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
`VertexAiGraphStoreConfig`	Configuration for lauching Vertex AI clusters with both graph store and compute pools
`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=..., vertex_ai_graph_store_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)
vertex_ai_graph_store_inferencer_config (global___VertexAiGraphStoreConfig | 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_GRAPH_STORE_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_graph_store_inferencer_config: global___VertexAiGraphStoreConfig[source]#

Return type:: global___VertexAiGraphStoreConfig

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=..., vertex_ai_graph_store_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)
vertex_ai_graph_store_trainer_config (global___VertexAiGraphStoreConfig | 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_GRAPH_STORE_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_graph_store_trainer_config: global___VertexAiGraphStoreConfig[source]#

Return type:: global___VertexAiGraphStoreConfig

property vertex_ai_trainer_config: global___VertexAiResourceConfig[source]#

Return type:: global___VertexAiResourceConfig

class snapchat.research.gbml.gigl_resource_config_pb2.VertexAiGraphStoreConfig(*, graph_store_pool=..., compute_pool=...)[source]#

Bases: google.protobuf.message.Message

Configuration for lauching Vertex AI clusters with both graph store and compute pools Under the hood, this uses Vertex AI Multi-Pool Training See https://cloud.google.com/vertex-ai/docs/training/distributed-training for more info. This cluster setup should be used when you want store your graph on separate machines from the compute machines e.g. you can get lots of big memory machines and separate gpu machines individually, but getting lots of gpu machines with lots of memory is challenging.

Parameters:

graph_store_pool (global___VertexAiResourceConfig | None)
compute_pool (global___VertexAiResourceConfig | 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.

COMPUTE_POOL_FIELD_NUMBER: int[source]#

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

GRAPH_STORE_POOL_FIELD_NUMBER: int[source]#

property compute_pool: global___VertexAiResourceConfig[source]#

Return type:: global___VertexAiResourceConfig

property graph_store_pool: 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=..., gcp_region_override=...)[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)
gcp_region_override (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]#

GCP_REGION_OVERRIDE_FIELD_NUMBER: int[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]#

gcp_region_override: str[source]#: Region override If provided, then the Vertex AI Job will be launched in the provided region. Otherwise, will launch jobs in the region specified at CommonComputeConfig.region ex: “us-west1” NOTE: If set, then there may be data egress costs from CommonComputeConfig.region -> gcp_region_override

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___VertexAiGraphStoreConfig[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___VertexAiGraphStoreConfig`
`global___VertexAiResourceConfig`
`global___VertexAiTrainerConfig`

snapchat.research.gbml.gigl_resource_config_pb2#

Attributes#

Classes#

Module Contents#