viam.proto.app.mltraining ========================= .. py:module:: viam.proto.app.mltraining .. autoapi-nested-parse:: @generated by Viam. Do not edit manually! Classes ------- .. autoapisummary:: viam.proto.app.mltraining.MLTrainingServiceBase viam.proto.app.mltraining.MLTrainingServiceStub viam.proto.app.mltraining.UnimplementedMLTrainingServiceBase viam.proto.app.mltraining.CancelTrainingJobRequest viam.proto.app.mltraining.CancelTrainingJobResponse viam.proto.app.mltraining.Container viam.proto.app.mltraining.DeleteCompletedTrainingJobRequest viam.proto.app.mltraining.DeleteCompletedTrainingJobResponse viam.proto.app.mltraining.GetTrainingJobLogsRequest viam.proto.app.mltraining.GetTrainingJobLogsResponse viam.proto.app.mltraining.GetTrainingJobRequest viam.proto.app.mltraining.GetTrainingJobResponse viam.proto.app.mltraining.ListSupportedContainersRequest viam.proto.app.mltraining.ListSupportedContainersResponse viam.proto.app.mltraining.ListTrainingJobsRequest viam.proto.app.mltraining.ListTrainingJobsResponse viam.proto.app.mltraining.ModelFramework viam.proto.app.mltraining.ModelType viam.proto.app.mltraining.SubmitCustomTrainingJobRequest viam.proto.app.mltraining.SubmitCustomTrainingJobResponse viam.proto.app.mltraining.SubmitTrainingJobRequest viam.proto.app.mltraining.SubmitTrainingJobResponse viam.proto.app.mltraining.TrainingJobLogEntry viam.proto.app.mltraining.TrainingJobMetadata viam.proto.app.mltraining.TrainingStatus Package Contents ---------------- .. py:class:: MLTrainingServiceBase Bases: :py:obj:`abc.ABC` Helper class that provides a standard way to create an ABC using inheritance. .. py:method:: SubmitTrainingJob(stream: grpclib.server.Stream[app.mltraining.v1.ml_training_pb2.SubmitTrainingJobRequest, app.mltraining.v1.ml_training_pb2.SubmitTrainingJobResponse]) -> None :abstractmethod: :async: .. py:method:: SubmitCustomTrainingJob(stream: grpclib.server.Stream[app.mltraining.v1.ml_training_pb2.SubmitCustomTrainingJobRequest, app.mltraining.v1.ml_training_pb2.SubmitCustomTrainingJobResponse]) -> None :abstractmethod: :async: .. py:method:: GetTrainingJob(stream: grpclib.server.Stream[app.mltraining.v1.ml_training_pb2.GetTrainingJobRequest, app.mltraining.v1.ml_training_pb2.GetTrainingJobResponse]) -> None :abstractmethod: :async: .. py:method:: ListTrainingJobs(stream: grpclib.server.Stream[app.mltraining.v1.ml_training_pb2.ListTrainingJobsRequest, app.mltraining.v1.ml_training_pb2.ListTrainingJobsResponse]) -> None :abstractmethod: :async: .. py:method:: CancelTrainingJob(stream: grpclib.server.Stream[app.mltraining.v1.ml_training_pb2.CancelTrainingJobRequest, app.mltraining.v1.ml_training_pb2.CancelTrainingJobResponse]) -> None :abstractmethod: :async: .. py:method:: DeleteCompletedTrainingJob(stream: grpclib.server.Stream[app.mltraining.v1.ml_training_pb2.DeleteCompletedTrainingJobRequest, app.mltraining.v1.ml_training_pb2.DeleteCompletedTrainingJobResponse]) -> None :abstractmethod: :async: .. py:method:: GetTrainingJobLogs(stream: grpclib.server.Stream[app.mltraining.v1.ml_training_pb2.GetTrainingJobLogsRequest, app.mltraining.v1.ml_training_pb2.GetTrainingJobLogsResponse]) -> None :abstractmethod: :async: .. py:method:: ListSupportedContainers(stream: grpclib.server.Stream[app.mltraining.v1.ml_training_pb2.ListSupportedContainersRequest, app.mltraining.v1.ml_training_pb2.ListSupportedContainersResponse]) -> None :abstractmethod: :async: .. py:method:: __mapping__() -> Dict[str, grpclib.const.Handler] .. py:class:: MLTrainingServiceStub(channel: grpclib.client.Channel) .. py:attribute:: SubmitTrainingJob .. py:attribute:: SubmitCustomTrainingJob .. py:attribute:: GetTrainingJob .. py:attribute:: ListTrainingJobs .. py:attribute:: CancelTrainingJob .. py:attribute:: DeleteCompletedTrainingJob .. py:attribute:: GetTrainingJobLogs .. py:attribute:: ListSupportedContainers .. py:class:: UnimplementedMLTrainingServiceBase Bases: :py:obj:`MLTrainingServiceBase` Helper class that provides a standard way to create an ABC using inheritance. .. py:method:: SubmitTrainingJob(stream: grpclib.server.Stream[app.mltraining.v1.ml_training_pb2.SubmitTrainingJobRequest, app.mltraining.v1.ml_training_pb2.SubmitTrainingJobResponse]) -> None :async: .. py:method:: SubmitCustomTrainingJob(stream: grpclib.server.Stream[app.mltraining.v1.ml_training_pb2.SubmitCustomTrainingJobRequest, app.mltraining.v1.ml_training_pb2.SubmitCustomTrainingJobResponse]) -> None :async: .. py:method:: GetTrainingJob(stream: grpclib.server.Stream[app.mltraining.v1.ml_training_pb2.GetTrainingJobRequest, app.mltraining.v1.ml_training_pb2.GetTrainingJobResponse]) -> None :async: .. py:method:: ListTrainingJobs(stream: grpclib.server.Stream[app.mltraining.v1.ml_training_pb2.ListTrainingJobsRequest, app.mltraining.v1.ml_training_pb2.ListTrainingJobsResponse]) -> None :async: .. py:method:: CancelTrainingJob(stream: grpclib.server.Stream[app.mltraining.v1.ml_training_pb2.CancelTrainingJobRequest, app.mltraining.v1.ml_training_pb2.CancelTrainingJobResponse]) -> None :async: .. py:method:: DeleteCompletedTrainingJob(stream: grpclib.server.Stream[app.mltraining.v1.ml_training_pb2.DeleteCompletedTrainingJobRequest, app.mltraining.v1.ml_training_pb2.DeleteCompletedTrainingJobResponse]) -> None :async: .. py:method:: GetTrainingJobLogs(stream: grpclib.server.Stream[app.mltraining.v1.ml_training_pb2.GetTrainingJobLogsRequest, app.mltraining.v1.ml_training_pb2.GetTrainingJobLogsResponse]) -> None :async: .. py:method:: ListSupportedContainers(stream: grpclib.server.Stream[app.mltraining.v1.ml_training_pb2.ListSupportedContainersRequest, app.mltraining.v1.ml_training_pb2.ListSupportedContainersResponse]) -> None :async: .. py:class:: CancelTrainingJobRequest(*, id: str = ...) Bases: :py:obj:`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. .. py:attribute:: id :type: str .. py:class:: CancelTrainingJobResponse Bases: :py:obj:`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. .. py:class:: Container(*, key: str = ..., uri: str = ..., framework: str = ..., description: str = ..., eol: google.protobuf.timestamp_pb2.Timestamp | None = ...) Bases: :py:obj:`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. .. py:attribute:: key :type: str .. py:attribute:: uri :type: str .. py:attribute:: framework :type: str .. py:attribute:: description :type: str .. py:method:: eol() -> google.protobuf.timestamp_pb2.Timestamp .. py:method:: HasField(field_name: _HasFieldArgType) -> bool 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, :exc:`ValueError` will be raised. :param field_name: The name of the field to check for presence. :type field_name: str :returns: Whether a value has been set for the named field. :rtype: bool :raises ValueError: if the `field_name` is not a member of this message. .. py:class:: DeleteCompletedTrainingJobRequest(*, id: str = ...) Bases: :py:obj:`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. .. py:attribute:: id :type: str .. py:class:: DeleteCompletedTrainingJobResponse Bases: :py:obj:`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. .. py:class:: GetTrainingJobLogsRequest(*, id: str = ..., page_token: str | None = ...) Bases: :py:obj:`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. .. py:attribute:: id :type: str .. py:attribute:: page_token :type: str .. py:method:: HasField(field_name: _HasFieldArgType) -> bool 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, :exc:`ValueError` will be raised. :param field_name: The name of the field to check for presence. :type field_name: str :returns: Whether a value has been set for the named field. :rtype: bool :raises ValueError: if the `field_name` is not a member of this message. .. py:method:: WhichOneof(oneof_group: _WhichOneofArgType__page_token) -> _WhichOneofReturnType__page_token | None Returns the name of the field that is set inside a oneof group. If no field is set, returns None. :param oneof_group: the name of the oneof group to check. :type oneof_group: str :returns: The name of the group that is set, or None. :rtype: str or None :raises ValueError: no group with the given name exists .. py:class:: GetTrainingJobLogsResponse(*, logs: collections.abc.Iterable[Global___TrainingJobLogEntry] | None = ..., next_page_token: str = ...) Bases: :py:obj:`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. .. py:attribute:: next_page_token :type: str .. py:method:: logs() -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[Global___TrainingJobLogEntry] .. py:class:: GetTrainingJobRequest(*, id: str = ...) Bases: :py:obj:`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. .. py:attribute:: id :type: str .. py:class:: GetTrainingJobResponse(*, metadata: Global___TrainingJobMetadata | None = ...) Bases: :py:obj:`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. .. py:method:: metadata() -> Global___TrainingJobMetadata .. py:method:: HasField(field_name: _HasFieldArgType) -> bool 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, :exc:`ValueError` will be raised. :param field_name: The name of the field to check for presence. :type field_name: str :returns: Whether a value has been set for the named field. :rtype: bool :raises ValueError: if the `field_name` is not a member of this message. .. py:class:: ListSupportedContainersRequest Bases: :py:obj:`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. .. py:class:: ListSupportedContainersResponse(*, container_map: collections.abc.Mapping[str, Global___Container] | None = ...) Bases: :py:obj:`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. .. py:class:: ContainerMapEntry(*, key: str = ..., value: Global___Container | None = ...) Bases: :py:obj:`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. .. py:attribute:: key :type: str .. py:method:: value() -> Global___Container .. py:method:: HasField(field_name: _HasFieldArgType) -> bool 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, :exc:`ValueError` will be raised. :param field_name: The name of the field to check for presence. :type field_name: str :returns: Whether a value has been set for the named field. :rtype: bool :raises ValueError: if the `field_name` is not a member of this message. .. py:method:: container_map() -> google.protobuf.internal.containers.MessageMap[str, Global___Container] ex value: container_map: { "tf:2.15": Container { key: "tf:2.15" uri: "us-docker.pkg.dev/vertex-ai/training/tf-gpu.2-15.py310:latest" description: "Tensorflow 2.15" eol: { seconds: 1772630400, nanos: 0 } // 2026-03-03T00:00:00Z } } .. py:class:: ListTrainingJobsRequest(*, organization_id: str = ..., status: Global___TrainingStatus = ...) Bases: :py:obj:`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. .. py:attribute:: organization_id :type: str .. py:attribute:: status :type: Global___TrainingStatus .. py:class:: ListTrainingJobsResponse(*, jobs: collections.abc.Iterable[Global___TrainingJobMetadata] | None = ...) Bases: :py:obj:`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. .. py:method:: jobs() -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[Global___TrainingJobMetadata] .. py:class:: ModelFramework Bases: :py:obj:`_ModelFramework` .. py:class:: ModelType Bases: :py:obj:`_ModelType` .. py:class:: SubmitCustomTrainingJobRequest(*, dataset_id: str = ..., registry_item_id: str = ..., registry_item_version: str = ..., organization_id: str = ..., model_name: str = ..., model_version: str = ..., arguments: collections.abc.Mapping[str, str] | None = ..., container_version: str = ...) Bases: :py:obj:`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. .. py:class:: ArgumentsEntry(*, key: str = ..., value: str = ...) Bases: :py:obj:`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. .. py:attribute:: key :type: str .. py:attribute:: value :type: str .. py:attribute:: dataset_id :type: str .. py:attribute:: registry_item_id :type: str .. py:attribute:: registry_item_version :type: str .. py:attribute:: organization_id :type: str .. py:attribute:: model_name :type: str .. py:attribute:: model_version :type: str .. py:attribute:: container_version :type: str .. py:method:: arguments() -> google.protobuf.internal.containers.ScalarMap[str, str] .. py:class:: SubmitCustomTrainingJobResponse(*, id: str = ...) Bases: :py:obj:`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. .. py:attribute:: id :type: str .. py:class:: SubmitTrainingJobRequest(*, dataset_id: str = ..., organization_id: str = ..., model_name: str = ..., model_version: str = ..., model_type: Global___ModelType = ..., model_framework: Global___ModelFramework = ..., tags: collections.abc.Iterable[str] | None = ...) Bases: :py:obj:`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. .. py:attribute:: dataset_id :type: str .. py:attribute:: organization_id :type: str .. py:attribute:: model_name :type: str .. py:attribute:: model_version :type: str .. py:attribute:: model_type :type: Global___ModelType .. py:attribute:: model_framework :type: Global___ModelFramework .. py:method:: tags() -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[str] .. py:class:: SubmitTrainingJobResponse(*, id: str = ...) Bases: :py:obj:`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. .. py:attribute:: id :type: str .. py:class:: TrainingJobLogEntry(*, level: str = ..., time: google.protobuf.timestamp_pb2.Timestamp | None = ..., message: str = ...) Bases: :py:obj:`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. .. py:attribute:: level :type: str .. py:attribute:: message :type: str .. py:method:: time() -> google.protobuf.timestamp_pb2.Timestamp .. py:method:: HasField(field_name: _HasFieldArgType) -> bool 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, :exc:`ValueError` will be raised. :param field_name: The name of the field to check for presence. :type field_name: str :returns: Whether a value has been set for the named field. :rtype: bool :raises ValueError: if the `field_name` is not a member of this message. .. py:class:: TrainingJobMetadata(*, id: str = ..., dataset_id: str = ..., organization_id: str = ..., model_name: str = ..., model_version: str = ..., model_type: Global___ModelType = ..., model_framework: Global___ModelFramework = ..., is_custom_job: bool = ..., registry_item_id: str = ..., registry_item_version: str = ..., status: Global___TrainingStatus = ..., error_status: google.rpc.status_pb2.Status | None = ..., created_on: google.protobuf.timestamp_pb2.Timestamp | None = ..., last_modified: google.protobuf.timestamp_pb2.Timestamp | None = ..., training_started: google.protobuf.timestamp_pb2.Timestamp | None = ..., training_ended: google.protobuf.timestamp_pb2.Timestamp | None = ..., synced_model_id: str = ..., tags: collections.abc.Iterable[str] | None = ..., arguments: collections.abc.Mapping[str, str] | None = ..., container_version: str = ...) Bases: :py:obj:`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. .. py:class:: ArgumentsEntry(*, key: str = ..., value: str = ...) Bases: :py:obj:`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. .. py:attribute:: key :type: str .. py:attribute:: value :type: str .. py:attribute:: id :type: str .. py:attribute:: dataset_id :type: str .. py:attribute:: organization_id :type: str .. py:attribute:: model_name :type: str .. py:attribute:: model_version :type: str .. py:attribute:: model_type :type: Global___ModelType .. py:attribute:: model_framework :type: Global___ModelFramework .. py:attribute:: is_custom_job :type: bool .. py:attribute:: registry_item_id :type: str .. py:attribute:: registry_item_version :type: str .. py:attribute:: status :type: Global___TrainingStatus .. py:attribute:: synced_model_id :type: str .. py:attribute:: container_version :type: str .. py:method:: error_status() -> google.rpc.status_pb2.Status .. py:method:: created_on() -> google.protobuf.timestamp_pb2.Timestamp .. py:method:: last_modified() -> google.protobuf.timestamp_pb2.Timestamp .. py:method:: training_started() -> google.protobuf.timestamp_pb2.Timestamp .. py:method:: training_ended() -> google.protobuf.timestamp_pb2.Timestamp .. py:method:: tags() -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[str] .. py:method:: arguments() -> google.protobuf.internal.containers.ScalarMap[str, str] .. py:method:: HasField(field_name: _HasFieldArgType) -> bool 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, :exc:`ValueError` will be raised. :param field_name: The name of the field to check for presence. :type field_name: str :returns: Whether a value has been set for the named field. :rtype: bool :raises ValueError: if the `field_name` is not a member of this message. .. py:class:: TrainingStatus Bases: :py:obj:`_TrainingStatus`