Bentoml

`zenml.integrations.bentoml` `special`

Initialization of the BentoML integration for ZenML.

The BentoML integration allows you to use the BentoML model serving to implement continuous model deployment.

`BentoMLIntegration (Integration)`

Definition of BentoML integration for ZenML.

Source code in zenml/integrations/bentoml/__init__.py

class BentoMLIntegration(Integration):
    """Definition of BentoML integration for ZenML."""

    NAME = BENTOML
    REQUIREMENTS = [
        "bentoml>=1.0.10",
    ]

    @classmethod
    def activate(cls) -> None:
        """Activate the BentoML integration."""
        from zenml.integrations.bentoml import materializers  # noqa
        from zenml.integrations.bentoml import model_deployers  # noqa
        from zenml.integrations.bentoml import services  # noqa

    @classmethod
    def flavors(cls) -> List[Type[Flavor]]:
        """Declare the stack component flavors for BentoML.

        Returns:
            List of stack component flavors for this integration.
        """
        from zenml.integrations.bentoml.flavors import (
            BentoMLModelDeployerFlavor,
        )

        return [BentoMLModelDeployerFlavor]

`activate()` `classmethod`

Activate the BentoML integration.

Source code in zenml/integrations/bentoml/__init__.py

@classmethod
def activate(cls) -> None:
    """Activate the BentoML integration."""
    from zenml.integrations.bentoml import materializers  # noqa
    from zenml.integrations.bentoml import model_deployers  # noqa
    from zenml.integrations.bentoml import services  # noqa

`flavors()` `classmethod`

Declare the stack component flavors for BentoML.

Returns:

Type	Description
`List[Type[zenml.stack.flavor.Flavor]]`	List of stack component flavors for this integration.

Source code in zenml/integrations/bentoml/__init__.py

@classmethod
def flavors(cls) -> List[Type[Flavor]]:
    """Declare the stack component flavors for BentoML.

    Returns:
        List of stack component flavors for this integration.
    """
    from zenml.integrations.bentoml.flavors import (
        BentoMLModelDeployerFlavor,
    )

    return [BentoMLModelDeployerFlavor]

`constants`

BentoML constants.

`flavors` `special`

BentoML integration flavors.

`bentoml_model_deployer_flavor`

BentoML model deployer flavor.

`BentoMLModelDeployerConfig (BaseModelDeployerConfig)`

Configuration for the BentoMLModelDeployer.

Source code in zenml/integrations/bentoml/flavors/bentoml_model_deployer_flavor.py

class BentoMLModelDeployerConfig(BaseModelDeployerConfig):
    """Configuration for the BentoMLModelDeployer."""

    service_path: str = ""

`BentoMLModelDeployerFlavor (BaseModelDeployerFlavor)`

Flavor for the BentoML model deployer.

Source code in zenml/integrations/bentoml/flavors/bentoml_model_deployer_flavor.py

class BentoMLModelDeployerFlavor(BaseModelDeployerFlavor):
    """Flavor for the BentoML model deployer."""

    @property
    def name(self) -> str:
        """Name of the flavor.

        Returns:
            Name of the flavor.
        """
        return BENTOML_MODEL_DEPLOYER_FLAVOR

    @property
    def docs_url(self) -> Optional[str]:
        """A url to point at docs explaining this flavor.

        Returns:
            A flavor docs url.
        """
        return self.generate_default_docs_url()

    @property
    def sdk_docs_url(self) -> Optional[str]:
        """A url to point at SDK docs explaining this flavor.

        Returns:
            A flavor SDK docs url.
        """
        return self.generate_default_sdk_docs_url()

    @property
    def logo_url(self) -> str:
        """A url to represent the flavor in the dashboard.

        Returns:
            The flavor logo.
        """
        return "https://public-flavor-logos.s3.eu-central-1.amazonaws.com/model_deployer/bentoml.png"

    @property
    def config_class(self) -> Type[BentoMLModelDeployerConfig]:
        """Returns `BentoMLModelDeployerConfig` config class.

        Returns:
                The config class.
        """
        return BentoMLModelDeployerConfig

    @property
    def implementation_class(self) -> Type["BentoMLModelDeployer"]:
        """Implementation class for this flavor.

        Returns:
            The implementation class.
        """
        from zenml.integrations.bentoml.model_deployers import (
            BentoMLModelDeployer,
        )

        return BentoMLModelDeployer

`config_class: Type[zenml.integrations.bentoml.flavors.bentoml_model_deployer_flavor.BentoMLModelDeployerConfig]` `property` `readonly`

Returns BentoMLModelDeployerConfig config class.

Returns:

Type	Description
`Type[zenml.integrations.bentoml.flavors.bentoml_model_deployer_flavor.BentoMLModelDeployerConfig]`	The config class.

`docs_url: Optional[str]` `property` `readonly`

A url to point at docs explaining this flavor.

Returns:

Type	Description
`Optional[str]`	A flavor docs url.

`implementation_class: Type[BentoMLModelDeployer]` `property` `readonly`

Implementation class for this flavor.

Returns:

Type	Description
`Type[BentoMLModelDeployer]`	The implementation class.

`logo_url: str` `property` `readonly`

A url to represent the flavor in the dashboard.

Returns:

Type	Description
`str`	The flavor logo.

`name: str` `property` `readonly`

Name of the flavor.

Returns:

Type	Description
`str`	Name of the flavor.

`sdk_docs_url: Optional[str]` `property` `readonly`

A url to point at SDK docs explaining this flavor.

Returns:

Type	Description
`Optional[str]`	A flavor SDK docs url.

`materializers` `special`

Initialization of the BentoML Bento Materializer.

`bentoml_bento_materializer`

Materializer for BentoML Bento objects.

`BentoMaterializer (BaseMaterializer)`

Materializer for Bentoml Bento objects.

Source code in zenml/integrations/bentoml/materializers/bentoml_bento_materializer.py

class BentoMaterializer(BaseMaterializer):
    """Materializer for Bentoml Bento objects."""

    ASSOCIATED_TYPES: ClassVar[Tuple[Type[Any], ...]] = (bento.Bento,)
    ASSOCIATED_ARTIFACT_TYPE: ClassVar[ArtifactType] = ArtifactType.DATA

    def load(self, data_type: Type[bento.Bento]) -> bento.Bento:
        """Read from artifact store and return a Bento object.

        Args:
            data_type: An bento.Bento type.

        Returns:
            An bento.Bento object.
        """
        with self.get_temporary_directory(delete_at_exit=False) as temp_dir:
            # Copy from artifact store to temporary directory
            io_utils.copy_dir(self.uri, temp_dir)

            # Load the Bento from the temporary directory
            imported_bento = Bento.import_from(
                os.path.join(temp_dir, DEFAULT_BENTO_FILENAME)
            )

            # Try save the Bento to the local BentoML store
            try:
                _ = bentoml.get(imported_bento.tag)
            except BentoMLException:
                imported_bento.save()
            return imported_bento

    def save(self, bento: bento.Bento) -> None:
        """Write to artifact store.

        Args:
            bento: An bento.Bento object.
        """
        with self.get_temporary_directory(delete_at_exit=True) as temp_dir:
            temp_bento_path = os.path.join(temp_dir, DEFAULT_BENTO_FILENAME)
            bentoml.export_bento(bento.tag, temp_bento_path)
            io_utils.copy_dir(temp_dir, self.uri)

    def extract_metadata(
        self, bento: bento.Bento
    ) -> Dict[str, "MetadataType"]:
        """Extract metadata from the given `Bento` object.

        Args:
            bento: The `Bento` object to extract metadata from.

        Returns:
            The extracted metadata as a dictionary.
        """
        return {
            "bento_info_name": bento.info.name,
            "bento_info_version": bento.info.version,
            "bento_tag_name": bento.tag.name,
            "bentoml_version": bento.info.bentoml_version,
        }

`extract_metadata(self, bento)`

Extract metadata from the given Bento object.

Parameters:

Name	Type	Description	Default
`bento`	`bentoml._internal.bento.bento.Bento`	The `Bento` object to extract metadata from.	required

Returns:

Type	Description
`Dict[str, MetadataType]`	The extracted metadata as a dictionary.

Source code in zenml/integrations/bentoml/materializers/bentoml_bento_materializer.py

def extract_metadata(
    self, bento: bento.Bento
) -> Dict[str, "MetadataType"]:
    """Extract metadata from the given `Bento` object.

    Args:
        bento: The `Bento` object to extract metadata from.

    Returns:
        The extracted metadata as a dictionary.
    """
    return {
        "bento_info_name": bento.info.name,
        "bento_info_version": bento.info.version,
        "bento_tag_name": bento.tag.name,
        "bentoml_version": bento.info.bentoml_version,
    }

`load(self, data_type)`

Read from artifact store and return a Bento object.

Parameters:

Name	Type	Description	Default
`data_type`	`Type[bentoml._internal.bento.bento.Bento]`	An bento.Bento type.	required

Returns:

Type	Description
`bentoml._internal.bento.bento.Bento`	An bento.Bento object.

Source code in zenml/integrations/bentoml/materializers/bentoml_bento_materializer.py

def load(self, data_type: Type[bento.Bento]) -> bento.Bento:
    """Read from artifact store and return a Bento object.

    Args:
        data_type: An bento.Bento type.

    Returns:
        An bento.Bento object.
    """
    with self.get_temporary_directory(delete_at_exit=False) as temp_dir:
        # Copy from artifact store to temporary directory
        io_utils.copy_dir(self.uri, temp_dir)

        # Load the Bento from the temporary directory
        imported_bento = Bento.import_from(
            os.path.join(temp_dir, DEFAULT_BENTO_FILENAME)
        )

        # Try save the Bento to the local BentoML store
        try:
            _ = bentoml.get(imported_bento.tag)
        except BentoMLException:
            imported_bento.save()
        return imported_bento

`save(self, bento)`

Write to artifact store.

Parameters:

Name	Type	Description	Default
`bento`	`bentoml._internal.bento.bento.Bento`	An bento.Bento object.	required

Source code in zenml/integrations/bentoml/materializers/bentoml_bento_materializer.py

def save(self, bento: bento.Bento) -> None:
    """Write to artifact store.

    Args:
        bento: An bento.Bento object.
    """
    with self.get_temporary_directory(delete_at_exit=True) as temp_dir:
        temp_bento_path = os.path.join(temp_dir, DEFAULT_BENTO_FILENAME)
        bentoml.export_bento(bento.tag, temp_bento_path)
        io_utils.copy_dir(temp_dir, self.uri)

`model_deployers` `special`

Initialization of the BentoML Model Deployer.

`bentoml_model_deployer`

Implementation of the BentoML Model Deployer.

`BentoMLModelDeployer (BaseModelDeployer)`

BentoML model deployer stack component implementation.

Source code in zenml/integrations/bentoml/model_deployers/bentoml_model_deployer.py

class BentoMLModelDeployer(BaseModelDeployer):
    """BentoML model deployer stack component implementation."""

    NAME: ClassVar[str] = "BentoML"
    FLAVOR: ClassVar[Type[BaseModelDeployerFlavor]] = (
        BentoMLModelDeployerFlavor
    )

    _service_path: Optional[str] = None

    @property
    def config(self) -> BentoMLModelDeployerConfig:
        """Returns the `BentoMLModelDeployerConfig` config.

        Returns:
            The configuration.
        """
        return cast(BentoMLModelDeployerConfig, self._config)

    @staticmethod
    def get_service_path(id_: UUID) -> str:
        """Get the path where local BentoML service information is stored.

        This includes the deployment service configuration, PID and log files
        are stored.

        Args:
            id_: The ID of the BentoML model deployer.

        Returns:
            The service path.
        """
        service_path = os.path.join(
            GlobalConfiguration().local_stores_path,
            str(id_),
        )
        create_dir_recursive_if_not_exists(service_path)
        return service_path

    @property
    def local_path(self) -> str:
        """Returns the path to the root directory.

        This is where all configurations for BentoML deployment daemon processes
        are stored.

        If the service path is not set in the config by the user, the path is
        set to a local default path according to the component ID.

        Returns:
            The path to the local service root directory.
        """
        if self._service_path is not None:
            return self._service_path

        if self.config.service_path:
            self._service_path = self.config.service_path
        else:
            self._service_path = self.get_service_path(self.id)

        create_dir_recursive_if_not_exists(self._service_path)
        return self._service_path

    @staticmethod
    def get_model_server_info(
        service_instance: BaseService,
    ) -> Dict[str, Optional[str]]:
        """Return implementation specific information on the model server.

        Args:
            service_instance: BentoML deployment service object

        Returns:
            A dictionary containing the model server information.

        Raises:
            ValueError: If the service type is not supported.
        """
        if (
            service_instance.SERVICE_TYPE.name
            == BENTOML_CONTAINER_DEPLOYMENT_SERVICE_NAME
        ):
            service_instance = cast(
                BentoMLContainerDeploymentService, service_instance
            )
        elif (
            service_instance.SERVICE_TYPE.name
            == BENTOML_LOCAL_DEPLOYMENT_SERVICE_NAME
        ):
            service_instance = cast(
                BentoMLLocalDeploymentService, service_instance
            )
        else:
            raise ValueError(
                f"Unsupported service type: {service_instance.SERVICE_TYPE.name}"
            )

        predictions_apis_urls = ""
        if service_instance.prediction_apis_urls is not None:  # type: ignore
            predictions_apis_urls = ", ".join(
                [
                    api
                    for api in service_instance.prediction_apis_urls  # type: ignore
                    if api is not None
                ]
            )

        service_config = service_instance.config
        assert isinstance(
            service_config,
            (BentoMLLocalDeploymentConfig, BentoMLContainerDeploymentConfig),
        )

        service_status = service_instance.status
        assert isinstance(
            service_status, (ContainerServiceStatus, LocalDaemonServiceStatus)
        )

        return {
            "HEALTH_CHECK_URL": service_instance.get_healthcheck_url(),
            "PREDICTION_URL": service_instance.get_prediction_url(),
            "BENTO_TAG": service_config.bento_tag,
            "MODEL_NAME": service_config.model_name,
            "MODEL_URI": service_config.model_uri,
            "BENTO_URI": service_config.bento_uri,
            "SERVICE_PATH": service_status.runtime_path,
            "DAEMON_PID": str(service_status.pid)
            if hasattr(service_status, "pid")
            else None,
            "PREDICTION_APIS_URLS": predictions_apis_urls,
        }

    def perform_deploy_model(
        self,
        id: UUID,
        config: ServiceConfig,
        timeout: int = DEFAULT_SERVICE_START_STOP_TIMEOUT,
    ) -> BaseService:
        """Create a new BentoML deployment service or update an existing one.

        This should serve the supplied model and deployment configuration.

        This method has two modes of operation, depending on the `replace`
        argument value:

          * if `replace` is False, calling this method will create a new BentoML
            deployment server to reflect the model and other configuration
            parameters specified in the supplied BentoML service `config`.

          * if `replace` is True, this method will first attempt to find an
            existing BentoML deployment service that is *equivalent* to the
            supplied configuration parameters. Two or more BentoML deployment
            services are considered equivalent if they have the same
            `pipeline_name`, `pipeline_step_name` and `model_name` configuration
            parameters. To put it differently, two BentoML deployment services
            are equivalent if they serve versions of the same model deployed by
            the same pipeline step. If an equivalent BentoML deployment is found,
            it will be updated in place to reflect the new configuration
            parameters.

        Callers should set `replace` to True if they want a continuous model
        deployment workflow that doesn't spin up a new BentoML deployment
        server for each new model version. If multiple equivalent BentoML
        deployment servers are found, one is selected at random to be updated
        and the others are deleted.

        Args:
            id: the UUID of the BentoML model deployer.
            config: the configuration of the model to be deployed with BentoML.
            timeout: the timeout in seconds to wait for the BentoML server
                to be provisioned and successfully started or updated. If set
                to 0, the method will return immediately after the BentoML
                server is provisioned, without waiting for it to fully start.

        Returns:
            The ZenML BentoML deployment service object that can be used to
            interact with the BentoML model http server.
        """
        service = self._create_new_service(
            id=id, timeout=timeout, config=config
        )
        logger.info(f"Created a new BentoML deployment service: {service}")
        return service

    def _clean_up_existing_service(
        self,
        timeout: int,
        force: bool,
        existing_service: BaseService,
    ) -> None:
        # stop the older service
        existing_service.stop(timeout=timeout, force=force)

        # assert that the service is either a BentoMLLocalDeploymentService or a BentoMLContainerDeploymentService
        if not isinstance(
            existing_service,
            (BentoMLLocalDeploymentService, BentoMLContainerDeploymentService),
        ):
            raise ValueError(
                f"Unsupported service type: {type(existing_service)}"
            )

        # delete the old configuration file
        if existing_service.status.runtime_path:
            shutil.rmtree(existing_service.status.runtime_path)

    # the step will receive a config from the user that mentions the number
    # of workers etc.the step implementation will create a new config using
    # all values from the user and add values like pipeline name, model_uri
    def _create_new_service(
        self, id: UUID, timeout: int, config: ServiceConfig
    ) -> BaseService:
        """Creates a new BentoMLDeploymentService.

        Args:
            id: the ID of the BentoML deployment service to be created or updated.
            timeout: the timeout in seconds to wait for the BentoML server
                to be provisioned and successfully started or updated.
            config: the configuration of the model to be deployed with BentoML.

        Returns:
            The BentoMLDeploymentService object that can be used to interact
            with the BentoML model server.

        Raises:
            ValueError: If the service type is not supported.
        """
        assert isinstance(
            config,
            (BentoMLLocalDeploymentConfig, BentoMLContainerDeploymentConfig),
        )
        # set the root runtime path with the stack component's UUID
        config.root_runtime_path = self.local_path
        # create a new service for the new model
        # if the config is of type BentoMLLocalDeploymentConfig, create a
        # BentoMLLocalDeploymentService, otherwise create a
        # BentoMLContainerDeploymentService
        service: BaseService
        if isinstance(config, BentoMLLocalDeploymentConfig):
            service = BentoMLLocalDeploymentService(uuid=id, config=config)
        elif isinstance(config, BentoMLContainerDeploymentConfig):
            service = BentoMLContainerDeploymentService(uuid=id, config=config)
        else:
            raise ValueError(f"Unsupported service type: {type(config)}")
        service.start(timeout=timeout)

        return service

    def perform_stop_model(
        self,
        service: BaseService,
        timeout: int = DEFAULT_SERVICE_START_STOP_TIMEOUT,
        force: bool = False,
    ) -> BaseService:
        """Method to stop a model server.

        Args:
            service: The service to stop.
            timeout: Timeout in seconds to wait for the service to stop.
            force: If True, force the service to stop.

        Returns:
            The stopped service.
        """
        service.stop(timeout=timeout, force=force)
        return service

    def perform_start_model(
        self,
        service: BaseService,
        timeout: int = DEFAULT_SERVICE_START_STOP_TIMEOUT,
    ) -> BaseService:
        """Method to start a model server.

        Args:
            service: The service to start.
            timeout: Timeout in seconds to wait for the service to start.

        Returns:
            The started service.
        """
        service.start(timeout=timeout)
        return service

    def perform_delete_model(
        self,
        service: BaseService,
        timeout: int = DEFAULT_SERVICE_START_STOP_TIMEOUT,
        force: bool = False,
    ) -> None:
        """Method to delete all configuration of a model server.

        Args:
            service: The service to delete.
            timeout: Timeout in seconds to wait for the service to stop.
            force: If True, force the service to stop.
        """
        self._clean_up_existing_service(
            existing_service=service, timeout=timeout, force=force
        )

`config: BentoMLModelDeployerConfig` `property` `readonly`

Returns the BentoMLModelDeployerConfig config.

Returns:

Type	Description
`BentoMLModelDeployerConfig`	The configuration.

`local_path: str` `property` `readonly`

Returns the path to the root directory.

This is where all configurations for BentoML deployment daemon processes are stored.

If the service path is not set in the config by the user, the path is set to a local default path according to the component ID.

Returns:

Type	Description
`str`	The path to the local service root directory.

`FLAVOR (BaseModelDeployerFlavor)`

Flavor for the BentoML model deployer.

Source code in zenml/integrations/bentoml/model_deployers/bentoml_model_deployer.py

class BentoMLModelDeployerFlavor(BaseModelDeployerFlavor):
    """Flavor for the BentoML model deployer."""

    @property
    def name(self) -> str:
        """Name of the flavor.

        Returns:
            Name of the flavor.
        """
        return BENTOML_MODEL_DEPLOYER_FLAVOR

    @property
    def docs_url(self) -> Optional[str]:
        """A url to point at docs explaining this flavor.

        Returns:
            A flavor docs url.
        """
        return self.generate_default_docs_url()

    @property
    def sdk_docs_url(self) -> Optional[str]:
        """A url to point at SDK docs explaining this flavor.

        Returns:
            A flavor SDK docs url.
        """
        return self.generate_default_sdk_docs_url()

    @property
    def logo_url(self) -> str:
        """A url to represent the flavor in the dashboard.

        Returns:
            The flavor logo.
        """
        return "https://public-flavor-logos.s3.eu-central-1.amazonaws.com/model_deployer/bentoml.png"

    @property
    def config_class(self) -> Type[BentoMLModelDeployerConfig]:
        """Returns `BentoMLModelDeployerConfig` config class.

        Returns:
                The config class.
        """
        return BentoMLModelDeployerConfig

    @property
    def implementation_class(self) -> Type["BentoMLModelDeployer"]:
        """Implementation class for this flavor.

        Returns:
            The implementation class.
        """
        from zenml.integrations.bentoml.model_deployers import (
            BentoMLModelDeployer,
        )

        return BentoMLModelDeployer

config_class: Type[zenml.integrations.bentoml.flavors.bentoml_model_deployer_flavor.BentoMLModelDeployerConfig] property readonly

Returns BentoMLModelDeployerConfig config class.

Returns:

Type	Description
`Type[zenml.integrations.bentoml.flavors.bentoml_model_deployer_flavor.BentoMLModelDeployerConfig]`	The config class.

docs_url: Optional[str] property readonly

A url to point at docs explaining this flavor.

Returns:

Type	Description
`Optional[str]`	A flavor docs url.

implementation_class: Type[BentoMLModelDeployer] property readonly

Implementation class for this flavor.

Returns:

Type	Description
`Type[BentoMLModelDeployer]`	The implementation class.

logo_url: str property readonly

A url to represent the flavor in the dashboard.

Returns:

Type	Description
`str`	The flavor logo.

name: str property readonly

Name of the flavor.

Returns:

Type	Description
`str`	Name of the flavor.

sdk_docs_url: Optional[str] property readonly

A url to point at SDK docs explaining this flavor.

Returns:

Type	Description
`Optional[str]`	A flavor SDK docs url.

`get_model_server_info(service_instance)` `staticmethod`

Return implementation specific information on the model server.

Parameters:

Name	Type	Description	Default
`service_instance`	`BaseService`	BentoML deployment service object	required

Returns:

Type	Description
`Dict[str, Optional[str]]`	A dictionary containing the model server information.

Exceptions:

Type	Description
`ValueError`	If the service type is not supported.

Source code in zenml/integrations/bentoml/model_deployers/bentoml_model_deployer.py

@staticmethod
def get_model_server_info(
    service_instance: BaseService,
) -> Dict[str, Optional[str]]:
    """Return implementation specific information on the model server.

    Args:
        service_instance: BentoML deployment service object

    Returns:
        A dictionary containing the model server information.

    Raises:
        ValueError: If the service type is not supported.
    """
    if (
        service_instance.SERVICE_TYPE.name
        == BENTOML_CONTAINER_DEPLOYMENT_SERVICE_NAME
    ):
        service_instance = cast(
            BentoMLContainerDeploymentService, service_instance
        )
    elif (
        service_instance.SERVICE_TYPE.name
        == BENTOML_LOCAL_DEPLOYMENT_SERVICE_NAME
    ):
        service_instance = cast(
            BentoMLLocalDeploymentService, service_instance
        )
    else:
        raise ValueError(
            f"Unsupported service type: {service_instance.SERVICE_TYPE.name}"
        )

    predictions_apis_urls = ""
    if service_instance.prediction_apis_urls is not None:  # type: ignore
        predictions_apis_urls = ", ".join(
            [
                api
                for api in service_instance.prediction_apis_urls  # type: ignore
                if api is not None
            ]
        )

    service_config = service_instance.config
    assert isinstance(
        service_config,
        (BentoMLLocalDeploymentConfig, BentoMLContainerDeploymentConfig),
    )

    service_status = service_instance.status
    assert isinstance(
        service_status, (ContainerServiceStatus, LocalDaemonServiceStatus)
    )

    return {
        "HEALTH_CHECK_URL": service_instance.get_healthcheck_url(),
        "PREDICTION_URL": service_instance.get_prediction_url(),
        "BENTO_TAG": service_config.bento_tag,
        "MODEL_NAME": service_config.model_name,
        "MODEL_URI": service_config.model_uri,
        "BENTO_URI": service_config.bento_uri,
        "SERVICE_PATH": service_status.runtime_path,
        "DAEMON_PID": str(service_status.pid)
        if hasattr(service_status, "pid")
        else None,
        "PREDICTION_APIS_URLS": predictions_apis_urls,
    }

`get_service_path(id_)` `staticmethod`

Get the path where local BentoML service information is stored.

This includes the deployment service configuration, PID and log files are stored.

Parameters:

Name	Type	Description	Default
`id_`	`UUID`	The ID of the BentoML model deployer.	required

Returns:

Type	Description
`str`	The service path.

Source code in zenml/integrations/bentoml/model_deployers/bentoml_model_deployer.py

@staticmethod
def get_service_path(id_: UUID) -> str:
    """Get the path where local BentoML service information is stored.

    This includes the deployment service configuration, PID and log files
    are stored.

    Args:
        id_: The ID of the BentoML model deployer.

    Returns:
        The service path.
    """
    service_path = os.path.join(
        GlobalConfiguration().local_stores_path,
        str(id_),
    )
    create_dir_recursive_if_not_exists(service_path)
    return service_path

`perform_delete_model(self, service, timeout=60, force=False)`

Method to delete all configuration of a model server.

Parameters:

Name	Type	Description	Default
`service`	`BaseService`	The service to delete.	required
`timeout`	`int`	Timeout in seconds to wait for the service to stop.	`60`
`force`	`bool`	If True, force the service to stop.	`False`

Source code in zenml/integrations/bentoml/model_deployers/bentoml_model_deployer.py

def perform_delete_model(
    self,
    service: BaseService,
    timeout: int = DEFAULT_SERVICE_START_STOP_TIMEOUT,
    force: bool = False,
) -> None:
    """Method to delete all configuration of a model server.

    Args:
        service: The service to delete.
        timeout: Timeout in seconds to wait for the service to stop.
        force: If True, force the service to stop.
    """
    self._clean_up_existing_service(
        existing_service=service, timeout=timeout, force=force
    )

`perform_deploy_model(self, id, config, timeout=60)`

Create a new BentoML deployment service or update an existing one.

This should serve the supplied model and deployment configuration.

This method has two modes of operation, depending on the replace argument value:

if replace is False, calling this method will create a new BentoML deployment server to reflect the model and other configuration parameters specified in the supplied BentoML service config.
if replace is True, this method will first attempt to find an existing BentoML deployment service that is equivalent to the supplied configuration parameters. Two or more BentoML deployment services are considered equivalent if they have the same pipeline_name, pipeline_step_name and model_name configuration parameters. To put it differently, two BentoML deployment services are equivalent if they serve versions of the same model deployed by the same pipeline step. If an equivalent BentoML deployment is found, it will be updated in place to reflect the new configuration parameters.

Callers should set replace to True if they want a continuous model deployment workflow that doesn't spin up a new BentoML deployment server for each new model version. If multiple equivalent BentoML deployment servers are found, one is selected at random to be updated and the others are deleted.

Parameters:

Name	Type	Description	Default
`id`	`UUID`	the UUID of the BentoML model deployer.	required
`config`	`ServiceConfig`	the configuration of the model to be deployed with BentoML.	required
`timeout`	`int`	the timeout in seconds to wait for the BentoML server to be provisioned and successfully started or updated. If set to 0, the method will return immediately after the BentoML server is provisioned, without waiting for it to fully start.	`60`

Returns:

Type	Description
`BaseService`	The ZenML BentoML deployment service object that can be used to interact with the BentoML model http server.

Source code in zenml/integrations/bentoml/model_deployers/bentoml_model_deployer.py

def perform_deploy_model(
    self,
    id: UUID,
    config: ServiceConfig,
    timeout: int = DEFAULT_SERVICE_START_STOP_TIMEOUT,
) -> BaseService:
    """Create a new BentoML deployment service or update an existing one.

    This should serve the supplied model and deployment configuration.

    This method has two modes of operation, depending on the `replace`
    argument value:

      * if `replace` is False, calling this method will create a new BentoML
        deployment server to reflect the model and other configuration
        parameters specified in the supplied BentoML service `config`.

      * if `replace` is True, this method will first attempt to find an
        existing BentoML deployment service that is *equivalent* to the
        supplied configuration parameters. Two or more BentoML deployment
        services are considered equivalent if they have the same
        `pipeline_name`, `pipeline_step_name` and `model_name` configuration
        parameters. To put it differently, two BentoML deployment services
        are equivalent if they serve versions of the same model deployed by
        the same pipeline step. If an equivalent BentoML deployment is found,
        it will be updated in place to reflect the new configuration
        parameters.

    Callers should set `replace` to True if they want a continuous model
    deployment workflow that doesn't spin up a new BentoML deployment
    server for each new model version. If multiple equivalent BentoML
    deployment servers are found, one is selected at random to be updated
    and the others are deleted.

    Args:
        id: the UUID of the BentoML model deployer.
        config: the configuration of the model to be deployed with BentoML.
        timeout: the timeout in seconds to wait for the BentoML server
            to be provisioned and successfully started or updated. If set
            to 0, the method will return immediately after the BentoML
            server is provisioned, without waiting for it to fully start.

    Returns:
        The ZenML BentoML deployment service object that can be used to
        interact with the BentoML model http server.
    """
    service = self._create_new_service(
        id=id, timeout=timeout, config=config
    )
    logger.info(f"Created a new BentoML deployment service: {service}")
    return service

`perform_start_model(self, service, timeout=60)`

Method to start a model server.

Parameters:

Name	Type	Description	Default
`service`	`BaseService`	The service to start.	required
`timeout`	`int`	Timeout in seconds to wait for the service to start.	`60`

Returns:

Type	Description
`BaseService`	The started service.

Source code in zenml/integrations/bentoml/model_deployers/bentoml_model_deployer.py

def perform_start_model(
    self,
    service: BaseService,
    timeout: int = DEFAULT_SERVICE_START_STOP_TIMEOUT,
) -> BaseService:
    """Method to start a model server.

    Args:
        service: The service to start.
        timeout: Timeout in seconds to wait for the service to start.

    Returns:
        The started service.
    """
    service.start(timeout=timeout)
    return service

`perform_stop_model(self, service, timeout=60, force=False)`

Method to stop a model server.

Parameters:

Name	Type	Description	Default
`service`	`BaseService`	The service to stop.	required
`timeout`	`int`	Timeout in seconds to wait for the service to stop.	`60`
`force`	`bool`	If True, force the service to stop.	`False`

Returns:

Type	Description
`BaseService`	The stopped service.

Source code in zenml/integrations/bentoml/model_deployers/bentoml_model_deployer.py

def perform_stop_model(
    self,
    service: BaseService,
    timeout: int = DEFAULT_SERVICE_START_STOP_TIMEOUT,
    force: bool = False,
) -> BaseService:
    """Method to stop a model server.

    Args:
        service: The service to stop.
        timeout: Timeout in seconds to wait for the service to stop.
        force: If True, force the service to stop.

    Returns:
        The stopped service.
    """
    service.stop(timeout=timeout, force=force)
    return service

`services` `special`

Initialization for BentoML services.

`bentoml_container_deployment`

Implementation for the BentoML container deployment service.

`BentoMLContainerDeploymentConfig (ContainerServiceConfig)`

BentoML container deployment configuration.

Source code in zenml/integrations/bentoml/services/bentoml_container_deployment.py

class BentoMLContainerDeploymentConfig(ContainerServiceConfig):
    """BentoML container deployment configuration."""

    model_name: str
    model_uri: str
    bento_tag: str
    bento_uri: Optional[str] = None
    platform: Optional[str] = None
    image: str = ""
    image_tag: Optional[str] = None
    features: Optional[List[str]] = None
    file: Optional[str] = None
    apis: List[str] = []
    working_dir: Optional[str] = None
    workers: int = 1
    backlog: int = 2048
    host: Optional[str] = None
    port: Optional[int] = None

`BentoMLContainerDeploymentEndpoint (ContainerServiceEndpoint)`

A service endpoint exposed by the BentoML container deployment service.

Attributes:

Name	Type	Description
`config`	`BentoMLContainerDeploymentEndpointConfig`	service endpoint configuration

Source code in zenml/integrations/bentoml/services/bentoml_container_deployment.py

class BentoMLContainerDeploymentEndpoint(ContainerServiceEndpoint):
    """A service endpoint exposed by the BentoML container deployment service.

    Attributes:
        config: service endpoint configuration
    """

    config: BentoMLContainerDeploymentEndpointConfig

    @property
    def prediction_url(self) -> Optional[str]:
        """Gets the prediction URL for the endpoint.

        Returns:
            the prediction URL for the endpoint
        """
        uri = self.status.uri
        if not uri:
            return None
        return os.path.join(uri, self.config.prediction_url_path)

`prediction_url: Optional[str]` `property` `readonly`

Gets the prediction URL for the endpoint.

Returns:

Type	Description
`Optional[str]`	the prediction URL for the endpoint

`BentoMLContainerDeploymentEndpointConfig (ContainerServiceEndpointConfig)`

BentoML container deployment service configuration.

Attributes:

Name	Type	Description
`prediction_url_path`	`str`	URI subpath for prediction requests

Source code in zenml/integrations/bentoml/services/bentoml_container_deployment.py

class BentoMLContainerDeploymentEndpointConfig(ContainerServiceEndpointConfig):
    """BentoML container deployment service configuration.

    Attributes:
        prediction_url_path: URI subpath for prediction requests
    """

    prediction_url_path: str

`BentoMLContainerDeploymentService (ContainerService, BaseDeploymentService)`

BentoML container deployment service.

Source code in zenml/integrations/bentoml/services/bentoml_container_deployment.py

class BentoMLContainerDeploymentService(
    ContainerService, BaseDeploymentService
):
    """BentoML container deployment service."""

    SERVICE_TYPE = ServiceType(
        name=BENTOML_CONTAINER_DEPLOYMENT_SERVICE_NAME,
        type="model-serving",
        flavor="bentoml",
        description="BentoML container prediction service",
        logo_url="https://public-flavor-logos.s3.eu-central-1.amazonaws.com/model_deployer/bentoml.png",
    )

    config: BentoMLContainerDeploymentConfig
    endpoint: BentoMLContainerDeploymentEndpoint

    def __init__(
        self,
        config: Union[BentoMLContainerDeploymentConfig, Dict[str, Any]],
        **attrs: Any,
    ) -> None:
        """Initialize the BentoML deployment service.

        Args:
            config: service configuration
            attrs: additional attributes to set on the service
        """
        # ensure that the endpoint is created before the service is initialized
        # TODO [ENG-700]: implement a service factory or builder for BentoML
        #   deployment services
        if (
            isinstance(config, BentoMLContainerDeploymentConfig)
            and "endpoint" not in attrs
        ):
            endpoint = BentoMLContainerDeploymentEndpoint(
                config=BentoMLContainerDeploymentEndpointConfig(
                    protocol=ServiceEndpointProtocol.HTTP,
                    port=config.port or BENTOML_DEFAULT_PORT,
                    ip_address=config.host or DEFAULT_LOCAL_SERVICE_IP_ADDRESS,
                    prediction_url_path=BENTOML_PREDICTION_URL_PATH,
                ),
                monitor=HTTPEndpointHealthMonitor(
                    config=HTTPEndpointHealthMonitorConfig(
                        healthcheck_uri_path=BENTOML_HEALTHCHECK_URL_PATH,
                    )
                ),
            )
            attrs["endpoint"] = endpoint
        super().__init__(config=config, **attrs)

    # override the is_running property to check if the bentoml container is running
    @property
    def is_running(self) -> bool:
        """Check if the service is currently running.

        This method will actively poll the external service to get its status
        and will return the result.

        Returns:
            True if the service is running and active (i.e. the endpoints are
            responsive, if any are configured), otherwise False.
        """
        self.update_status()
        return self.status.state == ServiceState.ACTIVE

    # override the container start method to use the root user
    def _start_container(self) -> None:
        """Start the service docker container associated with this service."""
        container = self.container

        if container:
            # the container exists, check if it is running
            if container.status == "running":
                logger.debug(
                    "Container for service '%s' is already running",
                    self,
                )
                return

            # the container is stopped or in an error state, remove it
            logger.debug(
                "Removing previous container for service '%s'",
                self,
            )
            container.remove(force=True)

        logger.debug("Starting container for service '%s'...", self)

        try:
            self.docker_client.images.get(self.config.image)
        except docker_errors.ImageNotFound:
            logger.debug(
                "Pulling container image '%s' for service '%s'...",
                self.config.image,
                self,
            )
            self.docker_client.images.pull(self.config.image)

        self._setup_runtime_path()

        ports: Dict[int, Optional[int]] = {}
        if self.endpoint:
            self.endpoint.prepare_for_start()
            if self.endpoint.status.port:
                ports[self.endpoint.status.port] = self.endpoint.status.port

        command, env = self._get_container_cmd()
        volumes = self._get_container_volumes()

        try:
            container = self.docker_client.containers.run(
                name=self.container_id,
                image=self.config.image,
                entrypoint=command,
                detach=True,
                volumes=volumes,
                environment=env,
                remove=False,
                auto_remove=False,
                ports=ports,
                user="root",
                labels={
                    "zenml-service-uuid": str(self.uuid),
                },
                working_dir="/home/bentoml/bento",
                extra_hosts={"host.docker.internal": "host-gateway"},
            )

            logger.debug(
                "Docker container for service '%s' started with ID: %s",
                self,
                self.container_id,
            )
        except docker_errors.DockerException as e:
            logger.error(
                "Docker container for service '%s' failed to start: %s",
                self,
                e,
            )

    def _containerize_and_push_bento(self) -> None:
        """Containerize the bento and push it to the container registry.

        Raises:
            Exception: If the bento containerization fails.
        """
        zenml_client = ZenMLClient()
        container_registry = zenml_client.active_stack.container_registry
        # a tuple of config image and image tag
        if self.config.image and self.config.image_tag:
            image_tag = (self.config.image, self.config.image_tag)
        else:
            # if container registry is present in the stack, name the image
            # with the container registry uri, else name the image with the bento tag
            if container_registry:
                image_name = (
                    f"{container_registry.config.uri}/{self.config.bento_tag}"
                )
                image_tag = (image_name,)  # type: ignore
                self.config.image = image_name
            else:
                # bentoml will use the bento tag as the name of the image
                image_tag = (self.config.bento_tag,)  # type: ignore
                self.config.image = self.config.bento_tag
        try:
            bentoml.container.build(
                bento_tag=self.config.bento_tag,
                backend="docker",  # hardcoding docker since container service only supports docker
                image_tag=image_tag,
                features=self.config.features,
                file=self.config.file,
                platform=self.config.platform,
            )

        except Exception as e:
            logger.error(f"Error containerizing the bento: {e}")
            raise e

        if container_registry:
            logger.info(
                f"Pushing bento to container registry {container_registry.config.uri}"
            )
            # push the bento to the image registry
            container_registry.push_image(self.config.image)
        else:
            logger.warning(
                "No container registry found in the active stack. "
                "Please add a container registry to your stack to push "
                "the bento to an image registry."
            )

    def provision(self) -> None:
        """Provision the service."""
        # containerize the bento
        self._containerize_and_push_bento()
        # run the container
        super().provision()

    def run(self) -> None:
        """Start the service.

        Raises:
            FileNotFoundError: If the bento file is not found.
            subprocess.CalledProcessError: If the bentoml serve command fails.
        """
        from bentoml._internal.service.loader import load

        logger.info("Starting BentoML container deployment service...")

        self.endpoint.prepare_for_start()

        if self.config.working_dir is None:
            if os.path.isdir(os.path.expanduser(self.config.bento_tag)):
                self.config.working_dir = os.path.expanduser(
                    self.config.bento_tag
                )
            else:
                self.config.working_dir = "."
        if sys.path[0] != self.config.working_dir:
            sys.path.insert(0, self.config.working_dir)

        _ = load(bento_identifier=".", working_dir=self.config.working_dir)
        # run bentoml serve command inside the container
        # Use subprocess for better control and error handling
        import subprocess

        try:
            subprocess.run(["bentoml", "serve"], check=True)
        except subprocess.CalledProcessError as e:
            logger.error(f"Failed to start BentoML service: {e}")
            raise
        except FileNotFoundError:
            logger.error(
                "BentoML command not found. Make sure it's installed and in the PATH."
            )
            raise

    @property
    def prediction_url(self) -> Optional[str]:
        """Get the URI where the http server is running.

        Returns:
            The URI where the http service can be accessed to get more information
            about the service and to make predictions.
        """
        if not self.is_running:
            return None
        return self.endpoint.prediction_url

    @property
    def prediction_apis_urls(self) -> Optional[List[str]]:
        """Get the URI where the prediction api services is answering requests.

        Returns:
            The URI where the prediction service apis can be contacted to process
            HTTP/REST inference requests, or None, if the service isn't running.
        """
        if not self.is_running:
            return None

        if self.config.apis:
            return [
                f"{self.endpoint.prediction_url}/{api}"
                for api in self.config.apis
            ]
        return None

    def predict(self, api_endpoint: str, data: Any) -> Any:
        """Make a prediction using the service.

        Args:
            data: data to make a prediction on
            api_endpoint: the api endpoint to make the prediction on

        Returns:
            The prediction result.

        Raises:
            Exception: if the service is not running
            ValueError: if the prediction endpoint is unknown.
        """
        if not self.is_running:
            raise Exception(
                "BentoML prediction service is not running. "
                "Please start the service before making predictions."
            )
        if self.endpoint.prediction_url is not None:
            client = Client.from_url(
                self.endpoint.prediction_url.replace("http://", "").rstrip("/")
            )
            result = client.call(api_endpoint, data)
        else:
            raise ValueError("No endpoint known for prediction.")
        return result

`is_running: bool` `property` `readonly`

Check if the service is currently running.

This method will actively poll the external service to get its status and will return the result.

Returns:

Type	Description
`bool`	True if the service is running and active (i.e. the endpoints are responsive, if any are configured), otherwise False.

`prediction_apis_urls: Optional[List[str]]` `property` `readonly`

Get the URI where the prediction api services is answering requests.

Returns:

Type	Description
`Optional[List[str]]`	The URI where the prediction service apis can be contacted to process HTTP/REST inference requests, or None, if the service isn't running.

`prediction_url: Optional[str]` `property` `readonly`

Get the URI where the http server is running.

Returns:

Type	Description
`Optional[str]`	The URI where the http service can be accessed to get more information about the service and to make predictions.

`init(self, config, **attrs)` `special`

Initialize the BentoML deployment service.

Parameters:

Name	Type	Description	Default
`config`	`Union[zenml.integrations.bentoml.services.bentoml_container_deployment.BentoMLContainerDeploymentConfig, Dict[str, Any]]`	service configuration	required
`attrs`	`Any`	additional attributes to set on the service	`{}`

Source code in zenml/integrations/bentoml/services/bentoml_container_deployment.py

def __init__(
    self,
    config: Union[BentoMLContainerDeploymentConfig, Dict[str, Any]],
    **attrs: Any,
) -> None:
    """Initialize the BentoML deployment service.

    Args:
        config: service configuration
        attrs: additional attributes to set on the service
    """
    # ensure that the endpoint is created before the service is initialized
    # TODO [ENG-700]: implement a service factory or builder for BentoML
    #   deployment services
    if (
        isinstance(config, BentoMLContainerDeploymentConfig)
        and "endpoint" not in attrs
    ):
        endpoint = BentoMLContainerDeploymentEndpoint(
            config=BentoMLContainerDeploymentEndpointConfig(
                protocol=ServiceEndpointProtocol.HTTP,
                port=config.port or BENTOML_DEFAULT_PORT,
                ip_address=config.host or DEFAULT_LOCAL_SERVICE_IP_ADDRESS,
                prediction_url_path=BENTOML_PREDICTION_URL_PATH,
            ),
            monitor=HTTPEndpointHealthMonitor(
                config=HTTPEndpointHealthMonitorConfig(
                    healthcheck_uri_path=BENTOML_HEALTHCHECK_URL_PATH,
                )
            ),
        )
        attrs["endpoint"] = endpoint
    super().__init__(config=config, **attrs)

`model_post_init(/, self, context)`

We need to both initialize private attributes and call the user-defined model_post_init method.

Source code in zenml/integrations/bentoml/services/bentoml_container_deployment.py

def wrapped_model_post_init(self: BaseModel, context: Any, /) -> None:
    """We need to both initialize private attributes and call the user-defined model_post_init
    method.
    """
    init_private_attributes(self, context)
    original_model_post_init(self, context)

`predict(self, api_endpoint, data)`

Make a prediction using the service.

Parameters:

Name	Type	Description	Default
`data`	`Any`	data to make a prediction on	required
`api_endpoint`	`str`	the api endpoint to make the prediction on	required

Returns:

Type	Description
`Any`	The prediction result.

Exceptions:

Type	Description
`Exception`	if the service is not running
`ValueError`	if the prediction endpoint is unknown.

Source code in zenml/integrations/bentoml/services/bentoml_container_deployment.py

def predict(self, api_endpoint: str, data: Any) -> Any:
    """Make a prediction using the service.

    Args:
        data: data to make a prediction on
        api_endpoint: the api endpoint to make the prediction on

    Returns:
        The prediction result.

    Raises:
        Exception: if the service is not running
        ValueError: if the prediction endpoint is unknown.
    """
    if not self.is_running:
        raise Exception(
            "BentoML prediction service is not running. "
            "Please start the service before making predictions."
        )
    if self.endpoint.prediction_url is not None:
        client = Client.from_url(
            self.endpoint.prediction_url.replace("http://", "").rstrip("/")
        )
        result = client.call(api_endpoint, data)
    else:
        raise ValueError("No endpoint known for prediction.")
    return result

`provision(self)`

Provision the service.

Source code in zenml/integrations/bentoml/services/bentoml_container_deployment.py

def provision(self) -> None:
    """Provision the service."""
    # containerize the bento
    self._containerize_and_push_bento()
    # run the container
    super().provision()

`run(self)`

Start the service.

Exceptions:

Type	Description
`FileNotFoundError`	If the bento file is not found.
`subprocess.CalledProcessError`	If the bentoml serve command fails.

Source code in zenml/integrations/bentoml/services/bentoml_container_deployment.py

def run(self) -> None:
    """Start the service.

    Raises:
        FileNotFoundError: If the bento file is not found.
        subprocess.CalledProcessError: If the bentoml serve command fails.
    """
    from bentoml._internal.service.loader import load

    logger.info("Starting BentoML container deployment service...")

    self.endpoint.prepare_for_start()

    if self.config.working_dir is None:
        if os.path.isdir(os.path.expanduser(self.config.bento_tag)):
            self.config.working_dir = os.path.expanduser(
                self.config.bento_tag
            )
        else:
            self.config.working_dir = "."
    if sys.path[0] != self.config.working_dir:
        sys.path.insert(0, self.config.working_dir)

    _ = load(bento_identifier=".", working_dir=self.config.working_dir)
    # run bentoml serve command inside the container
    # Use subprocess for better control and error handling
    import subprocess

    try:
        subprocess.run(["bentoml", "serve"], check=True)
    except subprocess.CalledProcessError as e:
        logger.error(f"Failed to start BentoML service: {e}")
        raise
    except FileNotFoundError:
        logger.error(
            "BentoML command not found. Make sure it's installed and in the PATH."
        )
        raise

`bentoml_local_deployment`

Implementation for the BentoML local deployment service.

`BentoMLDeploymentEndpoint (LocalDaemonServiceEndpoint)`

A service endpoint exposed by the BentoML deployment daemon.

Attributes:

Name	Type	Description
`config`	`BentoMLDeploymentEndpointConfig`	service endpoint configuration

Source code in zenml/integrations/bentoml/services/bentoml_local_deployment.py

class BentoMLDeploymentEndpoint(LocalDaemonServiceEndpoint):
    """A service endpoint exposed by the BentoML deployment daemon.

    Attributes:
        config: service endpoint configuration
    """

    config: BentoMLDeploymentEndpointConfig
    monitor: HTTPEndpointHealthMonitor

    @property
    def prediction_url(self) -> Optional[str]:
        """Gets the prediction URL for the endpoint.

        Returns:
            the prediction URL for the endpoint
        """
        uri = self.status.uri
        if not uri:
            return None
        return os.path.join(uri, self.config.prediction_url_path)

`prediction_url: Optional[str]` `property` `readonly`

Gets the prediction URL for the endpoint.

Returns:

Type	Description
`Optional[str]`	the prediction URL for the endpoint

`BentoMLDeploymentEndpointConfig (LocalDaemonServiceEndpointConfig)`

BentoML deployment service configuration.

Attributes:

Name	Type	Description
`prediction_url_path`	`str`	URI subpath for prediction requests

Source code in zenml/integrations/bentoml/services/bentoml_local_deployment.py

class BentoMLDeploymentEndpointConfig(LocalDaemonServiceEndpointConfig):
    """BentoML deployment service configuration.

    Attributes:
        prediction_url_path: URI subpath for prediction requests
    """

    prediction_url_path: str

`BentoMLLocalDeploymentConfig (LocalDaemonServiceConfig)`

BentoML model deployment configuration.

Attributes:

Name	Type	Description
`model_name`	`str`	name of the model to deploy
`model_uri`	`str`	URI of the model to deploy
`port`	`Optional[int]`	port to expose the service on
`bento_tag`	`str`	Bento package to deploy. A bento tag is a combination of the name of the bento and its version.
`workers`	`int`	number of workers to use
`backlog`	`int`	number of requests to queue
`production`	`bool`	whether to run in production mode
`working_dir`	`str`	working directory for the service
`host`	`Optional[str]`	host to expose the service on
`ssl_parameters`	`Optional[zenml.integrations.bentoml.services.bentoml_local_deployment.SSLBentoMLParametersConfig]`	SSL parameters for the Bentoml deployment

Source code in zenml/integrations/bentoml/services/bentoml_local_deployment.py

class BentoMLLocalDeploymentConfig(LocalDaemonServiceConfig):
    """BentoML model deployment configuration.

    Attributes:
        model_name: name of the model to deploy
        model_uri: URI of the model to deploy
        port: port to expose the service on
        bento_tag: Bento package to deploy. A bento tag is a combination of the
            name of the bento and its version.
        workers: number of workers to use
        backlog: number of requests to queue
        production: whether to run in production mode
        working_dir: working directory for the service
        host: host to expose the service on
        ssl_parameters: SSL parameters for the Bentoml deployment
    """

    model_name: str
    model_uri: str
    bento_tag: str
    bento_uri: Optional[str] = None
    apis: List[str] = []
    workers: int = 1
    port: Optional[int] = None
    backlog: int = 2048
    production: bool = False
    working_dir: str
    host: Optional[str] = None
    ssl_parameters: Optional[SSLBentoMLParametersConfig] = Field(
        default_factory=SSLBentoMLParametersConfig
    )

`BentoMLLocalDeploymentService (LocalDaemonService, BaseDeploymentService)`

BentoML deployment service used to start a local prediction server for BentoML models.

Attributes:

Name	Type	Description
`SERVICE_TYPE`	`ClassVar[zenml.services.service_type.ServiceType]`	a service type descriptor with information describing the BentoML deployment service class
`config`	`BentoMLLocalDeploymentConfig`	service configuration
`endpoint`	`BentoMLDeploymentEndpoint`	optional service endpoint

Source code in zenml/integrations/bentoml/services/bentoml_local_deployment.py

class BentoMLLocalDeploymentService(LocalDaemonService, BaseDeploymentService):
    """BentoML deployment service used to start a local prediction server for BentoML models.

    Attributes:
        SERVICE_TYPE: a service type descriptor with information describing
            the BentoML deployment service class
        config: service configuration
        endpoint: optional service endpoint
    """

    SERVICE_TYPE = ServiceType(
        name=BENTOML_LOCAL_DEPLOYMENT_SERVICE_NAME,
        type="model-serving",
        flavor="bentoml",
        description="BentoML local prediction service",
        logo_url="https://public-flavor-logos.s3.eu-central-1.amazonaws.com/model_deployer/bentoml.png",
    )

    config: BentoMLLocalDeploymentConfig
    endpoint: BentoMLDeploymentEndpoint

    def __init__(
        self,
        config: Union[BentoMLLocalDeploymentConfig, Dict[str, Any]],
        **attrs: Any,
    ) -> None:
        """Initialize the BentoML deployment service.

        Args:
            config: service configuration
            attrs: additional attributes to set on the service
        """
        # ensure that the endpoint is created before the service is initialized
        # TODO [ENG-700]: implement a service factory or builder for BentoML
        #   deployment services
        if (
            isinstance(config, BentoMLLocalDeploymentConfig)
            and "endpoint" not in attrs
        ):
            endpoint = BentoMLDeploymentEndpoint(
                config=BentoMLDeploymentEndpointConfig(
                    protocol=ServiceEndpointProtocol.HTTP,
                    port=config.port
                    if config.port is not None
                    else BENTOML_DEFAULT_PORT,
                    ip_address=config.host or DEFAULT_LOCAL_SERVICE_IP_ADDRESS,
                    prediction_url_path=BENTOML_PREDICTION_URL_PATH,
                ),
                monitor=HTTPEndpointHealthMonitor(
                    config=HTTPEndpointHealthMonitorConfig(
                        healthcheck_uri_path=BENTOML_HEALTHCHECK_URL_PATH,
                    )
                ),
            )
            attrs["endpoint"] = endpoint
        super().__init__(config=config, **attrs)

    def run(self) -> None:
        """Start the service."""
        from bentoml import Service
        from bentoml._internal.service.loader import load

        logger.info(
            "Starting BentoML prediction service as blocking "
            "process... press CTRL+C once to stop it."
        )

        self.endpoint.prepare_for_start()
        ssl_params = self.config.ssl_parameters or SSLBentoMLParametersConfig()
        # verify if to deploy in production mode or development mode
        logger.info("Running in production mode.")
        svc = load(
            bento_identifier=self.config.bento_tag,
            working_dir=self.config.working_dir or ".",
        )

        if isinstance(svc, Service):
            # bentoml<1.2
            from bentoml.serving import serve_http_production

            try:
                serve_http_production(
                    self.config.bento_tag,
                    working_dir=self.config.working_dir,
                    port=self.config.port,
                    api_workers=self.config.workers,
                    host=self.config.host or DEFAULT_LOCAL_SERVICE_IP_ADDRESS,
                    backlog=self.config.backlog,
                    ssl_certfile=ssl_params.ssl_certfile,
                    ssl_keyfile=ssl_params.ssl_keyfile,
                    ssl_keyfile_password=ssl_params.ssl_keyfile_password,
                    ssl_version=ssl_params.ssl_version,
                    ssl_cert_reqs=ssl_params.ssl_cert_reqs,
                    ssl_ca_certs=ssl_params.ssl_ca_certs,
                    ssl_ciphers=ssl_params.ssl_ciphers,
                )
            except KeyboardInterrupt:
                logger.info("Stopping BentoML prediction service...")
        else:
            # bentoml>=1.2
            from _bentoml_impl.server import serve_http  # type: ignore

            svc.inject_config()
            try:
                serve_http(
                    self.config.bento_tag,
                    working_dir=self.config.working_dir or ".",
                    host=self.config.host or DEFAULT_LOCAL_SERVICE_IP_ADDRESS,
                    port=self.config.port,
                    backlog=self.config.backlog,
                    ssl_certfile=ssl_params.ssl_certfile,
                    ssl_keyfile=ssl_params.ssl_keyfile,
                    ssl_keyfile_password=ssl_params.ssl_keyfile_password,
                    ssl_version=ssl_params.ssl_version,
                    ssl_cert_reqs=ssl_params.ssl_cert_reqs,
                    ssl_ca_certs=ssl_params.ssl_ca_certs,
                    ssl_ciphers=ssl_params.ssl_ciphers,
                )
            except KeyboardInterrupt:
                logger.info("Stopping BentoML prediction service...")

    @property
    def prediction_url(self) -> Optional[str]:
        """Get the URI where the http server is running.

        Returns:
            The URI where the http service can be accessed to get more information
            about the service and to make predictions.
        """
        if not self.is_running:
            return None
        return self.endpoint.prediction_url

    @property
    def prediction_apis_urls(self) -> Optional[List[str]]:
        """Get the URI where the prediction api services is answering requests.

        Returns:
            The URI where the prediction service apis can be contacted to process
            HTTP/REST inference requests, or None, if the service isn't running.
        """
        if not self.is_running:
            return None

        if self.config.apis:
            return [
                f"{self.endpoint.prediction_url}/{api}"
                for api in self.config.apis
            ]
        return None

    def predict(
        self, api_endpoint: str, data: "Any", sync: bool = True
    ) -> "Any":
        """Make a prediction using the service.

        Args:
            data: data to make a prediction on
            api_endpoint: the api endpoint to make the prediction on
            sync: if set to False, the prediction will be made asynchronously

        Returns:
            The prediction result.

        Raises:
            Exception: if the service is not running
            ValueError: if the prediction endpoint is unknown.
        """
        if not self.is_running:
            raise Exception(
                "BentoML prediction service is not running. "
                "Please start the service before making predictions."
            )
        if self.endpoint.prediction_url is None:
            raise ValueError("No endpoint known for prediction.")
        if sync:
            client = SyncHTTPClient(self.endpoint.prediction_url)
        else:
            client = AsyncHTTPClient(self.endpoint.prediction_url)
        result = client.call(api_endpoint, data)
        return result

`prediction_apis_urls: Optional[List[str]]` `property` `readonly`

Get the URI where the prediction api services is answering requests.

Returns:

Type	Description
`Optional[List[str]]`	The URI where the prediction service apis can be contacted to process HTTP/REST inference requests, or None, if the service isn't running.

`prediction_url: Optional[str]` `property` `readonly`

Get the URI where the http server is running.

Returns:

Type	Description
`Optional[str]`	The URI where the http service can be accessed to get more information about the service and to make predictions.

`init(self, config, **attrs)` `special`

Initialize the BentoML deployment service.

Parameters:

Name	Type	Description	Default
`config`	`Union[zenml.integrations.bentoml.services.bentoml_local_deployment.BentoMLLocalDeploymentConfig, Dict[str, Any]]`	service configuration	required
`attrs`	`Any`	additional attributes to set on the service	`{}`

Source code in zenml/integrations/bentoml/services/bentoml_local_deployment.py

def __init__(
    self,
    config: Union[BentoMLLocalDeploymentConfig, Dict[str, Any]],
    **attrs: Any,
) -> None:
    """Initialize the BentoML deployment service.

    Args:
        config: service configuration
        attrs: additional attributes to set on the service
    """
    # ensure that the endpoint is created before the service is initialized
    # TODO [ENG-700]: implement a service factory or builder for BentoML
    #   deployment services
    if (
        isinstance(config, BentoMLLocalDeploymentConfig)
        and "endpoint" not in attrs
    ):
        endpoint = BentoMLDeploymentEndpoint(
            config=BentoMLDeploymentEndpointConfig(
                protocol=ServiceEndpointProtocol.HTTP,
                port=config.port
                if config.port is not None
                else BENTOML_DEFAULT_PORT,
                ip_address=config.host or DEFAULT_LOCAL_SERVICE_IP_ADDRESS,
                prediction_url_path=BENTOML_PREDICTION_URL_PATH,
            ),
            monitor=HTTPEndpointHealthMonitor(
                config=HTTPEndpointHealthMonitorConfig(
                    healthcheck_uri_path=BENTOML_HEALTHCHECK_URL_PATH,
                )
            ),
        )
        attrs["endpoint"] = endpoint
    super().__init__(config=config, **attrs)

`predict(self, api_endpoint, data, sync=True)`

Make a prediction using the service.

Parameters:

Name	Type	Description	Default
`data`	`Any`	data to make a prediction on	required
`api_endpoint`	`str`	the api endpoint to make the prediction on	required
`sync`	`bool`	if set to False, the prediction will be made asynchronously	`True`

Returns:

Type	Description
`Any`	The prediction result.

Exceptions:

Type	Description
`Exception`	if the service is not running
`ValueError`	if the prediction endpoint is unknown.

Source code in zenml/integrations/bentoml/services/bentoml_local_deployment.py

def predict(
    self, api_endpoint: str, data: "Any", sync: bool = True
) -> "Any":
    """Make a prediction using the service.

    Args:
        data: data to make a prediction on
        api_endpoint: the api endpoint to make the prediction on
        sync: if set to False, the prediction will be made asynchronously

    Returns:
        The prediction result.

    Raises:
        Exception: if the service is not running
        ValueError: if the prediction endpoint is unknown.
    """
    if not self.is_running:
        raise Exception(
            "BentoML prediction service is not running. "
            "Please start the service before making predictions."
        )
    if self.endpoint.prediction_url is None:
        raise ValueError("No endpoint known for prediction.")
    if sync:
        client = SyncHTTPClient(self.endpoint.prediction_url)
    else:
        client = AsyncHTTPClient(self.endpoint.prediction_url)
    result = client.call(api_endpoint, data)
    return result

`run(self)`

Start the service.

Source code in zenml/integrations/bentoml/services/bentoml_local_deployment.py

def run(self) -> None:
    """Start the service."""
    from bentoml import Service
    from bentoml._internal.service.loader import load

    logger.info(
        "Starting BentoML prediction service as blocking "
        "process... press CTRL+C once to stop it."
    )

    self.endpoint.prepare_for_start()
    ssl_params = self.config.ssl_parameters or SSLBentoMLParametersConfig()
    # verify if to deploy in production mode or development mode
    logger.info("Running in production mode.")
    svc = load(
        bento_identifier=self.config.bento_tag,
        working_dir=self.config.working_dir or ".",
    )

    if isinstance(svc, Service):
        # bentoml<1.2
        from bentoml.serving import serve_http_production

        try:
            serve_http_production(
                self.config.bento_tag,
                working_dir=self.config.working_dir,
                port=self.config.port,
                api_workers=self.config.workers,
                host=self.config.host or DEFAULT_LOCAL_SERVICE_IP_ADDRESS,
                backlog=self.config.backlog,
                ssl_certfile=ssl_params.ssl_certfile,
                ssl_keyfile=ssl_params.ssl_keyfile,
                ssl_keyfile_password=ssl_params.ssl_keyfile_password,
                ssl_version=ssl_params.ssl_version,
                ssl_cert_reqs=ssl_params.ssl_cert_reqs,
                ssl_ca_certs=ssl_params.ssl_ca_certs,
                ssl_ciphers=ssl_params.ssl_ciphers,
            )
        except KeyboardInterrupt:
            logger.info("Stopping BentoML prediction service...")
    else:
        # bentoml>=1.2
        from _bentoml_impl.server import serve_http  # type: ignore

        svc.inject_config()
        try:
            serve_http(
                self.config.bento_tag,
                working_dir=self.config.working_dir or ".",
                host=self.config.host or DEFAULT_LOCAL_SERVICE_IP_ADDRESS,
                port=self.config.port,
                backlog=self.config.backlog,
                ssl_certfile=ssl_params.ssl_certfile,
                ssl_keyfile=ssl_params.ssl_keyfile,
                ssl_keyfile_password=ssl_params.ssl_keyfile_password,
                ssl_version=ssl_params.ssl_version,
                ssl_cert_reqs=ssl_params.ssl_cert_reqs,
                ssl_ca_certs=ssl_params.ssl_ca_certs,
                ssl_ciphers=ssl_params.ssl_ciphers,
            )
        except KeyboardInterrupt:
            logger.info("Stopping BentoML prediction service...")

`SSLBentoMLParametersConfig (BaseModel)`

BentoML SSL parameters configuration.

Attributes:

Name	Type	Description
`ssl_certfile`	`Optional[str]`	SSL certificate file
`ssl_keyfile`	`Optional[str]`	SSL key file
`ssl_keyfile_password`	`Optional[str]`	SSL key file password
`ssl_version`	`Optional[int]`	SSL version
`ssl_cert_reqs`	`Optional[int]`	SSL certificate requirements
`ssl_ca_certs`	`Optional[str]`	SSL CA certificates
`ssl_ciphers`	`Optional[str]`	SSL ciphers

Source code in zenml/integrations/bentoml/services/bentoml_local_deployment.py

class SSLBentoMLParametersConfig(BaseModel):
    """BentoML SSL parameters configuration.

    Attributes:
        ssl_certfile: SSL certificate file
        ssl_keyfile: SSL key file
        ssl_keyfile_password: SSL key file password
        ssl_version: SSL version
        ssl_cert_reqs: SSL certificate requirements
        ssl_ca_certs: SSL CA certificates
        ssl_ciphers: SSL ciphers
    """

    ssl_certfile: Optional[str] = None
    ssl_keyfile: Optional[str] = None
    ssl_keyfile_password: Optional[str] = None
    ssl_version: Optional[int] = None
    ssl_cert_reqs: Optional[int] = None
    ssl_ca_certs: Optional[str] = None
    ssl_ciphers: Optional[str] = None

`deployment_type`

BentoML Service Deployment Types.

`BentoMLDeploymentType (Enum)`

BentoML Service Deployment Types.

Source code in zenml/integrations/bentoml/services/deployment_type.py

class BentoMLDeploymentType(Enum):
    """BentoML Service Deployment Types."""

    LOCAL = "local"
    CONTAINER = "container"

`steps` `special`

Initialization of the BentoML standard interface steps.

`bento_builder`

Implementation of the BentoML bento builder step.

`bentoml_deployer`

Implementation of the BentoML model deployer pipeline step.

Bentoml

zenml.integrations.bentoml special

BentoMLIntegration (Integration)

activate() classmethod

flavors() classmethod

constants

flavors special

bentoml_model_deployer_flavor

BentoMLModelDeployerConfig (BaseModelDeployerConfig)

BentoMLModelDeployerFlavor (BaseModelDeployerFlavor)

config_class: Type[zenml.integrations.bentoml.flavors.bentoml_model_deployer_flavor.BentoMLModelDeployerConfig] property readonly

docs_url: Optional[str] property readonly

implementation_class: Type[BentoMLModelDeployer] property readonly

logo_url: str property readonly

name: str property readonly

sdk_docs_url: Optional[str] property readonly

materializers special

bentoml_bento_materializer

BentoMaterializer (BaseMaterializer)

extract_metadata(self, bento)

load(self, data_type)

save(self, bento)

model_deployers special

bentoml_model_deployer

BentoMLModelDeployer (BaseModelDeployer)

config: BentoMLModelDeployerConfig property readonly

local_path: str property readonly

FLAVOR (BaseModelDeployerFlavor)

get_model_server_info(service_instance) staticmethod

get_service_path(id_) staticmethod

perform_delete_model(self, service, timeout=60, force=False)

perform_deploy_model(self, id, config, timeout=60)

perform_start_model(self, service, timeout=60)

perform_stop_model(self, service, timeout=60, force=False)

services special

bentoml_container_deployment

BentoMLContainerDeploymentConfig (ContainerServiceConfig)

BentoMLContainerDeploymentEndpoint (ContainerServiceEndpoint)

prediction_url: Optional[str] property readonly

BentoMLContainerDeploymentEndpointConfig (ContainerServiceEndpointConfig)

BentoMLContainerDeploymentService (ContainerService, BaseDeploymentService)

is_running: bool property readonly

prediction_apis_urls: Optional[List[str]] property readonly

prediction_url: Optional[str] property readonly

__init__(self, config, **attrs) special

model_post_init(/, self, context)

predict(self, api_endpoint, data)

provision(self)

run(self)

bentoml_local_deployment

BentoMLDeploymentEndpoint (LocalDaemonServiceEndpoint)

prediction_url: Optional[str] property readonly

BentoMLDeploymentEndpointConfig (LocalDaemonServiceEndpointConfig)

BentoMLLocalDeploymentConfig (LocalDaemonServiceConfig)

BentoMLLocalDeploymentService (LocalDaemonService, BaseDeploymentService)

prediction_apis_urls: Optional[List[str]] property readonly

prediction_url: Optional[str] property readonly

__init__(self, config, **attrs) special

predict(self, api_endpoint, data, sync=True)

run(self)

SSLBentoMLParametersConfig (BaseModel)

deployment_type

BentoMLDeploymentType (Enum)

steps special

bento_builder

bentoml_deployer

`zenml.integrations.bentoml` `special`

`BentoMLIntegration (Integration)`

`activate()` `classmethod`

`flavors()` `classmethod`

`constants`

`flavors` `special`

`bentoml_model_deployer_flavor`

`BentoMLModelDeployerConfig (BaseModelDeployerConfig)`

`BentoMLModelDeployerFlavor (BaseModelDeployerFlavor)`

`config_class: Type[zenml.integrations.bentoml.flavors.bentoml_model_deployer_flavor.BentoMLModelDeployerConfig]` `property` `readonly`

`docs_url: Optional[str]` `property` `readonly`

`implementation_class: Type[BentoMLModelDeployer]` `property` `readonly`

`logo_url: str` `property` `readonly`

`name: str` `property` `readonly`

`sdk_docs_url: Optional[str]` `property` `readonly`

`materializers` `special`

`bentoml_bento_materializer`

`BentoMaterializer (BaseMaterializer)`

`extract_metadata(self, bento)`

`load(self, data_type)`

`save(self, bento)`

`model_deployers` `special`

`bentoml_model_deployer`

`BentoMLModelDeployer (BaseModelDeployer)`

`config: BentoMLModelDeployerConfig` `property` `readonly`

`local_path: str` `property` `readonly`

`FLAVOR (BaseModelDeployerFlavor)`

`get_model_server_info(service_instance)` `staticmethod`

`get_service_path(id_)` `staticmethod`

`perform_delete_model(self, service, timeout=60, force=False)`

`perform_deploy_model(self, id, config, timeout=60)`

`perform_start_model(self, service, timeout=60)`

`perform_stop_model(self, service, timeout=60, force=False)`

`services` `special`

`bentoml_container_deployment`

`BentoMLContainerDeploymentConfig (ContainerServiceConfig)`

`BentoMLContainerDeploymentEndpoint (ContainerServiceEndpoint)`

`prediction_url: Optional[str]` `property` `readonly`

`BentoMLContainerDeploymentEndpointConfig (ContainerServiceEndpointConfig)`

`BentoMLContainerDeploymentService (ContainerService, BaseDeploymentService)`

`is_running: bool` `property` `readonly`

`prediction_apis_urls: Optional[List[str]]` `property` `readonly`

`prediction_url: Optional[str]` `property` `readonly`

`init(self, config, **attrs)` `special`

`model_post_init(/, self, context)`

`predict(self, api_endpoint, data)`

`provision(self)`

`run(self)`

`bentoml_local_deployment`

`BentoMLDeploymentEndpoint (LocalDaemonServiceEndpoint)`

`prediction_url: Optional[str]` `property` `readonly`

`BentoMLDeploymentEndpointConfig (LocalDaemonServiceEndpointConfig)`

`BentoMLLocalDeploymentConfig (LocalDaemonServiceConfig)`

`BentoMLLocalDeploymentService (LocalDaemonService, BaseDeploymentService)`

`prediction_apis_urls: Optional[List[str]]` `property` `readonly`

`prediction_url: Optional[str]` `property` `readonly`

`init(self, config, **attrs)` `special`

`predict(self, api_endpoint, data, sync=True)`

`run(self)`

`SSLBentoMLParametersConfig (BaseModel)`

`deployment_type`

`BentoMLDeploymentType (Enum)`

`steps` `special`

`bento_builder`

`bentoml_deployer`