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B2

zenml.integrations.b2

Initialization of the Backblaze B2 integration.

The B2 integration registers a dedicated b2 artifact store flavor that reuses the S3-compatible implementation, defaulting the endpoint URL to Backblaze B2's S3 API. It depends on the same runtime libraries as the S3 integration (s3fs, boto3) and is intentionally a thin subclass: all filesystem behavior is inherited from the S3 artifact store.

Attributes

B2 = 'b2' module-attribute

B2_ARTIFACT_STORE_FLAVOR = 'b2' module-attribute

Classes

B2Integration

Bases: Integration

Definition of the Backblaze B2 integration for ZenML.

Methods:
flavors() -> List[Type[Flavor]] classmethod

Declare the stack component flavors for the B2 integration.

Returns:

Type Description
List[Type[Flavor]]

List of stack component flavors for this integration.
Source code in src/zenml/integrations/b2/__init__.py 
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@classmethod
def flavors(cls) -> List[Type[Flavor]]:
    """Declare the stack component flavors for the B2 integration.

    Returns:
        List of stack component flavors for this integration.
    """
    from zenml.integrations.b2.flavors import B2ArtifactStoreFlavor

    return [B2ArtifactStoreFlavor]

Flavor

Class for ZenML Flavors.

Attributes
config_class: Type[StackComponentConfig] abstractmethod property

Returns StackComponentConfig config class.

Returns:

Type Description
Type[StackComponentConfig]

The config class.
config_schema: Dict[str, Any] property

The config schema for a flavor.

Returns:

Type Description
Dict[str, Any]

The config schema.
display_name: Optional[str] property

The display name of the flavor.

By default, converts the technical name to a human-readable format. For example, "vm_kubernetes" becomes "VM Kubernetes". Flavors can override this to provide custom display names.

Returns:

Type Description
Optional[str]

The display name of the flavor.
docs_url: Optional[str] property

A url to point at docs explaining this flavor.

Returns:

Type Description
Optional[str]

A flavor docs url.
implementation_class: Type[StackComponent] abstractmethod property

Implementation class for this flavor.

Returns:

Type Description
Type[StackComponent]

The implementation class for this flavor.
logo_url: Optional[str] property

A url to represent the flavor in the dashboard.

Returns:

Type Description
Optional[str]

The flavor logo.
name: str abstractmethod property

The flavor name.

Returns:

Type Description
str

The flavor name.
sdk_docs_url: Optional[str] property

A url to point at SDK docs explaining this flavor.

Returns:

Type Description
Optional[str]

A flavor SDK docs url.
service_connector_requirements: Optional[ServiceConnectorRequirements] property

Service connector resource requirements for service connectors.

Specifies resource requirements that are used to filter the available service connector types that are compatible with this flavor.

Returns:

Type Description
Optional[ServiceConnectorRequirements]

Requirements for compatible service connectors, if a service
Optional[ServiceConnectorRequirements]

connector is required for this flavor.
type: StackComponentType abstractmethod property

The stack component type.

Returns:

Type Description
StackComponentType

The stack component type.
Methods:
from_model(flavor_model: FlavorResponse) -> Flavor classmethod

Loads a flavor from a model.

Parameters:

Name Type Description Default
flavor_model FlavorResponse

The model to load from.

 required

Raises:

Type Description
CustomFlavorImportError

If the custom flavor can't be imported.
ImportError

If the flavor can't be imported.

Returns:

Type Description
Flavor

The loaded flavor.
Source code in src/zenml/stack/flavor.py 
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@classmethod
def from_model(cls, flavor_model: FlavorResponse) -> "Flavor":
    """Loads a flavor from a model.

    Args:
        flavor_model: The model to load from.

    Raises:
        CustomFlavorImportError: If the custom flavor can't be imported.
        ImportError: If the flavor can't be imported.

    Returns:
        The loaded flavor.
    """
    try:
        _, flavor = validate_flavor_source(
            source=flavor_model.source,
            component_type=flavor_model.type,
            validate_component_classes=False,
        )
    except (TypeError, ValueError) as err:
        if flavor_model.is_custom:
            flavor_module, _, _ = flavor_model.source.rpartition(".")
            expected_file_path = os.path.join(
                source_utils.get_source_root(),
                flavor_module.replace(".", os.path.sep),
            )
            raise CustomFlavorImportError(
                f"Couldn't import custom flavor {flavor_model.name}: "
                f"{err}. Make sure the custom flavor class "
                f"`{flavor_model.source}` is importable. If it is part of "
                "a library, make sure it is installed. If "
                "it is a local code file, make sure it exists at "
                f"`{expected_file_path}.py`."
            ) from err
        else:
            raise ImportError(
                f"Couldn't import flavor {flavor_model.name}: {err}"
            ) from err
    return flavor
generate_default_docs_url() -> str

Generate the doc urls for all inbuilt and integration flavors.

Note that this method is not going to be useful for custom flavors, which do not have any docs in the main zenml docs.

Returns:

Type Description
str

The complete url to the zenml documentation
Source code in src/zenml/stack/flavor.py 
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def generate_default_docs_url(self) -> str:
    """Generate the doc urls for all inbuilt and integration flavors.

    Note that this method is not going to be useful for custom flavors,
    which do not have any docs in the main zenml docs.

    Returns:
        The complete url to the zenml documentation
    """
    component_type = self.type.plural.replace("_", "-")
    name = self.name.replace("_", "-")

    base = "https://docs.zenml.io"
    return f"{base}/stack-components/{component_type}/{name}"
generate_default_sdk_docs_url() -> str

Generate SDK docs url for a flavor.

Returns:

Type Description
str

The complete url to the zenml SDK docs
Source code in src/zenml/stack/flavor.py 
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def generate_default_sdk_docs_url(self) -> str:
    """Generate SDK docs url for a flavor.

    Returns:
        The complete url to the zenml SDK docs
    """
    from zenml import __version__

    base = f"https://sdkdocs.zenml.io/{__version__}"

    component_type = self.type.plural

    if "zenml.integrations" in self.__module__:
        # Get integration name out of module path which will look something
        #  like this "zenml.integrations.<integration>....
        integration = self.__module__.split(
            "zenml.integrations.", maxsplit=1
        )[1].split(".")[0]

        # Get the config class name to point to the specific class
        config_class_name = self.config_class.__name__

        return (
            f"{base}/integration_code_docs"
            f"/integrations-{integration}"
            f"#zenml.integrations.{integration}.flavors.{config_class_name}"
        )

    else:
        return (
            f"{base}/core_code_docs/core-{component_type}/"
            f"#{self.__module__}"
        )
to_model(integration: Optional[str] = None, is_custom: bool = True) -> FlavorRequest

Converts a flavor to a model.

Parameters:

Name Type Description Default
integration Optional[str]

The integration to use for the model.

 None
is_custom bool

Whether the flavor is a custom flavor.

 True

Returns:

Type Description
FlavorRequest

The model.
Source code in src/zenml/stack/flavor.py 
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def to_model(
    self,
    integration: Optional[str] = None,
    is_custom: bool = True,
) -> FlavorRequest:
    """Converts a flavor to a model.

    Args:
        integration: The integration to use for the model.
        is_custom: Whether the flavor is a custom flavor.

    Returns:
        The model.
    """
    connector_requirements = self.service_connector_requirements
    connector_type = (
        connector_requirements.connector_type
        if connector_requirements
        else None
    )
    resource_type = (
        connector_requirements.resource_type
        if connector_requirements
        else None
    )
    resource_id_attr = (
        connector_requirements.resource_id_attr
        if connector_requirements
        else None
    )

    model = FlavorRequest(
        name=self.name,
        display_name=self.display_name,
        type=self.type,
        source=source_utils.resolve(self.__class__).import_path,
        config_schema=self.config_schema,
        connector_type=connector_type,
        connector_resource_type=resource_type,
        connector_resource_id_attr=resource_id_attr,
        integration=integration,
        logo_url=self.logo_url,
        docs_url=self.docs_url,
        sdk_docs_url=self.sdk_docs_url,
        is_custom=is_custom,
    )
    return model

Integration

Base class for integration in ZenML.

Methods:
activate() -> None classmethod

Abstract method to activate the integration.

Source code in src/zenml/integrations/integration.py 
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@classmethod
def activate(cls) -> None:
    """Abstract method to activate the integration."""
check_installation() -> bool classmethod

Method to check whether the required packages are installed.

Returns:

Type Description
bool

True if all required packages are installed, False otherwise.
Source code in src/zenml/integrations/integration.py 
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@classmethod
def check_installation(cls) -> bool:
    """Method to check whether the required packages are installed.

    Returns:
        True if all required packages are installed, False otherwise.
    """
    for requirement in cls.get_requirements():
        parsed_requirement = Requirement(requirement)

        if not requirement_installed(parsed_requirement):
            logger.debug(
                "Requirement '%s' for integration '%s' is not installed "
                "or installed with the wrong version.",
                requirement,
                cls.NAME,
            )
            return False

        dependencies = get_dependencies(parsed_requirement)

        for dependency in dependencies:
            if not requirement_installed(dependency):
                logger.debug(
                    "Requirement '%s' for integration '%s' is not "
                    "installed or installed with the wrong version.",
                    dependency,
                    cls.NAME,
                )
                return False

    logger.debug(
        f"Integration '{cls.NAME}' is installed correctly with "
        f"requirements {cls.get_requirements()}."
    )
    return True
flavors() -> List[Type[Flavor]] classmethod

Abstract method to declare new stack component flavors.

Returns:

Type Description
List[Type[Flavor]]

A list of new stack component flavors.
Source code in src/zenml/integrations/integration.py 
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@classmethod
def flavors(cls) -> List[Type[Flavor]]:
    """Abstract method to declare new stack component flavors.

    Returns:
        A list of new stack component flavors.
    """
    return []
get_requirements(target_os: Optional[str] = None, python_version: Optional[str] = None) -> List[str] classmethod

Method to get the requirements for the integration.

Parameters:

Name Type Description Default
target_os Optional[str]

The target operating system to get the requirements for.

 None
python_version Optional[str]

The Python version to use for the requirements.

 None

Returns:

Type Description
List[str]

A list of requirements.
Source code in src/zenml/integrations/integration.py 
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@classmethod
def get_requirements(
    cls,
    target_os: Optional[str] = None,
    python_version: Optional[str] = None,
) -> List[str]:
    """Method to get the requirements for the integration.

    Args:
        target_os: The target operating system to get the requirements for.
        python_version: The Python version to use for the requirements.

    Returns:
        A list of requirements.
    """
    return cls.REQUIREMENTS
get_uninstall_requirements(target_os: Optional[str] = None) -> List[str] classmethod

Method to get the uninstall requirements for the integration.

Parameters:

Name Type Description Default
target_os Optional[str]

The target operating system to get the requirements for.

 None

Returns:

Type Description
List[str]

A list of requirements.
Source code in src/zenml/integrations/integration.py 
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@classmethod
def get_uninstall_requirements(
    cls, target_os: Optional[str] = None
) -> List[str]:
    """Method to get the uninstall requirements for the integration.

    Args:
        target_os: The target operating system to get the requirements for.

    Returns:
        A list of requirements.
    """
    ret = []
    for each in cls.get_requirements(target_os=target_os):
        is_ignored = False
        for ignored in cls.REQUIREMENTS_IGNORED_ON_UNINSTALL:
            if each.startswith(ignored):
                is_ignored = True
                break
        if not is_ignored:
            ret.append(each)
    return ret

Modules

artifact_stores

Initialization of the Backblaze B2 Artifact Store.

Classes
B2ArtifactStore(*args: Any, **kwargs: Any)

Bases: S3ArtifactStore

Artifact Store backed by a Backblaze B2 bucket via the S3 API.

Source code in src/zenml/integrations/s3/artifact_stores/s3_artifact_store.py 
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def __init__(
    self,
    *args: Any,
    **kwargs: Any,
) -> None:
    """Initializes the artifact store.

    Args:
        *args: Additional positional arguments.
        **kwargs: Additional keyword arguments.
    """
    super().__init__(*args, **kwargs)
    self._boto3_bucket_holder = None
Attributes
config: B2ArtifactStoreConfig property

Get the typed config of this artifact store.

Returns:

Type Description
B2ArtifactStoreConfig

The config of this artifact store.
filesystem: ZenMLS3Filesystem property

The B2 S3-compatible filesystem for this artifact store.

Returns:

Type Description
ZenMLS3Filesystem

The filesystem object.
Methods:
get_credentials() -> Tuple[Optional[str], Optional[str], Optional[str], Optional[str]]

Gets authentication credentials for the B2 filesystem.

Returns:

Type Description
Optional[str]

Tuple (key, secret, token, region) of credentials used to
Optional[str]

authenticate with the S3-compatible B2 filesystem.
Source code in src/zenml/integrations/b2/artifact_stores/b2_artifact_store.py 
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def get_credentials(
    self,
) -> Tuple[Optional[str], Optional[str], Optional[str], Optional[str]]:
    """Gets authentication credentials for the B2 filesystem.

    Returns:
        Tuple (key, secret, token, region) of credentials used to
        authenticate with the S3-compatible B2 filesystem.
    """
    key, secret, token, region = super().get_credentials()

    if key is None:
        key = os.environ.get(B2_KEY_ID_ENV_VAR)
    if secret is None:
        secret = os.environ.get(B2_APPLICATION_KEY_ENV_VAR)

    return key, secret, token, region
Modules
b2_artifact_store

Implementation of the Backblaze B2 Artifact Store.

B2's S3-compatible API behaves identically to S3 for the read/write operations ZenML needs, so this class subclasses :class:S3ArtifactStore. The B2-specific runtime fallbacks are applied here instead of in the config model so environment-derived values are not persisted during component registration.

Classes
B2ArtifactStore(*args: Any, **kwargs: Any)

Bases: S3ArtifactStore

Artifact Store backed by a Backblaze B2 bucket via the S3 API.

Source code in src/zenml/integrations/s3/artifact_stores/s3_artifact_store.py 
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def __init__(
    self,
    *args: Any,
    **kwargs: Any,
) -> None:
    """Initializes the artifact store.

    Args:
        *args: Additional positional arguments.
        **kwargs: Additional keyword arguments.
    """
    super().__init__(*args, **kwargs)
    self._boto3_bucket_holder = None
Attributes
config: B2ArtifactStoreConfig property

Get the typed config of this artifact store.

Returns:

Type Description
B2ArtifactStoreConfig

The config of this artifact store.
filesystem: ZenMLS3Filesystem property

The B2 S3-compatible filesystem for this artifact store.

Returns:

Type Description
ZenMLS3Filesystem

The filesystem object.
Methods:
get_credentials() -> Tuple[Optional[str], Optional[str], Optional[str], Optional[str]]

Gets authentication credentials for the B2 filesystem.

Returns:

Type Description
Optional[str]

Tuple (key, secret, token, region) of credentials used to
Optional[str]

authenticate with the S3-compatible B2 filesystem.
Source code in src/zenml/integrations/b2/artifact_stores/b2_artifact_store.py 
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def get_credentials(
    self,
) -> Tuple[Optional[str], Optional[str], Optional[str], Optional[str]]:
    """Gets authentication credentials for the B2 filesystem.

    Returns:
        Tuple (key, secret, token, region) of credentials used to
        authenticate with the S3-compatible B2 filesystem.
    """
    key, secret, token, region = super().get_credentials()

    if key is None:
        key = os.environ.get(B2_KEY_ID_ENV_VAR)
    if secret is None:
        secret = os.environ.get(B2_APPLICATION_KEY_ENV_VAR)

    return key, secret, token, region

flavors

Backblaze B2 integration flavors.

Classes
B2ArtifactStoreConfig(warn_about_plain_text_secrets: bool = False, **kwargs: Any)

Bases: S3ArtifactStoreConfig

Configuration for the Backblaze B2 Artifact Store.

Inherits all fields from :class:S3ArtifactStoreConfig. The bucket URI continues to use the s3:// scheme because the underlying filesystem (s3fs) addresses B2 buckets through the S3 API.

Source code in src/zenml/stack/stack_component.py 
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def __init__(
    self, warn_about_plain_text_secrets: bool = False, **kwargs: Any
) -> None:
    """Ensures that secret references don't clash with pydantic validation.

    StackComponents allow the specification of all their string attributes
    using secret references of the form `{{secret_name.key}}`. This however
    is only possible when the stack component does not perform any explicit
    validation of this attribute using pydantic validators. If this were
    the case, the validation would run on the secret reference and would
    fail or in the worst case, modify the secret reference and lead to
    unexpected behavior. This method ensures that no attributes that require
    custom pydantic validation are set as secret references.

    Args:
        warn_about_plain_text_secrets: If true, then warns about using
            plain-text secrets.
        **kwargs: Arguments to initialize this stack component.

    Raises:
        ValueError: If an attribute that requires custom pydantic validation
            is passed as a secret reference, or if the `name` attribute
            was passed as a secret reference.
    """
    for key, value in kwargs.items():
        try:
            field = self.__class__.model_fields[key]
        except KeyError:
            # Value for a private attribute or non-existing field, this
            # will fail during the upcoming pydantic validation
            continue

        if value is None:
            continue

        if not secret_utils.is_secret_reference(value):
            if (
                secret_utils.is_secret_field(field)
                and warn_about_plain_text_secrets
            ):
                logger.warning(
                    "You specified a plain-text value for the sensitive "
                    f"attribute `{key}` for a `{self.__class__.__name__}` "
                    "stack component. This is currently only a warning, "
                    "but future versions of ZenML will require you to pass "
                    "in sensitive information as secrets. Check out the "
                    "documentation on how to configure your stack "
                    "components with secrets here: "
                    "https://docs.zenml.io/deploying-zenml/deploying-zenml/secret-management"
                )
            continue

        if pydantic_utils.has_validators(
            pydantic_class=self.__class__, field_name=key
        ):
            raise ValueError(
                f"Passing the stack component attribute `{key}` as a "
                "secret reference is not allowed as additional validation "
                "is required for this attribute."
            )

    super().__init__(**kwargs)
B2ArtifactStoreFlavor

Bases: S3ArtifactStoreFlavor

Flavor of the Backblaze B2 artifact store.

Attributes
config_class: Type[B2ArtifactStoreConfig] property

The config class of the flavor.

Returns:

Type Description
Type[B2ArtifactStoreConfig]

The config class of the flavor.
display_name: str property

Display name of the flavor.

Returns:

Type Description
str

The display name of the flavor.
implementation_class: Type[B2ArtifactStore] property

Implementation class for this flavor.

Returns:

Type Description
Type[B2ArtifactStore]

The implementation class for this flavor.
logo_url: str property

Logo URL for the dashboard.

Returns:

Type Description
str

The flavor logo.
name: str property

Name of the flavor.

Returns:

Type Description
str

The name of the flavor.
service_connector_requirements: Optional[ServiceConnectorRequirements] property

Service connector resource requirements for B2.

A dedicated B2 service connector is not yet shipped; until one lands, no connector requirements are advertised so users wire credentials directly into the flavor config.

Returns:

Type Description
Optional[ServiceConnectorRequirements]

None: no service connector is currently required.
Modules
b2_artifact_store_flavor

Backblaze B2 artifact store flavor.

B2 is S3-compatible, so this flavor subclasses the S3 implementation rather than duplicating it. The config layer only validates user-provided values. Runtime fallbacks such as environment credentials and endpoint/user agent defaults are applied in the artifact store implementation so they are not persisted as stack component configuration.

Classes
B2ArtifactStoreConfig(warn_about_plain_text_secrets: bool = False, **kwargs: Any)

Bases: S3ArtifactStoreConfig

Configuration for the Backblaze B2 Artifact Store.

Inherits all fields from :class:S3ArtifactStoreConfig. The bucket URI continues to use the s3:// scheme because the underlying filesystem (s3fs) addresses B2 buckets through the S3 API.

Source code in src/zenml/stack/stack_component.py 
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def __init__(
    self, warn_about_plain_text_secrets: bool = False, **kwargs: Any
) -> None:
    """Ensures that secret references don't clash with pydantic validation.

    StackComponents allow the specification of all their string attributes
    using secret references of the form `{{secret_name.key}}`. This however
    is only possible when the stack component does not perform any explicit
    validation of this attribute using pydantic validators. If this were
    the case, the validation would run on the secret reference and would
    fail or in the worst case, modify the secret reference and lead to
    unexpected behavior. This method ensures that no attributes that require
    custom pydantic validation are set as secret references.

    Args:
        warn_about_plain_text_secrets: If true, then warns about using
            plain-text secrets.
        **kwargs: Arguments to initialize this stack component.

    Raises:
        ValueError: If an attribute that requires custom pydantic validation
            is passed as a secret reference, or if the `name` attribute
            was passed as a secret reference.
    """
    for key, value in kwargs.items():
        try:
            field = self.__class__.model_fields[key]
        except KeyError:
            # Value for a private attribute or non-existing field, this
            # will fail during the upcoming pydantic validation
            continue

        if value is None:
            continue

        if not secret_utils.is_secret_reference(value):
            if (
                secret_utils.is_secret_field(field)
                and warn_about_plain_text_secrets
            ):
                logger.warning(
                    "You specified a plain-text value for the sensitive "
                    f"attribute `{key}` for a `{self.__class__.__name__}` "
                    "stack component. This is currently only a warning, "
                    "but future versions of ZenML will require you to pass "
                    "in sensitive information as secrets. Check out the "
                    "documentation on how to configure your stack "
                    "components with secrets here: "
                    "https://docs.zenml.io/deploying-zenml/deploying-zenml/secret-management"
                )
            continue

        if pydantic_utils.has_validators(
            pydantic_class=self.__class__, field_name=key
        ):
            raise ValueError(
                f"Passing the stack component attribute `{key}` as a "
                "secret reference is not allowed as additional validation "
                "is required for this attribute."
            )

    super().__init__(**kwargs)
B2ArtifactStoreFlavor

Bases: S3ArtifactStoreFlavor

Flavor of the Backblaze B2 artifact store.

Attributes
config_class: Type[B2ArtifactStoreConfig] property

The config class of the flavor.

Returns:

Type Description
Type[B2ArtifactStoreConfig]

The config class of the flavor.
display_name: str property

Display name of the flavor.

Returns:

Type Description
str

The display name of the flavor.
implementation_class: Type[B2ArtifactStore] property

Implementation class for this flavor.

Returns:

Type Description
Type[B2ArtifactStore]

The implementation class for this flavor.
logo_url: str property

Logo URL for the dashboard.

Returns:

Type Description
str

The flavor logo.
name: str property

Name of the flavor.

Returns:

Type Description
str

The name of the flavor.
service_connector_requirements: Optional[ServiceConnectorRequirements] property

Service connector resource requirements for B2.

A dedicated B2 service connector is not yet shipped; until one lands, no connector requirements are advertised so users wire credentials directly into the flavor config.

Returns:

Type Description
Optional[ServiceConnectorRequirements]

None: no service connector is currently required.