Tekton
zenml.integrations.tekton
special
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.
TektonIntegration (Integration)
Definition of Tekton Integration for ZenML.
Source code in zenml/integrations/tekton/__init__.py
class TektonIntegration(Integration):
"""Definition of Tekton Integration for ZenML."""
NAME = TEKTON
REQUIREMENTS = ["kfp>=2.6.0", "kfp-kubernetes>=1.1.0"]
@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]
flavors()
classmethod
Declare the stack component flavors for the Tekton integration.
Returns:
| Type | Description |
|---|---|
List[Type[zenml.stack.flavor.Flavor]] |
List of stack component flavors for this integration. |
Source code in zenml/integrations/tekton/__init__.py
@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]
flavors
special
Tekton integration flavors.
tekton_orchestrator_flavor
Tekton orchestrator flavor.
TektonOrchestratorConfig (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 zenml/integrations/tekton/flavors/tekton_orchestrator_flavor.py
class TektonOrchestratorConfig(
BaseOrchestratorConfig, TektonOrchestratorSettings
):
"""Configuration for the Tekton orchestrator.
Attributes:
tekton_hostname: Hostname of the Tekton server.
kubernetes_context: 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: Name of the kubernetes namespace in which the
pods that run the pipeline steps should be running.
"""
tekton_hostname: Optional[str] = None
kubernetes_context: Optional[str] = None
kubernetes_namespace: str = "kubeflow"
@model_validator(mode="before")
@classmethod
@before_validator_handler
def _validate_deprecated_attrs(
cls, data: Dict[str, Any]
) -> Dict[str, Any]:
"""Pydantic root_validator for deprecated attributes.
This root validator is used for backwards compatibility purposes. E.g.
it handles attributes that are no longer available or that have become
mandatory in the meantime.
Args:
data: Values passed to the object constructor
Returns:
Values passed to the object constructor
"""
provisioning_attrs = [
"tekton_ui_port",
"skip_ui_daemon_provisioning",
]
# remove deprecated attributes from values dict
for attr in provisioning_attrs:
if attr in data:
del data[attr]
return data
@property
def is_remote(self) -> bool:
"""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:
True if this config is for a remote component, False otherwise.
"""
return True
@property
def is_local(self) -> bool:
"""Checks if this stack component is running locally.
Returns:
True if this config is for a local component, False otherwise.
"""
return False
is_local: bool
property
readonly
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
readonly
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 (BaseOrchestratorFlavor)
Flavor for the Tekton orchestrator.
Source code in zenml/integrations/tekton/flavors/tekton_orchestrator_flavor.py
class TektonOrchestratorFlavor(BaseOrchestratorFlavor):
"""Flavor for the Tekton orchestrator."""
@property
def name(self) -> str:
"""Name of the orchestrator flavor.
Returns:
Name of the orchestrator flavor.
"""
return TEKTON_ORCHESTRATOR_FLAVOR
@property
def service_connector_requirements(
self,
) -> Optional[ServiceConnectorRequirements]:
"""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:
Requirements for compatible service connectors, if a service
connector is required for this flavor.
"""
return ServiceConnectorRequirements(
resource_type=KUBERNETES_CLUSTER_RESOURCE_TYPE,
)
@property
def docs_url(self) -> Optional[str]:
"""A url to point at docs explaining this flavor.
Returns:
A flavor docs url.
"""
return self.generate_default_docs_url()
@property
def sdk_docs_url(self) -> Optional[str]:
"""A url to point at SDK docs explaining this flavor.
Returns:
A flavor SDK docs url.
"""
return self.generate_default_sdk_docs_url()
@property
def logo_url(self) -> str:
"""A url to represent the flavor in the dashboard.
Returns:
The flavor logo.
"""
return "https://public-flavor-logos.s3.eu-central-1.amazonaws.com/orchestrator/tekton.png"
@property
def config_class(self) -> Type[TektonOrchestratorConfig]:
"""Returns `TektonOrchestratorConfig` config class.
Returns:
The config class.
"""
return TektonOrchestratorConfig
@property
def implementation_class(self) -> Type["TektonOrchestrator"]:
"""Implementation class for this flavor.
Returns:
Implementation class for this flavor.
"""
from zenml.integrations.tekton.orchestrators import TektonOrchestrator
return TektonOrchestrator
config_class: Type[zenml.integrations.tekton.flavors.tekton_orchestrator_flavor.TektonOrchestratorConfig]
property
readonly
Returns TektonOrchestratorConfig config class.
Returns:
| Type | Description |
|---|---|
Type[zenml.integrations.tekton.flavors.tekton_orchestrator_flavor.TektonOrchestratorConfig] |
The config class. |
docs_url: Optional[str]
property
readonly
A url to point at docs explaining this flavor.
Returns:
| Type | Description |
|---|---|
Optional[str] |
A flavor docs url. |
implementation_class: Type[TektonOrchestrator]
property
readonly
Implementation class for this flavor.
Returns:
| Type | Description |
|---|---|
Type[TektonOrchestrator] |
Implementation class for this flavor. |
logo_url: str
property
readonly
A url to represent the flavor in the dashboard.
Returns:
| Type | Description |
|---|---|
str |
The flavor logo. |
name: str
property
readonly
Name of the orchestrator flavor.
Returns:
| Type | Description |
|---|---|
str |
Name of the orchestrator flavor. |
sdk_docs_url: Optional[str]
property
readonly
A url to point at SDK docs explaining this flavor.
Returns:
| Type | Description |
|---|---|
Optional[str] |
A flavor SDK docs url. |
service_connector_requirements: Optional[zenml.models.v2.misc.service_connector_type.ServiceConnectorRequirements]
property
readonly
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[zenml.models.v2.misc.service_connector_type.ServiceConnectorRequirements] |
Requirements for compatible service connectors, if a service connector is required for this flavor. |
TektonOrchestratorSettings (BaseSettings)
Settings for the Tekton orchestrator.
Attributes:
| Name | Type | Description |
|---|---|---|
synchronous |
bool |
If |
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_password |
Optional[str] |
Password to generate a session cookie for the kubeflow client. Both |
user_namespace |
Optional[str] |
The user namespace to use when creating experiments and runs. |
Source code in zenml/integrations/tekton/flavors/tekton_orchestrator_flavor.py
class TektonOrchestratorSettings(BaseSettings):
"""Settings for the Tekton orchestrator.
Attributes:
synchronous: 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: How many seconds to wait for synchronous runs.
client_args: Arguments to pass when initializing the KFP client.
client_username: Username to generate a session cookie for the kubeflow client. Both `client_username`
and `client_password` need to be set together.
client_password: Password to generate a session cookie for the kubeflow client. Both `client_username`
and `client_password` need to be set together.
user_namespace: The user namespace to use when creating experiments
and runs.
"""
synchronous: bool = True
timeout: int = 1200
client_args: Dict[str, Any] = {}
client_username: Optional[str] = SecretField(default=None)
client_password: Optional[str] = SecretField(default=None)
user_namespace: Optional[str] = None
node_selectors: Dict[str, str] = {}
node_affinity: Dict[str, List[str]] = {}
pod_settings: Optional[KubernetesPodSettings] = None
@model_validator(mode="before")
@classmethod
@before_validator_handler
def _validate_and_migrate_pod_settings(
cls, data: Dict[str, Any]
) -> Dict[str, Any]:
"""Validates settings and migrates pod settings from older version.
Args:
data: Dict representing user-specified runtime settings.
Returns:
Validated settings.
Raises:
ValueError: If username and password are not specified together.
"""
# Validate username and password for auth cookie logic
username = data.get("client_username")
password = data.get("client_password")
client_creds_error = "`client_username` and `client_password` both need to be set together."
if (username and password is None) or (password and username is None):
raise ValueError(client_creds_error)
return data
orchestrators
special
Initialization of the Tekton ZenML orchestrator.
tekton_orchestrator
Implementation of the Tekton orchestrator.
KubeClientKFPClient (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.
Source code in zenml/integrations/tekton/orchestrators/tekton_orchestrator.py
class KubeClientKFPClient(kfp.Client): # type: ignore[misc]
"""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.
"""
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)
def _load_config(self, *args: Any, **kwargs: Any) -> Any:
"""Loads the KFP configuration.
Initializes the KFP configuration from the Kubernetes client.
Args:
args: standard KFP client positional arguments.
kwargs: standard KFP client keyword arguments.
Returns:
The KFP configuration.
"""
from kfp_server_api.configuration import Configuration
kube_config = self._k8s_client.configuration
host = (
kube_config.host
+ "/"
+ self._KUBE_PROXY_PATH.format(kwargs.get("namespace", "kubeflow"))
)
config = Configuration(
host=host,
api_key=kube_config.api_key,
api_key_prefix=kube_config.api_key_prefix,
username=kube_config.username,
password=kube_config.password,
discard_unknown_keys=kube_config.discard_unknown_keys,
)
# Extra attributes not present in the Configuration constructor
keys = ["ssl_ca_cert", "cert_file", "key_file", "verify_ssl"]
for key in keys:
if key in kube_config.__dict__:
setattr(config, key, getattr(kube_config, key))
return config
__init__(self, client, *args, **kwargs)
special
Initializes the KFP client from a Kubernetes client.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
client |
kubernetes.client.ApiClient |
pre-configured Kubernetes client. |
required |
args |
Any |
standard KFP client positional arguments. |
() |
kwargs |
Any |
standard KFP client keyword arguments. |
{} |
Source code in zenml/integrations/tekton/orchestrators/tekton_orchestrator.py
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)
TektonOrchestrator (ContainerizedOrchestrator)
Orchestrator responsible for running pipelines using Tekton.
Source code in zenml/integrations/tekton/orchestrators/tekton_orchestrator.py
class TektonOrchestrator(ContainerizedOrchestrator):
"""Orchestrator responsible for running pipelines using Tekton."""
_k8s_client: Optional[k8s_client.ApiClient] = None
def _get_kfp_client(
self,
settings: TektonOrchestratorSettings,
) -> kfp.Client:
"""Creates a KFP client instance.
Args:
settings: Settings which can be used to
configure the client instance.
Returns:
A KFP client instance.
Raises:
RuntimeError: If the linked Kubernetes connector behaves
unexpectedly.
"""
connector = self.get_connector()
client_args = settings.client_args.copy()
# The kube_context, host and namespace are stack component
# configurations that refer to the Tekton deployment. We don't want
# these overwritten on a run by run basis by user settings
client_args["namespace"] = self.config.kubernetes_namespace
if connector:
client = connector.connect()
if not isinstance(client, k8s_client.ApiClient):
raise RuntimeError(
f"Expected a k8s_client.ApiClient while trying to use the "
f"linked connector, but got {type(client)}."
)
return KubeClientKFPClient(
client=client,
**client_args,
)
elif self.config.kubernetes_context:
client_args["kube_context"] = self.config.kubernetes_context
elif self.config.tekton_hostname:
client_args["host"] = self.config.tekton_hostname
# Handle username and password, ignore the case if one is passed and
# not the other. Also do not attempt to get cookie if cookie is
# already passed in client_args
if settings.client_username and settings.client_password:
# If cookie is already set, then ignore
if "cookie" in client_args:
logger.warning(
"Cookie already set in `client_args`, ignoring "
"`client_username` and `client_password`..."
)
else:
session_cookie = self._get_session_cookie(
username=settings.client_username,
password=settings.client_password,
)
client_args["cookies"] = session_cookie
return KFPClient(**client_args)
def _get_session_cookie(self, username: str, password: str) -> str:
"""Gets session cookie from username and password.
Args:
username: Username for tekoton host.
password: Password for tekoton host.
Raises:
RuntimeError: If the cookie fetching failed.
Returns:
Cookie with the prefix `authsession=`.
"""
if self.config.tekton_hostname is None:
raise RuntimeError(
"You must configure the tekoton orchestrator "
"with the `tekton_hostname` parameter which usually ends "
"with `/pipeline` (e.g. `https://mykubeflow.com/pipeline`). "
"Please update the current tekoton orchestrator with: "
f"`zenml orchestrator update {self.name} "
"--tekton_hostname=<MY_KUBEFLOW_HOST>`"
)
# Get cookie
logger.info(
f"Attempting to fetch session cookie from {self.config.tekton_hostname} "
"with supplied username and password..."
)
session = requests.Session()
try:
response = session.get(self.config.tekton_hostname)
response.raise_for_status()
except (
requests.exceptions.HTTPError,
requests.exceptions.ConnectionError,
requests.exceptions.Timeout,
requests.exceptions.RequestException,
) as e:
raise RuntimeError(
f"Error while trying to fetch tekoton cookie: {e}"
)
headers = {
"Content-Type": "application/x-www-form-urlencoded",
}
data = {"login": username, "password": password}
try:
response = session.post(response.url, headers=headers, data=data)
response.raise_for_status()
except requests.exceptions.HTTPError as errh:
raise RuntimeError(
f"Error while trying to fetch tekoton cookie: {errh}"
)
cookie_dict: Dict[str, str] = session.cookies.get_dict() # type: ignore[no-untyped-call]
if "authservice_session" not in cookie_dict:
raise RuntimeError("Invalid username and/or password!")
logger.info("Session cookie fetched successfully!")
return "authservice_session=" + str(cookie_dict["authservice_session"])
@property
def config(self) -> TektonOrchestratorConfig:
"""Returns the `TektonOrchestratorConfig` config.
Returns:
The configuration.
"""
return cast(TektonOrchestratorConfig, self._config)
@property
def settings_class(self) -> Optional[Type["BaseSettings"]]:
"""Settings class for the Tekton orchestrator.
Returns:
The settings class.
"""
return TektonOrchestratorSettings
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
@property
def validator(self) -> Optional[StackValidator]:
"""Ensures a stack with only remote components and a container registry.
Returns:
A `StackValidator` instance.
"""
def _validate(stack: "Stack") -> Tuple[bool, str]:
container_registry = stack.container_registry
# should not happen, because the stack validation takes care of
# this, but just in case
assert container_registry is not None
kubernetes_context = self.config.kubernetes_context
msg = f"'{self.name}' Tekton orchestrator error: "
if not self.connector:
if not kubernetes_context:
return False, (
f"{msg}you must either link this stack component to a "
"Kubernetes service connector (see the 'zenml "
"orchestrator connect' CLI command) or explicitly set "
"the `kubernetes_context` attribute to the name of "
"the Kubernetes config context pointing to the "
"cluster where you would like to run pipelines."
)
contexts, active_context = self.get_kubernetes_contexts()
if kubernetes_context not in contexts:
return False, (
f"{msg}could not find a Kubernetes context named "
f"'{kubernetes_context}' in the local "
"Kubernetes configuration. Please make sure that the "
"Kubernetes cluster is running and that the "
"kubeconfig file is configured correctly. To list all "
"configured contexts, run:\n\n"
" `kubectl config get-contexts`\n"
)
if kubernetes_context != active_context:
logger.warning(
f"{msg}the Kubernetes context '{kubernetes_context}' " # nosec
f"configured for the Tekton orchestrator is not "
f"the same as the active context in the local "
f"Kubernetes configuration. If this is not deliberate,"
f" you should update the orchestrator's "
f"`kubernetes_context` field by running:\n\n"
f" `zenml orchestrator update {self.name} "
f"--kubernetes_context={active_context}`\n"
f"To list all configured contexts, run:\n\n"
f" `kubectl config get-contexts`\n"
f"To set the active context to be the same as the one "
f"configured in the Tekton orchestrator and "
f"silence this warning, run:\n\n"
f" `kubectl config use-context "
f"{kubernetes_context}`\n"
)
silence_local_validations_msg = (
f"To silence this warning, set the "
f"`skip_local_validations` attribute to True in the "
f"orchestrator configuration by running:\n\n"
f" 'zenml orchestrator update {self.name} "
f"--skip_local_validations=True'\n"
)
if not self.config.is_local:
# if the orchestrator is not running in a local k3d cluster,
# we cannot have any other local components in our stack,
# because we cannot mount the local path into the container.
# This may result in problems when running the pipeline, "
# because the local components will not be available inside
# the Tekton containers.
# go through all stack components and identify those that
# advertise a local path where they persist information that
# they need to be available when running pipelines.
for stack_comp in stack.components.values():
local_path = stack_comp.local_path
if not local_path:
continue
return False, (
f"{msg}the Tekton orchestrator is configured to run "
f"pipelines in a remote Kubernetes cluster, but the "
f"'{stack_comp.name}' {stack_comp.type.value} is a "
f"local stack component and will not be available in "
f"the Tekton pipeline step.\n"
f"Please ensure that you always use non-local "
f"stack components with a remote Tekton orchestrator, "
f"otherwise you may run into pipeline execution "
f"problems. You should use a flavor of "
f"{stack_comp.type.value} other than "
f"'{stack_comp.flavor}'.\n"
+ silence_local_validations_msg
)
# if the orchestrator is remote, the container registry must
# also be remote.
if container_registry.config.is_local:
return False, (
f"{msg}the Tekton orchestrator is configured to run "
f"pipelines in a remote Kubernetes cluster, but the "
f"'{container_registry.name}' container registry URI "
f"'{container_registry.config.uri}' "
f"points to a local container registry. Please ensure "
f"that you always use non-local stack components with "
f"a remote Tekton orchestrator, otherwise you will "
f"run into problems. You should use a flavor of "
f"container registry other than "
f"'{container_registry.flavor}'.\n"
+ silence_local_validations_msg
)
return True, ""
return StackValidator(
required_components={
StackComponentType.CONTAINER_REGISTRY,
StackComponentType.IMAGE_BUILDER,
},
custom_validation_function=_validate,
)
def _create_dynamic_component(
self,
image: str,
command: List[str],
arguments: List[str],
component_name: str,
) -> dsl.PipelineTask:
"""Creates a dynamic container component for a Tekton pipeline.
Args:
image: The image to use for the component.
command: The command to use for the component.
arguments: The arguments to use for the component.
component_name: The name of the component.
Returns:
The dynamic container component.
"""
def dynamic_container_component() -> dsl.ContainerSpec:
"""Dynamic container component.
Returns:
The dynamic container component.
"""
_component = dsl.ContainerSpec(
image=image,
command=command,
args=arguments,
)
_component.__name__ = component_name
return _component
dynamic_func = FunctionType(
dynamic_container_component.__code__,
dynamic_container_component.__globals__,
name=component_name,
argdefs=dynamic_container_component.__defaults__,
closure=dynamic_container_component.__closure__,
)
return dsl.container_component(dynamic_func)
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,
)
def _upload_and_run_pipeline(
self,
deployment: "PipelineDeploymentResponse",
pipeline_file_path: str,
run_name: str,
) -> None:
"""Tries to upload and run a KFP pipeline.
Args:
deployment: The pipeline deployment.
pipeline_file_path: Path to the pipeline definition file.
run_name: The Tekton run name.
Raises:
RuntimeError: If Tekton API returns an error.
"""
pipeline_name = deployment.pipeline_configuration.name
settings = cast(
TektonOrchestratorSettings, self.get_settings(deployment)
)
user_namespace = settings.user_namespace
kubernetes_context = self.config.kubernetes_context
try:
if kubernetes_context:
logger.info(
"Running in kubernetes context '%s'.",
kubernetes_context,
)
elif self.config.tekton_hostname:
logger.info(
"Running on Tekton deployment '%s'.",
self.config.tekton_hostname,
)
elif self.connector:
logger.info(
"Running with Kubernetes credentials from connector '%s'.",
str(self.connector),
)
# upload the pipeline to Tekton and start it
client = self._get_kfp_client(settings=settings)
if deployment.schedule:
try:
experiment = client.get_experiment(
pipeline_name, namespace=user_namespace
)
logger.info(
"A recurring run has already been created with this "
"pipeline. Creating new recurring run now.."
)
except (ValueError, ApiException):
experiment = client.create_experiment(
pipeline_name, namespace=user_namespace
)
logger.info(
"Creating a new recurring run for pipeline '%s'.. ",
pipeline_name,
)
logger.info(
"You can see all recurring runs under the '%s' experiment.",
pipeline_name,
)
interval_seconds = (
deployment.schedule.interval_second.seconds
if deployment.schedule.interval_second
else None
)
result = client.create_recurring_run(
experiment_id=experiment.experiment_id,
job_name=run_name,
pipeline_package_path=pipeline_file_path,
enable_caching=False,
cron_expression=deployment.schedule.cron_expression,
start_time=deployment.schedule.utc_start_time,
end_time=deployment.schedule.utc_end_time,
interval_second=interval_seconds,
no_catchup=not deployment.schedule.catchup,
)
logger.info(
"Started recurring run with ID '%s'.",
result.recurring_run_id,
)
else:
logger.info(
"No schedule detected. Creating a one-off pipeline run.."
)
try:
result = client.create_run_from_pipeline_package(
pipeline_file_path,
arguments={},
run_name=run_name,
enable_caching=False,
namespace=user_namespace,
)
except ApiException:
raise RuntimeError(
f"Failed to create {run_name} on Tekton! "
"Please check stack component settings and "
"configuration!"
)
logger.info(
"Started one-off pipeline run with ID '%s'.", result.run_id
)
if settings.synchronous:
client.wait_for_run_completion(
run_id=result.run_id, timeout=settings.timeout
)
except urllib3.exceptions.HTTPError as error:
if kubernetes_context:
msg = (
f"Please make sure your kubernetes config is present and "
f"the '{kubernetes_context}' kubernetes context is "
"configured correctly."
)
elif self.connector:
msg = (
f"Please check that the '{self.connector}' connector "
f"linked to this component is configured correctly with "
"valid credentials."
)
else:
msg = ""
logger.warning(
f"Failed to upload Tekton pipeline: {error}. {msg}",
)
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
@property
def root_directory(self) -> str:
"""Returns path to the root directory.
Returns:
Path to the root directory.
"""
return os.path.join(
io_utils.get_global_config_directory(),
"tekton",
str(self.id),
)
@property
def pipeline_directory(self) -> str:
"""Path to a directory in which the Tekton pipeline files are stored.
Returns:
Path to the pipeline directory.
"""
return os.path.join(self.root_directory, "pipelines")
@property
def _pid_file_path(self) -> str:
"""Returns path to the daemon PID file.
Returns:
Path to the daemon PID file.
"""
return os.path.join(self.root_directory, "tekton_daemon.pid")
@property
def log_file(self) -> str:
"""Path of the daemon log file.
Returns:
Path of the daemon log file.
"""
return os.path.join(self.root_directory, "tekton_daemon.log")
def _configure_container_resources(
self,
dynamic_component: dsl.PipelineTask,
resource_settings: "ResourceSettings",
node_selector_constraint: Optional[Tuple[str, str]] = None,
) -> dsl.PipelineTask:
"""Adds resource requirements to the container.
Args:
dynamic_component: The dynamic component to add the resource
settings to.
resource_settings: The resource settings to use for this
container.
node_selector_constraint: Node selector constraint to apply to
the container.
Returns:
The dynamic component with the resource settings applied.
"""
# Set optional CPU, RAM and GPU constraints for the pipeline
if resource_settings:
cpu_limit = resource_settings.cpu_count or None
if cpu_limit is not None:
dynamic_component = dynamic_component.set_cpu_limit(str(cpu_limit))
memory_limit = resource_settings.get_memory() or None
if memory_limit is not None:
dynamic_component = dynamic_component.set_memory_limit(
memory_limit
)
gpu_limit = (
resource_settings.gpu_count
if resource_settings.gpu_count is not None
else 0
)
if node_selector_constraint:
(constraint_label, value) = node_selector_constraint
if gpu_limit is not None and gpu_limit > 0:
dynamic_component = (
dynamic_component.set_accelerator_type(value)
.set_accelerator_limit(gpu_limit)
.set_gpu_limit(gpu_limit)
)
elif constraint_label == "accelerator" and gpu_limit == 0:
logger.warning(
"GPU limit is set to 0 but a GPU type is specified. Ignoring GPU settings."
)
return dynamic_component
config: TektonOrchestratorConfig
property
readonly
Returns the TektonOrchestratorConfig config.
Returns:
| Type | Description |
|---|---|
TektonOrchestratorConfig |
The configuration. |
log_file: str
property
readonly
Path of the daemon log file.
Returns:
| Type | Description |
|---|---|
str |
Path of the daemon log file. |
pipeline_directory: str
property
readonly
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
readonly
Returns path to the root directory.
Returns:
| Type | Description |
|---|---|
str |
Path to the root directory. |
settings_class: Optional[Type[BaseSettings]]
property
readonly
Settings class for the Tekton orchestrator.
Returns:
| Type | Description |
|---|---|
Optional[Type[BaseSettings]] |
The settings class. |
validator: Optional[zenml.stack.stack_validator.StackValidator]
property
readonly
Ensures a stack with only remote components and a container registry.
Returns:
| Type | Description |
|---|---|
Optional[zenml.stack.stack_validator.StackValidator] |
A |
get_kubernetes_contexts(self)
Get the list of configured Kubernetes contexts and the active context.
Returns:
| Type | Description |
|---|---|
Tuple[List[str], Optional[str]] |
A tuple containing the list of configured Kubernetes contexts and the active context. |
Source code in zenml/integrations/tekton/orchestrators/tekton_orchestrator.py
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(self)
Returns the active orchestrator run id.
Exceptions:
| 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 zenml/integrations/tekton/orchestrators/tekton_orchestrator.py
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(self, deployment, stack, environment)
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 |
Exceptions:
| Type | Description |
|---|---|
RuntimeError |
If you try to run the pipelines in a notebook environment. |
Source code in zenml/integrations/tekton/orchestrators/tekton_orchestrator.py
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,
)