snapchat.research.gbml.gbml_config_pb2#

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

Attributes#

`DESCRIPTOR`
`global___GbmlConfig`

Classes#

GbmlConfig

TODO: document all protos with comments.

Module Contents#

class snapchat.research.gbml.gbml_config_pb2.GbmlConfig(*, task_metadata=..., graph_metadata=..., shared_config=..., dataset_config=..., trainer_config=..., inferencer_config=..., post_processor_config=..., metrics_config=..., profiler_config=..., feature_flags=...)[source]#

Bases: google.protobuf.message.Message

TODO: document all protos with comments.

Parameters:

task_metadata (global___GbmlConfig | None)
graph_metadata (snapchat.research.gbml.graph_schema_pb2.GraphMetadata | None)
shared_config (global___GbmlConfig | None)
dataset_config (global___GbmlConfig | None)
trainer_config (global___GbmlConfig | None)
inferencer_config (global___GbmlConfig | None)
post_processor_config (global___GbmlConfig | None)
metrics_config (global___GbmlConfig | None)
profiler_config (global___GbmlConfig | None)
feature_flags (collections.abc.Mapping[str, str] | None)

class DatasetConfig(*, data_preprocessor_config=..., subgraph_sampler_config=..., split_generator_config=...)[source]#

Bases: google.protobuf.message.Message

Contains config related to generating training data for a GML task.

Parameters:

data_preprocessor_config (global___GbmlConfig | None)
subgraph_sampler_config (global___GbmlConfig | None)
split_generator_config (global___GbmlConfig | None)

class DataPreprocessorConfig(*, data_preprocessor_config_cls_path=..., data_preprocessor_args=...)[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:

data_preprocessor_config_cls_path (str)
data_preprocessor_args (collections.abc.Mapping[str, str] | None)

class DataPreprocessorArgsEntry(*, 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

DATA_PREPROCESSOR_ARGS_FIELD_NUMBER: int[source]#

DATA_PREPROCESSOR_CONFIG_CLS_PATH_FIELD_NUMBER: int[source]#

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

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

Arguments to instantiate concrete DataPreprocessorConfig instance with.

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

data_preprocessor_config_cls_path: str[source]#: Uri pointing to user-written DataPreprocessorConfig class definition.

class SplitGeneratorConfig(*, split_strategy_cls_path=..., split_strategy_args=..., assigner_cls_path=..., assigner_args=...)[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:

split_strategy_cls_path (str)
split_strategy_args (collections.abc.Mapping[str, str] | None)
assigner_cls_path (str)
assigner_args (collections.abc.Mapping[str, str] | None)

class AssignerArgsEntry(*, 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]#

class SplitStrategyArgsEntry(*, 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

ASSIGNER_ARGS_FIELD_NUMBER: int[source]#

ASSIGNER_CLS_PATH_FIELD_NUMBER: int[source]#

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

SPLIT_STRATEGY_ARGS_FIELD_NUMBER: int[source]#

SPLIT_STRATEGY_CLS_PATH_FIELD_NUMBER: int[source]#

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

Arguments to instantiate concrete Assigner instance with.

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

assigner_cls_path: str[source]#: Module path to concrete Assigner instance

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

Arguments to instantiate concrete SplitStrategy instance with.

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

split_strategy_cls_path: str[source]#: Module path to concrete SplitStrategy instance.

class SubgraphSamplerConfig(*, num_hops=..., num_neighbors_to_sample=..., subgraph_sampling_strategy=..., num_positive_samples=..., experimental_flags=..., num_max_training_samples_to_output=..., num_user_defined_positive_samples=..., num_user_defined_negative_samples=..., graph_db_config=...)[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:

num_hops (int)
num_neighbors_to_sample (int)
subgraph_sampling_strategy (snapchat.research.gbml.subgraph_sampling_strategy_pb2.SubgraphSamplingStrategy | None)
num_positive_samples (int)
experimental_flags (collections.abc.Mapping[str, str] | None)
num_max_training_samples_to_output (int)
num_user_defined_positive_samples (int)
num_user_defined_negative_samples (int)
graph_db_config (global___GbmlConfig | None)

class ExperimentalFlagsEntry(*, 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.

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

EXPERIMENTAL_FLAGS_FIELD_NUMBER: int[source]#

GRAPH_DB_CONFIG_FIELD_NUMBER: int[source]#

NUM_HOPS_FIELD_NUMBER: int[source]#

NUM_MAX_TRAINING_SAMPLES_TO_OUTPUT_FIELD_NUMBER: int[source]#

NUM_NEIGHBORS_TO_SAMPLE_FIELD_NUMBER: int[source]#

NUM_POSITIVE_SAMPLES_FIELD_NUMBER: int[source]#

NUM_USER_DEFINED_NEGATIVE_SAMPLES_FIELD_NUMBER: int[source]#

NUM_USER_DEFINED_POSITIVE_SAMPLES_FIELD_NUMBER: int[source]#

SUBGRAPH_SAMPLING_STRATEGY_FIELD_NUMBER: int[source]#

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

(deprecated) number of hard negative samples (3,4hops) used in NodeAnchorBasedLinkPredictionTask also used in loss computation. Random negatives will always be used even when there are no hard negatives uint32 num_hard_negative_samples = 4;

Arguments for experimental_flags, can be permutation_strategy: ‘deterministic’ or ‘non-deterministic’

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

property graph_db_config: global___GbmlConfig[source]#

If specified, intention is to run ingestion into graphDB for subgraph sampler

Return type:: global___GbmlConfig

num_hops: int[source]#: number of hops for subgraph sampler to include

num_max_training_samples_to_output: int[source]#: max number of training samples (i.e. nodes to store as main samples for training) If this is not provided or is set to 0, all nodes will be included for training

num_neighbors_to_sample: int[source]#: num_neighbors_to_sample indicates the max number of neighbors to sample for each hop num_neighbors_to_sample can be set to -1 to indicate no sampling (include all neighbors)

num_positive_samples: int[source]#: number of positive samples (1hop) used in NodeAnchorBasedLinkPredictionTask as part of loss computation. It cannot be 0. And it’s recommended to be larger than 1 due to the split filtering logic in split generator, to guarantee most samples to have at least one positive for it to not be excluded in training.

num_user_defined_negative_samples: int[source]#: number of user defined negative samples. Treated as hard negative samples. Used in NodeAnchorBasedLinkPredictionTask Also used in loss computation. Random negatives will always be used even when there are no user defined hard negatives

num_user_defined_positive_samples: int[source]#: number of user defined positive samples. Used in NodeAnchorBasedLinkPredictionTask as part of loss computation. If num_user_defined_positive_samples is specified num_positive_samples will be ignored as positive samples will only be drawn from user defined positive samples.

property subgraph_sampling_strategy: snapchat.research.gbml.subgraph_sampling_strategy_pb2.SubgraphSamplingStrategy[source]#

num hops and num neighbors to sample is deprecated in favor of neighbor_sampling_strategy. Used to specify how the graphs which are used for message passing are constructed

Return type:: snapchat.research.gbml.subgraph_sampling_strategy_pb2.SubgraphSamplingStrategy

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.

DATA_PREPROCESSOR_CONFIG_FIELD_NUMBER: int[source]#

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

SPLIT_GENERATOR_CONFIG_FIELD_NUMBER: int[source]#

SUBGRAPH_SAMPLER_CONFIG_FIELD_NUMBER: int[source]#

property data_preprocessor_config: global___GbmlConfig[source]#

Return type:: global___GbmlConfig

property split_generator_config: global___GbmlConfig[source]#

Return type:: global___GbmlConfig

property subgraph_sampler_config: global___GbmlConfig[source]#

Return type:: global___GbmlConfig

class FeatureFlagsEntry(*, 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]#

class GraphDBConfig(*, graph_db_ingestion_cls_path=..., graph_db_ingestion_args=..., graph_db_args=..., graph_db_sampler_config=...)[source]#

Bases: google.protobuf.message.Message

Generic Configuration for a GraphDB connection.

Parameters:

graph_db_ingestion_cls_path (str)
graph_db_ingestion_args (collections.abc.Mapping[str, str] | None)
graph_db_args (collections.abc.Mapping[str, str] | None)
graph_db_sampler_config (global___GbmlConfig | None)

class GraphDBServiceConfig(*, graph_db_client_class_path=...)[source]#

Bases: google.protobuf.message.Message

Scala-specific configuration.

Parameters:: graph_db_client_class_path (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]#

GRAPH_DB_CLIENT_CLASS_PATH_FIELD_NUMBER: int[source]#

graph_db_client_class_path: str[source]#: Scala absolute class path pointing to an implementation of DBClient[DBResult] e.g. my.team.graph_db.DBClient.

class GraphDbArgsEntry(*, 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]#

class GraphDbIngestionArgsEntry(*, 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.

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

GRAPH_DB_ARGS_FIELD_NUMBER: int[source]#

GRAPH_DB_INGESTION_ARGS_FIELD_NUMBER: int[source]#

GRAPH_DB_INGESTION_CLS_PATH_FIELD_NUMBER: int[source]#

GRAPH_DB_SAMPLER_CONFIG_FIELD_NUMBER: int[source]#

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

General arguments required for graphDB (graph space, port, etc.) These are passed to both the Python and Scala implementations.

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

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

Arguments to instantiate concrete BaseIngestion instance with.

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

graph_db_ingestion_cls_path: str[source]#: Python class path pointing to user-written BaseIngestion` class definition. e.g. my.team.graph_db.BaseInjectionImpl. This class is currently, as an implementation detail, used for injestion only. We document this purely for information purposes and may change the implementation at any time.

property graph_db_sampler_config: global___GbmlConfig[source]#

If provided, then an implementation of a DBClient[DBResult] Scala class for a GraphDB. Intended to be used to inject specific implementations at runtime. The object constructed from this is currently, as an implementation detail, used for sampling only. We document this purely for information purposes and may change the implementation at any time.

Return type:: global___GbmlConfig

class InferencerConfig(*, inferencer_args=..., inferencer_cls_path=..., cls_path=..., command=..., inference_batch_size=...)[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:

inferencer_args (collections.abc.Mapping[str, str] | None)
inferencer_cls_path (str)
cls_path (str)
command (str)
inference_batch_size (int)

class InferencerArgsEntry(*, 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.

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

CLS_PATH_FIELD_NUMBER: int[source]#

COMMAND_FIELD_NUMBER: int[source]#

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

INFERENCER_ARGS_FIELD_NUMBER: int[source]#

INFERENCER_CLS_PATH_FIELD_NUMBER: int[source]#

INFERENCE_BATCH_SIZE_FIELD_NUMBER: int[source]#

cls_path: str[source]#: Path pointing to inferencer class definition.

command: str[source]#: Command to use for launching inference job

inference_batch_size: int[source]#: Optional. If set, will be used to batch inference samples to a specific size before call for inference is made Defaults to setting in python/gigl/src/inference/gnn_inferencer.py

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

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

inferencer_cls_path: str[source]#: (deprecated) Path to modeling task spec class path to construct model for inference. Used for the subgraph-sampling-based inference process.

class MetricsConfig(*, metrics_cls_path=..., metrics_args=...)[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:

metrics_cls_path (str)
metrics_args (collections.abc.Mapping[str, str] | None)

class MetricsArgsEntry(*, 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

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

METRICS_ARGS_FIELD_NUMBER: int[source]#

METRICS_CLS_PATH_FIELD_NUMBER: int[source]#

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

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

metrics_cls_path: str[source]#

class PostProcessorConfig(*, post_processor_args=..., post_processor_cls_path=...)[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:

post_processor_args (collections.abc.Mapping[str, str] | None)
post_processor_cls_path (str)

class PostProcessorArgsEntry(*, 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

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

POST_PROCESSOR_ARGS_FIELD_NUMBER: int[source]#

POST_PROCESSOR_CLS_PATH_FIELD_NUMBER: int[source]#

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

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

post_processor_cls_path: str[source]#

class ProfilerConfig(*, should_enable_profiler=..., profiler_log_dir=..., profiler_args=...)[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:

should_enable_profiler (bool)
profiler_log_dir (str)
profiler_args (collections.abc.Mapping[str, str] | None)

class ProfilerArgsEntry(*, 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

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

PROFILER_ARGS_FIELD_NUMBER: int[source]#

PROFILER_LOG_DIR_FIELD_NUMBER: int[source]#

SHOULD_ENABLE_PROFILER_FIELD_NUMBER: int[source]#

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

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

profiler_log_dir: str[source]#

should_enable_profiler: bool[source]#

class SharedConfig(*, preprocessed_metadata_uri=..., flattened_graph_metadata=..., dataset_metadata=..., trained_model_metadata=..., inference_metadata=..., postprocessed_metadata=..., shared_args=..., is_graph_directed=..., should_skip_training=..., should_skip_automatic_temp_asset_cleanup=..., should_skip_inference=..., should_skip_model_evaluation=..., should_include_isolated_nodes_in_training=...)[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:

preprocessed_metadata_uri (str)
flattened_graph_metadata (snapchat.research.gbml.flattened_graph_metadata_pb2.FlattenedGraphMetadata | None)
dataset_metadata (snapchat.research.gbml.dataset_metadata_pb2.DatasetMetadata | None)
trained_model_metadata (snapchat.research.gbml.trained_model_metadata_pb2.TrainedModelMetadata | None)
inference_metadata (snapchat.research.gbml.inference_metadata_pb2.InferenceMetadata | None)
postprocessed_metadata (snapchat.research.gbml.postprocessed_metadata_pb2.PostProcessedMetadata | None)
shared_args (collections.abc.Mapping[str, str] | None)
is_graph_directed (bool)
should_skip_training (bool)
should_skip_automatic_temp_asset_cleanup (bool)
should_skip_inference (bool)
should_skip_model_evaluation (bool)
should_include_isolated_nodes_in_training (bool)

class SharedArgsEntry(*, 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.

DATASET_METADATA_FIELD_NUMBER: int[source]#

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

FLATTENED_GRAPH_METADATA_FIELD_NUMBER: int[source]#

INFERENCE_METADATA_FIELD_NUMBER: int[source]#

IS_GRAPH_DIRECTED_FIELD_NUMBER: int[source]#

POSTPROCESSED_METADATA_FIELD_NUMBER: int[source]#

PREPROCESSED_METADATA_URI_FIELD_NUMBER: int[source]#

SHARED_ARGS_FIELD_NUMBER: int[source]#

SHOULD_INCLUDE_ISOLATED_NODES_IN_TRAINING_FIELD_NUMBER: int[source]#

SHOULD_SKIP_AUTOMATIC_TEMP_ASSET_CLEANUP_FIELD_NUMBER: int[source]#

SHOULD_SKIP_INFERENCE_FIELD_NUMBER: int[source]#

SHOULD_SKIP_MODEL_EVALUATION_FIELD_NUMBER: int[source]#

SHOULD_SKIP_TRAINING_FIELD_NUMBER: int[source]#

TRAINED_MODEL_METADATA_FIELD_NUMBER: int[source]#

property dataset_metadata: snapchat.research.gbml.dataset_metadata_pb2.DatasetMetadata[source]#

DatasetMetadata message, which designates location of SplitGenerator outputs.

Return type:: snapchat.research.gbml.dataset_metadata_pb2.DatasetMetadata

property flattened_graph_metadata: snapchat.research.gbml.flattened_graph_metadata_pb2.FlattenedGraphMetadata[source]#

FlattenedGraphMetadata message, which designates locations of GraphFlat outputs.

Return type:: snapchat.research.gbml.flattened_graph_metadata_pb2.FlattenedGraphMetadata

property inference_metadata: snapchat.research.gbml.inference_metadata_pb2.InferenceMetadata[source]#

InferenceMetadata message, which designates location of Inferencer outputs.

Return type:: snapchat.research.gbml.inference_metadata_pb2.InferenceMetadata

is_graph_directed: bool[source]#: is the graph directed or undirected (bidirectional)

property postprocessed_metadata: snapchat.research.gbml.postprocessed_metadata_pb2.PostProcessedMetadata[source]#

PostProcessedMetadata message, which designates location of PostProcessor outputs.

Return type:: snapchat.research.gbml.postprocessed_metadata_pb2.PostProcessedMetadata

preprocessed_metadata_uri: str[source]#: Uri where DataPreprocessor generates the PreprocessedMetadata proto.

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

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

should_include_isolated_nodes_in_training: bool[source]#: If set to true, will include isolated nodes in training data As isolated nodes do not have positive neighbors, self loop will be added SGS outputs training samples including isolated nodes, trainer adds self loops in training subgraphs

should_skip_automatic_temp_asset_cleanup: bool[source]#: If set to true, will skip automatic clean up of temp assets Useful if you are running hyper param tuning jobs and dont want to continuously run the whole pipeline

should_skip_inference: bool[source]#: to skip inference or not (for training only jobs)

should_skip_model_evaluation: bool[source]#: If set, we will not compute or export model metrics like MRR, etc Has a side effect if should_skip_training is set as well to result in not generating training samples and only RNNs needed for inference.

should_skip_training: bool[source]#: to skip training or not (inference only)

property trained_model_metadata: snapchat.research.gbml.trained_model_metadata_pb2.TrainedModelMetadata[source]#

TrainedModelMetadata message, which designates location of Trainer outputs.

Return type:: snapchat.research.gbml.trained_model_metadata_pb2.TrainedModelMetadata

class TaskMetadata(*, node_based_task_metadata=..., node_anchor_based_link_prediction_task_metadata=..., link_based_task_metadata=...)[source]#

Bases: google.protobuf.message.Message

Indicates the training task specification and metadata for the config.

Parameters:

node_based_task_metadata (global___GbmlConfig | None)
node_anchor_based_link_prediction_task_metadata (global___GbmlConfig | None)
link_based_task_metadata (global___GbmlConfig | None)

class LinkBasedTaskMetadata(*, supervision_edge_types=...)[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:: supervision_edge_types (collections.abc.Iterable[snapchat.research.gbml.graph_schema_pb2.EdgeType] | 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

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

SUPERVISION_EDGE_TYPES_FIELD_NUMBER: int[source]#

property supervision_edge_types: google.protobuf.internal.containers.RepeatedCompositeFieldContainer[snapchat.research.gbml.graph_schema_pb2.EdgeType][source]#

Return type:: google.protobuf.internal.containers.RepeatedCompositeFieldContainer[snapchat.research.gbml.graph_schema_pb2.EdgeType]

class NodeAnchorBasedLinkPredictionTaskMetadata(*, supervision_edge_types=...)[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:: supervision_edge_types (collections.abc.Iterable[snapchat.research.gbml.graph_schema_pb2.EdgeType] | 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

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

SUPERVISION_EDGE_TYPES_FIELD_NUMBER: int[source]#

property supervision_edge_types: google.protobuf.internal.containers.RepeatedCompositeFieldContainer[snapchat.research.gbml.graph_schema_pb2.EdgeType][source]#

Return type:: google.protobuf.internal.containers.RepeatedCompositeFieldContainer[snapchat.research.gbml.graph_schema_pb2.EdgeType]

class NodeBasedTaskMetadata(*, supervision_node_types=...)[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:: supervision_node_types (collections.abc.Iterable[str] | 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

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

SUPERVISION_NODE_TYPES_FIELD_NUMBER: int[source]#

property supervision_node_types: google.protobuf.internal.containers.RepeatedScalarFieldContainer[str][source]#

Return type:: google.protobuf.internal.containers.RepeatedScalarFieldContainer[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

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]#

LINK_BASED_TASK_METADATA_FIELD_NUMBER: int[source]#

NODE_ANCHOR_BASED_LINK_PREDICTION_TASK_METADATA_FIELD_NUMBER: int[source]#

NODE_BASED_TASK_METADATA_FIELD_NUMBER: int[source]#

property link_based_task_metadata: global___GbmlConfig[source]#

Return type:: global___GbmlConfig

property node_anchor_based_link_prediction_task_metadata: global___GbmlConfig[source]#

Return type:: global___GbmlConfig

property node_based_task_metadata: global___GbmlConfig[source]#

Return type:: global___GbmlConfig

class TrainerConfig(*, trainer_cls_path=..., trainer_args=..., cls_path=..., command=..., should_log_to_tensorboard=...)[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:

trainer_cls_path (str)
trainer_args (collections.abc.Mapping[str, str] | None)
cls_path (str)
command (str)
should_log_to_tensorboard (bool)

class TrainerArgsEntry(*, 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.

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

CLS_PATH_FIELD_NUMBER: int[source]#

COMMAND_FIELD_NUMBER: int[source]#

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

SHOULD_LOG_TO_TENSORBOARD_FIELD_NUMBER: int[source]#

TRAINER_ARGS_FIELD_NUMBER: int[source]#

TRAINER_CLS_PATH_FIELD_NUMBER: int[source]#

cls_path: str[source]#: Path pointing to trainer class definition.

command: str[source]#: Command to use for launching trainer job

should_log_to_tensorboard: bool[source]#: Weather to log to tensorboard or not (defaults to false)

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

Arguments to parameterize training process with.

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

trainer_cls_path: str[source]#: (deprecated) Uri pointing to user-written BaseTrainer class definition. Used for the subgraph-sampling-based training process.

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.

DATASET_CONFIG_FIELD_NUMBER: int[source]#

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

FEATURE_FLAGS_FIELD_NUMBER: int[source]#

GRAPH_METADATA_FIELD_NUMBER: int[source]#

INFERENCER_CONFIG_FIELD_NUMBER: int[source]#

METRICS_CONFIG_FIELD_NUMBER: int[source]#

POST_PROCESSOR_CONFIG_FIELD_NUMBER: int[source]#

PROFILER_CONFIG_FIELD_NUMBER: int[source]#

SHARED_CONFIG_FIELD_NUMBER: int[source]#

TASK_METADATA_FIELD_NUMBER: int[source]#

TRAINER_CONFIG_FIELD_NUMBER: int[source]#

property dataset_config: global___GbmlConfig[source]#

Return type:: global___GbmlConfig

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

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

property graph_metadata: snapchat.research.gbml.graph_schema_pb2.GraphMetadata[source]#

Return type:: snapchat.research.gbml.graph_schema_pb2.GraphMetadata

property inferencer_config: global___GbmlConfig[source]#

Return type:: global___GbmlConfig

property metrics_config: global___GbmlConfig[source]#

Return type:: global___GbmlConfig

property post_processor_config: global___GbmlConfig[source]#

Return type:: global___GbmlConfig

property profiler_config: global___GbmlConfig[source]#

Return type:: global___GbmlConfig

property shared_config: global___GbmlConfig[source]#

Return type:: global___GbmlConfig

property task_metadata: global___GbmlConfig[source]#

Return type:: global___GbmlConfig

property trainer_config: global___GbmlConfig[source]#

Return type:: global___GbmlConfig

snapchat.research.gbml.gbml_config_pb2.DESCRIPTOR: google.protobuf.descriptor.FileDescriptor[source]#

snapchat.research.gbml.gbml_config_pb2.global___GbmlConfig[source]#