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-tekton==1.4.1"]
@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)
pydantic-model
Configuration for the Tekton orchestrator.
Attributes:
| Name | Type | Description |
|---|---|---|
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. |
local |
bool |
If |
skip_local_validations |
bool |
If |
Source code in zenml/integrations/tekton/flavors/tekton_orchestrator_flavor.py
class TektonOrchestratorConfig( # type: ignore[misc] # https://github.com/pydantic/pydantic/issues/4173
BaseOrchestratorConfig, TektonOrchestratorSettings
):
"""Configuration for the Tekton orchestrator.
Attributes:
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.
local: If `True`, the orchestrator will assume it is connected to a
local kubernetes cluster and will perform additional validations and
operations to allow using the orchestrator in combination with other
local stack components that store data in the local filesystem
(i.e. it will mount the local stores directory into the pipeline
containers).
skip_local_validations: If `True`, the local validations will be
skipped.
"""
kubernetes_context: Optional[str] = None
kubernetes_namespace: str = "zenml"
local: bool = False
skip_local_validations: bool = False
@root_validator(pre=True)
def _validate_deprecated_attrs(
cls, values: 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:
values: 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 values:
del values[attr]
return values
@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 not self.local
@property
def is_local(self) -> bool:
"""Checks if this stack component is running locally.
This designation is used to determine if the stack component can be
shared with other users or if it is only usable on the local host.
Returns:
True if this config is for a local component, False otherwise.
"""
return self.local
is_local: bool
property
readonly
Checks if this stack component is running locally.
This designation is used to determine if the stack component can be shared with other users or if it is only usable on the local host.
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.service_connector_models.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.service_connector_models.ServiceConnectorRequirements] |
Requirements for compatible service connectors, if a service connector is required for this flavor. |
TektonOrchestratorSettings (BaseSettings)
pydantic-model
Settings for the Tekton orchestrator.
Attributes:
| Name | Type | Description |
|---|---|---|
pod_settings |
Optional[zenml.integrations.kubernetes.pod_settings.KubernetesPodSettings] |
Pod settings to apply. |
Source code in zenml/integrations/tekton/flavors/tekton_orchestrator_flavor.py
class TektonOrchestratorSettings(BaseSettings):
"""Settings for the Tekton orchestrator.
Attributes:
pod_settings: Pod settings to apply.
"""
pod_settings: Optional[KubernetesPodSettings] = None
orchestrators
special
Initialization of the Tekton ZenML orchestrator.
tekton_orchestrator
Implementation of the Tekton orchestrator.
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
@property
def kube_client(self) -> k8s_client.ApiClient:
"""Getter for the Kubernetes API client.
Returns:
The Kubernetes API client.
Raises:
RuntimeError: if the Kubernetes connector behaves unexpectedly.
"""
# Refresh the client also if the connector has expired
if self._k8s_client and not self.connector_has_expired():
return self._k8s_client
connector = self.get_connector()
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)}."
)
self._k8s_client = client
else:
k8s_config.load_kube_config(context=self.config.kubernetes_context)
self._k8s_client = k8s_client.ApiClient()
return self._k8s_client
@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
connector = self.get_connector()
msg = f"'{self.name}' Tekton orchestrator error: "
if not 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}' "
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.skip_local_validations
and 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 _configure_container_op(
self,
container_op: dsl.ContainerOp,
) -> None:
"""Makes changes in place to the configuration of the container op.
Configures persistent mounted volumes for each stack component that
writes to a local path.
Args:
container_op: The Tekton container operation to configure.
"""
volumes: Dict[str, k8s_client.V1Volume] = {}
stack = Client().active_stack
if self.config.is_local:
stack.check_local_paths()
local_stores_path = GlobalConfiguration().local_stores_path
host_path = k8s_client.V1HostPathVolumeSource(
path=local_stores_path, type="Directory"
)
volumes[local_stores_path] = k8s_client.V1Volume(
name="local-stores",
host_path=host_path,
)
logger.debug(
"Adding host path volume for the local ZenML stores (path: %s) "
"in Tekton pipelines container.",
local_stores_path,
)
if sys.platform == "win32":
# File permissions are not checked on Windows. This if clause
# prevents mypy from complaining about unused 'type: ignore'
# statements
pass
else:
# Run KFP containers in the context of the local UID/GID
# to ensure that the artifact and metadata stores can be shared
# with the local pipeline runs.
container_op.container.security_context = (
k8s_client.V1SecurityContext(
run_as_user=os.getuid(),
run_as_group=os.getgid(),
)
)
logger.debug(
"Setting security context UID and GID to local user/group "
"in Tekton pipelines container."
)
container_op.container.add_env_variable(
k8s_client.V1EnvVar(
name=ENV_ZENML_LOCAL_STORES_PATH,
value=local_stores_path,
)
)
container_op.add_pvolumes(volumes)
@staticmethod
def _configure_container_resources(
container_op: dsl.ContainerOp,
resource_settings: "ResourceSettings",
) -> None:
"""Adds resource requirements to the container.
Args:
container_op: The container operation to configure.
resource_settings: The resource settings to use for this
container.
"""
if resource_settings.cpu_count is not None:
container_op = container_op.set_cpu_limit(
str(resource_settings.cpu_count)
)
if resource_settings.gpu_count is not None:
container_op = container_op.set_gpu_limit(
resource_settings.gpu_count
)
if resource_settings.memory is not None:
memory_limit = resource_settings.memory[:-1]
container_op = container_op.set_memory_limit(memory_limit)
def prepare_or_run_pipeline(
self,
deployment: "PipelineDeploymentResponseModel",
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
)
def _construct_kfp_pipeline() -> None:
"""Create a container_op for each step.
This should contain the name of the docker image and configures the
entrypoint of the docker image to run the step.
Additionally, this gives each container_op information about its
direct downstream steps.
"""
# Dictionary of container_ops index by the associated step name
step_name_to_container_op: Dict[str, dsl.ContainerOp] = {}
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
)
)
container_op = dsl.ContainerOp(
name=step_name,
image=image,
command=command,
arguments=arguments,
)
settings = cast(
TektonOrchestratorSettings, self.get_settings(step)
)
self._configure_container_op(
container_op=container_op,
)
if settings.pod_settings:
apply_pod_settings(
container_op=container_op,
settings=settings.pod_settings,
)
container_op.container.add_env_variable(
k8s_client.V1EnvVar(
name=ENV_ZENML_TEKTON_RUN_ID,
value="$(context.pipelineRun.name)",
)
)
for key, value in environment.items():
container_op.container.add_env_variable(
k8s_client.V1EnvVar(
name=key,
value=value,
)
)
if self.requires_resources_in_orchestration_environment(step):
self._configure_container_resources(
container_op=container_op,
resource_settings=step.config.resource_settings,
)
# Find the upstream container ops of the current step and
# configure the current container op to run after them
for upstream_step_name in step.spec.upstream_steps:
upstream_container_op = step_name_to_container_op[
upstream_step_name
]
container_op.after(upstream_container_op)
# Update dictionary of container ops with the current one
step_name_to_container_op[step_name] = container_op
# 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"
)
# Set the run name, which Tekton reads from this attribute of the
# pipeline function
setattr(
_construct_kfp_pipeline,
"_component_human_name",
orchestrator_run_name,
)
pipeline_config = TektonPipelineConf()
pipeline_config.add_pipeline_label(
"pipelines.kubeflow.org/cache_enabled", "false"
)
TektonCompiler().compile(
_construct_kfp_pipeline,
pipeline_file_path,
tekton_pipeline_conf=pipeline_config,
)
logger.info(
"Writing Tekton workflow definition to `%s`.", pipeline_file_path
)
if deployment.schedule:
logger.warning(
"The Tekton Orchestrator currently does not support the "
"use of schedules. The `schedule` will be ignored "
"and the pipeline will be run immediately."
)
kubernetes_context = self.config.kubernetes_context
if kubernetes_context:
logger.info(
"Running Tekton pipeline in kubernetes context '%s' and "
"namespace '%s'.",
kubernetes_context,
self.config.kubernetes_namespace,
)
elif self.connector:
connector = self.get_connector()
assert connector is not None
logger.info(
"Running Tekton pipeline with Kubernetes credentials from "
"connector '%s'.",
connector.name or str(connector),
)
# Read the Tekton pipeline resource from the generated YAML file
with open(pipeline_file_path, "r") as f:
tekton_resource = yaml.safe_load(f)
# Upload the Tekton pipeline to the Kubernetes cluster
custom_objects_api = k8s_client.CustomObjectsApi(self.kube_client)
try:
logger.debug("Creating Tekton resource ...")
response = custom_objects_api.create_namespaced_custom_object(
group=tekton_resource["apiVersion"].split("/")[0],
version=tekton_resource["apiVersion"].split("/")[1],
namespace=self.config.kubernetes_namespace,
plural=tekton_resource["kind"].lower() + "s",
body=tekton_resource,
)
logger.debug("Tekton API response: %s", response)
except k8s_client.rest.ApiException as e:
logger.error("Exception when creating Tekton resource: %s", str(e))
raise RuntimeError(
f"Failed to upload Tekton pipeline: {str(e)}. "
f"Please make sure your Kubernetes cluster is running and "
f"accessible.",
)
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:
raise RuntimeError(
"Unable to read run id from environment variable "
f"{ENV_ZENML_TEKTON_RUN_ID}."
)
@property
def root_directory(self) -> str:
"""Returns path to the root directory for all files concerning this orchestrator.
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")
config: TektonOrchestratorConfig
property
readonly
Returns the TektonOrchestratorConfig config.
Returns:
| Type | Description |
|---|---|
TektonOrchestratorConfig |
The configuration. |
kube_client: ApiClient
property
readonly
Getter for the Kubernetes API client.
Returns:
| Type | Description |
|---|---|
ApiClient |
The Kubernetes API client. |
Exceptions:
| Type | Description |
|---|---|
RuntimeError |
if the Kubernetes connector behaves unexpectedly. |
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 for all files concerning this orchestrator.
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:
raise RuntimeError(
"Unable to read run id from environment variable "
f"{ENV_ZENML_TEKTON_RUN_ID}."
)
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 |
PipelineDeploymentResponseModel |
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: "PipelineDeploymentResponseModel",
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
)
def _construct_kfp_pipeline() -> None:
"""Create a container_op for each step.
This should contain the name of the docker image and configures the
entrypoint of the docker image to run the step.
Additionally, this gives each container_op information about its
direct downstream steps.
"""
# Dictionary of container_ops index by the associated step name
step_name_to_container_op: Dict[str, dsl.ContainerOp] = {}
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
)
)
container_op = dsl.ContainerOp(
name=step_name,
image=image,
command=command,
arguments=arguments,
)
settings = cast(
TektonOrchestratorSettings, self.get_settings(step)
)
self._configure_container_op(
container_op=container_op,
)
if settings.pod_settings:
apply_pod_settings(
container_op=container_op,
settings=settings.pod_settings,
)
container_op.container.add_env_variable(
k8s_client.V1EnvVar(
name=ENV_ZENML_TEKTON_RUN_ID,
value="$(context.pipelineRun.name)",
)
)
for key, value in environment.items():
container_op.container.add_env_variable(
k8s_client.V1EnvVar(
name=key,
value=value,
)
)
if self.requires_resources_in_orchestration_environment(step):
self._configure_container_resources(
container_op=container_op,
resource_settings=step.config.resource_settings,
)
# Find the upstream container ops of the current step and
# configure the current container op to run after them
for upstream_step_name in step.spec.upstream_steps:
upstream_container_op = step_name_to_container_op[
upstream_step_name
]
container_op.after(upstream_container_op)
# Update dictionary of container ops with the current one
step_name_to_container_op[step_name] = container_op
# 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"
)
# Set the run name, which Tekton reads from this attribute of the
# pipeline function
setattr(
_construct_kfp_pipeline,
"_component_human_name",
orchestrator_run_name,
)
pipeline_config = TektonPipelineConf()
pipeline_config.add_pipeline_label(
"pipelines.kubeflow.org/cache_enabled", "false"
)
TektonCompiler().compile(
_construct_kfp_pipeline,
pipeline_file_path,
tekton_pipeline_conf=pipeline_config,
)
logger.info(
"Writing Tekton workflow definition to `%s`.", pipeline_file_path
)
if deployment.schedule:
logger.warning(
"The Tekton Orchestrator currently does not support the "
"use of schedules. The `schedule` will be ignored "
"and the pipeline will be run immediately."
)
kubernetes_context = self.config.kubernetes_context
if kubernetes_context:
logger.info(
"Running Tekton pipeline in kubernetes context '%s' and "
"namespace '%s'.",
kubernetes_context,
self.config.kubernetes_namespace,
)
elif self.connector:
connector = self.get_connector()
assert connector is not None
logger.info(
"Running Tekton pipeline with Kubernetes credentials from "
"connector '%s'.",
connector.name or str(connector),
)
# Read the Tekton pipeline resource from the generated YAML file
with open(pipeline_file_path, "r") as f:
tekton_resource = yaml.safe_load(f)
# Upload the Tekton pipeline to the Kubernetes cluster
custom_objects_api = k8s_client.CustomObjectsApi(self.kube_client)
try:
logger.debug("Creating Tekton resource ...")
response = custom_objects_api.create_namespaced_custom_object(
group=tekton_resource["apiVersion"].split("/")[0],
version=tekton_resource["apiVersion"].split("/")[1],
namespace=self.config.kubernetes_namespace,
plural=tekton_resource["kind"].lower() + "s",
body=tekton_resource,
)
logger.debug("Tekton API response: %s", response)
except k8s_client.rest.ApiException as e:
logger.error("Exception when creating Tekton resource: %s", str(e))
raise RuntimeError(
f"Failed to upload Tekton pipeline: {str(e)}. "
f"Please make sure your Kubernetes cluster is running and "
f"accessible.",
)