Skip to content

Tekton

zenml.integrations.tekton

Initialization of the Tekton integration for ZenML.

The Tekton integration sub-module powers an alternative to the local orchestrator. You can enable it by registering the Tekton orchestrator with the CLI tool.

Attributes

TEKTON = 'tekton' module-attribute

TEKTON_ORCHESTRATOR_FLAVOR = 'tekton' 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 
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
@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 
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
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 
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
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. Custom flavors are then scoped by user and workspace

 True

Returns:

Type Description
FlavorRequest

The model.
Source code in src/zenml/stack/flavor.py 
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
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. Custom flavors
            are then scoped by user and workspace

    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
    )
    user = None
    workspace = None
    if is_custom:
        user = Client().active_user.id
        workspace = Client().active_workspace.id

    model_class = FlavorRequest if is_custom else InternalFlavorRequest
    model = model_class(
        user=user,
        workspace=workspace,
        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 
170
171
172
@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 
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
@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 
174
175
176
177
178
179
180
181
@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) -> 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

Returns:

Type Description
List[str]

A list of requirements.
Source code in src/zenml/integrations/integration.py 
135
136
137
138
139
140
141
142
143
144
145
@classmethod
def get_requirements(cls, target_os: 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.

    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 
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
@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 
183
184
185
186
187
188
189
190
@classmethod
def plugin_flavors(cls) -> List[Type["BasePluginFlavor"]]:
    """Abstract method to declare new plugin flavors.

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

StackComponentType

Bases: StrEnum

All possible types a StackComponent can have.

Attributes
plural: str property

Returns the plural of the enum value.

Returns:

Type Description
str

The plural of the enum value.

TektonIntegration

Bases: Integration

Definition of Tekton Integration for ZenML.

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

Declare the stack component flavors for the Tekton integration.

Returns:

Type Description
List[Type[Flavor]]

List of stack component flavors for this integration.
Source code in src/zenml/integrations/tekton/__init__.py 
37
38
39
40
41
42
43
44
45
46
@classmethod
def flavors(cls) -> List[Type[Flavor]]:
    """Declare the stack component flavors for the Tekton integration.

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

    return [TektonOrchestratorFlavor]

Modules

flavors

Tekton integration flavors.

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

Bases: BaseOrchestratorConfig, TektonOrchestratorSettings

Configuration for the Tekton orchestrator.

Attributes:

Name Type Description
tekton_hostname Optional[str]

Hostname of the Tekton server.
kubernetes_context Optional[str]

Name of a kubernetes context to run pipelines in. If the stack component is linked to a Kubernetes service connector, this field is ignored. Otherwise, it is mandatory.
kubernetes_namespace str

Name of the kubernetes namespace in which the pods that run the pipeline steps should be running.
Source code in src/zenml/stack/stack_component.py 
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
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_local: bool property

Checks if this stack component is running locally.

Returns:

Type Description
bool

True if this config is for a local component, False otherwise.
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.
TektonOrchestratorFlavor

Bases: BaseOrchestratorFlavor

Flavor for the Tekton orchestrator.

Attributes
config_class: Type[TektonOrchestratorConfig] property

Returns TektonOrchestratorConfig config class.

Returns:

Type Description
Type[TektonOrchestratorConfig]

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[TektonOrchestrator] property

Implementation class for this flavor.

Returns:

Type Description
Type[TektonOrchestrator]

Implementation class for this flavor.
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 orchestrator flavor.

Returns:

Type Description
str

Name of the orchestrator 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.
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.
Modules
tekton_orchestrator_flavor

Tekton orchestrator flavor.

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

Bases: BaseOrchestratorConfig, TektonOrchestratorSettings

Configuration for the Tekton orchestrator.

Attributes:

Name Type Description
tekton_hostname Optional[str]

Hostname of the Tekton server.
kubernetes_context Optional[str]

Name of a kubernetes context to run pipelines in. If the stack component is linked to a Kubernetes service connector, this field is ignored. Otherwise, it is mandatory.
kubernetes_namespace str

Name of the kubernetes namespace in which the pods that run the pipeline steps should be running.
Source code in src/zenml/stack/stack_component.py 
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
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_local: bool property

Checks if this stack component is running locally.

Returns:

Type Description
bool

True if this config is for a local component, False otherwise.
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.
TektonOrchestratorFlavor

Bases: BaseOrchestratorFlavor

Flavor for the Tekton orchestrator.

Attributes
config_class: Type[TektonOrchestratorConfig] property

Returns TektonOrchestratorConfig config class.

Returns:

Type Description
Type[TektonOrchestratorConfig]

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[TektonOrchestrator] property

Implementation class for this flavor.

Returns:

Type Description
Type[TektonOrchestrator]

Implementation class for this flavor.
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 orchestrator flavor.

Returns:

Type Description
str

Name of the orchestrator 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.
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.
TektonOrchestratorSettings(warn_about_plain_text_secrets: bool = False, **kwargs: Any)

Bases: BaseSettings

Settings for the Tekton orchestrator.

Attributes:

Name Type Description
synchronous bool

If True, the client running a pipeline using this orchestrator waits until all steps finish running. If False, the client returns immediately and the pipeline is executed asynchronously. Defaults to True. This setting only has an effect when specified on the pipeline and will be ignored if specified on steps.
timeout int

How many seconds to wait for synchronous runs.
client_args Dict[str, Any]

Arguments to pass when initializing the KFP client.
client_username Optional[str]

Username to generate a session cookie for the kubeflow client. Both client_username
client_password Optional[str]

Password to generate a session cookie for the kubeflow client. Both client_username
user_namespace Optional[str]

The user namespace to use when creating experiments and runs.
Source code in src/zenml/config/secret_reference_mixin.py 
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
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)
Functions

orchestrators

Initialization of the Tekton ZenML orchestrator.

Classes
TektonOrchestrator(name: str, id: UUID, config: StackComponentConfig, flavor: str, type: StackComponentType, user: Optional[UUID], workspace: 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: ContainerizedOrchestrator

Orchestrator responsible for running pipelines using Tekton.

Source code in src/zenml/stack/stack_component.py 
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
def __init__(
    self,
    name: str,
    id: UUID,
    config: StackComponentConfig,
    flavor: str,
    type: StackComponentType,
    user: Optional[UUID],
    workspace: 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.
        workspace: The ID of the workspace the component belongs to.
        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.workspace = workspace
    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: TektonOrchestratorConfig property

Returns the TektonOrchestratorConfig config.

Returns:

Type Description
TektonOrchestratorConfig

The configuration.
log_file: str property

Path of the daemon log file.

Returns:

Type Description
str

Path of the daemon log file.
pipeline_directory: str property

Path to a directory in which the Tekton pipeline files are stored.

Returns:

Type Description
str

Path to the pipeline directory.
root_directory: str property

Returns path to the root directory.

Returns:

Type Description
str

Path to the root directory.
settings_class: Optional[Type[BaseSettings]] property

Settings class for the Tekton orchestrator.

Returns:

Type Description
Optional[Type[BaseSettings]]

The settings class.
validator: Optional[StackValidator] property

Ensures a stack with only remote components and a container registry.

Returns:

Type Description
Optional[StackValidator]

A StackValidator instance.
Functions
get_kubernetes_contexts() -> Tuple[List[str], Optional[str]]

Get the list of configured Kubernetes contexts and the active context.

Returns:

Type Description
List[str]

A tuple containing the list of configured Kubernetes contexts and
Optional[str]

the active context.
Source code in src/zenml/integrations/tekton/orchestrators/tekton_orchestrator.py 
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
def get_kubernetes_contexts(self) -> Tuple[List[str], Optional[str]]:
    """Get the list of configured Kubernetes contexts and the active context.

    Returns:
        A tuple containing the list of configured Kubernetes contexts and
        the active context.
    """
    try:
        contexts, active_context = k8s_config.list_kube_config_contexts()
    except k8s_config.config_exception.ConfigException:
        return [], None

    context_names = [c["name"] for c in contexts]
    active_context_name = active_context["name"]
    return context_names, active_context_name
get_orchestrator_run_id() -> str

Returns the active orchestrator run id.

Raises:

Type Description
RuntimeError

If the environment variable specifying the run id is not set.

Returns:

Type Description
str

The orchestrator run id.
Source code in src/zenml/integrations/tekton/orchestrators/tekton_orchestrator.py 
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
def get_orchestrator_run_id(self) -> str:
    """Returns the active orchestrator run id.

    Raises:
        RuntimeError: If the environment variable specifying the run id
            is not set.

    Returns:
        The orchestrator run id.
    """
    try:
        return os.environ[ENV_ZENML_TEKTON_RUN_ID]
    except KeyError as e:
        raise RuntimeError(
            "Unable to read run id from environment variable "
            f"{ENV_ZENML_TEKTON_RUN_ID}."
        ) from e
prepare_or_run_pipeline(deployment: PipelineDeploymentResponse, stack: Stack, environment: Dict[str, str]) -> Any

Runs the pipeline on Tekton.

This function first compiles the ZenML pipeline into a Tekton yaml and then applies this configuration to run the pipeline.

Parameters:

Name Type Description Default
deployment PipelineDeploymentResponse

The pipeline deployment to prepare or run.

 required
stack Stack

The stack the pipeline will run on.

 required
environment Dict[str, str]

Environment variables to set in the orchestration environment.

 required

Raises:

Type Description
RuntimeError

If you try to run the pipelines in a notebook environment.
Source code in src/zenml/integrations/tekton/orchestrators/tekton_orchestrator.py 
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
def prepare_or_run_pipeline(
    self,
    deployment: "PipelineDeploymentResponse",
    stack: "Stack",
    environment: Dict[str, str],
) -> Any:
    """Runs the pipeline on Tekton.

    This function first compiles the ZenML pipeline into a Tekton yaml
    and then applies this configuration to run the pipeline.

    Args:
        deployment: The pipeline deployment to prepare or run.
        stack: The stack the pipeline will run on.
        environment: Environment variables to set in the orchestration
            environment.

    Raises:
        RuntimeError: If you try to run the pipelines in a notebook
            environment.
    """
    # First check whether the code running in a notebook
    if Environment.in_notebook():
        raise RuntimeError(
            "The Tekton orchestrator cannot run pipelines in a notebook "
            "environment. The reason is that it is non-trivial to create "
            "a Docker image of a notebook. Please consider refactoring "
            "your notebook cells into separate scripts in a Python module "
            "and run the code outside of a notebook when using this "
            "orchestrator."
        )

    assert stack.container_registry

    orchestrator_run_name = get_orchestrator_run_name(
        pipeline_name=deployment.pipeline_configuration.name
    ).replace("_", "-")

    def _create_dynamic_pipeline() -> Any:
        """Create a dynamic pipeline including each step.

        Returns:
            pipeline_func
        """
        step_name_to_dynamic_component: Dict[str, Any] = {}

        for step_name, step in deployment.step_configurations.items():
            image = self.get_image(
                deployment=deployment,
                step_name=step_name,
            )
            command = StepEntrypointConfiguration.get_entrypoint_command()
            arguments = (
                StepEntrypointConfiguration.get_entrypoint_arguments(
                    step_name=step_name,
                    deployment_id=deployment.id,
                )
            )
            dynamic_component = self._create_dynamic_component(
                image, command, arguments, step_name
            )
            step_settings = cast(
                TektonOrchestratorSettings, self.get_settings(step)
            )
            node_selector_constraint: Optional[Tuple[str, str]] = None
            pod_settings = step_settings.pod_settings
            if pod_settings:
                if pod_settings.host_ipc:
                    logger.warning(
                        "Host IPC is set to `True` but not supported in "
                        "this orchestrator. Ignoring..."
                    )
                if pod_settings.affinity:
                    logger.warning(
                        "Affinity is set but not supported in Tekton with "
                        "Tekton Pipelines 2.x. Ignoring..."
                    )
                if pod_settings.tolerations:
                    logger.warning(
                        "Tolerations are set but not supported in "
                        "Tekton with Tekton Pipelines 2.x. Ignoring..."
                    )
                if pod_settings.volumes:
                    logger.warning(
                        "Volumes are set but not supported in Tekton with "
                        "Tekton Pipelines 2.x. Ignoring..."
                    )
                if pod_settings.volume_mounts:
                    logger.warning(
                        "Volume mounts are set but not supported in "
                        "Tekton with Tekton Pipelines 2.x. Ignoring..."
                    )
                # apply pod settings
                if (
                    KFP_ACCELERATOR_NODE_SELECTOR_CONSTRAINT_LABEL
                    in pod_settings.node_selectors.keys()
                ):
                    node_selector_constraint = (
                        KFP_ACCELERATOR_NODE_SELECTOR_CONSTRAINT_LABEL,
                        pod_settings.node_selectors[
                            KFP_ACCELERATOR_NODE_SELECTOR_CONSTRAINT_LABEL
                        ],
                    )

            step_name_to_dynamic_component[step_name] = dynamic_component

        @dsl.pipeline(  # type: ignore[misc]
            display_name=orchestrator_run_name,
        )
        def dynamic_pipeline() -> None:
            """Dynamic pipeline."""
            # iterate through the components one by one
            # (from step_name_to_dynamic_component)
            for (
                component_name,
                component,
            ) in step_name_to_dynamic_component.items():
                # for each component, check to see what other steps are
                # upstream of it
                step = deployment.step_configurations[component_name]
                upstream_step_components = [
                    step_name_to_dynamic_component[upstream_step_name]
                    for upstream_step_name in step.spec.upstream_steps
                ]
                task = (
                    component()
                    .set_display_name(
                        name=component_name,
                    )
                    .set_caching_options(enable_caching=False)
                    .set_env_variable(
                        name=ENV_ZENML_TEKTON_RUN_ID,
                        value=dsl.PIPELINE_JOB_NAME_PLACEHOLDER,
                    )
                    .after(*upstream_step_components)
                )
                self._configure_container_resources(
                    task,
                    step.config.resource_settings,
                    node_selector_constraint,
                )

        return dynamic_pipeline

    def _update_yaml_with_environment(
        yaml_file_path: str, environment: Dict[str, str]
    ) -> None:
        """Updates the env section of the steps in the YAML file with the given environment variables.

        Args:
            yaml_file_path: The path to the YAML file to update.
            environment: A dictionary of environment variables to add.
        """
        pipeline_definition = yaml_utils.read_yaml(pipeline_file_path)

        # Iterate through each component and add the environment variables
        for executor in pipeline_definition["deploymentSpec"]["executors"]:
            if (
                "container"
                in pipeline_definition["deploymentSpec"]["executors"][
                    executor
                ]
            ):
                container = pipeline_definition["deploymentSpec"][
                    "executors"
                ][executor]["container"]
                if "env" not in container:
                    container["env"] = []
                for key, value in environment.items():
                    container["env"].append({"name": key, "value": value})

        yaml_utils.write_yaml(pipeline_file_path, pipeline_definition)

        print(
            f"Updated YAML file with environment variables at {yaml_file_path}"
        )

    # Get a filepath to use to save the finished yaml to
    fileio.makedirs(self.pipeline_directory)
    pipeline_file_path = os.path.join(
        self.pipeline_directory, f"{orchestrator_run_name}.yaml"
    )

    KFPCompiler().compile(
        pipeline_func=_create_dynamic_pipeline(),
        package_path=pipeline_file_path,
        pipeline_name=orchestrator_run_name,
    )

    # Let's update the YAML file with the environment variables
    _update_yaml_with_environment(pipeline_file_path, environment)

    logger.info(
        "Writing Tekton workflow definition to `%s`.", pipeline_file_path
    )

    # using the kfp client uploads the pipeline to Tekton pipelines and
    # runs it there
    self._upload_and_run_pipeline(
        deployment=deployment,
        pipeline_file_path=pipeline_file_path,
        run_name=orchestrator_run_name,
    )
Modules
tekton_orchestrator

Implementation of the Tekton orchestrator.

Classes
KubeClientKFPClient(client: k8s_client.ApiClient, *args: Any, **kwargs: Any)

Bases: Client

KFP client initialized from a Kubernetes client.

This is a workaround for the fact that the native KFP client does not support initialization from an existing Kubernetes client.

Initializes the KFP client from a Kubernetes client.

Parameters:

Name Type Description Default
client ApiClient

pre-configured Kubernetes client.

 required
args Any

standard KFP client positional arguments.

 ()
kwargs Any

standard KFP client keyword arguments.

 {}
Source code in src/zenml/integrations/tekton/orchestrators/tekton_orchestrator.py 
73
74
75
76
77
78
79
80
81
82
83
84
def __init__(
    self, client: k8s_client.ApiClient, *args: Any, **kwargs: Any
) -> None:
    """Initializes the KFP client from a Kubernetes client.

    Args:
        client: pre-configured Kubernetes client.
        args: standard KFP client positional arguments.
        kwargs: standard KFP client keyword arguments.
    """
    self._k8s_client = client
    super().__init__(*args, **kwargs)
Functions
TektonOrchestrator(name: str, id: UUID, config: StackComponentConfig, flavor: str, type: StackComponentType, user: Optional[UUID], workspace: 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: ContainerizedOrchestrator

Orchestrator responsible for running pipelines using Tekton.

Source code in src/zenml/stack/stack_component.py 
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
def __init__(
    self,
    name: str,
    id: UUID,
    config: StackComponentConfig,
    flavor: str,
    type: StackComponentType,
    user: Optional[UUID],
    workspace: 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.
        workspace: The ID of the workspace the component belongs to.
        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.workspace = workspace
    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: TektonOrchestratorConfig property

Returns the TektonOrchestratorConfig config.

Returns:

Type Description
TektonOrchestratorConfig

The configuration.
log_file: str property

Path of the daemon log file.

Returns:

Type Description
str

Path of the daemon log file.
pipeline_directory: str property

Path to a directory in which the Tekton pipeline files are stored.

Returns:

Type Description
str

Path to the pipeline directory.
root_directory: str property

Returns path to the root directory.

Returns:

Type Description
str

Path to the root directory.
settings_class: Optional[Type[BaseSettings]] property

Settings class for the Tekton orchestrator.

Returns:

Type Description
Optional[Type[BaseSettings]]

The settings class.
validator: Optional[StackValidator] property

Ensures a stack with only remote components and a container registry.

Returns:

Type Description
Optional[StackValidator]

A StackValidator instance.
Functions
get_kubernetes_contexts() -> Tuple[List[str], Optional[str]]

Get the list of configured Kubernetes contexts and the active context.

Returns:

Type Description
List[str]

A tuple containing the list of configured Kubernetes contexts and
Optional[str]

the active context.
Source code in src/zenml/integrations/tekton/orchestrators/tekton_orchestrator.py 
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
def get_kubernetes_contexts(self) -> Tuple[List[str], Optional[str]]:
    """Get the list of configured Kubernetes contexts and the active context.

    Returns:
        A tuple containing the list of configured Kubernetes contexts and
        the active context.
    """
    try:
        contexts, active_context = k8s_config.list_kube_config_contexts()
    except k8s_config.config_exception.ConfigException:
        return [], None

    context_names = [c["name"] for c in contexts]
    active_context_name = active_context["name"]
    return context_names, active_context_name
get_orchestrator_run_id() -> str

Returns the active orchestrator run id.

Raises:

Type Description
RuntimeError

If the environment variable specifying the run id is not set.

Returns:

Type Description
str

The orchestrator run id.
Source code in src/zenml/integrations/tekton/orchestrators/tekton_orchestrator.py 
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
def get_orchestrator_run_id(self) -> str:
    """Returns the active orchestrator run id.

    Raises:
        RuntimeError: If the environment variable specifying the run id
            is not set.

    Returns:
        The orchestrator run id.
    """
    try:
        return os.environ[ENV_ZENML_TEKTON_RUN_ID]
    except KeyError as e:
        raise RuntimeError(
            "Unable to read run id from environment variable "
            f"{ENV_ZENML_TEKTON_RUN_ID}."
        ) from e
prepare_or_run_pipeline(deployment: PipelineDeploymentResponse, stack: Stack, environment: Dict[str, str]) -> Any

Runs the pipeline on Tekton.

This function first compiles the ZenML pipeline into a Tekton yaml and then applies this configuration to run the pipeline.

Parameters:

Name Type Description Default
deployment PipelineDeploymentResponse

The pipeline deployment to prepare or run.

 required
stack Stack

The stack the pipeline will run on.

 required
environment Dict[str, str]

Environment variables to set in the orchestration environment.

 required

Raises:

Type Description
RuntimeError

If you try to run the pipelines in a notebook environment.
Source code in src/zenml/integrations/tekton/orchestrators/tekton_orchestrator.py 
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
def prepare_or_run_pipeline(
    self,
    deployment: "PipelineDeploymentResponse",
    stack: "Stack",
    environment: Dict[str, str],
) -> Any:
    """Runs the pipeline on Tekton.

    This function first compiles the ZenML pipeline into a Tekton yaml
    and then applies this configuration to run the pipeline.

    Args:
        deployment: The pipeline deployment to prepare or run.
        stack: The stack the pipeline will run on.
        environment: Environment variables to set in the orchestration
            environment.

    Raises:
        RuntimeError: If you try to run the pipelines in a notebook
            environment.
    """
    # First check whether the code running in a notebook
    if Environment.in_notebook():
        raise RuntimeError(
            "The Tekton orchestrator cannot run pipelines in a notebook "
            "environment. The reason is that it is non-trivial to create "
            "a Docker image of a notebook. Please consider refactoring "
            "your notebook cells into separate scripts in a Python module "
            "and run the code outside of a notebook when using this "
            "orchestrator."
        )

    assert stack.container_registry

    orchestrator_run_name = get_orchestrator_run_name(
        pipeline_name=deployment.pipeline_configuration.name
    ).replace("_", "-")

    def _create_dynamic_pipeline() -> Any:
        """Create a dynamic pipeline including each step.

        Returns:
            pipeline_func
        """
        step_name_to_dynamic_component: Dict[str, Any] = {}

        for step_name, step in deployment.step_configurations.items():
            image = self.get_image(
                deployment=deployment,
                step_name=step_name,
            )
            command = StepEntrypointConfiguration.get_entrypoint_command()
            arguments = (
                StepEntrypointConfiguration.get_entrypoint_arguments(
                    step_name=step_name,
                    deployment_id=deployment.id,
                )
            )
            dynamic_component = self._create_dynamic_component(
                image, command, arguments, step_name
            )
            step_settings = cast(
                TektonOrchestratorSettings, self.get_settings(step)
            )
            node_selector_constraint: Optional[Tuple[str, str]] = None
            pod_settings = step_settings.pod_settings
            if pod_settings:
                if pod_settings.host_ipc:
                    logger.warning(
                        "Host IPC is set to `True` but not supported in "
                        "this orchestrator. Ignoring..."
                    )
                if pod_settings.affinity:
                    logger.warning(
                        "Affinity is set but not supported in Tekton with "
                        "Tekton Pipelines 2.x. Ignoring..."
                    )
                if pod_settings.tolerations:
                    logger.warning(
                        "Tolerations are set but not supported in "
                        "Tekton with Tekton Pipelines 2.x. Ignoring..."
                    )
                if pod_settings.volumes:
                    logger.warning(
                        "Volumes are set but not supported in Tekton with "
                        "Tekton Pipelines 2.x. Ignoring..."
                    )
                if pod_settings.volume_mounts:
                    logger.warning(
                        "Volume mounts are set but not supported in "
                        "Tekton with Tekton Pipelines 2.x. Ignoring..."
                    )
                # apply pod settings
                if (
                    KFP_ACCELERATOR_NODE_SELECTOR_CONSTRAINT_LABEL
                    in pod_settings.node_selectors.keys()
                ):
                    node_selector_constraint = (
                        KFP_ACCELERATOR_NODE_SELECTOR_CONSTRAINT_LABEL,
                        pod_settings.node_selectors[
                            KFP_ACCELERATOR_NODE_SELECTOR_CONSTRAINT_LABEL
                        ],
                    )

            step_name_to_dynamic_component[step_name] = dynamic_component

        @dsl.pipeline(  # type: ignore[misc]
            display_name=orchestrator_run_name,
        )
        def dynamic_pipeline() -> None:
            """Dynamic pipeline."""
            # iterate through the components one by one
            # (from step_name_to_dynamic_component)
            for (
                component_name,
                component,
            ) in step_name_to_dynamic_component.items():
                # for each component, check to see what other steps are
                # upstream of it
                step = deployment.step_configurations[component_name]
                upstream_step_components = [
                    step_name_to_dynamic_component[upstream_step_name]
                    for upstream_step_name in step.spec.upstream_steps
                ]
                task = (
                    component()
                    .set_display_name(
                        name=component_name,
                    )
                    .set_caching_options(enable_caching=False)
                    .set_env_variable(
                        name=ENV_ZENML_TEKTON_RUN_ID,
                        value=dsl.PIPELINE_JOB_NAME_PLACEHOLDER,
                    )
                    .after(*upstream_step_components)
                )
                self._configure_container_resources(
                    task,
                    step.config.resource_settings,
                    node_selector_constraint,
                )

        return dynamic_pipeline

    def _update_yaml_with_environment(
        yaml_file_path: str, environment: Dict[str, str]
    ) -> None:
        """Updates the env section of the steps in the YAML file with the given environment variables.

        Args:
            yaml_file_path: The path to the YAML file to update.
            environment: A dictionary of environment variables to add.
        """
        pipeline_definition = yaml_utils.read_yaml(pipeline_file_path)

        # Iterate through each component and add the environment variables
        for executor in pipeline_definition["deploymentSpec"]["executors"]:
            if (
                "container"
                in pipeline_definition["deploymentSpec"]["executors"][
                    executor
                ]
            ):
                container = pipeline_definition["deploymentSpec"][
                    "executors"
                ][executor]["container"]
                if "env" not in container:
                    container["env"] = []
                for key, value in environment.items():
                    container["env"].append({"name": key, "value": value})

        yaml_utils.write_yaml(pipeline_file_path, pipeline_definition)

        print(
            f"Updated YAML file with environment variables at {yaml_file_path}"
        )

    # Get a filepath to use to save the finished yaml to
    fileio.makedirs(self.pipeline_directory)
    pipeline_file_path = os.path.join(
        self.pipeline_directory, f"{orchestrator_run_name}.yaml"
    )

    KFPCompiler().compile(
        pipeline_func=_create_dynamic_pipeline(),
        package_path=pipeline_file_path,
        pipeline_name=orchestrator_run_name,
    )

    # Let's update the YAML file with the environment variables
    _update_yaml_with_environment(pipeline_file_path, environment)

    logger.info(
        "Writing Tekton workflow definition to `%s`.", pipeline_file_path
    )

    # using the kfp client uploads the pipeline to Tekton pipelines and
    # runs it there
    self._upload_and_run_pipeline(
        deployment=deployment,
        pipeline_file_path=pipeline_file_path,
        run_name=orchestrator_run_name,
    )
Functions Modules