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Modal

zenml.integrations.modal

Modal integration for cloud-native step execution.

The Modal integration sub-module provides a step operator flavor that allows executing steps on Modal's cloud infrastructure.

Attributes

MODAL = 'modal' module-attribute

MODAL_STEP_OPERATOR_FLAVOR = 'modal' module-attribute

Classes

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.
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.
Functions
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 = source_utils.load(flavor_model.source)()
    except (ModuleNotFoundError, ImportError, NotImplementedError) as err:
        if flavor_model.is_custom:
            flavor_module, _ = flavor_model.source.rsplit(".", maxsplit=1)
            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`."
            )
        else:
            raise ImportError(
                f"Couldn't import flavor {flavor_model.name}: {err}"
            )
    return cast(Flavor, 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
    """
    from zenml import __version__

    component_type = self.type.plural.replace("_", "-")
    name = self.name.replace("_", "-")

    try:
        is_latest = is_latest_zenml_version()
    except RuntimeError:
        # We assume in error cases that we are on the latest version
        is_latest = True

    if is_latest:
        base = "https://docs.zenml.io"
    else:
        base = f"https://zenml-io.gitbook.io/zenml-legacy-documentation/v/{__version__}"
    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]

        return (
            f"{base}/integration_code_docs"
            f"/integrations-{integration}/#{self.__module__}"
        )

    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,
        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.

Functions
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 r in cls.get_requirements():
        try:
            # First check if the base package is installed
            dist = pkg_resources.get_distribution(r)

            # Next, check if the dependencies (including extras) are
            # installed
            deps: List[Requirement] = []

            _, extras = parse_requirement(r)
            if extras:
                extra_list = extras[1:-1].split(",")
                for extra in extra_list:
                    try:
                        requirements = dist.requires(extras=[extra])  # type: ignore[arg-type]
                    except pkg_resources.UnknownExtra as e:
                        logger.debug(f"Unknown extra: {str(e)}")
                        return False
                    deps.extend(requirements)
            else:
                deps = dist.requires()

            for ri in deps:
                try:
                    # Remove the "extra == ..." part from the requirement string
                    cleaned_req = re.sub(
                        r"; extra == \"\w+\"", "", str(ri)
                    )
                    pkg_resources.get_distribution(cleaned_req)
                except pkg_resources.DistributionNotFound as e:
                    logger.debug(
                        f"Unable to find required dependency "
                        f"'{e.req}' for requirement '{r}' "
                        f"necessary for integration '{cls.NAME}'."
                    )
                    return False
                except pkg_resources.VersionConflict as e:
                    logger.debug(
                        f"Package version '{e.dist}' does not match "
                        f"version '{e.req}' required by '{r}' "
                        f"necessary for integration '{cls.NAME}'."
                    )
                    return False

        except pkg_resources.DistributionNotFound as e:
            logger.debug(
                f"Unable to find required package '{e.req}' for "
                f"integration {cls.NAME}."
            )
            return False
        except pkg_resources.VersionConflict as e:
            logger.debug(
                f"Package version '{e.dist}' does not match version "
                f"'{e.req}' necessary for integration {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
plugin_flavors() -> List[Type[BasePluginFlavor]] classmethod

Abstract method to declare new plugin flavors.

Returns:

Type Description
List[Type[BasePluginFlavor]]

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

    Returns:
        A list of new plugin flavors.
    """
    return []

ModalIntegration

Bases: Integration

Definition of Modal integration for ZenML.

Functions
flavors() -> List[Type[Flavor]] classmethod

Declare the stack component flavors for the Modal integration.

Returns:

Type Description
List[Type[Flavor]]

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

    Returns:
        List of new stack component flavors.
    """
    from zenml.integrations.modal.flavors import ModalStepOperatorFlavor

    return [ModalStepOperatorFlavor]

Modules

flavors

Modal integration flavors.

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

Bases: BaseStepOperatorConfig, ModalStepOperatorSettings

Configuration for the Modal step operator.

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/getting-started/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)
Attributes
is_remote: bool property

Checks if this stack component is running remotely.

This designation is used to determine if the stack component can be used with a local ZenML database or if it requires a remote ZenML server.

Returns:

Type Description
bool

True if this config is for a remote component, False otherwise.
ModalStepOperatorFlavor

Bases: BaseStepOperatorFlavor

Modal step operator flavor.

Attributes
config_class: Type[ModalStepOperatorConfig] property

Returns ModalStepOperatorConfig config class.

Returns:

Type Description
Type[ModalStepOperatorConfig]

The config class.
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[ModalStepOperator] property

Implementation class for this flavor.

Returns:

Type Description
Type[ModalStepOperator]

The implementation class.
logo_url: str property

A url to represent the flavor in 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.
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.
ModalStepOperatorSettings(warn_about_plain_text_secrets: bool = False, **kwargs: Any)

Bases: BaseSettings

Settings for the Modal step operator.

Specifying the region and cloud provider is only available for Enterprise and Team plan customers.

Certain combinations of settings are not available. It is suggested to err on the side of looser settings rather than more restrictive ones to avoid pipeline execution failures. In the case of failures, however, Modal provides detailed error messages that can help identify what is incompatible. See more in the Modal docs at https://modal.com/docs/guide/region-selection.

Attributes:

Name Type Description
gpu Optional[str]

The type of GPU to use for the step execution.
region Optional[str]

The region to use for the step execution.
cloud Optional[str]

The cloud provider to use for the step execution.
Source code in src/zenml/config/secret_reference_mixin.py 
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def __init__(
    self, warn_about_plain_text_secrets: bool = False, **kwargs: Any
) -> None:
    """Ensures that secret references are only passed for valid fields.

    This method ensures that secret references are not passed for fields
    that explicitly prevent them or require pydantic validation.

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

    Raises:
        ValueError: If an attribute that requires custom pydantic validation
            or an attribute which explicitly disallows secret references
            is 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}`. 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 values with secrets "
                    "here: https://docs.zenml.io/getting-started/deploying-zenml/secret-management"
                )
            continue

        if secret_utils.is_clear_text_field(field):
            raise ValueError(
                f"Passing the `{key}` attribute as a secret reference is "
                "not allowed."
            )

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

    super().__init__(**kwargs)
Modules
modal_step_operator_flavor

Modal step operator flavor.

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

Bases: BaseStepOperatorConfig, ModalStepOperatorSettings

Configuration for the Modal step operator.

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/getting-started/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)
Attributes
is_remote: bool property

Checks if this stack component is running remotely.

This designation is used to determine if the stack component can be used with a local ZenML database or if it requires a remote ZenML server.

Returns:

Type Description
bool

True if this config is for a remote component, False otherwise.
ModalStepOperatorFlavor

Bases: BaseStepOperatorFlavor

Modal step operator flavor.

Attributes
config_class: Type[ModalStepOperatorConfig] property

Returns ModalStepOperatorConfig config class.

Returns:

Type Description
Type[ModalStepOperatorConfig]

The config class.
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[ModalStepOperator] property

Implementation class for this flavor.

Returns:

Type Description
Type[ModalStepOperator]

The implementation class.
logo_url: str property

A url to represent the flavor in 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.
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.
ModalStepOperatorSettings(warn_about_plain_text_secrets: bool = False, **kwargs: Any)

Bases: BaseSettings

Settings for the Modal step operator.

Specifying the region and cloud provider is only available for Enterprise and Team plan customers.

Certain combinations of settings are not available. It is suggested to err on the side of looser settings rather than more restrictive ones to avoid pipeline execution failures. In the case of failures, however, Modal provides detailed error messages that can help identify what is incompatible. See more in the Modal docs at https://modal.com/docs/guide/region-selection.

Attributes:

Name Type Description
gpu Optional[str]

The type of GPU to use for the step execution.
region Optional[str]

The region to use for the step execution.
cloud Optional[str]

The cloud provider to use for the step execution.
Source code in src/zenml/config/secret_reference_mixin.py 
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def __init__(
    self, warn_about_plain_text_secrets: bool = False, **kwargs: Any
) -> None:
    """Ensures that secret references are only passed for valid fields.

    This method ensures that secret references are not passed for fields
    that explicitly prevent them or require pydantic validation.

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

    Raises:
        ValueError: If an attribute that requires custom pydantic validation
            or an attribute which explicitly disallows secret references
            is 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}`. 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 values with secrets "
                    "here: https://docs.zenml.io/getting-started/deploying-zenml/secret-management"
                )
            continue

        if secret_utils.is_clear_text_field(field):
            raise ValueError(
                f"Passing the `{key}` attribute as a secret reference is "
                "not allowed."
            )

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

    super().__init__(**kwargs)

step_operators

Modal step operator.

Classes
ModalStepOperator(name: str, id: UUID, config: StackComponentConfig, flavor: str, type: StackComponentType, user: Optional[UUID], created: datetime, updated: datetime, labels: Optional[Dict[str, Any]] = None, connector_requirements: Optional[ServiceConnectorRequirements] = None, connector: Optional[UUID] = None, connector_resource_id: Optional[str] = None, *args: Any, **kwargs: Any)

Bases: BaseStepOperator

Step operator to run a step on Modal.

This class defines code that can set up a Modal environment and run functions in it.

Source code in src/zenml/stack/stack_component.py 
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def __init__(
    self,
    name: str,
    id: UUID,
    config: StackComponentConfig,
    flavor: str,
    type: StackComponentType,
    user: Optional[UUID],
    created: datetime,
    updated: datetime,
    labels: Optional[Dict[str, Any]] = None,
    connector_requirements: Optional[ServiceConnectorRequirements] = None,
    connector: Optional[UUID] = None,
    connector_resource_id: Optional[str] = None,
    *args: Any,
    **kwargs: Any,
):
    """Initializes a StackComponent.

    Args:
        name: The name of the component.
        id: The unique ID of the component.
        config: The config of the component.
        flavor: The flavor of the component.
        type: The type of the component.
        user: The ID of the user who created the component.
        created: The creation time of the component.
        updated: The last update time of the component.
        labels: The labels of the component.
        connector_requirements: The requirements for the connector.
        connector: The ID of a connector linked to the component.
        connector_resource_id: The custom resource ID to access through
            the connector.
        *args: Additional positional arguments.
        **kwargs: Additional keyword arguments.

    Raises:
        ValueError: If a secret reference is passed as name.
    """
    if secret_utils.is_secret_reference(name):
        raise ValueError(
            "Passing the `name` attribute of a stack component as a "
            "secret reference is not allowed."
        )

    self.id = id
    self.name = name
    self._config = config
    self.flavor = flavor
    self.type = type
    self.user = user
    self.created = created
    self.updated = updated
    self.labels = labels
    self.connector_requirements = connector_requirements
    self.connector = connector
    self.connector_resource_id = connector_resource_id
    self._connector_instance: Optional[ServiceConnector] = None
Attributes
config: ModalStepOperatorConfig property

Get the Modal step operator configuration.

Returns:

Type Description
ModalStepOperatorConfig

The Modal step operator configuration.
settings_class: Optional[Type[BaseSettings]] property

Get the settings class for the Modal step operator.

Returns:

Type Description
Optional[Type[BaseSettings]]

The Modal step operator settings class.
validator: Optional[StackValidator] property

Get the stack validator for the Modal step operator.

Returns:

Type Description
Optional[StackValidator]

The stack validator.
Functions
get_docker_builds(deployment: PipelineDeploymentBase) -> List[BuildConfiguration]

Get the Docker build configurations for the Modal step operator.

Parameters:

Name Type Description Default
deployment PipelineDeploymentBase

The pipeline deployment.

 required

Returns:

Type Description
List[BuildConfiguration]

A list of Docker build configurations.
Source code in src/zenml/integrations/modal/step_operators/modal_step_operator.py 
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def get_docker_builds(
    self, deployment: "PipelineDeploymentBase"
) -> List["BuildConfiguration"]:
    """Get the Docker build configurations for the Modal step operator.

    Args:
        deployment: The pipeline deployment.

    Returns:
        A list of Docker build configurations.
    """
    builds = []
    for step_name, step in deployment.step_configurations.items():
        if step.config.step_operator == self.name:
            build = BuildConfiguration(
                key=MODAL_STEP_OPERATOR_DOCKER_IMAGE_KEY,
                settings=step.config.docker_settings,
                step_name=step_name,
            )
            builds.append(build)

    return builds
launch(info: StepRunInfo, entrypoint_command: List[str], environment: Dict[str, str]) -> None

Launch a step run on Modal.

Parameters:

Name Type Description Default
info StepRunInfo

The step run information.

 required
entrypoint_command List[str]

The entrypoint command for the step.

 required
environment Dict[str, str]

The environment variables for the step.

 required

Raises:

Type Description
RuntimeError

If no Docker credentials are found for the container registry.
ValueError

If no container registry is found in the stack.
Source code in src/zenml/integrations/modal/step_operators/modal_step_operator.py 
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def launch(
    self,
    info: "StepRunInfo",
    entrypoint_command: List[str],
    environment: Dict[str, str],
) -> None:
    """Launch a step run on Modal.

    Args:
        info: The step run information.
        entrypoint_command: The entrypoint command for the step.
        environment: The environment variables for the step.

    Raises:
        RuntimeError: If no Docker credentials are found for the container registry.
        ValueError: If no container registry is found in the stack.
    """
    settings = cast(ModalStepOperatorSettings, self.get_settings(info))
    image_name = info.get_image(key=MODAL_STEP_OPERATOR_DOCKER_IMAGE_KEY)
    zc = Client()
    stack = zc.active_stack

    if not stack.container_registry:
        raise ValueError(
            "No Container registry found in the stack. "
            "Please add a container registry and ensure "
            "it is correctly configured."
        )

    if docker_creds := stack.container_registry.credentials:
        docker_username, docker_password = docker_creds
    else:
        raise RuntimeError(
            "No Docker credentials found for the container registry."
        )

    my_secret = modal.secret._Secret.from_dict(
        {
            "REGISTRY_USERNAME": docker_username,
            "REGISTRY_PASSWORD": docker_password,
        }
    )

    spec = modal.image.DockerfileSpec(
        commands=[f"FROM {image_name}"], context_files={}
    )

    zenml_image = modal.Image._from_args(
        dockerfile_function=lambda *_, **__: spec,
        force_build=False,
        image_registry_config=modal.image._ImageRegistryConfig(
            api_pb2.REGISTRY_AUTH_TYPE_STATIC_CREDS, my_secret
        ),
    ).env(environment)

    resource_settings = info.config.resource_settings
    gpu_values = get_gpu_values(settings, resource_settings)

    app = modal.App(
        f"zenml-{info.run_name}-{info.step_run_id}-{info.pipeline_step_name}"
    )

    async def run_sandbox() -> asyncio.Future[None]:
        loop = asyncio.get_event_loop()
        future = loop.create_future()
        with modal.enable_output():
            async with app.run():
                memory_mb = resource_settings.get_memory(ByteUnit.MB)
                memory_int = (
                    int(memory_mb) if memory_mb is not None else None
                )
                sb = await modal.Sandbox.create.aio(
                    "bash",
                    "-c",
                    " ".join(entrypoint_command),
                    image=zenml_image,
                    gpu=gpu_values,
                    cpu=resource_settings.cpu_count,
                    memory=memory_int,
                    cloud=settings.cloud,
                    region=settings.region,
                    app=app,
                    timeout=86400,  # 24h, the max Modal allows
                )

                await sb.wait.aio()

        future.set_result(None)
        return future

    asyncio.run(run_sandbox())
Modules
modal_step_operator

Modal step operator implementation.

Classes
ModalStepOperator(name: str, id: UUID, config: StackComponentConfig, flavor: str, type: StackComponentType, user: Optional[UUID], created: datetime, updated: datetime, labels: Optional[Dict[str, Any]] = None, connector_requirements: Optional[ServiceConnectorRequirements] = None, connector: Optional[UUID] = None, connector_resource_id: Optional[str] = None, *args: Any, **kwargs: Any)

Bases: BaseStepOperator

Step operator to run a step on Modal.

This class defines code that can set up a Modal environment and run functions in it.

Source code in src/zenml/stack/stack_component.py 
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def __init__(
    self,
    name: str,
    id: UUID,
    config: StackComponentConfig,
    flavor: str,
    type: StackComponentType,
    user: Optional[UUID],
    created: datetime,
    updated: datetime,
    labels: Optional[Dict[str, Any]] = None,
    connector_requirements: Optional[ServiceConnectorRequirements] = None,
    connector: Optional[UUID] = None,
    connector_resource_id: Optional[str] = None,
    *args: Any,
    **kwargs: Any,
):
    """Initializes a StackComponent.

    Args:
        name: The name of the component.
        id: The unique ID of the component.
        config: The config of the component.
        flavor: The flavor of the component.
        type: The type of the component.
        user: The ID of the user who created the component.
        created: The creation time of the component.
        updated: The last update time of the component.
        labels: The labels of the component.
        connector_requirements: The requirements for the connector.
        connector: The ID of a connector linked to the component.
        connector_resource_id: The custom resource ID to access through
            the connector.
        *args: Additional positional arguments.
        **kwargs: Additional keyword arguments.

    Raises:
        ValueError: If a secret reference is passed as name.
    """
    if secret_utils.is_secret_reference(name):
        raise ValueError(
            "Passing the `name` attribute of a stack component as a "
            "secret reference is not allowed."
        )

    self.id = id
    self.name = name
    self._config = config
    self.flavor = flavor
    self.type = type
    self.user = user
    self.created = created
    self.updated = updated
    self.labels = labels
    self.connector_requirements = connector_requirements
    self.connector = connector
    self.connector_resource_id = connector_resource_id
    self._connector_instance: Optional[ServiceConnector] = None
Attributes
config: ModalStepOperatorConfig property

Get the Modal step operator configuration.

Returns:

Type Description
ModalStepOperatorConfig

The Modal step operator configuration.
settings_class: Optional[Type[BaseSettings]] property

Get the settings class for the Modal step operator.

Returns:

Type Description
Optional[Type[BaseSettings]]

The Modal step operator settings class.
validator: Optional[StackValidator] property

Get the stack validator for the Modal step operator.

Returns:

Type Description
Optional[StackValidator]

The stack validator.
Functions
get_docker_builds(deployment: PipelineDeploymentBase) -> List[BuildConfiguration]

Get the Docker build configurations for the Modal step operator.

Parameters:

Name Type Description Default
deployment PipelineDeploymentBase

The pipeline deployment.

 required

Returns:

Type Description
List[BuildConfiguration]

A list of Docker build configurations.
Source code in src/zenml/integrations/modal/step_operators/modal_step_operator.py 
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def get_docker_builds(
    self, deployment: "PipelineDeploymentBase"
) -> List["BuildConfiguration"]:
    """Get the Docker build configurations for the Modal step operator.

    Args:
        deployment: The pipeline deployment.

    Returns:
        A list of Docker build configurations.
    """
    builds = []
    for step_name, step in deployment.step_configurations.items():
        if step.config.step_operator == self.name:
            build = BuildConfiguration(
                key=MODAL_STEP_OPERATOR_DOCKER_IMAGE_KEY,
                settings=step.config.docker_settings,
                step_name=step_name,
            )
            builds.append(build)

    return builds
launch(info: StepRunInfo, entrypoint_command: List[str], environment: Dict[str, str]) -> None

Launch a step run on Modal.

Parameters:

Name Type Description Default
info StepRunInfo

The step run information.

 required
entrypoint_command List[str]

The entrypoint command for the step.

 required
environment Dict[str, str]

The environment variables for the step.

 required

Raises:

Type Description
RuntimeError

If no Docker credentials are found for the container registry.
ValueError

If no container registry is found in the stack.
Source code in src/zenml/integrations/modal/step_operators/modal_step_operator.py 
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def launch(
    self,
    info: "StepRunInfo",
    entrypoint_command: List[str],
    environment: Dict[str, str],
) -> None:
    """Launch a step run on Modal.

    Args:
        info: The step run information.
        entrypoint_command: The entrypoint command for the step.
        environment: The environment variables for the step.

    Raises:
        RuntimeError: If no Docker credentials are found for the container registry.
        ValueError: If no container registry is found in the stack.
    """
    settings = cast(ModalStepOperatorSettings, self.get_settings(info))
    image_name = info.get_image(key=MODAL_STEP_OPERATOR_DOCKER_IMAGE_KEY)
    zc = Client()
    stack = zc.active_stack

    if not stack.container_registry:
        raise ValueError(
            "No Container registry found in the stack. "
            "Please add a container registry and ensure "
            "it is correctly configured."
        )

    if docker_creds := stack.container_registry.credentials:
        docker_username, docker_password = docker_creds
    else:
        raise RuntimeError(
            "No Docker credentials found for the container registry."
        )

    my_secret = modal.secret._Secret.from_dict(
        {
            "REGISTRY_USERNAME": docker_username,
            "REGISTRY_PASSWORD": docker_password,
        }
    )

    spec = modal.image.DockerfileSpec(
        commands=[f"FROM {image_name}"], context_files={}
    )

    zenml_image = modal.Image._from_args(
        dockerfile_function=lambda *_, **__: spec,
        force_build=False,
        image_registry_config=modal.image._ImageRegistryConfig(
            api_pb2.REGISTRY_AUTH_TYPE_STATIC_CREDS, my_secret
        ),
    ).env(environment)

    resource_settings = info.config.resource_settings
    gpu_values = get_gpu_values(settings, resource_settings)

    app = modal.App(
        f"zenml-{info.run_name}-{info.step_run_id}-{info.pipeline_step_name}"
    )

    async def run_sandbox() -> asyncio.Future[None]:
        loop = asyncio.get_event_loop()
        future = loop.create_future()
        with modal.enable_output():
            async with app.run():
                memory_mb = resource_settings.get_memory(ByteUnit.MB)
                memory_int = (
                    int(memory_mb) if memory_mb is not None else None
                )
                sb = await modal.Sandbox.create.aio(
                    "bash",
                    "-c",
                    " ".join(entrypoint_command),
                    image=zenml_image,
                    gpu=gpu_values,
                    cpu=resource_settings.cpu_count,
                    memory=memory_int,
                    cloud=settings.cloud,
                    region=settings.region,
                    app=app,
                    timeout=86400,  # 24h, the max Modal allows
                )

                await sb.wait.aio()

        future.set_result(None)
        return future

    asyncio.run(run_sandbox())
Functions
get_gpu_values(settings: ModalStepOperatorSettings, resource_settings: ResourceSettings) -> Optional[str]

Get the GPU values for the Modal step operator.

Parameters:

Name Type Description Default
settings ModalStepOperatorSettings

The Modal step operator settings.

 required
resource_settings ResourceSettings

The resource settings.

 required

Returns:

Type Description
Optional[str]

The GPU string if a count is specified, otherwise the GPU type.
Source code in src/zenml/integrations/modal/step_operators/modal_step_operator.py 
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def get_gpu_values(
    settings: ModalStepOperatorSettings, resource_settings: ResourceSettings
) -> Optional[str]:
    """Get the GPU values for the Modal step operator.

    Args:
        settings: The Modal step operator settings.
        resource_settings: The resource settings.

    Returns:
        The GPU string if a count is specified, otherwise the GPU type.
    """
    if not settings.gpu:
        return None
    gpu_count = resource_settings.gpu_count
    return f"{settings.gpu}:{gpu_count}" if gpu_count else settings.gpu