.gigl_resource_config_pb2.global___VertexAiTrainerConfig[source]#

`Component_Config_Populator`
`Component_Config_Validator`
`Component_Data_Preprocessor`
`Component_Inferencer`
`Component_Split_Generator`
`Component_Subgraph_Sampler`
`Component_Trainer`
`Component_Unknown`
`DESCRIPTOR`
`global___Component`
`global___DataPreprocessorConfig`
`global___DataflowResourceConfig`
`global___DistributedTrainerConfig`
`global___GiglResourceConfig`
`global___InferencerResourceConfig`
`global___KFPResourceConfig`
`global___KFPTrainerConfig`
`global___LocalResourceConfig`
`global___LocalTrainerConfig`
`global___SharedResourceConfig`
`global___SparkResourceConfig`
`global___TrainerResourceConfig`
`global___VertexAiResourceConfig`
`global___VertexAiTrainerConfig`

snapchat.research.gbml.gigl_resource_config_pb2#

Attributes#

Classes#

Module Contents#