Config
zenml.config
special
The config module contains classes and functions that manage user-specific configuration.
ZenML's configuration is stored in a file called
config.yaml, located on the user's directory for configuration files.
(The exact location differs from operating system to operating system.)
The GlobalConfiguration class is the main class in this module. It provides
a Pydantic configuration object that is used to store and retrieve
configuration. This GlobalConfiguration object handles the serialization and
deserialization of the configuration options that are stored in the file in
order to persist the configuration across sessions.
base_settings
Base class for all ZenML settings.
BaseSettings (SecretReferenceMixin)
Base class for settings.
The LEVEL class variable defines on which level the settings can be
specified. By default, subclasses can be defined on both pipelines and
steps.
Source code in zenml/config/base_settings.py
class BaseSettings(SecretReferenceMixin):
"""Base class for settings.
The `LEVEL` class variable defines on which level the settings can be
specified. By default, subclasses can be defined on both pipelines and
steps.
"""
LEVEL: ClassVar[ConfigurationLevel] = (
ConfigurationLevel.PIPELINE | ConfigurationLevel.STEP
)
model_config = ConfigDict(
# public attributes are immutable
frozen=True,
# allow extra attributes so this class can be used to parse dicts
# of arbitrary subclasses
extra="allow",
)
ConfigurationLevel (IntFlag)
Settings configuration level.
Bit flag that can be used to specify where a BaseSettings subclass
can be specified.
Source code in zenml/config/base_settings.py
class ConfigurationLevel(IntFlag):
"""Settings configuration level.
Bit flag that can be used to specify where a `BaseSettings` subclass
can be specified.
"""
STEP = auto()
PIPELINE = auto()
build_configuration
Build configuration class.
BuildConfiguration (BaseModel)
Configuration of Docker builds.
Attributes:
| Name | Type | Description |
|---|---|---|
key |
str |
The key to store the build. |
settings |
DockerSettings |
Settings for the build. |
step_name |
Optional[str] |
Name of the step for which this image will be built. |
entrypoint |
Optional[str] |
Optional entrypoint for the image. |
extra_files |
Dict[str, str] |
Extra files to include in the Docker image. |
Source code in zenml/config/build_configuration.py
class BuildConfiguration(BaseModel):
"""Configuration of Docker builds.
Attributes:
key: The key to store the build.
settings: Settings for the build.
step_name: Name of the step for which this image will be built.
entrypoint: Optional entrypoint for the image.
extra_files: Extra files to include in the Docker image.
"""
key: str
settings: DockerSettings
step_name: Optional[str] = None
entrypoint: Optional[str] = None
extra_files: Dict[str, str] = {}
def compute_settings_checksum(
self,
stack: "Stack",
code_repository: Optional["BaseCodeRepository"] = None,
) -> str:
"""Checksum for all build settings.
Args:
stack: The stack for which to compute the checksum. This is needed
to gather the stack integration requirements in case the
Docker settings specify to install them.
code_repository: Optional code repository that will be used to
download files inside the image.
Returns:
The checksum.
"""
hash_ = hashlib.md5() # nosec
settings_json = json.dumps(
self.settings.model_dump(
mode="json", exclude={"prevent_build_reuse"}
),
sort_keys=True,
default=json_utils.pydantic_encoder,
)
hash_.update(settings_json.encode())
if self.entrypoint:
hash_.update(self.entrypoint.encode())
for destination, source in self.extra_files.items():
hash_.update(destination.encode())
hash_.update(source.encode())
from zenml.utils.pipeline_docker_image_builder import (
PipelineDockerImageBuilder,
)
pass_code_repo = self.should_download_files_from_code_repository(
code_repository=code_repository
)
requirements_files = (
PipelineDockerImageBuilder.gather_requirements_files(
docker_settings=self.settings,
stack=stack,
code_repository=code_repository if pass_code_repo else None,
log=False,
)
)
for _, requirements, _ in requirements_files:
hash_.update(requirements.encode())
return hash_.hexdigest()
def should_include_files(
self,
code_repository: Optional["BaseCodeRepository"],
) -> bool:
"""Whether files should be included in the image.
Args:
code_repository: Code repository that can be used to download files
inside the image.
Returns:
Whether files should be included in the image.
"""
if self.should_download_files(code_repository=code_repository):
return False
return self.settings.allow_including_files_in_images
def should_download_files(
self,
code_repository: Optional["BaseCodeRepository"],
) -> bool:
"""Whether files should be downloaded in the image.
Args:
code_repository: Code repository that can be used to download files
inside the image.
Returns:
Whether files should be downloaded in the image.
"""
if self.should_download_files_from_code_repository(
code_repository=code_repository
):
return True
if self.settings.allow_download_from_artifact_store:
return True
return False
def should_download_files_from_code_repository(
self,
code_repository: Optional["BaseCodeRepository"],
) -> bool:
"""Whether files should be downloaded from the code repository.
Args:
code_repository: Code repository that can be used to download files
inside the image.
Returns:
Whether files should be downloaded from the code repository.
"""
if (
code_repository
and self.settings.allow_download_from_code_repository
):
return True
return False
compute_settings_checksum(self, stack, code_repository=None)
Checksum for all build settings.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
stack |
Stack |
The stack for which to compute the checksum. This is needed to gather the stack integration requirements in case the Docker settings specify to install them. |
required |
code_repository |
Optional[BaseCodeRepository] |
Optional code repository that will be used to download files inside the image. |
None |
Returns:
| Type | Description |
|---|---|
str |
The checksum. |
Source code in zenml/config/build_configuration.py
def compute_settings_checksum(
self,
stack: "Stack",
code_repository: Optional["BaseCodeRepository"] = None,
) -> str:
"""Checksum for all build settings.
Args:
stack: The stack for which to compute the checksum. This is needed
to gather the stack integration requirements in case the
Docker settings specify to install them.
code_repository: Optional code repository that will be used to
download files inside the image.
Returns:
The checksum.
"""
hash_ = hashlib.md5() # nosec
settings_json = json.dumps(
self.settings.model_dump(
mode="json", exclude={"prevent_build_reuse"}
),
sort_keys=True,
default=json_utils.pydantic_encoder,
)
hash_.update(settings_json.encode())
if self.entrypoint:
hash_.update(self.entrypoint.encode())
for destination, source in self.extra_files.items():
hash_.update(destination.encode())
hash_.update(source.encode())
from zenml.utils.pipeline_docker_image_builder import (
PipelineDockerImageBuilder,
)
pass_code_repo = self.should_download_files_from_code_repository(
code_repository=code_repository
)
requirements_files = (
PipelineDockerImageBuilder.gather_requirements_files(
docker_settings=self.settings,
stack=stack,
code_repository=code_repository if pass_code_repo else None,
log=False,
)
)
for _, requirements, _ in requirements_files:
hash_.update(requirements.encode())
return hash_.hexdigest()
should_download_files(self, code_repository)
Whether files should be downloaded in the image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
code_repository |
Optional[BaseCodeRepository] |
Code repository that can be used to download files inside the image. |
required |
Returns:
| Type | Description |
|---|---|
bool |
Whether files should be downloaded in the image. |
Source code in zenml/config/build_configuration.py
def should_download_files(
self,
code_repository: Optional["BaseCodeRepository"],
) -> bool:
"""Whether files should be downloaded in the image.
Args:
code_repository: Code repository that can be used to download files
inside the image.
Returns:
Whether files should be downloaded in the image.
"""
if self.should_download_files_from_code_repository(
code_repository=code_repository
):
return True
if self.settings.allow_download_from_artifact_store:
return True
return False
should_download_files_from_code_repository(self, code_repository)
Whether files should be downloaded from the code repository.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
code_repository |
Optional[BaseCodeRepository] |
Code repository that can be used to download files inside the image. |
required |
Returns:
| Type | Description |
|---|---|
bool |
Whether files should be downloaded from the code repository. |
Source code in zenml/config/build_configuration.py
def should_download_files_from_code_repository(
self,
code_repository: Optional["BaseCodeRepository"],
) -> bool:
"""Whether files should be downloaded from the code repository.
Args:
code_repository: Code repository that can be used to download files
inside the image.
Returns:
Whether files should be downloaded from the code repository.
"""
if (
code_repository
and self.settings.allow_download_from_code_repository
):
return True
return False
should_include_files(self, code_repository)
Whether files should be included in the image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
code_repository |
Optional[BaseCodeRepository] |
Code repository that can be used to download files inside the image. |
required |
Returns:
| Type | Description |
|---|---|
bool |
Whether files should be included in the image. |
Source code in zenml/config/build_configuration.py
def should_include_files(
self,
code_repository: Optional["BaseCodeRepository"],
) -> bool:
"""Whether files should be included in the image.
Args:
code_repository: Code repository that can be used to download files
inside the image.
Returns:
Whether files should be included in the image.
"""
if self.should_download_files(code_repository=code_repository):
return False
return self.settings.allow_including_files_in_images
compiler
Class for compiling ZenML pipelines into a serializable format.
Compiler
Compiles ZenML pipelines to serializable representations.
Source code in zenml/config/compiler.py
class Compiler:
"""Compiles ZenML pipelines to serializable representations."""
def compile(
self,
pipeline: "Pipeline",
stack: "Stack",
run_configuration: PipelineRunConfiguration,
) -> PipelineDeploymentBase:
"""Compiles a ZenML pipeline to a serializable representation.
Args:
pipeline: The pipeline to compile.
stack: The stack on which the pipeline will run.
run_configuration: The run configuration for this pipeline.
Returns:
The compiled pipeline deployment.
"""
logger.debug("Compiling pipeline `%s`.", pipeline.name)
# Copy the pipeline before we apply any run-level configurations, so
# we don't mess with the pipeline object/step objects in any way
pipeline = copy.deepcopy(pipeline)
self._apply_run_configuration(
pipeline=pipeline, config=run_configuration
)
convert_component_shortcut_settings_keys(
pipeline.configuration.settings, stack=stack
)
self._apply_stack_default_settings(pipeline=pipeline, stack=stack)
if run_configuration.run_name:
self._verify_run_name(run_configuration.run_name)
pipeline_settings = self._filter_and_validate_settings(
settings=pipeline.configuration.settings,
configuration_level=ConfigurationLevel.PIPELINE,
stack=stack,
)
with pipeline.__suppress_configure_warnings__():
pipeline.configure(settings=pipeline_settings, merge=False)
settings_to_passdown = {
key: settings
for key, settings in pipeline_settings.items()
if ConfigurationLevel.STEP in settings.LEVEL
}
steps = {
invocation_id: self._compile_step_invocation(
invocation=invocation,
pipeline_settings=settings_to_passdown,
pipeline_extra=pipeline.configuration.extra,
stack=stack,
step_config=run_configuration.steps.get(invocation_id),
pipeline_failure_hook_source=pipeline.configuration.failure_hook_source,
pipeline_success_hook_source=pipeline.configuration.success_hook_source,
)
for invocation_id, invocation in self._get_sorted_invocations(
pipeline=pipeline
)
}
self._ensure_required_stack_components_exist(stack=stack, steps=steps)
run_name = run_configuration.run_name or get_default_run_name(
pipeline_name=pipeline.name
)
client_version, server_version = get_zenml_versions()
step_specs = [step.spec for step in steps.values()]
pipeline_spec = self._compute_pipeline_spec(
pipeline=pipeline, step_specs=step_specs
)
deployment = PipelineDeploymentBase(
run_name_template=run_name,
pipeline_configuration=pipeline.configuration,
step_configurations=steps,
client_environment=get_run_environment_dict(),
client_version=client_version,
server_version=server_version,
pipeline_version_hash=pipeline._compute_unique_identifier(
pipeline_spec=pipeline_spec
),
pipeline_spec=pipeline_spec,
)
logger.debug("Compiled pipeline deployment: %s", deployment)
return deployment
def compile_spec(self, pipeline: "Pipeline") -> PipelineSpec:
"""Compiles a ZenML pipeline to a pipeline spec.
This method can be used when a pipeline spec is needed but the full
deployment including stack information is not required.
Args:
pipeline: The pipeline to compile.
Returns:
The compiled pipeline spec.
"""
logger.debug(
"Compiling pipeline spec for pipeline `%s`.", pipeline.name
)
# Copy the pipeline before we connect the steps, so we don't mess with
# the pipeline object/step objects in any way
pipeline = copy.deepcopy(pipeline)
invocations = [
self._get_step_spec(invocation=invocation)
for _, invocation in self._get_sorted_invocations(
pipeline=pipeline
)
]
pipeline_spec = self._compute_pipeline_spec(
pipeline=pipeline, step_specs=invocations
)
logger.debug("Compiled pipeline spec: %s", pipeline_spec)
return pipeline_spec
def _apply_run_configuration(
self, pipeline: "Pipeline", config: PipelineRunConfiguration
) -> None:
"""Applies run configurations to the pipeline and its steps.
Args:
pipeline: The pipeline to configure.
config: The run configurations.
"""
with pipeline.__suppress_configure_warnings__():
pipeline.configure(
enable_cache=config.enable_cache,
enable_artifact_metadata=config.enable_artifact_metadata,
enable_artifact_visualization=config.enable_artifact_visualization,
enable_step_logs=config.enable_step_logs,
settings=config.settings,
extra=config.extra,
model=config.model,
parameters=config.parameters,
)
invalid_step_configs = set(config.steps) - set(pipeline.invocations)
if invalid_step_configs:
logger.warning(
f"Configuration for step invocations {invalid_step_configs} "
"cannot be applied to any pipeline step invocations, "
"ignoring..."
)
for key in invalid_step_configs:
config.steps.pop(key)
# Override `enable_cache` of all steps if set at run level
if config.enable_cache is not None:
for invocation in pipeline.invocations.values():
invocation.step.configure(enable_cache=config.enable_cache)
# Override `enable_artifact_metadata` of all steps if set at run level
if config.enable_artifact_metadata is not None:
for invocation in pipeline.invocations.values():
invocation.step.configure(
enable_artifact_metadata=config.enable_artifact_metadata
)
# Override `enable_artifact_visualization` if set at run level
if config.enable_artifact_visualization is not None:
for invocation in pipeline.invocations.values():
invocation.step.configure(
enable_artifact_visualization=config.enable_artifact_visualization
)
# Override `enable_step_logs` if set at run level
if config.enable_step_logs is not None:
for invocation in pipeline.invocations.values():
invocation.step.configure(
enable_step_logs=config.enable_step_logs
)
def _apply_stack_default_settings(
self, pipeline: "Pipeline", stack: "Stack"
) -> None:
"""Applies stack default settings to a pipeline.
Args:
pipeline: The pipeline to which to apply the default settings.
stack: The stack containing potential default settings.
"""
pipeline_settings = pipeline.configuration.settings
for component in stack.components.values():
if not component.settings_class:
continue
settings_key = settings_utils.get_stack_component_setting_key(
component
)
default_settings = self._get_default_settings(component)
if settings_key in pipeline_settings:
combined_settings = pydantic_utils.update_model(
default_settings, update=pipeline_settings[settings_key]
)
pipeline_settings[settings_key] = combined_settings
else:
pipeline_settings[settings_key] = default_settings
with pipeline.__suppress_configure_warnings__():
pipeline.configure(settings=pipeline_settings, merge=False)
def _get_default_settings(
self,
stack_component: "StackComponent",
) -> "BaseSettings":
"""Gets default settings configured on a stack component.
Args:
stack_component: The stack component for which to get the settings.
Returns:
The settings configured on the stack component.
"""
assert stack_component.settings_class
# Exclude additional config attributes that aren't part of the settings
field_names = set(stack_component.settings_class.model_fields.keys())
default_settings = stack_component.settings_class.model_validate(
stack_component.config.model_dump(
include=field_names, exclude_unset=True, exclude_defaults=True
)
)
return default_settings
@staticmethod
def _verify_run_name(run_name: str) -> None:
"""Verifies that the run name contains only valid placeholders.
Args:
run_name: The run name to verify.
Raises:
ValueError: If the run name contains invalid placeholders.
"""
valid_placeholder_names = {"date", "time"}
placeholders = {
v[1] for v in string.Formatter().parse(run_name) if v[1]
}
if not placeholders.issubset(valid_placeholder_names):
raise ValueError(
f"Invalid run name {run_name}. Only the placeholders "
f"{valid_placeholder_names} are allowed in run names."
)
def _verify_upstream_steps(
self, invocation: "StepInvocation", pipeline: "Pipeline"
) -> None:
"""Verifies the upstream steps for a step invocation.
Args:
invocation: The step invocation for which to verify the upstream
steps.
pipeline: The parent pipeline of the invocation.
Raises:
RuntimeError: If an upstream step is missing.
"""
available_steps = set(pipeline.invocations)
invalid_upstream_steps = invocation.upstream_steps - available_steps
if invalid_upstream_steps:
raise RuntimeError(
f"Invalid upstream steps: {invalid_upstream_steps}. Available "
f"steps in this pipeline: {available_steps}."
)
def _filter_and_validate_settings(
self,
settings: Dict[str, "BaseSettings"],
configuration_level: ConfigurationLevel,
stack: "Stack",
) -> Dict[str, "BaseSettings"]:
"""Filters and validates settings.
Args:
settings: The settings to check.
configuration_level: The level on which these settings
were configured.
stack: The stack on which the pipeline will run.
Raises:
TypeError: If settings with an unsupported configuration
level were specified.
Returns:
The filtered settings.
"""
validated_settings = {}
for key, settings_instance in settings.items():
resolver = SettingsResolver(key=key, settings=settings_instance)
try:
settings_instance = resolver.resolve(stack=stack)
except KeyError:
logger.info(
"Not including stack component settings with key `%s`.",
key,
)
continue
if configuration_level not in settings_instance.LEVEL:
raise TypeError(
f"The settings class {settings_instance.__class__} can not "
f"be specified on a {configuration_level.name} level."
)
validated_settings[key] = settings_instance
return validated_settings
def _get_step_spec(
self,
invocation: "StepInvocation",
) -> StepSpec:
"""Gets the spec for a step invocation.
Args:
invocation: The invocation for which to get the spec.
Returns:
The step spec.
"""
inputs = {
key: InputSpec(
step_name=artifact.invocation_id,
output_name=artifact.output_name,
)
for key, artifact in invocation.input_artifacts.items()
}
return StepSpec(
source=invocation.step.resolve(),
upstream_steps=sorted(invocation.upstream_steps),
inputs=inputs,
pipeline_parameter_name=invocation.id,
)
def _compile_step_invocation(
self,
invocation: "StepInvocation",
pipeline_settings: Dict[str, "BaseSettings"],
pipeline_extra: Dict[str, Any],
stack: "Stack",
step_config: Optional["StepConfigurationUpdate"],
pipeline_failure_hook_source: Optional["Source"] = None,
pipeline_success_hook_source: Optional["Source"] = None,
) -> Step:
"""Compiles a ZenML step.
Args:
invocation: The step invocation to compile.
pipeline_settings: settings configured on the
pipeline of the step.
pipeline_extra: Extra values configured on the pipeline of the step.
stack: The stack on which the pipeline will be run.
step_config: Run configuration for the step.
pipeline_failure_hook_source: Source for the failure hook.
pipeline_success_hook_source: Source for the success hook.
Returns:
The compiled step.
"""
# Copy the invocation (including its referenced step) before we apply
# the step configuration which is exclusive to this invocation.
invocation = copy.deepcopy(invocation)
step = invocation.step
if step_config:
step._apply_configuration(
step_config, runtime_parameters=invocation.parameters
)
convert_component_shortcut_settings_keys(
step.configuration.settings, stack=stack
)
step_spec = self._get_step_spec(invocation=invocation)
step_settings = self._filter_and_validate_settings(
settings=step.configuration.settings,
configuration_level=ConfigurationLevel.STEP,
stack=stack,
)
step_extra = step.configuration.extra
step_on_failure_hook_source = step.configuration.failure_hook_source
step_on_success_hook_source = step.configuration.success_hook_source
step.configure(
settings=pipeline_settings,
extra=pipeline_extra,
on_failure=pipeline_failure_hook_source,
on_success=pipeline_success_hook_source,
merge=False,
)
step.configure(
settings=step_settings,
extra=step_extra,
on_failure=step_on_failure_hook_source,
on_success=step_on_success_hook_source,
merge=True,
)
parameters_to_ignore = (
set(step_config.parameters) if step_config else set()
)
complete_step_configuration = invocation.finalize(
parameters_to_ignore=parameters_to_ignore
)
return Step(spec=step_spec, config=complete_step_configuration)
def _get_sorted_invocations(
self,
pipeline: "Pipeline",
) -> List[Tuple[str, "StepInvocation"]]:
"""Sorts the step invocations of a pipeline using topological sort.
The resulting list of invocations will be in an order that can be
executed sequentially without any conflicts.
Args:
pipeline: The pipeline of which to sort the invocations
Returns:
The sorted steps.
"""
from zenml.orchestrators.dag_runner import reverse_dag
from zenml.orchestrators.topsort import topsorted_layers
# Sort step names using topological sort
dag: Dict[str, List[str]] = {}
for name, step in pipeline.invocations.items():
self._verify_upstream_steps(invocation=step, pipeline=pipeline)
dag[name] = list(step.upstream_steps)
reversed_dag: Dict[str, List[str]] = reverse_dag(dag)
layers = topsorted_layers(
nodes=list(dag),
get_node_id_fn=lambda node: node,
get_parent_nodes=lambda node: dag[node],
get_child_nodes=lambda node: reversed_dag[node],
)
sorted_step_names = [step for layer in layers for step in layer]
sorted_invocations: List[Tuple[str, "StepInvocation"]] = [
(name_in_pipeline, pipeline.invocations[name_in_pipeline])
for name_in_pipeline in sorted_step_names
]
return sorted_invocations
@staticmethod
def _ensure_required_stack_components_exist(
stack: "Stack", steps: Mapping[str, "Step"]
) -> None:
"""Ensures that the stack components required for each step exist.
Args:
stack: The stack on which the pipeline should be deployed.
steps: The steps of the pipeline.
Raises:
StackValidationError: If a required stack component is missing.
"""
available_step_operators = (
{stack.step_operator.name} if stack.step_operator else set()
)
available_experiment_trackers = (
{stack.experiment_tracker.name}
if stack.experiment_tracker
else set()
)
for name, step in steps.items():
step_operator = step.config.step_operator
if step_operator and step_operator not in available_step_operators:
raise StackValidationError(
f"Step `{name}` requires step operator "
f"'{step_operator}' which is not configured in "
f"the stack '{stack.name}'. Available step operators: "
f"{available_step_operators}."
)
experiment_tracker = step.config.experiment_tracker
if (
experiment_tracker
and experiment_tracker not in available_experiment_trackers
):
raise StackValidationError(
f"Step `{name}` requires experiment tracker "
f"'{experiment_tracker}' which is not "
f"configured in the stack '{stack.name}'. Available "
f"experiment trackers: {available_experiment_trackers}."
)
@staticmethod
def _compute_pipeline_spec(
pipeline: "Pipeline", step_specs: List["StepSpec"]
) -> "PipelineSpec":
"""Computes the pipeline spec.
Args:
pipeline: The pipeline for which to compute the spec.
step_specs: The step specs for the pipeline.
Returns:
The pipeline spec.
Raises:
ValueError: If the pipeline has no steps.
"""
if not step_specs:
raise ValueError(
f"Pipeline '{pipeline.name}' cannot be compiled because it has "
f"no steps. Please make sure that your steps are decorated "
"with `@step` and that at least one step is called within the "
"pipeline. For more information, see "
"https://docs.zenml.io/user-guide/starter-guide."
)
additional_spec_args: Dict[str, Any] = {
"source": pipeline.resolve(),
"parameters": pipeline._parameters,
}
return PipelineSpec(steps=step_specs, **additional_spec_args)
compile(self, pipeline, stack, run_configuration)
Compiles a ZenML pipeline to a serializable representation.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
pipeline |
Pipeline |
The pipeline to compile. |
required |
stack |
Stack |
The stack on which the pipeline will run. |
required |
run_configuration |
PipelineRunConfiguration |
The run configuration for this pipeline. |
required |
Returns:
| Type | Description |
|---|---|
PipelineDeploymentBase |
The compiled pipeline deployment. |
Source code in zenml/config/compiler.py
def compile(
self,
pipeline: "Pipeline",
stack: "Stack",
run_configuration: PipelineRunConfiguration,
) -> PipelineDeploymentBase:
"""Compiles a ZenML pipeline to a serializable representation.
Args:
pipeline: The pipeline to compile.
stack: The stack on which the pipeline will run.
run_configuration: The run configuration for this pipeline.
Returns:
The compiled pipeline deployment.
"""
logger.debug("Compiling pipeline `%s`.", pipeline.name)
# Copy the pipeline before we apply any run-level configurations, so
# we don't mess with the pipeline object/step objects in any way
pipeline = copy.deepcopy(pipeline)
self._apply_run_configuration(
pipeline=pipeline, config=run_configuration
)
convert_component_shortcut_settings_keys(
pipeline.configuration.settings, stack=stack
)
self._apply_stack_default_settings(pipeline=pipeline, stack=stack)
if run_configuration.run_name:
self._verify_run_name(run_configuration.run_name)
pipeline_settings = self._filter_and_validate_settings(
settings=pipeline.configuration.settings,
configuration_level=ConfigurationLevel.PIPELINE,
stack=stack,
)
with pipeline.__suppress_configure_warnings__():
pipeline.configure(settings=pipeline_settings, merge=False)
settings_to_passdown = {
key: settings
for key, settings in pipeline_settings.items()
if ConfigurationLevel.STEP in settings.LEVEL
}
steps = {
invocation_id: self._compile_step_invocation(
invocation=invocation,
pipeline_settings=settings_to_passdown,
pipeline_extra=pipeline.configuration.extra,
stack=stack,
step_config=run_configuration.steps.get(invocation_id),
pipeline_failure_hook_source=pipeline.configuration.failure_hook_source,
pipeline_success_hook_source=pipeline.configuration.success_hook_source,
)
for invocation_id, invocation in self._get_sorted_invocations(
pipeline=pipeline
)
}
self._ensure_required_stack_components_exist(stack=stack, steps=steps)
run_name = run_configuration.run_name or get_default_run_name(
pipeline_name=pipeline.name
)
client_version, server_version = get_zenml_versions()
step_specs = [step.spec for step in steps.values()]
pipeline_spec = self._compute_pipeline_spec(
pipeline=pipeline, step_specs=step_specs
)
deployment = PipelineDeploymentBase(
run_name_template=run_name,
pipeline_configuration=pipeline.configuration,
step_configurations=steps,
client_environment=get_run_environment_dict(),
client_version=client_version,
server_version=server_version,
pipeline_version_hash=pipeline._compute_unique_identifier(
pipeline_spec=pipeline_spec
),
pipeline_spec=pipeline_spec,
)
logger.debug("Compiled pipeline deployment: %s", deployment)
return deployment
compile_spec(self, pipeline)
Compiles a ZenML pipeline to a pipeline spec.
This method can be used when a pipeline spec is needed but the full deployment including stack information is not required.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
pipeline |
Pipeline |
The pipeline to compile. |
required |
Returns:
| Type | Description |
|---|---|
PipelineSpec |
The compiled pipeline spec. |
Source code in zenml/config/compiler.py
def compile_spec(self, pipeline: "Pipeline") -> PipelineSpec:
"""Compiles a ZenML pipeline to a pipeline spec.
This method can be used when a pipeline spec is needed but the full
deployment including stack information is not required.
Args:
pipeline: The pipeline to compile.
Returns:
The compiled pipeline spec.
"""
logger.debug(
"Compiling pipeline spec for pipeline `%s`.", pipeline.name
)
# Copy the pipeline before we connect the steps, so we don't mess with
# the pipeline object/step objects in any way
pipeline = copy.deepcopy(pipeline)
invocations = [
self._get_step_spec(invocation=invocation)
for _, invocation in self._get_sorted_invocations(
pipeline=pipeline
)
]
pipeline_spec = self._compute_pipeline_spec(
pipeline=pipeline, step_specs=invocations
)
logger.debug("Compiled pipeline spec: %s", pipeline_spec)
return pipeline_spec
convert_component_shortcut_settings_keys(settings, stack)
Convert component shortcut settings keys.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
settings |
Dict[str, BaseSettings] |
Dictionary of settings. |
required |
stack |
Stack |
The stack that the pipeline will run on. |
required |
Exceptions:
| Type | Description |
|---|---|
ValueError |
If stack component settings were defined both using the full and the shortcut key. |
Source code in zenml/config/compiler.py
def convert_component_shortcut_settings_keys(
settings: Dict[str, "BaseSettings"], stack: "Stack"
) -> None:
"""Convert component shortcut settings keys.
Args:
settings: Dictionary of settings.
stack: The stack that the pipeline will run on.
Raises:
ValueError: If stack component settings were defined both using the
full and the shortcut key.
"""
for component in stack.components.values():
shortcut_key = str(component.type)
if component_settings := settings.pop(shortcut_key, None):
key = settings_utils.get_stack_component_setting_key(component)
if key in settings:
raise ValueError(
f"Duplicate settings provided for your {shortcut_key} "
f"using the keys {shortcut_key} and {key}. Remove settings "
"for one of them to fix this error."
)
settings[key] = component_settings
get_zenml_versions()
Returns the version of ZenML on the client and server side.
Returns:
| Type | Description |
|---|---|
Tuple[str, str] |
the ZenML versions on the client and server side respectively. |
Source code in zenml/config/compiler.py
def get_zenml_versions() -> Tuple[str, str]:
"""Returns the version of ZenML on the client and server side.
Returns:
the ZenML versions on the client and server side respectively.
"""
from zenml.client import Client
client = Client()
server_version = client.zen_store.get_store_info().version
return __version__, server_version
constants
ZenML settings constants.
docker_settings
Docker settings.
DockerBuildConfig (BaseModel)
Configuration for a Docker build.
Attributes:
| Name | Type | Description |
|---|---|---|
build_options |
Dict[str, Any] |
Additional options that will be passed unmodified to the Docker build call when building an image. You can use this to for example specify build args or a target stage. See https://docker-py.readthedocs.io/en/stable/images.html#docker.models.images.ImageCollection.build for a full list of available options. |
dockerignore |
Optional[str] |
Path to a dockerignore file to use when building the Docker image. |
Source code in zenml/config/docker_settings.py
class DockerBuildConfig(BaseModel):
"""Configuration for a Docker build.
Attributes:
build_options: Additional options that will be passed unmodified to the
Docker build call when building an image. You can use this to for
example specify build args or a target stage. See
https://docker-py.readthedocs.io/en/stable/images.html#docker.models.images.ImageCollection.build
for a full list of available options.
dockerignore: Path to a dockerignore file to use when building the
Docker image.
"""
build_options: Dict[str, Any] = {}
dockerignore: Optional[str] = None
DockerSettings (BaseSettings)
Settings for building Docker images to run ZenML pipelines.
Build process:
- No
dockerfilespecified: If any of the options regarding requirements, environment variables or copying files require us to build an image, ZenML will build this image. Otherwise, theparent_imagewill be used to run the pipeline. dockerfilespecified: ZenML will first build an image based on the specified Dockerfile. If any of the options regarding requirements, environment variables or copying files require an additional image built on top of that, ZenML will build a second image. If not, the image build from the specified Dockerfile will be used to run the pipeline.
Requirements installation order:
Depending on the configuration of this object, requirements will be
installed in the following order (each step optional):
- The packages installed in your local python environment
- The packages required by the stack unless this is disabled by setting
install_stack_requirements=False.
- The packages specified via the required_integrations
- The packages specified via the requirements attribute
Attributes:
| Name | Type | Description |
|---|---|---|
parent_image |
Optional[str] |
Full name of the Docker image that should be used as the parent for the image that will be built. Defaults to a ZenML image built for the active Python and ZenML version. Additional notes:
* If you specify a custom image here, you need to make sure it has
ZenML installed.
* If this is a non-local image, the environment which is running
the pipeline and building the Docker image needs to be able to pull
this image.
* If a custom |
dockerfile |
Optional[str] |
Path to a custom Dockerfile that should be built. Depending on the other values you specify in this object, the resulting image will be used directly to run your pipeline or ZenML will use it as a parent image to build on top of. See the general docstring of this class for more information. Additional notes:
* If you specify this, the |
build_context_root |
Optional[str] |
Build context root for the Docker build, only used
when the |
parent_image_build_config |
Optional[zenml.config.docker_settings.DockerBuildConfig] |
Configuration for the parent image build. |
skip_build |
bool |
If set to |
prevent_build_reuse |
bool |
Prevent the reuse of an existing build. |
target_repository |
Optional[str] |
Name of the Docker repository to which the image should be pushed. This repository will be appended to the registry URI of the container registry of your stack and should therefore not include any registry. If not specified, the default repository name configured in the container registry stack component settings will be used. |
python_package_installer |
PythonPackageInstaller |
The package installer to use for python packages. |
python_package_installer_args |
Dict[str, Any] |
Arguments to pass to the python package installer. |
replicate_local_python_environment |
Union[List[str], zenml.config.docker_settings.PythonEnvironmentExportMethod] |
If not |
requirements |
Union[NoneType, str, List[str]] |
Path to a requirements file or a list of required pip
packages. During the image build, these requirements will be
installed using pip. If you need to use a different tool to
resolve and/or install your packages, please use a custom parent
image or specify a custom |
required_integrations |
List[str] |
List of ZenML integrations that should be installed. All requirements for the specified integrations will be installed inside the Docker image. |
required_hub_plugins |
List[str] |
DEPRECATED/UNUSED. |
install_stack_requirements |
bool |
If |
apt_packages |
List[str] |
APT packages to install inside the Docker image. |
environment |
Dict[str, Any] |
Dictionary of environment variables to set inside the Docker image. |
build_config |
Optional[zenml.config.docker_settings.DockerBuildConfig] |
Configuration for the main image build. |
user |
Optional[str] |
If not |
allow_including_files_in_images |
bool |
If |
allow_download_from_code_repository |
bool |
If |
allow_download_from_artifact_store |
bool |
If |
build_options |
Dict[str, Any] |
DEPRECATED, use parent_image_build_config.build_options instead. |
dockerignore |
Optional[str] |
DEPRECATED, use build_config.dockerignore instead. |
copy_files |
bool |
DEPRECATED/UNUSED. |
copy_global_config |
bool |
DEPRECATED/UNUSED. |
source_files |
Optional[str] |
DEPRECATED. Use allow_including_files_in_images, allow_download_from_code_repository and allow_download_from_artifact_store instead. |
Source code in zenml/config/docker_settings.py
class DockerSettings(BaseSettings):
"""Settings for building Docker images to run ZenML pipelines.
Build process:
--------------
* No `dockerfile` specified: If any of the options regarding
requirements, environment variables or copying files require us to build an
image, ZenML will build this image. Otherwise, the `parent_image` will be
used to run the pipeline.
* `dockerfile` specified: ZenML will first build an image based on the
specified Dockerfile. If any of the options regarding
requirements, environment variables or copying files require an additional
image built on top of that, ZenML will build a second image. If not, the
image build from the specified Dockerfile will be used to run the pipeline.
Requirements installation order:
--------------------------------
Depending on the configuration of this object, requirements will be
installed in the following order (each step optional):
- The packages installed in your local python environment
- The packages required by the stack unless this is disabled by setting
`install_stack_requirements=False`.
- The packages specified via the `required_integrations`
- The packages specified via the `requirements` attribute
Attributes:
parent_image: Full name of the Docker image that should be
used as the parent for the image that will be built. Defaults to
a ZenML image built for the active Python and ZenML version.
Additional notes:
* If you specify a custom image here, you need to make sure it has
ZenML installed.
* If this is a non-local image, the environment which is running
the pipeline and building the Docker image needs to be able to pull
this image.
* If a custom `dockerfile` is specified for this settings
object, this parent image will be ignored.
dockerfile: Path to a custom Dockerfile that should be built. Depending
on the other values you specify in this object, the resulting
image will be used directly to run your pipeline or ZenML will use
it as a parent image to build on top of. See the general docstring
of this class for more information.
Additional notes:
* If you specify this, the `parent_image` attribute will be ignored.
* If you specify this, the image built from this Dockerfile needs
to have ZenML installed.
build_context_root: Build context root for the Docker build, only used
when the `dockerfile` attribute is set. If this is left empty, the
build context will only contain the Dockerfile.
parent_image_build_config: Configuration for the parent image build.
skip_build: If set to `True`, the parent image will be used directly to
run the steps of your pipeline.
prevent_build_reuse: Prevent the reuse of an existing build.
target_repository: Name of the Docker repository to which the
image should be pushed. This repository will be appended to the
registry URI of the container registry of your stack and should
therefore **not** include any registry. If not specified, the
default repository name configured in the container registry
stack component settings will be used.
python_package_installer: The package installer to use for python
packages.
python_package_installer_args: Arguments to pass to the python package
installer.
replicate_local_python_environment: If not `None`, ZenML will use the
specified method to generate a requirements file that replicates
the packages installed in the currently running python environment.
This requirements file will then be installed in the Docker image.
requirements: Path to a requirements file or a list of required pip
packages. During the image build, these requirements will be
installed using pip. If you need to use a different tool to
resolve and/or install your packages, please use a custom parent
image or specify a custom `dockerfile`.
required_integrations: List of ZenML integrations that should be
installed. All requirements for the specified integrations will
be installed inside the Docker image.
required_hub_plugins: DEPRECATED/UNUSED.
install_stack_requirements: If `True`, ZenML will automatically detect
if components of your active stack are part of a ZenML integration
and install the corresponding requirements and apt packages.
If you set this to `False` or use custom components in your stack,
you need to make sure these get installed by specifying them in
the `requirements` and `apt_packages` attributes.
apt_packages: APT packages to install inside the Docker image.
environment: Dictionary of environment variables to set inside the
Docker image.
build_config: Configuration for the main image build.
user: If not `None`, will set the user, make it owner of the `/app`
directory which contains all the user code and run the container
entrypoint as this user.
allow_including_files_in_images: If `True`, code can be included in the
Docker images if code download from a code repository or artifact
store is disabled or not possible.
allow_download_from_code_repository: If `True`, code can be downloaded
from a code repository if possible.
allow_download_from_artifact_store: If `True`, code can be downloaded
from the artifact store.
build_options: DEPRECATED, use parent_image_build_config.build_options
instead.
dockerignore: DEPRECATED, use build_config.dockerignore instead.
copy_files: DEPRECATED/UNUSED.
copy_global_config: DEPRECATED/UNUSED.
source_files: DEPRECATED. Use allow_including_files_in_images,
allow_download_from_code_repository and
allow_download_from_artifact_store instead.
"""
parent_image: Optional[str] = None
dockerfile: Optional[str] = None
build_context_root: Optional[str] = None
parent_image_build_config: Optional[DockerBuildConfig] = None
skip_build: bool = False
prevent_build_reuse: bool = False
target_repository: Optional[str] = None
python_package_installer: PythonPackageInstaller = (
PythonPackageInstaller.PIP
)
python_package_installer_args: Dict[str, Any] = {}
replicate_local_python_environment: Optional[
Union[List[str], PythonEnvironmentExportMethod]
] = Field(default=None, union_mode="left_to_right")
requirements: Union[None, str, List[str]] = Field(
default=None, union_mode="left_to_right"
)
required_integrations: List[str] = []
install_stack_requirements: bool = True
apt_packages: List[str] = []
environment: Dict[str, Any] = {}
user: Optional[str] = None
build_config: Optional[DockerBuildConfig] = None
allow_including_files_in_images: bool = True
allow_download_from_code_repository: bool = True
allow_download_from_artifact_store: bool = True
# Deprecated attributes
build_options: Dict[str, Any] = {}
dockerignore: Optional[str] = None
copy_files: bool = True
copy_global_config: bool = True
source_files: Optional[str] = None
required_hub_plugins: List[str] = []
_deprecation_validator = deprecation_utils.deprecate_pydantic_attributes(
"copy_files",
"copy_global_config",
"source_files",
"required_hub_plugins",
)
@model_validator(mode="before")
@classmethod
@before_validator_handler
def _migrate_source_files(cls, data: Dict[str, Any]) -> Dict[str, Any]:
"""Migrate old source_files values.
Args:
data: The model data.
Raises:
ValueError: If an invalid source file mode is specified.
Returns:
The migrated data.
"""
source_files = data.get("source_files", None)
if source_files is None:
return data
replacement_attributes = [
"allow_including_files_in_images",
"allow_download_from_code_repository",
"allow_download_from_artifact_store",
]
if any(v in data for v in replacement_attributes):
logger.warning(
"Both `source_files` and one of %s specified for the "
"DockerSettings, ignoring the `source_files` value.",
replacement_attributes,
)
return data
allow_including_files_in_images = False
allow_download_from_code_repository = False
allow_download_from_artifact_store = False
if source_files == "download":
allow_download_from_code_repository = True
elif source_files == "include":
allow_including_files_in_images = True
elif source_files == "download_or_include":
allow_including_files_in_images = True
allow_download_from_code_repository = True
elif source_files == "ignore":
pass
else:
raise ValueError(f"Invalid source file mode `{source_files}`.")
data["allow_including_files_in_images"] = (
allow_including_files_in_images
)
data["allow_download_from_code_repository"] = (
allow_download_from_code_repository
)
data["allow_download_from_artifact_store"] = (
allow_download_from_artifact_store
)
return data
@model_validator(mode="after")
def _validate_skip_build(self) -> "DockerSettings":
"""Ensures that a parent image is passed when trying to skip the build.
Returns:
The validated settings values.
Raises:
ValueError: If the build should be skipped but no parent image
was specified.
"""
if self.skip_build and not self.parent_image:
raise ValueError(
"Docker settings that specify `skip_build=True` must always "
"contain a `parent_image`. This parent image will be used "
"to run the steps of your pipeline directly without additional "
"Docker builds on top of it."
)
return self
model_config = ConfigDict(
# public attributes are immutable
frozen=True,
# prevent extra attributes during model initialization
extra="forbid",
)
PythonEnvironmentExportMethod (Enum)
Different methods to export the local Python environment.
Source code in zenml/config/docker_settings.py
class PythonEnvironmentExportMethod(Enum):
"""Different methods to export the local Python environment."""
PIP_FREEZE = "pip_freeze"
POETRY_EXPORT = "poetry_export"
@property
def command(self) -> str:
"""Shell command that outputs local python packages.
The output string must be something that can be interpreted as a
requirements file for pip once it's written to a file.
Returns:
Shell command.
"""
return {
PythonEnvironmentExportMethod.PIP_FREEZE: "pip freeze",
PythonEnvironmentExportMethod.POETRY_EXPORT: "poetry export --format=requirements.txt",
}[self]
PythonPackageInstaller (Enum)
Different installers for python packages.
Source code in zenml/config/docker_settings.py
class PythonPackageInstaller(Enum):
"""Different installers for python packages."""
PIP = "pip"
UV = "uv"
global_config
Functionality to support ZenML GlobalConfiguration.
GlobalConfigMetaClass (ModelMetaclass)
Global configuration metaclass.
This metaclass is used to enforce a singleton instance of the GlobalConfiguration class with the following additional properties:
- the GlobalConfiguration is initialized automatically on import with the default configuration, if no config file exists yet.
- the GlobalConfiguration undergoes a schema migration if the version of the config file is older than the current version of the ZenML package.
- a default store is set if no store is configured yet.
Source code in zenml/config/global_config.py
class GlobalConfigMetaClass(ModelMetaclass):
"""Global configuration metaclass.
This metaclass is used to enforce a singleton instance of the
GlobalConfiguration class with the following additional properties:
* the GlobalConfiguration is initialized automatically on import with the
default configuration, if no config file exists yet.
* the GlobalConfiguration undergoes a schema migration if the version of the
config file is older than the current version of the ZenML package.
* a default store is set if no store is configured yet.
"""
def __init__(cls, *args: Any, **kwargs: Any) -> None:
"""Initialize a singleton class.
Args:
*args: positional arguments
**kwargs: keyword arguments
"""
super().__init__(*args, **kwargs)
cls._global_config: Optional["GlobalConfiguration"] = None
def __call__(cls, *args: Any, **kwargs: Any) -> "GlobalConfiguration":
"""Create or return the default global config instance.
Args:
*args: positional arguments
**kwargs: keyword arguments
Returns:
The global GlobalConfiguration instance.
"""
if not cls._global_config:
cls._global_config = cast(
"GlobalConfiguration", super().__call__(*args, **kwargs)
)
cls._global_config._migrate_config()
return cls._global_config
__call__(cls, *args, **kwargs)
special
Create or return the default global config instance.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
*args |
Any |
positional arguments |
() |
**kwargs |
Any |
keyword arguments |
{} |
Returns:
| Type | Description |
|---|---|
GlobalConfiguration |
The global GlobalConfiguration instance. |
Source code in zenml/config/global_config.py
def __call__(cls, *args: Any, **kwargs: Any) -> "GlobalConfiguration":
"""Create or return the default global config instance.
Args:
*args: positional arguments
**kwargs: keyword arguments
Returns:
The global GlobalConfiguration instance.
"""
if not cls._global_config:
cls._global_config = cast(
"GlobalConfiguration", super().__call__(*args, **kwargs)
)
cls._global_config._migrate_config()
return cls._global_config
__init__(cls, *args, **kwargs)
special
Initialize a singleton class.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
*args |
Any |
positional arguments |
() |
**kwargs |
Any |
keyword arguments |
{} |
Source code in zenml/config/global_config.py
def __init__(cls, *args: Any, **kwargs: Any) -> None:
"""Initialize a singleton class.
Args:
*args: positional arguments
**kwargs: keyword arguments
"""
super().__init__(*args, **kwargs)
cls._global_config: Optional["GlobalConfiguration"] = None
GlobalConfiguration (BaseModel)
Stores global configuration options.
Configuration options are read from a config file, but can be overwritten
by environment variables. See GlobalConfiguration.__getattribute__ for
more details.
Attributes:
| Name | Type | Description |
|---|---|---|
user_id |
Unique user id. |
|
user_email |
Email address associated with this client. |
|
user_email_opt_in |
Whether the user has opted in to email communication. |
|
analytics_opt_in |
If a user agreed to sending analytics or not. |
|
version |
Version of ZenML that was last used to create or update the global config. |
|
store |
Store configuration. |
|
active_stack_id |
The ID of the active stack. |
|
active_workspace_name |
The name of the active workspace. |
Source code in zenml/config/global_config.py
class GlobalConfiguration(BaseModel, metaclass=GlobalConfigMetaClass):
"""Stores global configuration options.
Configuration options are read from a config file, but can be overwritten
by environment variables. See `GlobalConfiguration.__getattribute__` for
more details.
Attributes:
user_id: Unique user id.
user_email: Email address associated with this client.
user_email_opt_in: Whether the user has opted in to email communication.
analytics_opt_in: If a user agreed to sending analytics or not.
version: Version of ZenML that was last used to create or update the
global config.
store: Store configuration.
active_stack_id: The ID of the active stack.
active_workspace_name: The name of the active workspace.
"""
user_id: uuid.UUID = Field(default_factory=uuid.uuid4)
user_email: Optional[str] = None
user_email_opt_in: Optional[bool] = None
analytics_opt_in: bool = True
version: Optional[str] = None
store: Optional[SerializeAsAny[StoreConfiguration]] = None
active_stack_id: Optional[uuid.UUID] = None
active_workspace_name: Optional[str] = None
_zen_store: Optional["BaseZenStore"] = None
_active_workspace: Optional["WorkspaceResponse"] = None
_active_stack: Optional["StackResponse"] = None
def __init__(self, **data: Any) -> None:
"""Initializes a GlobalConfiguration using values from the config file.
GlobalConfiguration is a singleton class: only one instance can exist.
Calling this constructor multiple times will always yield the same
instance.
Args:
data: Custom configuration options.
"""
config_values = self._read_config()
config_values.update(data)
super().__init__(**config_values)
if not fileio.exists(self._config_file):
self._write_config()
@classmethod
def get_instance(cls) -> Optional["GlobalConfiguration"]:
"""Return the GlobalConfiguration singleton instance.
Returns:
The GlobalConfiguration singleton instance or None, if the
GlobalConfiguration hasn't been initialized yet.
"""
return cls._global_config
@classmethod
def _reset_instance(
cls, config: Optional["GlobalConfiguration"] = None
) -> None:
"""Reset the GlobalConfiguration singleton instance.
This method is only meant for internal use and testing purposes.
Args:
config: The GlobalConfiguration instance to set as the global
singleton. If None, the global GlobalConfiguration singleton is
reset to an empty value.
"""
cls._global_config = config
if config:
config._write_config()
@field_validator("version")
@classmethod
def _validate_version(cls, value: Optional[str]) -> Optional[str]:
"""Validate the version attribute.
Args:
value: The version attribute value.
Returns:
The version attribute value.
Raises:
RuntimeError: If the version parsing fails.
"""
if value is None:
return value
if not isinstance(version.parse(value), version.Version):
# If the version parsing fails, it returns a `LegacyVersion`
# instead. Check to make sure it's an actual `Version` object
# which represents a valid version.
raise RuntimeError(
f"Invalid version in global configuration: {value}."
)
return value
def __setattr__(self, key: str, value: Any) -> None:
"""Sets an attribute and persists it in the global configuration.
Args:
key: The attribute name.
value: The attribute value.
"""
super().__setattr__(key, value)
if key.startswith("_"):
return
self._write_config()
def __custom_getattribute__(self, key: str) -> Any:
"""Gets an attribute value for a specific key.
If a value for this attribute was specified using an environment
variable called `$(CONFIG_ENV_VAR_PREFIX)$(ATTRIBUTE_NAME)` and its
value can be parsed to the attribute type, the value from this
environment variable is returned instead.
Args:
key: The attribute name.
Returns:
The attribute value.
"""
value = super().__getattribute__(key)
if key.startswith("_") or key not in type(self).model_fields:
return value
environment_variable_name = f"{CONFIG_ENV_VAR_PREFIX}{key.upper()}"
try:
environment_variable_value = os.environ[environment_variable_name]
# set the environment variable value to leverage Pydantic's type
# conversion and validation
super().__setattr__(key, environment_variable_value)
return_value = super().__getattribute__(key)
# set back the old value as we don't want to permanently store
# the environment variable value here
super().__setattr__(key, value)
return return_value
except (ValidationError, KeyError, TypeError):
return value
if not TYPE_CHECKING:
# When defining __getattribute__, mypy allows accessing non-existent
# attributes without failing
# (see https://github.com/python/mypy/issues/13319).
__getattribute__ = __custom_getattribute__
def _migrate_config(self) -> None:
"""Migrates the global config to the latest version."""
curr_version = version.parse(__version__)
if self.version is None:
logger.info(
"Initializing the ZenML global configuration version to %s",
curr_version,
)
else:
config_version = version.parse(self.version)
if config_version > curr_version:
logger.error(
"The ZenML global configuration version (%s) is higher "
"than the version of ZenML currently being used (%s). "
"Read more about this issue and how to solve it here: "
"`https://docs.zenml.io/reference/global-settings#version-mismatch-downgrading`",
config_version,
curr_version,
)
# TODO [ENG-899]: Give more detailed instruction on how to
# resolve version mismatch.
return
if config_version == curr_version:
return
logger.info(
"Migrating the ZenML global configuration from version %s "
"to version %s...",
config_version,
curr_version,
)
# this will also trigger rewriting the config file to disk
# to ensure the schema migration results are persisted
self.version = __version__
def _read_config(self) -> Dict[str, Any]:
"""Reads configuration options from disk.
If the config file doesn't exist yet, this method returns an empty
dictionary.
Returns:
A dictionary containing the configuration options.
"""
config_file = self._config_file
config_values = {}
if fileio.exists(config_file):
config_values = yaml_utils.read_yaml(config_file)
if config_values is None:
# This can happen for example if the config file is empty
config_values = {}
elif not isinstance(config_values, dict):
logger.warning(
"The global configuration file is not a valid YAML file. "
"Creating a new global configuration file."
)
config_values = {}
return config_values
def _write_config(self) -> None:
"""Writes the global configuration options to disk."""
# We never write the configuration file in a ZenML server environment
# because this is a long-running process and the global configuration
# variables are supplied via environment variables.
if ENV_ZENML_SERVER in os.environ:
logger.info(
"Not writing the global configuration to disk in a ZenML "
"server environment."
)
return
config_file = self._config_file
yaml_dict = self.model_dump(mode="json", exclude_none=True)
logger.debug(f"Writing config to {config_file}")
if not fileio.exists(config_file):
io_utils.create_dir_recursive_if_not_exists(self.config_directory)
yaml_utils.write_yaml(config_file, yaml_dict)
def _configure_store(
self,
config: StoreConfiguration,
skip_default_registrations: bool = False,
**kwargs: Any,
) -> None:
"""Configure the global zen store.
This method creates and initializes the global store according to the
supplied configuration.
Args:
config: The new store configuration to use.
skip_default_registrations: If `True`, the creation of the default
stack and user in the store will be skipped.
**kwargs: Additional keyword arguments to pass to the store
constructor.
"""
from zenml.zen_stores.base_zen_store import BaseZenStore
if self.store == config and self._zen_store:
# TODO: Do we actually need to create/initialize the store here
# or can we just return instead? We think this is just getting
# called for default registrations.
BaseZenStore.create_store(
config, skip_default_registrations, **kwargs
)
return
# TODO: Revisit the flow regarding the registration of the default
# entities once the analytics v1 is removed.
store = BaseZenStore.create_store(config, True, **kwargs)
logger.debug(f"Configuring the global store to {store.config}")
self.store = store.config
self._zen_store = store
if not skip_default_registrations:
store._initialize_database()
# Sanitize the global configuration to reflect the new store
self._sanitize_config()
self._write_config()
local_stores_path = Path(self.local_stores_path)
local_stores_path.mkdir(parents=True, exist_ok=True)
def _sanitize_config(self) -> None:
"""Sanitize and save the global configuration.
This method is called to ensure that the active stack and workspace
are set to their default values, if possible.
"""
# If running in a ZenML server environment, the active stack and
# workspace are not relevant
if ENV_ZENML_SERVER in os.environ:
return
active_workspace, active_stack = self.zen_store.validate_active_config(
self.active_workspace_name,
self.active_stack_id,
config_name="global",
)
self.active_workspace_name = active_workspace.name
self._active_workspace = active_workspace
self.set_active_stack(active_stack)
@property
def _config_file(self) -> str:
"""Path to the file where global configuration options are stored.
Returns:
The path to the global configuration file.
"""
return os.path.join(self.config_directory, "config.yaml")
@property
def config_directory(self) -> str:
"""Directory where the global configuration file is located.
Returns:
The directory where the global configuration file is located.
"""
return io_utils.get_global_config_directory()
@property
def local_stores_path(self) -> str:
"""Path where local stores information is stored.
Returns:
The path where local stores information is stored.
"""
if ENV_ZENML_LOCAL_STORES_PATH in os.environ:
return os.environ[ENV_ZENML_LOCAL_STORES_PATH]
return os.path.join(
self.config_directory,
LOCAL_STORES_DIRECTORY_NAME,
)
def get_config_environment_vars(self) -> Dict[str, str]:
"""Convert the global configuration to environment variables.
Returns:
Environment variables dictionary.
"""
environment_vars = {}
for key in self.model_fields.keys():
if key == "store":
# The store configuration uses its own environment variable
# naming scheme
continue
value = getattr(self, key)
if value is not None:
environment_vars[CONFIG_ENV_VAR_PREFIX + key.upper()] = str(
value
)
store_dict = self.store_configuration.model_dump(exclude_none=True)
# The secrets store and backup secrets store configurations use their
# own environment variables naming scheme
secrets_store_dict = store_dict.pop("secrets_store", None) or {}
backup_secrets_store_dict = (
store_dict.pop("backup_secrets_store", None) or {}
)
for key, value in store_dict.items():
if key in ["username", "password"]:
# Never include the username and password in the env vars. Use
# the API token instead.
continue
environment_vars[ENV_ZENML_STORE_PREFIX + key.upper()] = str(value)
for key, value in secrets_store_dict.items():
environment_vars[ENV_ZENML_SECRETS_STORE_PREFIX + key.upper()] = (
str(value)
)
for key, value in backup_secrets_store_dict.items():
environment_vars[
ENV_ZENML_BACKUP_SECRETS_STORE_PREFIX + key.upper()
] = str(value)
return environment_vars
def _get_store_configuration(
self, baseline: Optional[StoreConfiguration] = None
) -> StoreConfiguration:
"""Get the store configuration.
This method computes a store configuration starting from a baseline and
applying the environment variables on top. If no baseline is provided,
the following are used as a baseline:
* the current store configuration, if it exists (e.g. if a store was
configured in the global configuration file or explicitly set in the
global configuration by calling `set_store`), or
* the default store configuration, otherwise
Args:
baseline: Optional baseline store configuration to use.
Returns:
The store configuration.
"""
from zenml.zen_stores.base_zen_store import BaseZenStore
# Step 1: Create a baseline store configuration
if baseline is not None:
# Use the provided baseline store configuration
store = baseline
elif self.store is not None:
# Use the current store configuration as a baseline
store = self.store
else:
# Start with the default store configuration as a baseline
store = self.get_default_store()
# Step 2: Replace or update the baseline store configuration with the
# environment variables
env_store_config: Dict[str, str] = {}
env_secrets_store_config: Dict[str, str] = {}
env_backup_secrets_store_config: Dict[str, str] = {}
for k, v in os.environ.items():
if k.startswith(ENV_ZENML_STORE_PREFIX):
env_store_config[k[len(ENV_ZENML_STORE_PREFIX) :].lower()] = v
elif k.startswith(ENV_ZENML_SECRETS_STORE_PREFIX):
env_secrets_store_config[
k[len(ENV_ZENML_SECRETS_STORE_PREFIX) :].lower()
] = v
elif k.startswith(ENV_ZENML_BACKUP_SECRETS_STORE_PREFIX):
env_backup_secrets_store_config[
k[len(ENV_ZENML_BACKUP_SECRETS_STORE_PREFIX) :].lower()
] = v
if len(env_store_config):
# As a convenience, we also infer the store type from the URL if
# not explicitly set in the environment variables.
if "type" not in env_store_config and "url" in env_store_config:
env_store_config["type"] = BaseZenStore.get_store_type(
env_store_config["url"]
)
# We distinguish between two cases here: the environment variables
# are used to completely replace the store configuration (i.e. when
# the store type or URL is set using the environment variables), or
# they are only used to update the store configuration. In the first
# case, we replace the baseline store configuration with the
# environment variables. In the second case, we only merge the
# environment variables into the baseline store config.
if "type" in env_store_config:
logger.debug(
"Using environment variables to configure the store"
)
store = StoreConfiguration(
**env_store_config,
)
else:
logger.debug(
"Using environment variables to update the default store"
)
store = store.model_copy(update=env_store_config, deep=True)
# Step 3: Replace or update the baseline secrets store configuration
# with the environment variables. This only applies to SQL stores.
if store.type == StoreType.SQL:
# We distinguish between two cases here: the environment
# variables are used to completely replace the secrets store
# configuration (i.e. when the secrets store type is set using
# the environment variable), or they are only used to update the
# store configuration. In the first case, we replace the
# baseline secrets store configuration with the environment
# variables. In the second case, we only merge the environment
# variables into the baseline secrets store config (if any is
# set).
if len(env_secrets_store_config):
if "type" in env_secrets_store_config:
logger.debug(
"Using environment variables to configure the secrets "
"store"
)
store.secrets_store = SecretsStoreConfiguration(
**env_secrets_store_config
)
elif store.secrets_store:
logger.debug(
"Using environment variables to update the secrets "
"store"
)
store.secrets_store = store.secrets_store.model_copy(
update=env_secrets_store_config, deep=True
)
if len(env_backup_secrets_store_config):
if "type" in env_backup_secrets_store_config:
logger.debug(
"Using environment variables to configure the backup "
"secrets store"
)
store.backup_secrets_store = SecretsStoreConfiguration(
**env_backup_secrets_store_config
)
elif store.backup_secrets_store:
logger.debug(
"Using environment variables to update the backup "
"secrets store"
)
store.backup_secrets_store = (
store.backup_secrets_store.model_copy(
update=env_backup_secrets_store_config, deep=True
)
)
return store
@property
def store_configuration(self) -> StoreConfiguration:
"""Get the current store configuration.
Returns:
The store configuration.
"""
# If the zen store is already initialized, we can get the store
# configuration from there and disregard the global configuration.
if self._zen_store is not None:
return self._zen_store.config
return self._get_store_configuration()
def get_default_store(self) -> StoreConfiguration:
"""Get the default SQLite store configuration.
Returns:
The default SQLite store configuration.
"""
from zenml.zen_stores.base_zen_store import BaseZenStore
return BaseZenStore.get_default_store_config(
path=os.path.join(
self.local_stores_path,
DEFAULT_STORE_DIRECTORY_NAME,
)
)
def set_default_store(self) -> None:
"""Initializes and sets the default store configuration.
Call this method to initialize or revert the store configuration to the
default store.
"""
# Apply the environment variables to the default store configuration
default_store_cfg = self._get_store_configuration(
baseline=self.get_default_store()
)
self._configure_store(default_store_cfg)
logger.debug("Using the default store for the global config.")
def uses_default_store(self) -> bool:
"""Check if the global configuration uses the default store.
Returns:
`True` if the global configuration uses the default store.
"""
return self.store_configuration.url == self.get_default_store().url
def set_store(
self,
config: StoreConfiguration,
skip_default_registrations: bool = False,
**kwargs: Any,
) -> None:
"""Update the active store configuration.
Call this method to validate and update the active store configuration.
Args:
config: The new store configuration to use.
skip_default_registrations: If `True`, the creation of the default
stack and user in the store will be skipped.
**kwargs: Additional keyword arguments to pass to the store
constructor.
"""
# Apply the environment variables to the custom store configuration
config = self._get_store_configuration(baseline=config)
self._configure_store(config, skip_default_registrations, **kwargs)
logger.info("Updated the global store configuration.")
@property
def zen_store(self) -> "BaseZenStore":
"""Initialize and/or return the global zen store.
If the store hasn't been initialized yet, it is initialized when this
property is first accessed according to the global store configuration.
Returns:
The current zen store.
"""
if self._zen_store is None:
self._configure_store(self.store_configuration)
assert self._zen_store is not None
return self._zen_store
def set_active_workspace(
self, workspace: "WorkspaceResponse"
) -> "WorkspaceResponse":
"""Set the workspace for the local client.
Args:
workspace: The workspace to set active.
Returns:
The workspace that was set active.
"""
self.active_workspace_name = workspace.name
self._active_workspace = workspace
# Sanitize the global configuration to reflect the new workspace
self._sanitize_config()
return workspace
def set_active_stack(self, stack: "StackResponse") -> None:
"""Set the active stack for the local client.
Args:
stack: The model of the stack to set active.
"""
self.active_stack_id = stack.id
self._active_stack = stack
def get_active_workspace(self) -> "WorkspaceResponse":
"""Get a model of the active workspace for the local client.
Returns:
The model of the active workspace.
"""
workspace_name = self.get_active_workspace_name()
if self._active_workspace is not None:
return self._active_workspace
workspace = self.zen_store.get_workspace(
workspace_name_or_id=workspace_name,
)
return self.set_active_workspace(workspace)
def get_active_workspace_name(self) -> str:
"""Get the name of the active workspace.
If the active workspace doesn't exist yet, the ZenStore is reinitialized.
Returns:
The name of the active workspace.
"""
if self.active_workspace_name is None:
_ = self.zen_store
assert self.active_workspace_name is not None
return self.active_workspace_name
def get_active_stack_id(self) -> UUID:
"""Get the ID of the active stack.
If the active stack doesn't exist yet, the ZenStore is reinitialized.
Returns:
The active stack ID.
"""
if self.active_stack_id is None:
_ = self.zen_store
assert self.active_stack_id is not None
return self.active_stack_id
model_config = ConfigDict(
# Validate attributes when assigning them. We need to set this in order
# to have a mix of mutable and immutable attributes
validate_assignment=True,
# Allow extra attributes from configs of previous ZenML versions to
# permit downgrading
extra="allow",
)
config_directory: str
property
readonly
Directory where the global configuration file is located.
Returns:
| Type | Description |
|---|---|
str |
The directory where the global configuration file is located. |
local_stores_path: str
property
readonly
Path where local stores information is stored.
Returns:
| Type | Description |
|---|---|
str |
The path where local stores information is stored. |
store_configuration: StoreConfiguration
property
readonly
Get the current store configuration.
Returns:
| Type | Description |
|---|---|
StoreConfiguration |
The store configuration. |
zen_store: BaseZenStore
property
readonly
Initialize and/or return the global zen store.
If the store hasn't been initialized yet, it is initialized when this property is first accessed according to the global store configuration.
Returns:
| Type | Description |
|---|---|
BaseZenStore |
The current zen store. |
__custom_getattribute__(self, key)
special
Gets an attribute value for a specific key.
If a value for this attribute was specified using an environment
variable called $(CONFIG_ENV_VAR_PREFIX)$(ATTRIBUTE_NAME) and its
value can be parsed to the attribute type, the value from this
environment variable is returned instead.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
key |
str |
The attribute name. |
required |
Returns:
| Type | Description |
|---|---|
Any |
The attribute value. |
Source code in zenml/config/global_config.py
def __custom_getattribute__(self, key: str) -> Any:
"""Gets an attribute value for a specific key.
If a value for this attribute was specified using an environment
variable called `$(CONFIG_ENV_VAR_PREFIX)$(ATTRIBUTE_NAME)` and its
value can be parsed to the attribute type, the value from this
environment variable is returned instead.
Args:
key: The attribute name.
Returns:
The attribute value.
"""
value = super().__getattribute__(key)
if key.startswith("_") or key not in type(self).model_fields:
return value
environment_variable_name = f"{CONFIG_ENV_VAR_PREFIX}{key.upper()}"
try:
environment_variable_value = os.environ[environment_variable_name]
# set the environment variable value to leverage Pydantic's type
# conversion and validation
super().__setattr__(key, environment_variable_value)
return_value = super().__getattribute__(key)
# set back the old value as we don't want to permanently store
# the environment variable value here
super().__setattr__(key, value)
return return_value
except (ValidationError, KeyError, TypeError):
return value
__getattribute__(self, key)
special
Gets an attribute value for a specific key.
If a value for this attribute was specified using an environment
variable called $(CONFIG_ENV_VAR_PREFIX)$(ATTRIBUTE_NAME) and its
value can be parsed to the attribute type, the value from this
environment variable is returned instead.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
key |
str |
The attribute name. |
required |
Returns:
| Type | Description |
|---|---|
Any |
The attribute value. |
Source code in zenml/config/global_config.py
def __custom_getattribute__(self, key: str) -> Any:
"""Gets an attribute value for a specific key.
If a value for this attribute was specified using an environment
variable called `$(CONFIG_ENV_VAR_PREFIX)$(ATTRIBUTE_NAME)` and its
value can be parsed to the attribute type, the value from this
environment variable is returned instead.
Args:
key: The attribute name.
Returns:
The attribute value.
"""
value = super().__getattribute__(key)
if key.startswith("_") or key not in type(self).model_fields:
return value
environment_variable_name = f"{CONFIG_ENV_VAR_PREFIX}{key.upper()}"
try:
environment_variable_value = os.environ[environment_variable_name]
# set the environment variable value to leverage Pydantic's type
# conversion and validation
super().__setattr__(key, environment_variable_value)
return_value = super().__getattribute__(key)
# set back the old value as we don't want to permanently store
# the environment variable value here
super().__setattr__(key, value)
return return_value
except (ValidationError, KeyError, TypeError):
return value
__init__(self, **data)
special
Initializes a GlobalConfiguration using values from the config file.
GlobalConfiguration is a singleton class: only one instance can exist. Calling this constructor multiple times will always yield the same instance.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
data |
Any |
Custom configuration options. |
{} |
Source code in zenml/config/global_config.py
def __init__(self, **data: Any) -> None:
"""Initializes a GlobalConfiguration using values from the config file.
GlobalConfiguration is a singleton class: only one instance can exist.
Calling this constructor multiple times will always yield the same
instance.
Args:
data: Custom configuration options.
"""
config_values = self._read_config()
config_values.update(data)
super().__init__(**config_values)
if not fileio.exists(self._config_file):
self._write_config()
__setattr__(self, key, value)
special
Sets an attribute and persists it in the global configuration.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
key |
str |
The attribute name. |
required |
value |
Any |
The attribute value. |
required |
Source code in zenml/config/global_config.py
def __setattr__(self, key: str, value: Any) -> None:
"""Sets an attribute and persists it in the global configuration.
Args:
key: The attribute name.
value: The attribute value.
"""
super().__setattr__(key, value)
if key.startswith("_"):
return
self._write_config()
get_active_stack_id(self)
Get the ID of the active stack.
If the active stack doesn't exist yet, the ZenStore is reinitialized.
Returns:
| Type | Description |
|---|---|
UUID |
The active stack ID. |
Source code in zenml/config/global_config.py
def get_active_stack_id(self) -> UUID:
"""Get the ID of the active stack.
If the active stack doesn't exist yet, the ZenStore is reinitialized.
Returns:
The active stack ID.
"""
if self.active_stack_id is None:
_ = self.zen_store
assert self.active_stack_id is not None
return self.active_stack_id
get_active_workspace(self)
Get a model of the active workspace for the local client.
Returns:
| Type | Description |
|---|---|
WorkspaceResponse |
The model of the active workspace. |
Source code in zenml/config/global_config.py
def get_active_workspace(self) -> "WorkspaceResponse":
"""Get a model of the active workspace for the local client.
Returns:
The model of the active workspace.
"""
workspace_name = self.get_active_workspace_name()
if self._active_workspace is not None:
return self._active_workspace
workspace = self.zen_store.get_workspace(
workspace_name_or_id=workspace_name,
)
return self.set_active_workspace(workspace)
get_active_workspace_name(self)
Get the name of the active workspace.
If the active workspace doesn't exist yet, the ZenStore is reinitialized.
Returns:
| Type | Description |
|---|---|
str |
The name of the active workspace. |
Source code in zenml/config/global_config.py
def get_active_workspace_name(self) -> str:
"""Get the name of the active workspace.
If the active workspace doesn't exist yet, the ZenStore is reinitialized.
Returns:
The name of the active workspace.
"""
if self.active_workspace_name is None:
_ = self.zen_store
assert self.active_workspace_name is not None
return self.active_workspace_name
get_config_environment_vars(self)
Convert the global configuration to environment variables.
Returns:
| Type | Description |
|---|---|
Dict[str, str] |
Environment variables dictionary. |
Source code in zenml/config/global_config.py
def get_config_environment_vars(self) -> Dict[str, str]:
"""Convert the global configuration to environment variables.
Returns:
Environment variables dictionary.
"""
environment_vars = {}
for key in self.model_fields.keys():
if key == "store":
# The store configuration uses its own environment variable
# naming scheme
continue
value = getattr(self, key)
if value is not None:
environment_vars[CONFIG_ENV_VAR_PREFIX + key.upper()] = str(
value
)
store_dict = self.store_configuration.model_dump(exclude_none=True)
# The secrets store and backup secrets store configurations use their
# own environment variables naming scheme
secrets_store_dict = store_dict.pop("secrets_store", None) or {}
backup_secrets_store_dict = (
store_dict.pop("backup_secrets_store", None) or {}
)
for key, value in store_dict.items():
if key in ["username", "password"]:
# Never include the username and password in the env vars. Use
# the API token instead.
continue
environment_vars[ENV_ZENML_STORE_PREFIX + key.upper()] = str(value)
for key, value in secrets_store_dict.items():
environment_vars[ENV_ZENML_SECRETS_STORE_PREFIX + key.upper()] = (
str(value)
)
for key, value in backup_secrets_store_dict.items():
environment_vars[
ENV_ZENML_BACKUP_SECRETS_STORE_PREFIX + key.upper()
] = str(value)
return environment_vars
get_default_store(self)
Get the default SQLite store configuration.
Returns:
| Type | Description |
|---|---|
StoreConfiguration |
The default SQLite store configuration. |
Source code in zenml/config/global_config.py
def get_default_store(self) -> StoreConfiguration:
"""Get the default SQLite store configuration.
Returns:
The default SQLite store configuration.
"""
from zenml.zen_stores.base_zen_store import BaseZenStore
return BaseZenStore.get_default_store_config(
path=os.path.join(
self.local_stores_path,
DEFAULT_STORE_DIRECTORY_NAME,
)
)
get_instance()
classmethod
Return the GlobalConfiguration singleton instance.
Returns:
| Type | Description |
|---|---|
Optional[GlobalConfiguration] |
The GlobalConfiguration singleton instance or None, if the GlobalConfiguration hasn't been initialized yet. |
Source code in zenml/config/global_config.py
@classmethod
def get_instance(cls) -> Optional["GlobalConfiguration"]:
"""Return the GlobalConfiguration singleton instance.
Returns:
The GlobalConfiguration singleton instance or None, if the
GlobalConfiguration hasn't been initialized yet.
"""
return cls._global_config
model_post_init(/, self, context)
This function is meant to behave like a BaseModel method to initialise private attributes.
It takes context as an argument since that's what pydantic-core passes when calling it.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
self |
BaseModel |
The BaseModel instance. |
required |
context |
Any |
The context. |
required |
Source code in zenml/config/global_config.py
def init_private_attributes(self: BaseModel, context: Any, /) -> None:
"""This function is meant to behave like a BaseModel method to initialise private attributes.
It takes context as an argument since that's what pydantic-core passes when calling it.
Args:
self: The BaseModel instance.
context: The context.
"""
if getattr(self, '__pydantic_private__', None) is None:
pydantic_private = {}
for name, private_attr in self.__private_attributes__.items():
default = private_attr.get_default()
if default is not PydanticUndefined:
pydantic_private[name] = default
object_setattr(self, '__pydantic_private__', pydantic_private)
set_active_stack(self, stack)
Set the active stack for the local client.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
stack |
StackResponse |
The model of the stack to set active. |
required |
Source code in zenml/config/global_config.py
def set_active_stack(self, stack: "StackResponse") -> None:
"""Set the active stack for the local client.
Args:
stack: The model of the stack to set active.
"""
self.active_stack_id = stack.id
self._active_stack = stack
set_active_workspace(self, workspace)
Set the workspace for the local client.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
workspace |
WorkspaceResponse |
The workspace to set active. |
required |
Returns:
| Type | Description |
|---|---|
WorkspaceResponse |
The workspace that was set active. |
Source code in zenml/config/global_config.py
def set_active_workspace(
self, workspace: "WorkspaceResponse"
) -> "WorkspaceResponse":
"""Set the workspace for the local client.
Args:
workspace: The workspace to set active.
Returns:
The workspace that was set active.
"""
self.active_workspace_name = workspace.name
self._active_workspace = workspace
# Sanitize the global configuration to reflect the new workspace
self._sanitize_config()
return workspace
set_default_store(self)
Initializes and sets the default store configuration.
Call this method to initialize or revert the store configuration to the default store.
Source code in zenml/config/global_config.py
def set_default_store(self) -> None:
"""Initializes and sets the default store configuration.
Call this method to initialize or revert the store configuration to the
default store.
"""
# Apply the environment variables to the default store configuration
default_store_cfg = self._get_store_configuration(
baseline=self.get_default_store()
)
self._configure_store(default_store_cfg)
logger.debug("Using the default store for the global config.")
set_store(self, config, skip_default_registrations=False, **kwargs)
Update the active store configuration.
Call this method to validate and update the active store configuration.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
config |
StoreConfiguration |
The new store configuration to use. |
required |
skip_default_registrations |
bool |
If |
False |
**kwargs |
Any |
Additional keyword arguments to pass to the store constructor. |
{} |
Source code in zenml/config/global_config.py
def set_store(
self,
config: StoreConfiguration,
skip_default_registrations: bool = False,
**kwargs: Any,
) -> None:
"""Update the active store configuration.
Call this method to validate and update the active store configuration.
Args:
config: The new store configuration to use.
skip_default_registrations: If `True`, the creation of the default
stack and user in the store will be skipped.
**kwargs: Additional keyword arguments to pass to the store
constructor.
"""
# Apply the environment variables to the custom store configuration
config = self._get_store_configuration(baseline=config)
self._configure_store(config, skip_default_registrations, **kwargs)
logger.info("Updated the global store configuration.")
uses_default_store(self)
Check if the global configuration uses the default store.
Returns:
| Type | Description |
|---|---|
bool |
|
Source code in zenml/config/global_config.py
def uses_default_store(self) -> bool:
"""Check if the global configuration uses the default store.
Returns:
`True` if the global configuration uses the default store.
"""
return self.store_configuration.url == self.get_default_store().url
pipeline_configurations
Pipeline configuration classes.
PipelineConfiguration (PipelineConfigurationUpdate)
Pipeline configuration class.
Source code in zenml/config/pipeline_configurations.py
class PipelineConfiguration(PipelineConfigurationUpdate):
"""Pipeline configuration class."""
name: str
@field_validator("name")
@classmethod
def ensure_pipeline_name_allowed(cls, name: str) -> str:
"""Ensures the pipeline name is allowed.
Args:
name: Name of the pipeline.
Returns:
The validated name of the pipeline.
Raises:
ValueError: If the name is not allowed.
"""
if name in DISALLOWED_PIPELINE_NAMES:
raise ValueError(
f"Pipeline name '{name}' is not allowed since '{name}' is a "
"reserved key word. Please choose another name."
)
return name
@property
def docker_settings(self) -> "DockerSettings":
"""Docker settings of this pipeline.
Returns:
The Docker settings of this pipeline.
"""
from zenml.config import DockerSettings
model_or_dict: SettingsOrDict = self.settings.get(
DOCKER_SETTINGS_KEY, {}
)
return DockerSettings.model_validate(model_or_dict)
docker_settings: DockerSettings
property
readonly
Docker settings of this pipeline.
Returns:
| Type | Description |
|---|---|
DockerSettings |
The Docker settings of this pipeline. |
ensure_pipeline_name_allowed(name)
classmethod
Ensures the pipeline name is allowed.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
name |
str |
Name of the pipeline. |
required |
Returns:
| Type | Description |
|---|---|
str |
The validated name of the pipeline. |
Exceptions:
| Type | Description |
|---|---|
ValueError |
If the name is not allowed. |
Source code in zenml/config/pipeline_configurations.py
@field_validator("name")
@classmethod
def ensure_pipeline_name_allowed(cls, name: str) -> str:
"""Ensures the pipeline name is allowed.
Args:
name: Name of the pipeline.
Returns:
The validated name of the pipeline.
Raises:
ValueError: If the name is not allowed.
"""
if name in DISALLOWED_PIPELINE_NAMES:
raise ValueError(
f"Pipeline name '{name}' is not allowed since '{name}' is a "
"reserved key word. Please choose another name."
)
return name
PipelineConfigurationUpdate (StrictBaseModel)
Class for pipeline configuration updates.
Source code in zenml/config/pipeline_configurations.py
class PipelineConfigurationUpdate(StrictBaseModel):
"""Class for pipeline configuration updates."""
enable_cache: Optional[bool] = None
enable_artifact_metadata: Optional[bool] = None
enable_artifact_visualization: Optional[bool] = None
enable_step_logs: Optional[bool] = None
settings: Dict[str, SerializeAsAny[BaseSettings]] = {}
extra: Dict[str, Any] = {}
failure_hook_source: Optional[SourceWithValidator] = None
success_hook_source: Optional[SourceWithValidator] = None
model: Optional[Model] = None
parameters: Optional[Dict[str, Any]] = None
retry: Optional[StepRetryConfig] = None
pipeline_run_configuration
Pipeline run configuration class.
PipelineRunConfiguration (StrictBaseModel, YAMLSerializationMixin)
Class for pipeline run configurations.
Source code in zenml/config/pipeline_run_configuration.py
class PipelineRunConfiguration(
StrictBaseModel, pydantic_utils.YAMLSerializationMixin
):
"""Class for pipeline run configurations."""
run_name: Optional[str] = None
enable_cache: Optional[bool] = None
enable_artifact_metadata: Optional[bool] = None
enable_artifact_visualization: Optional[bool] = None
enable_step_logs: Optional[bool] = None
schedule: Optional[Schedule] = None
build: Union[PipelineBuildBase, UUID, None] = Field(
default=None, union_mode="left_to_right"
)
steps: Dict[str, StepConfigurationUpdate] = {}
settings: Dict[str, SerializeAsAny[BaseSettings]] = {}
extra: Dict[str, Any] = {}
model: Optional[Model] = None
parameters: Optional[Dict[str, Any]] = None
retry: Optional[StepRetryConfig] = None
failure_hook_source: Optional[SourceWithValidator] = None
success_hook_source: Optional[SourceWithValidator] = None
pipeline_spec
Pipeline configuration classes.
PipelineSpec (StrictBaseModel)
Specification of a pipeline.
Source code in zenml/config/pipeline_spec.py
class PipelineSpec(StrictBaseModel):
"""Specification of a pipeline."""
# Versions:
# - 0.2: Legacy BasePipeline in release <=0.39.1, the upstream steps and
# inputs in the step specs refer to the step names, not the pipeline
# parameter names
# - 0.3: Legacy BasePipeline in release >0.39.1, the upstream steps and
# inputs in the step specs refer to the pipeline parameter names
# - 0.4: New Pipeline class, the upstream steps and
# inputs in the step specs refer to the pipeline parameter names
version: str = "0.4"
source: Optional[SourceWithValidator] = None
parameters: Dict[str, Any] = {}
steps: List[StepSpec]
def __eq__(self, other: Any) -> bool:
"""Returns whether the other object is referring to the same pipeline.
Args:
other: The other object to compare to.
Returns:
True if the other object is referring to the same pipeline.
"""
if isinstance(other, PipelineSpec):
return self.steps == other.steps
return NotImplemented
@property
def json_with_string_sources(self) -> str:
"""JSON representation with sources replaced by their import path.
Returns:
The JSON representation.
"""
from packaging import version
dict_ = self.model_dump()
if self.source:
dict_["source"] = self.source.import_path
for step_dict in dict_["steps"]:
step_dict["source"] = Source.model_validate(
step_dict["source"]
).import_path
if version.parse(self.version) < version.parse("0.4"):
# Keep backwards compatibility with old pipeline versions
dict_.pop("source")
dict_.pop("parameters")
return json.dumps(dict_, sort_keys=False, default=pydantic_encoder)
json_with_string_sources: str
property
readonly
JSON representation with sources replaced by their import path.
Returns:
| Type | Description |
|---|---|
str |
The JSON representation. |
__eq__(self, other)
special
Returns whether the other object is referring to the same pipeline.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
other |
Any |
The other object to compare to. |
required |
Returns:
| Type | Description |
|---|---|
bool |
True if the other object is referring to the same pipeline. |
Source code in zenml/config/pipeline_spec.py
def __eq__(self, other: Any) -> bool:
"""Returns whether the other object is referring to the same pipeline.
Args:
other: The other object to compare to.
Returns:
True if the other object is referring to the same pipeline.
"""
if isinstance(other, PipelineSpec):
return self.steps == other.steps
return NotImplemented
resource_settings
Resource settings class used to specify resources for a step.
ByteUnit (Enum)
Enum for byte units.
Source code in zenml/config/resource_settings.py
class ByteUnit(Enum):
"""Enum for byte units."""
KB = "KB"
KIB = "KiB"
MB = "MB"
MIB = "MiB"
GB = "GB"
GIB = "GiB"
TB = "TB"
TIB = "TiB"
PB = "PB"
PIB = "PiB"
@property
def byte_value(self) -> int:
"""Returns the amount of bytes that this unit represents.
Returns:
The byte value of this unit.
"""
return {
ByteUnit.KB: 10**3,
ByteUnit.KIB: 1 << 10,
ByteUnit.MB: 10**6,
ByteUnit.MIB: 1 << 20,
ByteUnit.GB: 10**9,
ByteUnit.GIB: 1 << 30,
ByteUnit.TB: 10**12,
ByteUnit.TIB: 1 << 40,
ByteUnit.PB: 10**15,
ByteUnit.PIB: 1 << 50,
}[self]
ResourceSettings (BaseSettings)
Hardware resource settings.
Attributes:
| Name | Type | Description |
|---|---|---|
cpu_count |
Optional[float] |
The amount of CPU cores that should be configured. |
gpu_count |
Optional[int] |
The amount of GPUs that should be configured. |
memory |
Optional[str] |
The amount of memory that should be configured. |
Source code in zenml/config/resource_settings.py
class ResourceSettings(BaseSettings):
"""Hardware resource settings.
Attributes:
cpu_count: The amount of CPU cores that should be configured.
gpu_count: The amount of GPUs that should be configured.
memory: The amount of memory that should be configured.
"""
cpu_count: Optional[PositiveFloat] = None
gpu_count: Optional[NonNegativeInt] = None
memory: Optional[str] = Field(pattern=MEMORY_REGEX, default=None)
@property
def empty(self) -> bool:
"""Returns if this object is "empty" (=no values configured) or not.
Returns:
`True` if no values were configured, `False` otherwise.
"""
# To detect whether this config is empty (= no values specified), we
# check if there are any attributes which are explicitly set to any
# value other than `None`.
return len(self.model_dump(exclude_unset=True, exclude_none=True)) == 0
def get_memory(
self, unit: Union[str, ByteUnit] = ByteUnit.GB
) -> Optional[float]:
"""Gets the memory configuration in a specific unit.
Args:
unit: The unit to which the memory should be converted.
Raises:
ValueError: If the memory string is invalid.
Returns:
The memory configuration converted to the requested unit, or None
if no memory was configured.
"""
if not self.memory:
return None
if isinstance(unit, str):
unit = ByteUnit(unit)
memory = self.memory
for memory_unit in ByteUnit:
if memory.endswith(memory_unit.value):
memory_value = int(memory[: -len(memory_unit.value)])
return memory_value * memory_unit.byte_value / unit.byte_value
else:
# Should never happen due to the regex validation
raise ValueError(f"Unable to parse memory unit from '{memory}'.")
model_config = SettingsConfigDict(
# public attributes are immutable
frozen=True,
# prevent extra attributes during model initialization
extra="forbid",
)
empty: bool
property
readonly
Returns if this object is "empty" (=no values configured) or not.
Returns:
| Type | Description |
|---|---|
bool |
|
get_memory(self, unit=<ByteUnit.GB: 'GB'>)
Gets the memory configuration in a specific unit.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
unit |
Union[str, zenml.config.resource_settings.ByteUnit] |
The unit to which the memory should be converted. |
<ByteUnit.GB: 'GB'> |
Exceptions:
| Type | Description |
|---|---|
ValueError |
If the memory string is invalid. |
Returns:
| Type | Description |
|---|---|
Optional[float] |
The memory configuration converted to the requested unit, or None if no memory was configured. |
Source code in zenml/config/resource_settings.py
def get_memory(
self, unit: Union[str, ByteUnit] = ByteUnit.GB
) -> Optional[float]:
"""Gets the memory configuration in a specific unit.
Args:
unit: The unit to which the memory should be converted.
Raises:
ValueError: If the memory string is invalid.
Returns:
The memory configuration converted to the requested unit, or None
if no memory was configured.
"""
if not self.memory:
return None
if isinstance(unit, str):
unit = ByteUnit(unit)
memory = self.memory
for memory_unit in ByteUnit:
if memory.endswith(memory_unit.value):
memory_value = int(memory[: -len(memory_unit.value)])
return memory_value * memory_unit.byte_value / unit.byte_value
else:
# Should never happen due to the regex validation
raise ValueError(f"Unable to parse memory unit from '{memory}'.")
retry_config
Retry configuration for a step.
StepRetryConfig (StrictBaseModel)
Retry configuration for a step.
Delay is an integer (specified in seconds).
Source code in zenml/config/retry_config.py
class StepRetryConfig(StrictBaseModel):
"""Retry configuration for a step.
Delay is an integer (specified in seconds).
"""
max_retries: int = 1
delay: int = 0 # in seconds
backoff: int = 0
schedule
Class for defining a pipeline schedule.
Schedule (BaseModel)
Class for defining a pipeline schedule.
Attributes:
| Name | Type | Description |
|---|---|---|
name |
Optional[str] |
Optional name to give to the schedule. If not set, a default name will be generated based on the pipeline name and the current date and time. |
cron_expression |
Optional[str] |
Cron expression for the pipeline schedule. If a value for this is set it takes precedence over the start time + interval. |
start_time |
Optional[datetime.datetime] |
datetime object to indicate when to start the schedule. |
end_time |
Optional[datetime.datetime] |
datetime object to indicate when to end the schedule. |
interval_second |
Optional[datetime.timedelta] |
datetime timedelta indicating the seconds between two recurring runs for a periodic schedule. |
catchup |
bool |
Whether the recurring run should catch up if behind schedule. For example, if the recurring run is paused for a while and re-enabled afterward. If catchup=True, the scheduler will catch up on (backfill) each missed interval. Otherwise, it only schedules the latest interval if more than one interval is ready to be scheduled. Usually, if your pipeline handles backfill internally, you should turn catchup off to avoid duplicate backfill. |
run_once_start_time |
Optional[datetime.datetime] |
datetime object to indicate when to run the pipeline once. This is useful for one-off runs. |
Source code in zenml/config/schedule.py
class Schedule(BaseModel):
"""Class for defining a pipeline schedule.
Attributes:
name: Optional name to give to the schedule. If not set, a default name
will be generated based on the pipeline name and the current date
and time.
cron_expression: Cron expression for the pipeline schedule. If a value
for this is set it takes precedence over the start time + interval.
start_time: datetime object to indicate when to start the schedule.
end_time: datetime object to indicate when to end the schedule.
interval_second: datetime timedelta indicating the seconds between two
recurring runs for a periodic schedule.
catchup: Whether the recurring run should catch up if behind schedule.
For example, if the recurring run is paused for a while and
re-enabled afterward. If catchup=True, the scheduler will catch
up on (backfill) each missed interval. Otherwise, it only
schedules the latest interval if more than one interval is ready to
be scheduled. Usually, if your pipeline handles backfill
internally, you should turn catchup off to avoid duplicate backfill.
run_once_start_time: datetime object to indicate when to run the
pipeline once. This is useful for one-off runs.
"""
name: Optional[str] = None
cron_expression: Optional[str] = None
start_time: Optional[datetime.datetime] = None
end_time: Optional[datetime.datetime] = None
interval_second: Optional[datetime.timedelta] = None
catchup: bool = False
run_once_start_time: Optional[datetime.datetime] = None
@model_validator(mode="after")
def _ensure_cron_or_periodic_schedule_configured(self) -> "Schedule":
"""Ensures that the cron expression or start time + interval are set.
Returns:
All schedule attributes.
Raises:
ValueError: If no cron expression or start time + interval were
provided.
"""
periodic_schedule = self.start_time and self.interval_second
if self.cron_expression and periodic_schedule:
logger.warning(
"This schedule was created with a cron expression as well as "
"values for `start_time` and `interval_seconds`. The resulting "
"behavior depends on the concrete orchestrator implementation "
"but will usually ignore the interval and use the cron "
"expression."
)
return self
elif self.cron_expression and self.run_once_start_time:
logger.warning(
"This schedule was created with a cron expression as well as "
"a value for `run_once_start_time`. The resulting behavior "
"depends on the concrete orchestrator implementation but will "
"usually ignore the `run_once_start_time`."
)
return self
elif (
self.cron_expression
or periodic_schedule
or self.run_once_start_time
):
return self
else:
raise ValueError(
"Either a cron expression, a start time and interval seconds "
"or a run once start time "
"need to be set for a valid schedule."
)
@property
def utc_start_time(self) -> Optional[str]:
"""Optional ISO-formatted string of the UTC start time.
Returns:
Optional ISO-formatted string of the UTC start time.
"""
if not self.start_time:
return None
return self.start_time.astimezone(datetime.timezone.utc).isoformat()
@property
def utc_end_time(self) -> Optional[str]:
"""Optional ISO-formatted string of the UTC end time.
Returns:
Optional ISO-formatted string of the UTC end time.
"""
if not self.end_time:
return None
return self.end_time.astimezone(datetime.timezone.utc).isoformat()
utc_end_time: Optional[str]
property
readonly
Optional ISO-formatted string of the UTC end time.
Returns:
| Type | Description |
|---|---|
Optional[str] |
Optional ISO-formatted string of the UTC end time. |
utc_start_time: Optional[str]
property
readonly
Optional ISO-formatted string of the UTC start time.
Returns:
| Type | Description |
|---|---|
Optional[str] |
Optional ISO-formatted string of the UTC start time. |
secret_reference_mixin
Secret reference mixin implementation.
SecretReferenceMixin (BaseModel)
Mixin class for secret references in pydantic model attributes.
Source code in zenml/config/secret_reference_mixin.py
class SecretReferenceMixin(BaseModel):
"""Mixin class for secret references in pydantic model attributes."""
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/manage-the-deployed-services/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)
def __custom_getattribute__(self, key: str) -> Any:
"""Returns the (potentially resolved) attribute value for the given key.
An attribute value may be either specified directly, or as a secret
reference. In case of a secret reference, this method resolves the
reference and returns the secret value instead.
Args:
key: The key for which to get the attribute value.
Raises:
KeyError: If the secret or secret key don't exist.
Returns:
The (potentially resolved) attribute value.
"""
value = super().__getattribute__(key)
if not secret_utils.is_secret_reference(value):
return value
from zenml.client import Client
secret_ref = secret_utils.parse_secret_reference(value)
# Try to resolve the secret using the secret store
try:
secret = Client().get_secret_by_name_and_scope(
name=secret_ref.name,
)
except (KeyError, NotImplementedError):
raise KeyError(
f"Failed to resolve secret reference for attribute {key}: "
f"The secret {secret_ref.name} does not exist."
)
if secret_ref.key not in secret.values:
raise KeyError(
f"Failed to resolve secret reference for attribute {key}: "
f"The secret {secret_ref.name} does not contain a value "
f"for key {secret_ref.key}. Available keys: "
f"{set(secret.values.keys())}."
)
return secret.secret_values[secret_ref.key]
if not TYPE_CHECKING:
# When defining __getattribute__, mypy allows accessing non-existent
# attributes without failing
# (see https://github.com/python/mypy/issues/13319).
__getattribute__ = __custom_getattribute__
@property
def required_secrets(self) -> Set[secret_utils.SecretReference]:
"""All required secrets for this object.
Returns:
The required secrets of this object.
"""
return {
secret_utils.parse_secret_reference(v)
for v in self.model_dump().values()
if secret_utils.is_secret_reference(v)
}
required_secrets: Set[zenml.utils.secret_utils.SecretReference]
property
readonly
All required secrets for this object.
Returns:
| Type | Description |
|---|---|
Set[zenml.utils.secret_utils.SecretReference] |
The required secrets of this object. |
__custom_getattribute__(self, key)
special
Returns the (potentially resolved) attribute value for the given key.
An attribute value may be either specified directly, or as a secret reference. In case of a secret reference, this method resolves the reference and returns the secret value instead.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
key |
str |
The key for which to get the attribute value. |
required |
Exceptions:
| Type | Description |
|---|---|
KeyError |
If the secret or secret key don't exist. |
Returns:
| Type | Description |
|---|---|
Any |
The (potentially resolved) attribute value. |
Source code in zenml/config/secret_reference_mixin.py
def __custom_getattribute__(self, key: str) -> Any:
"""Returns the (potentially resolved) attribute value for the given key.
An attribute value may be either specified directly, or as a secret
reference. In case of a secret reference, this method resolves the
reference and returns the secret value instead.
Args:
key: The key for which to get the attribute value.
Raises:
KeyError: If the secret or secret key don't exist.
Returns:
The (potentially resolved) attribute value.
"""
value = super().__getattribute__(key)
if not secret_utils.is_secret_reference(value):
return value
from zenml.client import Client
secret_ref = secret_utils.parse_secret_reference(value)
# Try to resolve the secret using the secret store
try:
secret = Client().get_secret_by_name_and_scope(
name=secret_ref.name,
)
except (KeyError, NotImplementedError):
raise KeyError(
f"Failed to resolve secret reference for attribute {key}: "
f"The secret {secret_ref.name} does not exist."
)
if secret_ref.key not in secret.values:
raise KeyError(
f"Failed to resolve secret reference for attribute {key}: "
f"The secret {secret_ref.name} does not contain a value "
f"for key {secret_ref.key}. Available keys: "
f"{set(secret.values.keys())}."
)
return secret.secret_values[secret_ref.key]
__getattribute__(self, key)
special
Returns the (potentially resolved) attribute value for the given key.
An attribute value may be either specified directly, or as a secret reference. In case of a secret reference, this method resolves the reference and returns the secret value instead.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
key |
str |
The key for which to get the attribute value. |
required |
Exceptions:
| Type | Description |
|---|---|
KeyError |
If the secret or secret key don't exist. |
Returns:
| Type | Description |
|---|---|
Any |
The (potentially resolved) attribute value. |
Source code in zenml/config/secret_reference_mixin.py
def __custom_getattribute__(self, key: str) -> Any:
"""Returns the (potentially resolved) attribute value for the given key.
An attribute value may be either specified directly, or as a secret
reference. In case of a secret reference, this method resolves the
reference and returns the secret value instead.
Args:
key: The key for which to get the attribute value.
Raises:
KeyError: If the secret or secret key don't exist.
Returns:
The (potentially resolved) attribute value.
"""
value = super().__getattribute__(key)
if not secret_utils.is_secret_reference(value):
return value
from zenml.client import Client
secret_ref = secret_utils.parse_secret_reference(value)
# Try to resolve the secret using the secret store
try:
secret = Client().get_secret_by_name_and_scope(
name=secret_ref.name,
)
except (KeyError, NotImplementedError):
raise KeyError(
f"Failed to resolve secret reference for attribute {key}: "
f"The secret {secret_ref.name} does not exist."
)
if secret_ref.key not in secret.values:
raise KeyError(
f"Failed to resolve secret reference for attribute {key}: "
f"The secret {secret_ref.name} does not contain a value "
f"for key {secret_ref.key}. Available keys: "
f"{set(secret.values.keys())}."
)
return secret.secret_values[secret_ref.key]
__init__(self, warn_about_plain_text_secrets=False, **kwargs)
special
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.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
warn_about_plain_text_secrets |
bool |
If true, then warns about using plain-text secrets. |
False |
**kwargs |
Any |
Arguments to initialize this object. |
{} |
Exceptions:
| Type | Description |
|---|---|
ValueError |
If an attribute that requires custom pydantic validation or an attribute which explicitly disallows secret references is passed as a secret reference. |
Source code in zenml/config/secret_reference_mixin.py
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/manage-the-deployed-services/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)
secrets_store_config
Functionality to support ZenML secrets store configurations.
SecretsStoreConfiguration (BaseModel)
Generic secrets store configuration.
The store configurations of concrete secrets store implementations must inherit from this class and validate any extra attributes that are configured in addition to those defined in this class.
Attributes:
| Name | Type | Description |
|---|---|---|
type |
SecretsStoreType |
The type of store backend. |
class_path |
Optional[str] |
The Python class path of the store backend. Should point to
a subclass of |
Source code in zenml/config/secrets_store_config.py
class SecretsStoreConfiguration(BaseModel):
"""Generic secrets store configuration.
The store configurations of concrete secrets store implementations must
inherit from this class and validate any extra attributes that are
configured in addition to those defined in this class.
Attributes:
type: The type of store backend.
class_path: The Python class path of the store backend. Should point to
a subclass of `BaseSecretsStore`. This is optional and only
required if the store backend is not one of the built-in
implementations.
"""
type: SecretsStoreType
class_path: Optional[str] = None
@model_validator(mode="after")
def validate_custom(self) -> "SecretsStoreConfiguration":
"""Validate that class_path is set for custom secrets stores.
Returns:
Validated settings.
Raises:
ValueError: If class_path is not set when using a custom secrets
store.
"""
if not self.type:
return self
if self.type == SecretsStoreType.CUSTOM:
if self.class_path is None:
raise ValueError(
"A class_path must be set when using a custom secrets "
"store implementation."
)
elif self.class_path is not None:
raise ValueError(
f"The class_path attribute is not supported for the "
f"{self.type} secrets store type."
)
return self
model_config = ConfigDict(
# Validate attributes when assigning them. We need to set this in order
# to have a mix of mutable and immutable attributes
validate_assignment=True,
# Allow extra attributes to be set in the base class. The concrete
# classes are responsible for validating the attributes.
extra="allow",
)
validate_custom(self)
Validate that class_path is set for custom secrets stores.
Returns:
| Type | Description |
|---|---|
SecretsStoreConfiguration |
Validated settings. |
Exceptions:
| Type | Description |
|---|---|
ValueError |
If class_path is not set when using a custom secrets store. |
Source code in zenml/config/secrets_store_config.py
@model_validator(mode="after")
def validate_custom(self) -> "SecretsStoreConfiguration":
"""Validate that class_path is set for custom secrets stores.
Returns:
Validated settings.
Raises:
ValueError: If class_path is not set when using a custom secrets
store.
"""
if not self.type:
return self
if self.type == SecretsStoreType.CUSTOM:
if self.class_path is None:
raise ValueError(
"A class_path must be set when using a custom secrets "
"store implementation."
)
elif self.class_path is not None:
raise ValueError(
f"The class_path attribute is not supported for the "
f"{self.type} secrets store type."
)
return self
server_config
Functionality to support ZenML GlobalConfiguration.
ServerConfiguration (BaseModel)
ZenML Server configuration attributes.
All these attributes can be set through the environment with the ZENML_SERVER_-Prefix.
The value of the ZENML_SERVER_DEPLOYMENT_TYPE environment variable will be extracted to deployment_type.
Attributes:
| Name | Type | Description |
|---|---|---|
deployment_type |
ServerDeploymentType |
The type of ZenML server deployment that is running. |
server_url |
Optional[str] |
The URL where the ZenML server API is reachable. Must be configured for features that involve triggering workloads from the ZenML dashboard (e.g., running pipelines). If not specified, the clients will use the same URL used to connect them to the ZenML server. |
dashboard_url |
Optional[str] |
The URL where the ZenML dashboard is reachable.
If not specified, the |
root_url_path |
str |
The root URL path for the ZenML API and dashboard. |
auth_scheme |
AuthScheme |
The authentication scheme used by the ZenML server. |
jwt_token_algorithm |
str |
The algorithm used to sign and verify JWT tokens. |
jwt_token_issuer |
Optional[str] |
The issuer of the JWT tokens. If not specified, the issuer is set to the ZenML Server ID. |
jwt_token_audience |
Optional[str] |
The audience of the JWT tokens. If not specified, the audience is set to the ZenML Server ID. |
jwt_token_leeway_seconds |
int |
The leeway in seconds allowed when verifying the expiration time of JWT tokens. |
jwt_token_expire_minutes |
Optional[int] |
The expiration time of JWT tokens in minutes. If not specified, generated JWT tokens will not be set to expire. |
jwt_secret_key |
str |
The secret key used to sign and verify JWT tokens. If not specified, a random secret key is generated. |
auth_cookie_name |
Optional[str] |
The name of the http-only cookie used to store the JWT token. If not specified, the cookie name is set to a value computed from the ZenML server ID. |
auth_cookie_domain |
Optional[str] |
The domain of the http-only cookie used to store the JWT token. If not specified, the cookie will be valid for the domain where the ZenML server is running. |
cors_allow_origins |
Optional[List[str]] |
The origins allowed to make cross-origin requests to the ZenML server. If not specified, all origins are allowed. |
max_failed_device_auth_attempts |
int |
The maximum number of failed OAuth 2.0 device authentication attempts before the device is locked. |
device_auth_timeout |
int |
The timeout in seconds after which a pending OAuth 2.0 device authorization request expires. |
device_auth_polling_interval |
int |
The polling interval in seconds used to poll the OAuth 2.0 device authorization endpoint. |
device_expiration_minutes |
Optional[int] |
The time in minutes that an OAuth 2.0 device is
allowed to be used to authenticate with the ZenML server. If not
set or if |
trusted_device_expiration_minutes |
Optional[int] |
The time in minutes that a trusted OAuth 2.0
device is allowed to be used to authenticate with the ZenML server.
If not set or if |
external_login_url |
Optional[str] |
The login URL of an external authenticator service
to use with the |
external_user_info_url |
Optional[str] |
The user info URL of an external authenticator
service to use with the |
external_cookie_name |
Optional[str] |
The name of the http-only cookie used to store the
bearer token used to authenticate with the external authenticator
service. Must be specified if the |
external_server_id |
Optional[uuid.UUID] |
The ID of the ZenML server to use with the
|
metadata |
Dict[str, Any] |
Additional metadata to be associated with the ZenML server. |
rbac_implementation_source |
Optional[str] |
Source pointing to a class implementing
the RBAC interface defined by
|
feature_gate_implementation_source |
Optional[str] |
Source pointing to a class
implementing the feature gate interface defined by
|
workload_manager_implementation_source |
Optional[str] |
Source pointing to a class implementing the workload management interface. |
pipeline_run_auth_window |
int |
The default time window in minutes for which a pipeline run action is allowed to authenticate with the ZenML server. |
login_rate_limit_minute |
int |
The number of login attempts allowed per minute. |
login_rate_limit_day |
int |
The number of login attempts allowed per day. |
secure_headers_server |
Union[bool, str] |
Custom value to be set in the |
secure_headers_hsts |
Union[bool, str] |
The server header value to be set in the HTTP
header |
secure_headers_xfo |
Union[bool, str] |
The server header value to be set in the HTTP
header |
secure_headers_xxp |
Union[bool, str] |
The server header value to be set in the HTTP
header |
secure_headers_content |
Union[bool, str] |
The server header value to be set in the HTTP
header |
secure_headers_csp |
Union[bool, str] |
The server header value to be set in the HTTP
header |
secure_headers_referrer |
Union[bool, str] |
The server header value to be set in the HTTP
header |
secure_headers_cache |
Union[bool, str] |
The server header value to be set in the HTTP
header |
secure_headers_permissions |
Union[bool, str] |
The server header value to be set in the
HTTP header |
server_name |
str |
The name of the ZenML server. Used only during initial deployment. Can be changed later as a part of the server settings. |
display_announcements |
bool |
Whether to display announcements about ZenML in the dashboard. Used only during initial deployment. Can be changed later as a part of the server settings. |
display_updates |
bool |
Whether to display notifications about ZenML updates in the dashboard. Used only during initial deployment. Can be changed later as a part of the server settings. |
auto_activate |
bool |
Whether to automatically activate the server and create a default admin user account with an empty password during the initial deployment. |
max_request_body_size_in_bytes |
int |
The maximum size of the request body in bytes. If not specified, the default value of 256 Kb will be used. |
Source code in zenml/config/server_config.py
class ServerConfiguration(BaseModel):
"""ZenML Server configuration attributes.
All these attributes can be set through the environment with the `ZENML_SERVER_`-Prefix.
The value of the `ZENML_SERVER_DEPLOYMENT_TYPE` environment variable will be extracted to deployment_type.
Attributes:
deployment_type: The type of ZenML server deployment that is running.
server_url: The URL where the ZenML server API is reachable. Must be
configured for features that involve triggering workloads from the
ZenML dashboard (e.g., running pipelines). If not specified, the
clients will use the same URL used to connect them to the ZenML
server.
dashboard_url: The URL where the ZenML dashboard is reachable.
If not specified, the `server_url` value is used. This should be
configured if the dashboard is served from a different URL than the
ZenML server.
root_url_path: The root URL path for the ZenML API and dashboard.
auth_scheme: The authentication scheme used by the ZenML server.
jwt_token_algorithm: The algorithm used to sign and verify JWT tokens.
jwt_token_issuer: The issuer of the JWT tokens. If not specified, the
issuer is set to the ZenML Server ID.
jwt_token_audience: The audience of the JWT tokens. If not specified,
the audience is set to the ZenML Server ID.
jwt_token_leeway_seconds: The leeway in seconds allowed when verifying
the expiration time of JWT tokens.
jwt_token_expire_minutes: The expiration time of JWT tokens in minutes.
If not specified, generated JWT tokens will not be set to expire.
jwt_secret_key: The secret key used to sign and verify JWT tokens. If
not specified, a random secret key is generated.
auth_cookie_name: The name of the http-only cookie used to store the JWT
token. If not specified, the cookie name is set to a value computed
from the ZenML server ID.
auth_cookie_domain: The domain of the http-only cookie used to store the
JWT token. If not specified, the cookie will be valid for the
domain where the ZenML server is running.
cors_allow_origins: The origins allowed to make cross-origin requests
to the ZenML server. If not specified, all origins are allowed.
max_failed_device_auth_attempts: The maximum number of failed OAuth 2.0
device authentication attempts before the device is locked.
device_auth_timeout: The timeout in seconds after which a pending OAuth
2.0 device authorization request expires.
device_auth_polling_interval: The polling interval in seconds used to
poll the OAuth 2.0 device authorization endpoint.
device_expiration_minutes: The time in minutes that an OAuth 2.0 device is
allowed to be used to authenticate with the ZenML server. If not
set or if `jwt_token_expire_minutes` is not set, the devices are
allowed to be used indefinitely. This controls the expiration time
of the JWT tokens issued to clients after they have authenticated
with the ZenML server using an OAuth 2.0 device.
trusted_device_expiration_minutes: The time in minutes that a trusted OAuth 2.0
device is allowed to be used to authenticate with the ZenML server.
If not set or if `jwt_token_expire_minutes` is not set, the devices
are allowed to be used indefinitely. This controls the expiration
time of the JWT tokens issued to clients after they have
authenticated with the ZenML server using an OAuth 2.0 device
that has been marked as trusted.
external_login_url: The login URL of an external authenticator service
to use with the `EXTERNAL` authentication scheme.
external_user_info_url: The user info URL of an external authenticator
service to use with the `EXTERNAL` authentication scheme.
external_cookie_name: The name of the http-only cookie used to store the
bearer token used to authenticate with the external authenticator
service. Must be specified if the `EXTERNAL` authentication scheme
is used.
external_server_id: The ID of the ZenML server to use with the
`EXTERNAL` authentication scheme. If not specified, the regular
ZenML server ID is used.
metadata: Additional metadata to be associated with the ZenML server.
rbac_implementation_source: Source pointing to a class implementing
the RBAC interface defined by
`zenml.zen_server.rbac_interface.RBACInterface`. If not specified,
RBAC will not be enabled for this server.
feature_gate_implementation_source: Source pointing to a class
implementing the feature gate interface defined by
`zenml.zen_server.feature_gate.feature_gate_interface.FeatureGateInterface`.
If not specified, feature usage will not be gated/tracked for this
server.
workload_manager_implementation_source: Source pointing to a class
implementing the workload management interface.
pipeline_run_auth_window: The default time window in minutes for which
a pipeline run action is allowed to authenticate with the ZenML
server.
login_rate_limit_minute: The number of login attempts allowed per minute.
login_rate_limit_day: The number of login attempts allowed per day.
secure_headers_server: Custom value to be set in the `Server` HTTP
header to identify the server. If not specified, or if set to one of
the reserved values `enabled`, `yes`, `true`, `on`, the `Server`
header will be set to the default value (ZenML server ID). If set to
one of the reserved values `disabled`, `no`, `none`, `false`, `off`
or to an empty string, the `Server` header will not be included in
responses.
secure_headers_hsts: The server header value to be set in the HTTP
header `Strict-Transport-Security`. If not specified, or if set to
one of the reserved values `enabled`, `yes`, `true`, `on`, the
`Strict-Transport-Security` header will be set to the default value
(`max-age=63072000; includeSubdomains`). If set to one of
the reserved values `disabled`, `no`, `none`, `false`, `off` or to
an empty string, the `Strict-Transport-Security` header will not be
included in responses.
secure_headers_xfo: The server header value to be set in the HTTP
header `X-Frame-Options`. If not specified, or if set to one of the
reserved values `enabled`, `yes`, `true`, `on`, the `X-Frame-Options`
header will be set to the default value (`SAMEORIGIN`). If set to
one of the reserved values `disabled`, `no`, `none`, `false`, `off`
or to an empty string, the `X-Frame-Options` header will not be
included in responses.
secure_headers_xxp: The server header value to be set in the HTTP
header `X-XSS-Protection`. If not specified, or if set to one of the
reserved values `enabled`, `yes`, `true`, `on`, the `X-XSS-Protection`
header will be set to the default value (`0`). If set to one of the
reserved values `disabled`, `no`, `none`, `false`, `off` or
to an empty string, the `X-XSS-Protection` header will not be
included in responses. NOTE: this header is deprecated and should
always be set to `0`. The `Content-Security-Policy` header should be
used instead.
secure_headers_content: The server header value to be set in the HTTP
header `X-Content-Type-Options`. If not specified, or if set to one
of the reserved values `enabled`, `yes`, `true`, `on`, the
`X-Content-Type-Options` header will be set to the default value
(`nosniff`). If set to one of the reserved values `disabled`, `no`,
`none`, `false`, `off` or to an empty string, the
`X-Content-Type-Options` header will not be included in responses.
secure_headers_csp: The server header value to be set in the HTTP
header `Content-Security-Policy`. If not specified, or if set to one
of the reserved values `enabled`, `yes`, `true`, `on`, the
`Content-Security-Policy` header will be set to a default value
that is compatible with the ZenML dashboard. If set to one of the
reserved values `disabled`, `no`, `none`, `false`, `off` or to an
empty string, the `Content-Security-Policy` header will not be
included in responses.
secure_headers_referrer: The server header value to be set in the HTTP
header `Referrer-Policy`. If not specified, or if set to one of the
reserved values `enabled`, `yes`, `true`, `on`, the `Referrer-Policy`
header will be set to the default value
(`no-referrer-when-downgrade`). If set to one of the reserved values
`disabled`, `no`, `none`, `false`, `off` or to an empty string, the
`Referrer-Policy` header will not be included in responses.
secure_headers_cache: The server header value to be set in the HTTP
header `Cache-Control`. If not specified, or if set to one of the
reserved values `enabled`, `yes`, `true`, `on`, the `Cache-Control`
header will be set to the default value
(`no-store, no-cache, must-revalidate`). If set to one of the
reserved values `disabled`, `no`, `none`, `false`, `off` or to an
empty string, the `Cache-Control` header will not be included in
responses.
secure_headers_permissions: The server header value to be set in the
HTTP header `Permissions-Policy`. If not specified, or if set to one
of the reserved values `enabled`, `yes`, `true`, `on`, the
`Permissions-Policy` header will be set to the default value
(`accelerometer=(), camera=(), geolocation=(), gyroscope=(),
magnetometer=(), microphone=(), payment=(), usb=()`). If set to
one of the reserved values `disabled`, `no`, `none`, `false`, `off`
or to an empty string, the `Permissions-Policy` header will not be
included in responses.
server_name: The name of the ZenML server. Used only during initial
deployment. Can be changed later as a part of the server settings.
display_announcements: Whether to display announcements about ZenML in
the dashboard. Used only during initial deployment. Can be changed
later as a part of the server settings.
display_updates: Whether to display notifications about ZenML updates in
the dashboard. Used only during initial deployment. Can be changed
later as a part of the server settings.
auto_activate: Whether to automatically activate the server and create a
default admin user account with an empty password during the initial
deployment.
max_request_body_size_in_bytes: The maximum size of the request body in
bytes. If not specified, the default value of 256 Kb will be used.
"""
deployment_type: ServerDeploymentType = ServerDeploymentType.OTHER
server_url: Optional[str] = None
dashboard_url: Optional[str] = None
root_url_path: str = ""
metadata: Dict[str, Any] = {}
auth_scheme: AuthScheme = AuthScheme.OAUTH2_PASSWORD_BEARER
jwt_token_algorithm: str = DEFAULT_ZENML_JWT_TOKEN_ALGORITHM
jwt_token_issuer: Optional[str] = None
jwt_token_audience: Optional[str] = None
jwt_token_leeway_seconds: int = DEFAULT_ZENML_JWT_TOKEN_LEEWAY
jwt_token_expire_minutes: Optional[int] = None
jwt_secret_key: str = Field(default_factory=generate_jwt_secret_key)
auth_cookie_name: Optional[str] = None
auth_cookie_domain: Optional[str] = None
cors_allow_origins: Optional[List[str]] = None
max_failed_device_auth_attempts: int = (
DEFAULT_ZENML_SERVER_MAX_DEVICE_AUTH_ATTEMPTS
)
device_auth_timeout: int = DEFAULT_ZENML_SERVER_DEVICE_AUTH_TIMEOUT
device_auth_polling_interval: int = (
DEFAULT_ZENML_SERVER_DEVICE_AUTH_POLLING
)
device_expiration_minutes: Optional[int] = None
trusted_device_expiration_minutes: Optional[int] = None
external_login_url: Optional[str] = None
external_user_info_url: Optional[str] = None
external_cookie_name: Optional[str] = None
external_server_id: Optional[UUID] = None
rbac_implementation_source: Optional[str] = None
feature_gate_implementation_source: Optional[str] = None
workload_manager_implementation_source: Optional[str] = None
pipeline_run_auth_window: int = (
DEFAULT_ZENML_SERVER_PIPELINE_RUN_AUTH_WINDOW
)
rate_limit_enabled: bool = False
login_rate_limit_minute: int = DEFAULT_ZENML_SERVER_LOGIN_RATE_LIMIT_MINUTE
login_rate_limit_day: int = DEFAULT_ZENML_SERVER_LOGIN_RATE_LIMIT_DAY
secure_headers_server: Union[bool, str] = Field(
default=True,
union_mode="left_to_right",
)
secure_headers_hsts: Union[bool, str] = Field(
default=DEFAULT_ZENML_SERVER_SECURE_HEADERS_HSTS,
union_mode="left_to_right",
)
secure_headers_xfo: Union[bool, str] = Field(
default=DEFAULT_ZENML_SERVER_SECURE_HEADERS_XFO,
union_mode="left_to_right",
)
secure_headers_xxp: Union[bool, str] = Field(
default=DEFAULT_ZENML_SERVER_SECURE_HEADERS_XXP,
union_mode="left_to_right",
)
secure_headers_content: Union[bool, str] = Field(
default=DEFAULT_ZENML_SERVER_SECURE_HEADERS_CONTENT,
union_mode="left_to_right",
)
secure_headers_csp: Union[bool, str] = Field(
default=DEFAULT_ZENML_SERVER_SECURE_HEADERS_CSP,
union_mode="left_to_right",
)
secure_headers_referrer: Union[bool, str] = Field(
default=DEFAULT_ZENML_SERVER_SECURE_HEADERS_REFERRER,
union_mode="left_to_right",
)
secure_headers_cache: Union[bool, str] = Field(
default=DEFAULT_ZENML_SERVER_SECURE_HEADERS_CACHE,
union_mode="left_to_right",
)
secure_headers_permissions: Union[bool, str] = Field(
default=DEFAULT_ZENML_SERVER_SECURE_HEADERS_PERMISSIONS,
union_mode="left_to_right",
)
server_name: str = DEFAULT_ZENML_SERVER_NAME
display_announcements: bool = True
display_updates: bool = True
auto_activate: bool = False
thread_pool_size: int = DEFAULT_ZENML_SERVER_THREAD_POOL_SIZE
max_request_body_size_in_bytes: int = (
DEFAULT_ZENML_SERVER_MAX_REQUEST_BODY_SIZE_IN_BYTES
)
_deployment_id: Optional[UUID] = None
@model_validator(mode="before")
@classmethod
@before_validator_handler
def _validate_config(cls, data: Dict[str, Any]) -> Dict[str, Any]:
"""Validate the server configuration.
Args:
data: The server configuration values.
Returns:
The validated server configuration values.
Raises:
ValueError: If the server configuration is invalid.
"""
if data.get("auth_scheme") == AuthScheme.EXTERNAL:
# If the authentication scheme is set to `EXTERNAL`, the
# external authenticator URLs must be specified.
if not data.get("external_login_url") or not data.get(
"external_user_info_url"
):
raise ValueError(
"The external login and user info authenticator "
"URLs must be specified when using the EXTERNAL "
"authentication scheme."
)
# If the authentication scheme is set to `EXTERNAL`, the
# external cookie name must be specified.
if not data.get("external_cookie_name"):
raise ValueError(
"The external cookie name must be specified when "
"using the EXTERNAL authentication scheme."
)
if cors_allow_origins := data.get("cors_allow_origins"):
origins = cors_allow_origins.split(",")
data["cors_allow_origins"] = origins
else:
data["cors_allow_origins"] = ["*"]
# if metadata is a string, convert it to a dictionary
if isinstance(data.get("metadata"), str):
try:
data["metadata"] = json.loads(data["metadata"])
except json.JSONDecodeError as e:
raise ValueError(
f"The server metadata is not a valid JSON string: {e}"
)
# if one of the secure headers options is set to a boolean value, set
# the corresponding value
for k, v in data.copy().items():
if k.startswith("secure_headers_") and isinstance(v, str):
if v.lower() in ["disabled", "no", "none", "false", "off", ""]:
data[k] = False
if v.lower() in ["enabled", "yes", "true", "on"]:
# Revert to the default value if the header is enabled
del data[k]
# Handle merging of user-defined secure_headers_csp value with the default value
if "secure_headers_csp" in data:
user_defined_csp = data["secure_headers_csp"]
if isinstance(user_defined_csp, str):
# Parse the user-defined CSP string into a dictionary
user_defined_csp_dict = {}
for directive in user_defined_csp.split(";"):
directive = directive.strip()
if directive:
key, value = directive.split(" ", 1)
user_defined_csp_dict[key] = value.strip("'\"")
# Merge the user-defined CSP dictionary with the default CSP dictionary
default_csp_dict = {}
for directive in DEFAULT_ZENML_SERVER_SECURE_HEADERS_CSP.split(
";"
):
directive = directive.strip()
if directive:
key, value = directive.split(" ", 1)
default_csp_dict[key] = value.strip("'\"")
merged_csp_dict = {**default_csp_dict, **user_defined_csp_dict}
# Convert the merged CSP dictionary back to a string
merged_csp_str = "; ".join(
f"{key} {value}" for key, value in merged_csp_dict.items()
)
data["secure_headers_csp"] = merged_csp_str
return data
@property
def deployment_id(self) -> UUID:
"""Get the ZenML server deployment ID.
Returns:
The ZenML server deployment ID.
"""
from zenml.config.global_config import GlobalConfiguration
if self._deployment_id:
return self._deployment_id
self._deployment_id = (
GlobalConfiguration().zen_store.get_deployment_id()
)
return self._deployment_id
@property
def rbac_enabled(self) -> bool:
"""Whether RBAC is enabled on the server or not.
Returns:
Whether RBAC is enabled on the server or not.
"""
return self.rbac_implementation_source is not None
@property
def feature_gate_enabled(self) -> bool:
"""Whether feature gating is enabled on the server or not.
Returns:
Whether feature gating is enabled on the server or not.
"""
return self.feature_gate_implementation_source is not None
@property
def workload_manager_enabled(self) -> bool:
"""Whether workload management is enabled on the server or not.
Returns:
Whether workload management is enabled on the server or not.
"""
return self.workload_manager_implementation_source is not None
def get_jwt_token_issuer(self) -> str:
"""Get the JWT token issuer.
If not configured, the issuer is set to the ZenML Server ID.
Returns:
The JWT token issuer.
"""
if self.jwt_token_issuer:
return self.jwt_token_issuer
self.jwt_token_issuer = str(self.deployment_id)
return self.jwt_token_issuer
def get_jwt_token_audience(self) -> str:
"""Get the JWT token audience.
If not configured, the audience is set to the ZenML Server ID.
Returns:
The JWT token audience.
"""
if self.jwt_token_audience:
return self.jwt_token_audience
self.jwt_token_audience = str(self.deployment_id)
return self.jwt_token_audience
def get_auth_cookie_name(self) -> str:
"""Get the authentication cookie name.
If not configured, the cookie name is set to a value computed from the
ZenML server ID.
Returns:
The authentication cookie name.
"""
if self.auth_cookie_name:
return self.auth_cookie_name
self.auth_cookie_name = f"zenml-server-{self.deployment_id}"
return self.auth_cookie_name
def get_external_server_id(self) -> UUID:
"""Get the external server ID.
If not configured, the regular ZenML server ID is used.
Returns:
The external server ID.
"""
if self.external_server_id:
return self.external_server_id
self.external_server_id = self.deployment_id
return self.external_server_id
@classmethod
def get_server_config(cls) -> "ServerConfiguration":
"""Get the server configuration.
Returns:
The server configuration.
"""
env_server_config: Dict[str, Any] = {}
for k, v in os.environ.items():
if v == "":
continue
if k.startswith(ENV_ZENML_SERVER_PREFIX):
env_server_config[
k[len(ENV_ZENML_SERVER_PREFIX) :].lower()
] = v
return ServerConfiguration(**env_server_config)
model_config = ConfigDict(
# Allow extra attributes from configs of previous ZenML versions to
# permit downgrading
extra="allow",
)
deployment_id: UUID
property
readonly
Get the ZenML server deployment ID.
Returns:
| Type | Description |
|---|---|
UUID |
The ZenML server deployment ID. |
feature_gate_enabled: bool
property
readonly
Whether feature gating is enabled on the server or not.
Returns:
| Type | Description |
|---|---|
bool |
Whether feature gating is enabled on the server or not. |
rbac_enabled: bool
property
readonly
Whether RBAC is enabled on the server or not.
Returns:
| Type | Description |
|---|---|
bool |
Whether RBAC is enabled on the server or not. |
workload_manager_enabled: bool
property
readonly
Whether workload management is enabled on the server or not.
Returns:
| Type | Description |
|---|---|
bool |
Whether workload management is enabled on the server or not. |
get_auth_cookie_name(self)
Get the authentication cookie name.
If not configured, the cookie name is set to a value computed from the ZenML server ID.
Returns:
| Type | Description |
|---|---|
str |
The authentication cookie name. |
Source code in zenml/config/server_config.py
def get_auth_cookie_name(self) -> str:
"""Get the authentication cookie name.
If not configured, the cookie name is set to a value computed from the
ZenML server ID.
Returns:
The authentication cookie name.
"""
if self.auth_cookie_name:
return self.auth_cookie_name
self.auth_cookie_name = f"zenml-server-{self.deployment_id}"
return self.auth_cookie_name
get_external_server_id(self)
Get the external server ID.
If not configured, the regular ZenML server ID is used.
Returns:
| Type | Description |
|---|---|
UUID |
The external server ID. |
Source code in zenml/config/server_config.py
def get_external_server_id(self) -> UUID:
"""Get the external server ID.
If not configured, the regular ZenML server ID is used.
Returns:
The external server ID.
"""
if self.external_server_id:
return self.external_server_id
self.external_server_id = self.deployment_id
return self.external_server_id
get_jwt_token_audience(self)
Get the JWT token audience.
If not configured, the audience is set to the ZenML Server ID.
Returns:
| Type | Description |
|---|---|
str |
The JWT token audience. |
Source code in zenml/config/server_config.py
def get_jwt_token_audience(self) -> str:
"""Get the JWT token audience.
If not configured, the audience is set to the ZenML Server ID.
Returns:
The JWT token audience.
"""
if self.jwt_token_audience:
return self.jwt_token_audience
self.jwt_token_audience = str(self.deployment_id)
return self.jwt_token_audience
get_jwt_token_issuer(self)
Get the JWT token issuer.
If not configured, the issuer is set to the ZenML Server ID.
Returns:
| Type | Description |
|---|---|
str |
The JWT token issuer. |
Source code in zenml/config/server_config.py
def get_jwt_token_issuer(self) -> str:
"""Get the JWT token issuer.
If not configured, the issuer is set to the ZenML Server ID.
Returns:
The JWT token issuer.
"""
if self.jwt_token_issuer:
return self.jwt_token_issuer
self.jwt_token_issuer = str(self.deployment_id)
return self.jwt_token_issuer
get_server_config()
classmethod
Get the server configuration.
Returns:
| Type | Description |
|---|---|
ServerConfiguration |
The server configuration. |
Source code in zenml/config/server_config.py
@classmethod
def get_server_config(cls) -> "ServerConfiguration":
"""Get the server configuration.
Returns:
The server configuration.
"""
env_server_config: Dict[str, Any] = {}
for k, v in os.environ.items():
if v == "":
continue
if k.startswith(ENV_ZENML_SERVER_PREFIX):
env_server_config[
k[len(ENV_ZENML_SERVER_PREFIX) :].lower()
] = v
return ServerConfiguration(**env_server_config)
model_post_init(/, self, context)
This function is meant to behave like a BaseModel method to initialise private attributes.
It takes context as an argument since that's what pydantic-core passes when calling it.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
self |
BaseModel |
The BaseModel instance. |
required |
context |
Any |
The context. |
required |
Source code in zenml/config/server_config.py
def init_private_attributes(self: BaseModel, context: Any, /) -> None:
"""This function is meant to behave like a BaseModel method to initialise private attributes.
It takes context as an argument since that's what pydantic-core passes when calling it.
Args:
self: The BaseModel instance.
context: The context.
"""
if getattr(self, '__pydantic_private__', None) is None:
pydantic_private = {}
for name, private_attr in self.__private_attributes__.items():
default = private_attr.get_default()
if default is not PydanticUndefined:
pydantic_private[name] = default
object_setattr(self, '__pydantic_private__', pydantic_private)
generate_jwt_secret_key()
Generate a random JWT secret key.
This key is used to sign and verify generated JWT tokens.
Returns:
| Type | Description |
|---|---|
str |
A random JWT secret key. |
Source code in zenml/config/server_config.py
def generate_jwt_secret_key() -> str:
"""Generate a random JWT secret key.
This key is used to sign and verify generated JWT tokens.
Returns:
A random JWT secret key.
"""
return token_hex(32)
settings_resolver
Class for resolving settings.
SettingsResolver
Class for resolving settings.
This class converts a BaseSettings instance to the correct subclass
depending on the key for which these settings were specified.
Source code in zenml/config/settings_resolver.py
class SettingsResolver:
"""Class for resolving settings.
This class converts a `BaseSettings` instance to the correct subclass
depending on the key for which these settings were specified.
"""
def __init__(self, key: str, settings: "BaseSettings"):
"""Checks if the settings key is valid.
Args:
key: Settings key.
settings: The settings.
Raises:
ValueError: If the settings key is invalid.
"""
if not settings_utils.is_valid_setting_key(key):
raise ValueError(
f"Invalid setting key `{key}`. Setting keys can either refer "
"to general settings (available keys: "
f"{set(settings_utils.get_general_settings())}) or stack "
"component specific settings. Stack component specific keys "
"are of the format "
"`<STACK_COMPONENT_TYPE>.<STACK_COMPONENT_FLAVOR>`."
)
self._key = key
self._settings = settings
def resolve(self, stack: "Stack") -> "BaseSettings":
"""Resolves settings for the given stack.
Args:
stack: The stack for which to resolve the settings.
Returns:
The resolved settings.
"""
if settings_utils.is_general_setting_key(self._key):
target_class = self._resolve_general_settings_class()
else:
target_class = self._resolve_stack_component_setting_class(
stack=stack
)
return self._convert_settings(target_class=target_class)
def _resolve_general_settings_class(
self,
) -> Type["BaseSettings"]:
"""Resolves general settings.
Returns:
The resolved settings.
"""
return settings_utils.get_general_settings()[self._key]
def _resolve_stack_component_setting_class(
self, stack: "Stack"
) -> Type["BaseSettings"]:
"""Resolves stack component settings with the given stack.
Args:
stack: The stack to use for resolving.
Raises:
KeyError: If the stack contains no settings for the key.
Returns:
The resolved settings.
"""
settings_class = stack.setting_classes.get(self._key)
if not settings_class:
raise KeyError(
f"Failed to resolve settings for key {self._key}: "
"No settings for this key exist in the stack. "
"Available settings: "
f"{set(stack.setting_classes)}"
)
return settings_class
def _convert_settings(self, target_class: Type["T"]) -> "T":
"""Converts the settings to their correct class.
Args:
target_class: The correct settings class.
Raises:
SettingsResolvingError: If the conversion failed.
Returns:
The converted settings.
"""
settings_dict = dict(self._settings)
try:
return target_class(**settings_dict)
except ValidationError:
raise SettingsResolvingError(
f"Failed to convert settings `{settings_dict}` to expected "
f"class {target_class}."
)
__init__(self, key, settings)
special
Checks if the settings key is valid.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
key |
str |
Settings key. |
required |
settings |
BaseSettings |
The settings. |
required |
Exceptions:
| Type | Description |
|---|---|
ValueError |
If the settings key is invalid. |
Source code in zenml/config/settings_resolver.py
def __init__(self, key: str, settings: "BaseSettings"):
"""Checks if the settings key is valid.
Args:
key: Settings key.
settings: The settings.
Raises:
ValueError: If the settings key is invalid.
"""
if not settings_utils.is_valid_setting_key(key):
raise ValueError(
f"Invalid setting key `{key}`. Setting keys can either refer "
"to general settings (available keys: "
f"{set(settings_utils.get_general_settings())}) or stack "
"component specific settings. Stack component specific keys "
"are of the format "
"`<STACK_COMPONENT_TYPE>.<STACK_COMPONENT_FLAVOR>`."
)
self._key = key
self._settings = settings
resolve(self, stack)
Resolves settings for the given stack.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
stack |
Stack |
The stack for which to resolve the settings. |
required |
Returns:
| Type | Description |
|---|---|
BaseSettings |
The resolved settings. |
Source code in zenml/config/settings_resolver.py
def resolve(self, stack: "Stack") -> "BaseSettings":
"""Resolves settings for the given stack.
Args:
stack: The stack for which to resolve the settings.
Returns:
The resolved settings.
"""
if settings_utils.is_general_setting_key(self._key):
target_class = self._resolve_general_settings_class()
else:
target_class = self._resolve_stack_component_setting_class(
stack=stack
)
return self._convert_settings(target_class=target_class)
source
Source classes.
CodeRepositorySource (Source)
Source representing an object from a code repository.
Attributes:
| Name | Type | Description |
|---|---|---|
repository_id |
UUID |
The code repository ID. |
commit |
str |
The commit. |
subdirectory |
str |
The subdirectory of the source root inside the code repository. |
Source code in zenml/config/source.py
class CodeRepositorySource(Source):
"""Source representing an object from a code repository.
Attributes:
repository_id: The code repository ID.
commit: The commit.
subdirectory: The subdirectory of the source root inside the code
repository.
"""
repository_id: UUID
commit: str
subdirectory: str
type: SourceType = SourceType.CODE_REPOSITORY
@field_validator("type")
@classmethod
def _validate_type(cls, value: SourceType) -> SourceType:
"""Validate the source type.
Args:
value: The source type.
Raises:
ValueError: If the source type is not `CODE_REPOSITORY`.
Returns:
The source type.
"""
if value != SourceType.CODE_REPOSITORY:
raise ValueError("Invalid source type.")
return value
DistributionPackageSource (Source)
Source representing an object from a distribution package.
Attributes:
| Name | Type | Description |
|---|---|---|
package_name |
str |
Name of the package. |
version |
Optional[str] |
The package version. |
Source code in zenml/config/source.py
class DistributionPackageSource(Source):
"""Source representing an object from a distribution package.
Attributes:
package_name: Name of the package.
version: The package version.
"""
package_name: str
version: Optional[str] = None
type: SourceType = SourceType.DISTRIBUTION_PACKAGE
@field_validator("type")
@classmethod
def _validate_type(cls, value: SourceType) -> SourceType:
"""Validate the source type.
Args:
value: The source type.
Raises:
ValueError: If the source type is not `DISTRIBUTION_PACKAGE`.
Returns:
The source type.
"""
if value != SourceType.DISTRIBUTION_PACKAGE:
raise ValueError("Invalid source type.")
return value
NotebookSource (Source)
Source representing an object defined in a notebook.
Attributes:
| Name | Type | Description |
|---|---|---|
replacement_module |
Optional[str] |
Name of the module from which this source should be loaded in case the code is not running in a notebook. |
artifact_store_id |
Optional[uuid.UUID] |
ID of the artifact store in which the replacement module code is stored. |
Source code in zenml/config/source.py
class NotebookSource(Source):
"""Source representing an object defined in a notebook.
Attributes:
replacement_module: Name of the module from which this source should
be loaded in case the code is not running in a notebook.
artifact_store_id: ID of the artifact store in which the replacement
module code is stored.
"""
replacement_module: Optional[str] = None
artifact_store_id: Optional[UUID] = None
type: SourceType = SourceType.NOTEBOOK
@field_validator("type")
@classmethod
def _validate_type(cls, value: SourceType) -> SourceType:
"""Validate the source type.
Args:
value: The source type.
Raises:
ValueError: If the source type is not `NOTEBOOK`.
Returns:
The source type.
"""
if value != SourceType.NOTEBOOK:
raise ValueError("Invalid source type.")
return value
@field_validator("module")
@classmethod
def _validate_module(cls, value: str) -> str:
"""Validate the module.
Args:
value: The module.
Raises:
ValueError: If the module is not `__main__`.
Returns:
The module.
"""
if value != "__main__":
raise ValueError("Invalid module for notebook source.")
return value
Source (BaseModel)
Source specification.
A source specifies a module name as well as an optional attribute of that module. These values can be used to import the module and get the value of the attribute inside the module.
Examples:
The source Source(module="zenml.config.source", attribute="Source")
references the class that this docstring is describing. This class is
defined in the zenml.config.source module and the name of the
attribute is the class name Source.
Attributes:
| Name | Type | Description |
|---|---|---|
module |
str |
The module name. |
attribute |
Optional[str] |
Optional name of the attribute inside the module. |
type |
SourceType |
The type of the source. |
Source code in zenml/config/source.py
class Source(BaseModel):
"""Source specification.
A source specifies a module name as well as an optional attribute of that
module. These values can be used to import the module and get the value
of the attribute inside the module.
Example:
The source `Source(module="zenml.config.source", attribute="Source")`
references the class that this docstring is describing. This class is
defined in the `zenml.config.source` module and the name of the
attribute is the class name `Source`.
Attributes:
module: The module name.
attribute: Optional name of the attribute inside the module.
type: The type of the source.
"""
module: str
attribute: Optional[str] = None
type: SourceType
@classmethod
def from_import_path(
cls, import_path: str, is_module_path: bool = False
) -> "Source":
"""Creates a source from an import path.
Args:
import_path: The import path.
is_module_path: If the import path points to a module or not.
Raises:
ValueError: If the import path is empty.
Returns:
The source.
"""
if not import_path:
raise ValueError(
"Invalid empty import path. The import path needs to refer "
"to a Python module and an optional attribute of that module."
)
# Remove internal version pins for backwards compatibility
if "@" in import_path:
import_path = import_path.split("@", 1)[0]
if is_module_path or "." not in import_path:
module = import_path
attribute = None
else:
module, attribute = import_path.rsplit(".", maxsplit=1)
return Source(
module=module, attribute=attribute, type=SourceType.UNKNOWN
)
@property
def import_path(self) -> str:
"""The import path of the source.
Returns:
The import path of the source.
"""
if self.attribute:
return f"{self.module}.{self.attribute}"
else:
return self.module
@property
def is_internal(self) -> bool:
"""If the source is internal (=from the zenml package).
Returns:
True if the source is internal, False otherwise
"""
if self.type not in {SourceType.UNKNOWN, SourceType.INTERNAL}:
return False
return self.module.split(".", maxsplit=1)[0] == "zenml"
@property
def is_module_source(self) -> bool:
"""If the source is a module source.
Returns:
If the source is a module source.
"""
return self.attribute is None
model_config = ConfigDict(extra="allow")
def model_dump(self, **kwargs: Any) -> Dict[str, Any]:
"""Dump the source as a dictionary.
Args:
**kwargs: Additional keyword arguments.
Returns:
The source as a dictionary.
"""
return super().model_dump(serialize_as_any=True, **kwargs)
def model_dump_json(self, **kwargs: Any) -> str:
"""Dump the source as a JSON string.
Args:
**kwargs: Additional keyword arguments.
Returns:
The source as a JSON string.
"""
return super().model_dump_json(serialize_as_any=True, **kwargs)
import_path: str
property
readonly
The import path of the source.
Returns:
| Type | Description |
|---|---|
str |
The import path of the source. |
is_internal: bool
property
readonly
If the source is internal (=from the zenml package).
Returns:
| Type | Description |
|---|---|
bool |
True if the source is internal, False otherwise |
is_module_source: bool
property
readonly
If the source is a module source.
Returns:
| Type | Description |
|---|---|
bool |
If the source is a module source. |
from_import_path(import_path, is_module_path=False)
classmethod
Creates a source from an import path.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
import_path |
str |
The import path. |
required |
is_module_path |
bool |
If the import path points to a module or not. |
False |
Exceptions:
| Type | Description |
|---|---|
ValueError |
If the import path is empty. |
Returns:
| Type | Description |
|---|---|
Source |
The source. |
Source code in zenml/config/source.py
@classmethod
def from_import_path(
cls, import_path: str, is_module_path: bool = False
) -> "Source":
"""Creates a source from an import path.
Args:
import_path: The import path.
is_module_path: If the import path points to a module or not.
Raises:
ValueError: If the import path is empty.
Returns:
The source.
"""
if not import_path:
raise ValueError(
"Invalid empty import path. The import path needs to refer "
"to a Python module and an optional attribute of that module."
)
# Remove internal version pins for backwards compatibility
if "@" in import_path:
import_path = import_path.split("@", 1)[0]
if is_module_path or "." not in import_path:
module = import_path
attribute = None
else:
module, attribute = import_path.rsplit(".", maxsplit=1)
return Source(
module=module, attribute=attribute, type=SourceType.UNKNOWN
)
model_dump(self, **kwargs)
Dump the source as a dictionary.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
**kwargs |
Any |
Additional keyword arguments. |
{} |
Returns:
| Type | Description |
|---|---|
Dict[str, Any] |
The source as a dictionary. |
Source code in zenml/config/source.py
def model_dump(self, **kwargs: Any) -> Dict[str, Any]:
"""Dump the source as a dictionary.
Args:
**kwargs: Additional keyword arguments.
Returns:
The source as a dictionary.
"""
return super().model_dump(serialize_as_any=True, **kwargs)
model_dump_json(self, **kwargs)
Dump the source as a JSON string.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
**kwargs |
Any |
Additional keyword arguments. |
{} |
Returns:
| Type | Description |
|---|---|
str |
The source as a JSON string. |
Source code in zenml/config/source.py
def model_dump_json(self, **kwargs: Any) -> str:
"""Dump the source as a JSON string.
Args:
**kwargs: Additional keyword arguments.
Returns:
The source as a JSON string.
"""
return super().model_dump_json(serialize_as_any=True, **kwargs)
SourceType (Enum)
Enum representing different types of sources.
Source code in zenml/config/source.py
class SourceType(Enum):
"""Enum representing different types of sources."""
USER = "user"
BUILTIN = "builtin"
INTERNAL = "internal"
DISTRIBUTION_PACKAGE = "distribution_package"
CODE_REPOSITORY = "code_repository"
NOTEBOOK = "notebook"
UNKNOWN = "unknown"
convert_source(source)
Converts an old source string to a source object.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
source |
Any |
Source string or object. |
required |
Returns:
| Type | Description |
|---|---|
Any |
The converted source. |
Source code in zenml/config/source.py
def convert_source(source: Any) -> Any:
"""Converts an old source string to a source object.
Args:
source: Source string or object.
Returns:
The converted source.
"""
if isinstance(source, str):
source = Source.from_import_path(source)
return source
step_configurations
Pipeline configuration classes.
ArtifactConfiguration (PartialArtifactConfiguration)
Class representing a complete input/output artifact configuration.
Source code in zenml/config/step_configurations.py
class ArtifactConfiguration(PartialArtifactConfiguration):
"""Class representing a complete input/output artifact configuration."""
materializer_source: Tuple[SourceWithValidator, ...]
@field_validator("materializer_source", mode="before")
@classmethod
def _convert_source(
cls, value: Union[Source, Dict[str, Any], str, Tuple[Source, ...]]
) -> Tuple[Source, ...]:
"""Converts old source strings to tuples of source objects.
Args:
value: Source string or object.
Returns:
The converted source.
"""
if isinstance(value, str):
value = (Source.from_import_path(value),)
elif isinstance(value, dict):
value = (Source.model_validate(value),)
elif isinstance(value, Source):
value = (value,)
return value
InputSpec (StrictBaseModel)
Step input specification.
Source code in zenml/config/step_configurations.py
class InputSpec(StrictBaseModel):
"""Step input specification."""
step_name: str
output_name: str
PartialArtifactConfiguration (StrictBaseModel)
Class representing a partial input/output artifact configuration.
Source code in zenml/config/step_configurations.py
class PartialArtifactConfiguration(StrictBaseModel):
"""Class representing a partial input/output artifact configuration."""
materializer_source: Optional[Tuple[SourceWithValidator, ...]] = None
# TODO: This could be moved to the `PipelineDeployment` as it's the same
# for all steps/outputs
default_materializer_source: Optional[SourceWithValidator] = None
artifact_config: Optional[ArtifactConfig] = None
@model_validator(mode="before")
@classmethod
@before_validator_handler
def _remove_deprecated_attributes(
cls, data: Dict[str, Any]
) -> Dict[str, Any]:
"""Removes deprecated attributes from the values dict.
Args:
data: The values dict used to instantiate the model.
Returns:
The values dict without deprecated attributes.
"""
deprecated_attributes = ["artifact_source"]
for deprecated_attribute in deprecated_attributes:
if deprecated_attribute in data:
data.pop(deprecated_attribute)
return data
@field_validator("materializer_source", mode="before")
@classmethod
def _convert_source(
cls,
value: Union[None, Source, Dict[str, Any], str, Tuple[Source, ...]],
) -> Optional[Tuple[Source, ...]]:
"""Converts old source strings to tuples of source objects.
Args:
value: Source string or object.
Returns:
The converted source.
"""
if isinstance(value, str):
value = (Source.from_import_path(value),)
elif isinstance(value, dict):
value = (Source.model_validate(value),)
elif isinstance(value, Source):
value = (value,)
return value
PartialStepConfiguration (StepConfigurationUpdate)
Class representing a partial step configuration.
Source code in zenml/config/step_configurations.py
class PartialStepConfiguration(StepConfigurationUpdate):
"""Class representing a partial step configuration."""
name: str
caching_parameters: Mapping[str, Any] = {}
external_input_artifacts: Mapping[str, ExternalArtifactConfiguration] = {}
model_artifacts_or_metadata: Mapping[str, ModelVersionDataLazyLoader] = {}
client_lazy_loaders: Mapping[str, ClientLazyLoader] = {}
outputs: Mapping[str, PartialArtifactConfiguration] = {}
# TODO: In Pydantic v2, the `model_` is a protected namespaces for all
# fields defined under base models. If not handled, this raises a warning.
# It is possible to suppress this warning message with the following
# configuration, however the ultimate solution is to rename these fields.
# Even though they do not cause any problems right now, if we are not
# careful we might overwrite some fields protected by pydantic.
model_config = ConfigDict(protected_namespaces=())
# Override the deprecation validator as we do not want to deprecate the
# `name`` attribute on this class.
_deprecation_validator = deprecation_utils.deprecate_pydantic_attributes()
@model_validator(mode="before")
@classmethod
@before_validator_handler
def _remove_deprecated_attributes(cls, data: Any) -> Any:
"""Removes deprecated attributes from the values dict.
Args:
data: The values dict used to instantiate the model.
Returns:
The values dict without deprecated attributes.
"""
deprecated_attributes = ["docstring", "inputs"]
for deprecated_attribute in deprecated_attributes:
if deprecated_attribute in data:
data.pop(deprecated_attribute)
return data
Step (StrictBaseModel)
Class representing a ZenML step.
Source code in zenml/config/step_configurations.py
class Step(StrictBaseModel):
"""Class representing a ZenML step."""
spec: StepSpec
config: StepConfiguration
StepConfiguration (PartialStepConfiguration)
Step configuration class.
Source code in zenml/config/step_configurations.py
class StepConfiguration(PartialStepConfiguration):
"""Step configuration class."""
outputs: Mapping[str, ArtifactConfiguration] = {}
@property
def resource_settings(self) -> "ResourceSettings":
"""Resource settings of this step configuration.
Returns:
The resource settings of this step configuration.
"""
from zenml.config import ResourceSettings
model_or_dict: SettingsOrDict = self.settings.get(
RESOURCE_SETTINGS_KEY, {}
)
if isinstance(model_or_dict, BaseSettings):
model_or_dict = model_or_dict.model_dump()
return ResourceSettings.model_validate(model_or_dict)
@property
def docker_settings(self) -> "DockerSettings":
"""Docker settings of this step configuration.
Returns:
The Docker settings of this step configuration.
"""
from zenml.config import DockerSettings
model_or_dict: SettingsOrDict = self.settings.get(
DOCKER_SETTINGS_KEY, {}
)
if isinstance(model_or_dict, BaseSettings):
model_or_dict = model_or_dict.model_dump()
return DockerSettings.model_validate(model_or_dict)
docker_settings: DockerSettings
property
readonly
Docker settings of this step configuration.
Returns:
| Type | Description |
|---|---|
DockerSettings |
The Docker settings of this step configuration. |
resource_settings: ResourceSettings
property
readonly
Resource settings of this step configuration.
Returns:
| Type | Description |
|---|---|
ResourceSettings |
The resource settings of this step configuration. |
StepConfigurationUpdate (StrictBaseModel)
Class for step configuration updates.
Source code in zenml/config/step_configurations.py
class StepConfigurationUpdate(StrictBaseModel):
"""Class for step configuration updates."""
enable_cache: Optional[bool] = None
enable_artifact_metadata: Optional[bool] = None
enable_artifact_visualization: Optional[bool] = None
enable_step_logs: Optional[bool] = None
step_operator: Optional[str] = None
experiment_tracker: Optional[str] = None
parameters: Dict[str, Any] = {}
settings: Dict[str, SerializeAsAny[BaseSettings]] = {}
extra: Dict[str, Any] = {}
failure_hook_source: Optional[SourceWithValidator] = None
success_hook_source: Optional[SourceWithValidator] = None
model: Optional[Model] = None
retry: Optional[StepRetryConfig] = None
outputs: Mapping[str, PartialArtifactConfiguration] = {}
StepSpec (StrictBaseModel)
Specification of a pipeline.
Source code in zenml/config/step_configurations.py
class StepSpec(StrictBaseModel):
"""Specification of a pipeline."""
source: SourceWithValidator
upstream_steps: List[str]
inputs: Dict[str, InputSpec] = {}
# The default value is to ensure compatibility with specs of version <0.2
pipeline_parameter_name: str = ""
def __eq__(self, other: Any) -> bool:
"""Returns whether the other object is referring to the same step.
This is the case if the other objects is a `StepSpec` with the same
`upstream_steps` and a `source` that meets one of the following
conditions:
- it is the same as the `source` of this step
- it refers to the same absolute path as the `source` of this step
Args:
other: The other object to compare to.
Returns:
True if the other object is referring to the same step.
"""
if isinstance(other, StepSpec):
if self.upstream_steps != other.upstream_steps:
return False
if self.inputs != other.inputs:
return False
if self.pipeline_parameter_name != other.pipeline_parameter_name:
return False
return self.source.import_path == other.source.import_path
return NotImplemented
__eq__(self, other)
special
Returns whether the other object is referring to the same step.
This is the case if the other objects is a StepSpec with the same
upstream_steps and a source that meets one of the following
!!! conditions
- it is the same as the source of this step
- it refers to the same absolute path as the source of this step
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
other |
Any |
The other object to compare to. |
required |
Returns:
| Type | Description |
|---|---|
bool |
True if the other object is referring to the same step. |
Source code in zenml/config/step_configurations.py
def __eq__(self, other: Any) -> bool:
"""Returns whether the other object is referring to the same step.
This is the case if the other objects is a `StepSpec` with the same
`upstream_steps` and a `source` that meets one of the following
conditions:
- it is the same as the `source` of this step
- it refers to the same absolute path as the `source` of this step
Args:
other: The other object to compare to.
Returns:
True if the other object is referring to the same step.
"""
if isinstance(other, StepSpec):
if self.upstream_steps != other.upstream_steps:
return False
if self.inputs != other.inputs:
return False
if self.pipeline_parameter_name != other.pipeline_parameter_name:
return False
return self.source.import_path == other.source.import_path
return NotImplemented
step_run_info
Step run info.
StepRunInfo (StrictBaseModel)
All information necessary to run a step.
Source code in zenml/config/step_run_info.py
class StepRunInfo(StrictBaseModel):
"""All information necessary to run a step."""
step_run_id: UUID
run_id: UUID
run_name: str
pipeline_step_name: str
config: StepConfiguration
pipeline: PipelineConfiguration
force_write_logs: Callable[..., Any]
def get_image(self, key: str) -> str:
"""Gets the Docker image for the given key.
Args:
key: The key for which to get the image.
Raises:
RuntimeError: If the run does not have an associated build.
Returns:
The image name or digest.
"""
from zenml.client import Client
run = Client().get_pipeline_run(self.run_id)
if not run.build:
raise RuntimeError(
f"Missing build for run {run.id}. This is probably because "
"the build was manually deleted."
)
return run.build.get_image(
component_key=key, step=self.pipeline_step_name
)
get_image(self, key)
Gets the Docker image for the given key.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
key |
str |
The key for which to get the image. |
required |
Exceptions:
| Type | Description |
|---|---|
RuntimeError |
If the run does not have an associated build. |
Returns:
| Type | Description |
|---|---|
str |
The image name or digest. |
Source code in zenml/config/step_run_info.py
def get_image(self, key: str) -> str:
"""Gets the Docker image for the given key.
Args:
key: The key for which to get the image.
Raises:
RuntimeError: If the run does not have an associated build.
Returns:
The image name or digest.
"""
from zenml.client import Client
run = Client().get_pipeline_run(self.run_id)
if not run.build:
raise RuntimeError(
f"Missing build for run {run.id}. This is probably because "
"the build was manually deleted."
)
return run.build.get_image(
component_key=key, step=self.pipeline_step_name
)
store_config
Functionality to support ZenML store configurations.
StoreConfiguration (BaseModel)
Generic store configuration.
The store configurations of concrete store implementations must inherit from this class and validate any extra attributes that are configured in addition to those defined in this class.
Attributes:
| Name | Type | Description |
|---|---|---|
type |
StoreType |
The type of store backend. |
url |
str |
The URL of the store backend. |
secrets_store |
Optional[zenml.config.secrets_store_config.SecretsStoreConfiguration] |
The configuration of the secrets store to use to store secrets. If not set, secrets management is disabled. |
backup_secrets_store |
Optional[zenml.config.secrets_store_config.SecretsStoreConfiguration] |
The configuration of the secrets store to use to store backups of secrets. If not set, backup and restore of secrets are disabled. |
Source code in zenml/config/store_config.py
class StoreConfiguration(BaseModel):
"""Generic store configuration.
The store configurations of concrete store implementations must inherit from
this class and validate any extra attributes that are configured in addition
to those defined in this class.
Attributes:
type: The type of store backend.
url: The URL of the store backend.
secrets_store: The configuration of the secrets store to use to store
secrets. If not set, secrets management is disabled.
backup_secrets_store: The configuration of the secrets store to use to
store backups of secrets. If not set, backup and restore of secrets
are disabled.
"""
type: StoreType
url: str
secrets_store: Optional[SerializeAsAny[SecretsStoreConfiguration]] = None
backup_secrets_store: Optional[
SerializeAsAny[SecretsStoreConfiguration]
] = None
@classmethod
def supports_url_scheme(cls, url: str) -> bool:
"""Check if a URL scheme is supported by this store.
Concrete store configuration classes should override this method to
check if a URL scheme is supported by the store.
Args:
url: The URL to check.
Returns:
True if the URL scheme is supported, False otherwise.
"""
return True
@model_validator(mode="before")
@classmethod
@before_validator_handler
def validate_store_config(cls, data: Dict[str, Any]) -> Dict[str, Any]:
"""Validate the secrets store configuration.
Args:
data: The values of the store configuration.
Returns:
The values of the store configuration.
"""
if data.get("secrets_store") is None:
return data
# Remove the legacy REST secrets store configuration since it is no
# longer supported/needed
secrets_store = data["secrets_store"]
if isinstance(secrets_store, dict):
secrets_store_type = secrets_store.get("type")
if secrets_store_type == "rest":
del data["secrets_store"]
return data
model_config = ConfigDict(
# Validate attributes when assigning them. We need to set this in order
# to have a mix of mutable and immutable attributes
validate_assignment=True,
# Allow extra attributes to be set in the base class. The concrete
# classes are responsible for validating the attributes.
extra="allow",
)
supports_url_scheme(url)
classmethod
Check if a URL scheme is supported by this store.
Concrete store configuration classes should override this method to check if a URL scheme is supported by the store.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
The URL to check. |
required |
Returns:
| Type | Description |
|---|---|
bool |
True if the URL scheme is supported, False otherwise. |
Source code in zenml/config/store_config.py
@classmethod
def supports_url_scheme(cls, url: str) -> bool:
"""Check if a URL scheme is supported by this store.
Concrete store configuration classes should override this method to
check if a URL scheme is supported by the store.
Args:
url: The URL to check.
Returns:
True if the URL scheme is supported, False otherwise.
"""
return True
validate_store_config(data, validation_info)
classmethod
Wrapper method to handle the raw data.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
cls |
the class handler |
required | |
data |
Any |
the raw input data |
required |
validation_info |
ValidationInfo |
the context of the validation. |
required |
Returns:
| Type | Description |
|---|---|
Any |
the validated data |
Source code in zenml/config/store_config.py
def before_validator(
cls: Type[BaseModel], data: Any, validation_info: ValidationInfo
) -> Any:
"""Wrapper method to handle the raw data.
Args:
cls: the class handler
data: the raw input data
validation_info: the context of the validation.
Returns:
the validated data
"""
data = model_validator_data_handler(
raw_data=data, base_class=cls, validation_info=validation_info
)
return method(cls=cls, data=data)
strict_base_model
Strict immutable pydantic model.
StrictBaseModel (BaseModel)
Immutable pydantic model which prevents extra attributes.
Source code in zenml/config/strict_base_model.py
class StrictBaseModel(BaseModel):
"""Immutable pydantic model which prevents extra attributes."""
model_config = ConfigDict(frozen=True, extra="forbid")