Steps
zenml.steps
special
Initializer for ZenML steps.
A step is a single piece or stage of a ZenML pipeline. Think of each step as being one of the nodes of a Directed Acyclic Graph (or DAG). Steps are responsible for one aspect of processing or interacting with the data / artifacts in the pipeline.
ZenML currently implements a basic step interface, but there will be other more customized interfaces (layered in a hierarchy) for specialized implementations. Conceptually, a Step is a discrete and independent part of a pipeline that is responsible for one particular aspect of data manipulation inside a ZenML pipeline.
Steps can be subclassed from the BaseStep class, or used via our @step
decorator.
base_step
Base Step for ZenML.
BaseStep
Abstract base class for all ZenML steps.
Attributes:
| Name | Type | Description |
|---|---|---|
name |
The name of this step. |
|
pipeline_parameter_name |
Optional[str] |
The name of the pipeline parameter for which this step was passed as an argument. |
enable_cache |
A boolean indicating if caching is enabled for this step. |
|
custom_step_operator |
Optional name of a custom step operator to use for this step. |
|
requires_context |
A boolean indicating if this step requires a
|
Source code in zenml/steps/base_step.py
class BaseStep(metaclass=BaseStepMeta):
"""Abstract base class for all ZenML steps.
Attributes:
name: The name of this step.
pipeline_parameter_name: The name of the pipeline parameter for which
this step was passed as an argument.
enable_cache: A boolean indicating if caching is enabled for this step.
custom_step_operator: Optional name of a custom step operator to use
for this step.
requires_context: A boolean indicating if this step requires a
`StepContext` object during execution.
"""
# TODO [ENG-156]: Ensure these are ordered
INPUT_SIGNATURE: ClassVar[Dict[str, Type[Any]]] = None # type: ignore[assignment] # noqa
OUTPUT_SIGNATURE: ClassVar[Dict[str, Type[Any]]] = None # type: ignore[assignment] # noqa
CONFIG_PARAMETER_NAME: ClassVar[Optional[str]] = None
CONFIG_CLASS: ClassVar[Optional[Type[BaseStepConfig]]] = None
CONTEXT_PARAMETER_NAME: ClassVar[Optional[str]] = None
PARAM_SPEC: Dict[str, Any] = {}
INPUT_SPEC: Dict[str, Type[BaseArtifact]] = {}
OUTPUT_SPEC: Dict[str, Type[BaseArtifact]] = {}
INSTANCE_CONFIGURATION: Dict[str, Any] = {}
def __init__(self, *args: Any, **kwargs: Any) -> None:
"""Initializes a step.
Args:
*args: Positional arguments passed to the step.
**kwargs: Keyword arguments passed to the step.
"""
self.name = self.__class__.__name__
self.pipeline_parameter_name: Optional[str] = None
kwargs.update(getattr(self, INSTANCE_CONFIGURATION))
# This value is only used in `BaseStep.__created_by_functional_api()`
kwargs.pop(PARAM_CREATED_BY_FUNCTIONAL_API, None)
self.requires_context = bool(self.CONTEXT_PARAMETER_NAME)
self.custom_step_operator = kwargs.pop(PARAM_CUSTOM_STEP_OPERATOR, None)
self._resource_configuration = (
kwargs.pop(PARAM_RESOURCE_CONFIGURATION, None)
or ResourceConfiguration()
)
enable_cache = kwargs.pop(PARAM_ENABLE_CACHE, None)
if enable_cache is None:
if self.requires_context:
# Using the StepContext inside a step provides access to
# external resources which might influence the step execution.
# We therefore disable caching unless it is explicitly enabled
enable_cache = False
logger.debug(
"Step '%s': Step context required and caching not "
"explicitly enabled.",
self.name,
)
else:
# Default to cache enabled if not explicitly set
enable_cache = True
logger.debug(
"Step '%s': Caching %s.",
self.name,
"enabled" if enable_cache else "disabled",
)
self.enable_cache = enable_cache
self._explicit_materializers: Dict[str, Type[BaseMaterializer]] = {}
self._component: Optional[_ZenMLSimpleComponent] = None
self._has_been_called = False
self._upstream_steps: Set[str] = set()
self._verify_init_arguments(*args, **kwargs)
self._verify_output_spec()
@abstractmethod
def entrypoint(self, *args: Any, **kwargs: Any) -> Any:
"""Abstract method for core step logic.
Args:
*args: Positional arguments passed to the step.
**kwargs: Keyword arguments passed to the step.
Returns:
The output of the step.
"""
@classmethod
def _created_by_functional_api(cls) -> bool:
"""Returns if the step class was created by the functional API.
Returns:
`True` if the class was created by the functional API,
`False` otherwise.
"""
return cls.INSTANCE_CONFIGURATION.get(
PARAM_CREATED_BY_FUNCTIONAL_API, False
)
def get_materializers(
self, ensure_complete: bool = False
) -> Dict[str, Type[BaseMaterializer]]:
"""Returns available materializers for the outputs of this step.
Args:
ensure_complete: If set to `True`, this method will raise a
`StepInterfaceError` if no materializer can be found for an
output.
Returns:
A dictionary mapping output names to `BaseMaterializer` subclasses.
If no explicit materializer was set using
`step.with_return_materializers(...)`, this checks the
default materializer registry to find a materializer for the
type of the output. If no materializer is registered, the
output of this method will not contain an entry for this output.
Raises:
StepInterfaceError: (Only if `ensure_complete` is set to `True`)
If an output does not have an explicit materializer assigned
to it and there is no default materializer registered for
the output type.
"""
materializers = self._explicit_materializers.copy()
for output_name, output_type in self.OUTPUT_SIGNATURE.items():
if output_name in materializers:
# Materializer for this output was set explicitly
pass
elif default_materializer_registry.is_registered(output_type):
materializer = default_materializer_registry[output_type]
materializers[output_name] = materializer
else:
if ensure_complete:
raise StepInterfaceError(
f"Unable to find materializer for output "
f"'{output_name}' of type `{output_type}` in step "
f"'{self.name}'. Please make sure to either "
f"explicitly set a materializer for step outputs "
f"using `step.with_return_materializers(...)` or "
f"registering a default materializer for specific "
f"types by subclassing `BaseMaterializer` and setting "
f"its `ASSOCIATED_TYPES` class variable.",
url="https://docs.zenml.io/developer-guide/advanced-usage/materializer",
)
return materializers
@property
def upstream_steps(self) -> Set[str]:
"""Names of the upstream steps of this step.
This property will only contain the full set of upstream steps once
it's parent pipeline `connect(...)` method was called.
Returns:
Set of upstream step names.
"""
return self._upstream_steps
def after(self, step: "BaseStep") -> None:
"""Adds an upstream step to the this step.
Calling this method makes sure this step only starts running once the
given step has successfully finished executing.
**Note**: This can only be called inside the pipeline connect function
which is decorated with the `@pipeline` decorator. Any calls outside
this function will be ignored.
Example:
The following pipeline will run its steps sequentially in the following
order: step_2 -> step_1 -> step_3
```python
@pipeline
def example_pipeline(step_1, step_2, step_3):
step_1.after(step_2)
step_3(step_1(), step_2())
```
Args:
step: A step which should finish executing before this step is
started.
"""
self._upstream_steps.add(step.name)
def _reset(self) -> None:
"""Resets internal values that get set during a pipeline run."""
self._component = None
self._has_been_called = False
self._upstream_steps.clear()
@property
def _internal_execution_parameters(self) -> Dict[str, Any]:
"""Internal ZenML execution parameters for this step.
Returns:
A dictionary containing the ZenML internal execution parameters
"""
parameters = {
PARAM_PIPELINE_PARAMETER_NAME: self.pipeline_parameter_name,
PARAM_CUSTOM_STEP_OPERATOR: self.custom_step_operator,
}
if self.enable_cache:
# Caching is enabled so we compute a hash of the step function code
# and materializers to catch changes in the step behavior
# If the step was defined using the functional api, only track
# changes to the entrypoint function. Otherwise track changes to
# the entire step class.
source_object = (
self.entrypoint
if self._created_by_functional_api()
else self.__class__
)
parameters["step_source"] = get_hashed_source(source_object)
for name, materializer in self.get_materializers().items():
key = f"{name}_materializer_source"
parameters[key] = get_hashed_source(materializer)
else:
# Add a random string to the execution properties to disable caching
random_string = f"{random.getrandbits(128):032x}"
parameters["disable_cache"] = random_string
return {
INTERNAL_EXECUTION_PARAMETER_PREFIX + key: value
for key, value in parameters.items()
}
def _verify_init_arguments(self, *args: Any, **kwargs: Any) -> None:
"""Verifies the initialization args and kwargs of this step.
This method makes sure that there is only a config object passed at
initialization and that it was passed using the correct name and
type specified in the step declaration.
If the correct config object was found, additionally saves the
config parameters to `self.PARAM_SPEC`.
Args:
*args: The args passed to the init method of this step.
**kwargs: The kwargs passed to the init method of this step.
Raises:
StepInterfaceError: If there are too many arguments or arguments
with a wrong name/type.
"""
maximum_arg_count = 1 if self.CONFIG_CLASS else 0
arg_count = len(args) + len(kwargs)
if arg_count > maximum_arg_count:
raise StepInterfaceError(
f"Too many arguments ({arg_count}, expected: "
f"{maximum_arg_count}) passed when creating a "
f"'{self.name}' step."
)
if self.CONFIG_PARAMETER_NAME and self.CONFIG_CLASS:
if args:
config = args[0]
elif kwargs:
key, config = kwargs.popitem()
if key != self.CONFIG_PARAMETER_NAME:
raise StepInterfaceError(
f"Unknown keyword argument '{key}' when creating a "
f"'{self.name}' step, only expected a single "
f"argument with key '{self.CONFIG_PARAMETER_NAME}'."
)
else:
# This step requires configuration parameters but no config
# object was passed as an argument. The parameters might be
# set via default values in the config class or in a
# configuration file, so we continue for now and verify
# that all parameters are set before running the step
return
if not isinstance(config, self.CONFIG_CLASS):
raise StepInterfaceError(
f"`{config}` object passed when creating a "
f"'{self.name}' step is not a "
f"`{self.CONFIG_CLASS.__name__}` instance."
)
self.PARAM_SPEC = config.dict()
def _verify_output_spec(self) -> None:
"""Verifies the explicitly set output artifact types of this step.
Raises:
StepInterfaceError: If an output artifact type is specified for a
non-existent step output or the artifact type is not allowed
for the corresponding output type.
"""
for output_name, artifact_type in self.OUTPUT_SPEC.items():
if output_name not in self.OUTPUT_SIGNATURE:
raise StepInterfaceError(
f"Found explicit artifact type for unrecognized output "
f"'{output_name}' in step '{self.name}'. Output "
f"artifact types can only be specified for the outputs "
f"of this step: {set(self.OUTPUT_SIGNATURE)}."
)
if not issubclass(artifact_type, BaseArtifact):
raise StepInterfaceError(
f"Invalid artifact type ({artifact_type}) for output "
f"'{output_name}' of step '{self.name}'. Only "
f"`BaseArtifact` subclasses are allowed as artifact types."
)
output_type = self.OUTPUT_SIGNATURE[output_name]
allowed_artifact_types = set(
type_registry.get_artifact_type(output_type)
)
if artifact_type not in allowed_artifact_types:
raise StepInterfaceError(
f"Artifact type `{artifact_type}` for output "
f"'{output_name}' of step '{self.name}' is not an "
f"allowed artifact type for the defined output type "
f"`{output_type}`. Allowed artifact types: "
f"{allowed_artifact_types}. If you want to extend the "
f"allowed artifact types, implement a custom "
f"`BaseMaterializer` subclass and set its "
f"`ASSOCIATED_ARTIFACT_TYPES` and `ASSOCIATED_TYPES` "
f"accordingly."
)
def _update_and_verify_parameter_spec(self) -> None:
"""Verifies and prepares the config parameters for running this step.
When the step requires config parameters, this method:
- checks if config parameters were set via a config object or file
- tries to set missing config parameters from default values of the
config class
Raises:
MissingStepParameterError: If no value could be found for one or
more config parameters.
"""
if self.CONFIG_CLASS:
# we need to store a value for all config keys inside the
# metadata store to make sure caching works as expected
missing_keys = []
for name, field in self.CONFIG_CLASS.__fields__.items():
if name in self.PARAM_SPEC:
# a value for this parameter has been set already
continue
if field.required:
# this field has no default value set and therefore needs
# to be passed via an initialized config object
missing_keys.append(name)
else:
# use default value from the pydantic config class
self.PARAM_SPEC[name] = field.default
if missing_keys:
raise MissingStepParameterError(
self.name, missing_keys, self.CONFIG_CLASS
)
def _prepare_input_artifacts(
self, *artifacts: Channel, **kw_artifacts: Channel
) -> Dict[str, Channel]:
"""Verifies and prepares the input artifacts for running this step.
Args:
*artifacts: Positional input artifacts passed to
the __call__ method.
**kw_artifacts: Keyword input artifacts passed to
the __call__ method.
Returns:
Dictionary containing both the positional and keyword input
artifacts.
Raises:
StepInterfaceError: If there are too many or too few artifacts.
"""
input_artifact_keys = list(self.INPUT_SIGNATURE.keys())
if len(artifacts) > len(input_artifact_keys):
raise StepInterfaceError(
f"Too many input artifacts for step '{self.name}'. "
f"This step expects {len(input_artifact_keys)} artifact(s) "
f"but got {len(artifacts) + len(kw_artifacts)}."
)
combined_artifacts = {}
for i, artifact in enumerate(artifacts):
if not isinstance(artifact, Channel):
raise StepInterfaceError(
f"Wrong argument type (`{type(artifact)}`) for positional "
f"argument {i} of step '{self.name}'. Only outputs "
f"from previous steps can be used as arguments when "
f"connecting steps."
)
key = input_artifact_keys[i]
combined_artifacts[key] = artifact
for key, artifact in kw_artifacts.items():
if key in combined_artifacts:
# an artifact for this key was already set by
# the positional input artifacts
raise StepInterfaceError(
f"Unexpected keyword argument '{key}' for step "
f"'{self.name}'. An artifact for this key was "
f"already passed as a positional argument."
)
if not isinstance(artifact, Channel):
raise StepInterfaceError(
f"Wrong argument type (`{type(artifact)}`) for argument "
f"'{key}' of step '{self.name}'. Only outputs from "
f"previous steps can be used as arguments when "
f"connecting steps."
)
combined_artifacts[key] = artifact
# check if there are any missing or unexpected artifacts
expected_artifacts = set(self.INPUT_SIGNATURE.keys())
actual_artifacts = set(combined_artifacts.keys())
missing_artifacts = expected_artifacts - actual_artifacts
unexpected_artifacts = actual_artifacts - expected_artifacts
if missing_artifacts:
raise StepInterfaceError(
f"Missing input artifact(s) for step "
f"'{self.name}': {missing_artifacts}."
)
if unexpected_artifacts:
raise StepInterfaceError(
f"Unexpected input artifact(s) for step "
f"'{self.name}': {unexpected_artifacts}. This step "
f"only requires the following artifacts: {expected_artifacts}."
)
return combined_artifacts
# TODO [ENG-157]: replaces Channels with ZenML class (BaseArtifact?)
def __call__(
self, *artifacts: Channel, **kw_artifacts: Channel
) -> Union[Channel, List[Channel]]:
"""Generates a component when called.
Args:
*artifacts: Positional input artifacts passed to
the __call__ method.
**kw_artifacts: Keyword input artifacts passed to
the __call__ method.
Returns:
A single output artifact or a list of output artifacts.
Raises:
StepInterfaceError: If the step has already been called.
"""
if self._has_been_called:
raise StepInterfaceError(
f"Step {self.name} has already been called. A ZenML step "
f"instance can only be called once per pipeline run."
)
self._has_been_called = True
self._update_and_verify_parameter_spec()
# Prepare the input artifacts and spec
input_artifacts = self._prepare_input_artifacts(
*artifacts, **kw_artifacts
)
for channel in input_artifacts.values():
self._upstream_steps.add(channel.producer_component_id)
self.INPUT_SPEC = {
arg_name: artifact_type.type
for arg_name, artifact_type in input_artifacts.items()
}
# make sure we have registered materializers for each output
materializers = self.get_materializers(ensure_complete=True)
# Prepare the output artifacts and spec
for key, value in self.OUTPUT_SIGNATURE.items():
verified_types = type_registry.get_artifact_type(value)
if key not in self.OUTPUT_SPEC:
self.OUTPUT_SPEC[key] = verified_types[0]
execution_parameters = {
**self.PARAM_SPEC,
**self._internal_execution_parameters,
}
# Convert execution parameter values to strings
try:
execution_parameters = {
k: json.dumps(v) for k, v in execution_parameters.items()
}
except TypeError as e:
raise StepInterfaceError(
f"Failed to serialize execution parameters for step "
f"'{self.name}'. Please make sure to only use "
f"json serializable parameter values."
) from e
component_class = generate_component_class(
step_name=self.name,
step_module=self.__module__,
input_spec=self.INPUT_SPEC,
output_spec=self.OUTPUT_SPEC,
execution_parameter_names=set(execution_parameters),
step_function=self.entrypoint,
materializers=materializers,
enable_cache=self.enable_cache,
)
self._component = component_class(
**input_artifacts, **execution_parameters
)
# Resolve the returns in the right order.
returns = [self.component.outputs[key] for key in self.OUTPUT_SIGNATURE]
# If its one return we just return the one channel not as a list
if len(returns) == 1:
return returns[0]
else:
return returns
@property
def resource_configuration(self) -> ResourceConfiguration:
"""The resource configuration for this step.
Returns:
The (potentially empty) resource configuration for this step.
"""
return self._resource_configuration
@property
def component(self) -> _ZenMLSimpleComponent:
"""Returns a TFX component.
Returns:
A TFX component.
Raises:
StepInterfaceError: If you are trying to access the step component
before creating it.
"""
if not self._component:
raise StepInterfaceError(
"Trying to access the step component "
"before creating it via calling the step."
)
return self._component
@property
def executor_operator(self) -> Type[BaseExecutorOperator]:
"""Executor operator class that should be used to run this step.
Returns:
A TFX executor operator class.
"""
if self.custom_step_operator:
from zenml.step_operators.step_executor_operator import (
StepExecutorOperator,
)
return StepExecutorOperator
else:
return PythonExecutorOperator
def with_return_materializers(
self: T,
materializers: Union[
Type[BaseMaterializer], Dict[str, Type[BaseMaterializer]]
],
) -> T:
"""Register materializers for step outputs.
If a single materializer is passed, it will be used for all step
outputs. Otherwise, the dictionary keys specify the output names
for which the materializers will be used.
Args:
materializers: The materializers for the outputs of this step.
Returns:
The object that this method was called on.
Raises:
StepInterfaceError: If a materializer is not a `BaseMaterializer`
subclass or a materializer for a non-existent output is given.
"""
def _is_materializer_class(value: Any) -> bool:
"""Checks whether the given object is a `BaseMaterializer` subclass.
Args:
value: The object to check.
Returns:
True if the object is a `BaseMaterializer` subclass, False otherwise.
"""
is_class = isinstance(value, type)
return is_class and issubclass(value, BaseMaterializer)
if isinstance(materializers, dict):
allowed_output_names = set(self.OUTPUT_SIGNATURE)
for output_name, materializer in materializers.items():
if output_name not in allowed_output_names:
raise StepInterfaceError(
f"Got unexpected materializers for non-existent "
f"output '{output_name}' in step '{self.name}'. "
f"Only materializers for the outputs "
f"{allowed_output_names} of this step can"
f" be registered."
)
if not _is_materializer_class(materializer):
raise StepInterfaceError(
f"Got unexpected object `{materializer}` as "
f"materializer for output '{output_name}' of step "
f"'{self.name}'. Only `BaseMaterializer` "
f"subclasses are allowed."
)
self._explicit_materializers[output_name] = materializer
elif _is_materializer_class(materializers):
# Set the materializer for all outputs of this step
self._explicit_materializers = {
key: materializers for key in self.OUTPUT_SIGNATURE
}
else:
raise StepInterfaceError(
f"Got unexpected object `{materializers}` as output "
f"materializer for step '{self.name}'. Only "
f"`BaseMaterializer` subclasses or dictionaries mapping "
f"output names to `BaseMaterializer` subclasses are allowed "
f"as input when specifying return materializers."
)
return self
component: _ZenMLSimpleComponent
property
readonly
Returns a TFX component.
Returns:
| Type | Description |
|---|---|
_ZenMLSimpleComponent |
A TFX component. |
Exceptions:
| Type | Description |
|---|---|
StepInterfaceError |
If you are trying to access the step component before creating it. |
executor_operator: Type[tfx.orchestration.portable.base_executor_operator.BaseExecutorOperator]
property
readonly
Executor operator class that should be used to run this step.
Returns:
| Type | Description |
|---|---|
Type[tfx.orchestration.portable.base_executor_operator.BaseExecutorOperator] |
A TFX executor operator class. |
resource_configuration: ResourceConfiguration
property
readonly
The resource configuration for this step.
Returns:
| Type | Description |
|---|---|
ResourceConfiguration |
The (potentially empty) resource configuration for this step. |
upstream_steps: Set[str]
property
readonly
Names of the upstream steps of this step.
This property will only contain the full set of upstream steps once
it's parent pipeline connect(...) method was called.
Returns:
| Type | Description |
|---|---|
Set[str] |
Set of upstream step names. |
__call__(self, *artifacts, **kw_artifacts)
special
Generates a component when called.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
*artifacts |
Channel |
Positional input artifacts passed to the call method. |
() |
**kw_artifacts |
Channel |
Keyword input artifacts passed to the call method. |
{} |
Returns:
| Type | Description |
|---|---|
Union[tfx.types.channel.Channel, List[tfx.types.channel.Channel]] |
A single output artifact or a list of output artifacts. |
Exceptions:
| Type | Description |
|---|---|
StepInterfaceError |
If the step has already been called. |
Source code in zenml/steps/base_step.py
def __call__(
self, *artifacts: Channel, **kw_artifacts: Channel
) -> Union[Channel, List[Channel]]:
"""Generates a component when called.
Args:
*artifacts: Positional input artifacts passed to
the __call__ method.
**kw_artifacts: Keyword input artifacts passed to
the __call__ method.
Returns:
A single output artifact or a list of output artifacts.
Raises:
StepInterfaceError: If the step has already been called.
"""
if self._has_been_called:
raise StepInterfaceError(
f"Step {self.name} has already been called. A ZenML step "
f"instance can only be called once per pipeline run."
)
self._has_been_called = True
self._update_and_verify_parameter_spec()
# Prepare the input artifacts and spec
input_artifacts = self._prepare_input_artifacts(
*artifacts, **kw_artifacts
)
for channel in input_artifacts.values():
self._upstream_steps.add(channel.producer_component_id)
self.INPUT_SPEC = {
arg_name: artifact_type.type
for arg_name, artifact_type in input_artifacts.items()
}
# make sure we have registered materializers for each output
materializers = self.get_materializers(ensure_complete=True)
# Prepare the output artifacts and spec
for key, value in self.OUTPUT_SIGNATURE.items():
verified_types = type_registry.get_artifact_type(value)
if key not in self.OUTPUT_SPEC:
self.OUTPUT_SPEC[key] = verified_types[0]
execution_parameters = {
**self.PARAM_SPEC,
**self._internal_execution_parameters,
}
# Convert execution parameter values to strings
try:
execution_parameters = {
k: json.dumps(v) for k, v in execution_parameters.items()
}
except TypeError as e:
raise StepInterfaceError(
f"Failed to serialize execution parameters for step "
f"'{self.name}'. Please make sure to only use "
f"json serializable parameter values."
) from e
component_class = generate_component_class(
step_name=self.name,
step_module=self.__module__,
input_spec=self.INPUT_SPEC,
output_spec=self.OUTPUT_SPEC,
execution_parameter_names=set(execution_parameters),
step_function=self.entrypoint,
materializers=materializers,
enable_cache=self.enable_cache,
)
self._component = component_class(
**input_artifacts, **execution_parameters
)
# Resolve the returns in the right order.
returns = [self.component.outputs[key] for key in self.OUTPUT_SIGNATURE]
# If its one return we just return the one channel not as a list
if len(returns) == 1:
return returns[0]
else:
return returns
__init__(self, *args, **kwargs)
special
Initializes a step.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
*args |
Any |
Positional arguments passed to the step. |
() |
**kwargs |
Any |
Keyword arguments passed to the step. |
{} |
Source code in zenml/steps/base_step.py
def __init__(self, *args: Any, **kwargs: Any) -> None:
"""Initializes a step.
Args:
*args: Positional arguments passed to the step.
**kwargs: Keyword arguments passed to the step.
"""
self.name = self.__class__.__name__
self.pipeline_parameter_name: Optional[str] = None
kwargs.update(getattr(self, INSTANCE_CONFIGURATION))
# This value is only used in `BaseStep.__created_by_functional_api()`
kwargs.pop(PARAM_CREATED_BY_FUNCTIONAL_API, None)
self.requires_context = bool(self.CONTEXT_PARAMETER_NAME)
self.custom_step_operator = kwargs.pop(PARAM_CUSTOM_STEP_OPERATOR, None)
self._resource_configuration = (
kwargs.pop(PARAM_RESOURCE_CONFIGURATION, None)
or ResourceConfiguration()
)
enable_cache = kwargs.pop(PARAM_ENABLE_CACHE, None)
if enable_cache is None:
if self.requires_context:
# Using the StepContext inside a step provides access to
# external resources which might influence the step execution.
# We therefore disable caching unless it is explicitly enabled
enable_cache = False
logger.debug(
"Step '%s': Step context required and caching not "
"explicitly enabled.",
self.name,
)
else:
# Default to cache enabled if not explicitly set
enable_cache = True
logger.debug(
"Step '%s': Caching %s.",
self.name,
"enabled" if enable_cache else "disabled",
)
self.enable_cache = enable_cache
self._explicit_materializers: Dict[str, Type[BaseMaterializer]] = {}
self._component: Optional[_ZenMLSimpleComponent] = None
self._has_been_called = False
self._upstream_steps: Set[str] = set()
self._verify_init_arguments(*args, **kwargs)
self._verify_output_spec()
after(self, step)
Adds an upstream step to the this step.
Calling this method makes sure this step only starts running once the given step has successfully finished executing.
Note: This can only be called inside the pipeline connect function
which is decorated with the @pipeline decorator. Any calls outside
this function will be ignored.
Examples:
The following pipeline will run its steps sequentially in the following order: step_2 -> step_1 -> step_3
@pipeline
def example_pipeline(step_1, step_2, step_3):
step_1.after(step_2)
step_3(step_1(), step_2())
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
step |
BaseStep |
A step which should finish executing before this step is started. |
required |
Source code in zenml/steps/base_step.py
def after(self, step: "BaseStep") -> None:
"""Adds an upstream step to the this step.
Calling this method makes sure this step only starts running once the
given step has successfully finished executing.
**Note**: This can only be called inside the pipeline connect function
which is decorated with the `@pipeline` decorator. Any calls outside
this function will be ignored.
Example:
The following pipeline will run its steps sequentially in the following
order: step_2 -> step_1 -> step_3
```python
@pipeline
def example_pipeline(step_1, step_2, step_3):
step_1.after(step_2)
step_3(step_1(), step_2())
```
Args:
step: A step which should finish executing before this step is
started.
"""
self._upstream_steps.add(step.name)
entrypoint(self, *args, **kwargs)
Abstract method for core step logic.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
*args |
Any |
Positional arguments passed to the step. |
() |
**kwargs |
Any |
Keyword arguments passed to the step. |
{} |
Returns:
| Type | Description |
|---|---|
Any |
The output of the step. |
Source code in zenml/steps/base_step.py
@abstractmethod
def entrypoint(self, *args: Any, **kwargs: Any) -> Any:
"""Abstract method for core step logic.
Args:
*args: Positional arguments passed to the step.
**kwargs: Keyword arguments passed to the step.
Returns:
The output of the step.
"""
get_materializers(self, ensure_complete=False)
Returns available materializers for the outputs of this step.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ensure_complete |
bool |
If set to |
False |
Returns:
| Type | Description |
|---|---|
Dict[str, Type[zenml.materializers.base_materializer.BaseMaterializer]] |
A dictionary mapping output names to |
Exceptions:
| Type | Description |
|---|---|
StepInterfaceError |
(Only if |
Source code in zenml/steps/base_step.py
def get_materializers(
self, ensure_complete: bool = False
) -> Dict[str, Type[BaseMaterializer]]:
"""Returns available materializers for the outputs of this step.
Args:
ensure_complete: If set to `True`, this method will raise a
`StepInterfaceError` if no materializer can be found for an
output.
Returns:
A dictionary mapping output names to `BaseMaterializer` subclasses.
If no explicit materializer was set using
`step.with_return_materializers(...)`, this checks the
default materializer registry to find a materializer for the
type of the output. If no materializer is registered, the
output of this method will not contain an entry for this output.
Raises:
StepInterfaceError: (Only if `ensure_complete` is set to `True`)
If an output does not have an explicit materializer assigned
to it and there is no default materializer registered for
the output type.
"""
materializers = self._explicit_materializers.copy()
for output_name, output_type in self.OUTPUT_SIGNATURE.items():
if output_name in materializers:
# Materializer for this output was set explicitly
pass
elif default_materializer_registry.is_registered(output_type):
materializer = default_materializer_registry[output_type]
materializers[output_name] = materializer
else:
if ensure_complete:
raise StepInterfaceError(
f"Unable to find materializer for output "
f"'{output_name}' of type `{output_type}` in step "
f"'{self.name}'. Please make sure to either "
f"explicitly set a materializer for step outputs "
f"using `step.with_return_materializers(...)` or "
f"registering a default materializer for specific "
f"types by subclassing `BaseMaterializer` and setting "
f"its `ASSOCIATED_TYPES` class variable.",
url="https://docs.zenml.io/developer-guide/advanced-usage/materializer",
)
return materializers
with_return_materializers(self, materializers)
Register materializers for step outputs.
If a single materializer is passed, it will be used for all step outputs. Otherwise, the dictionary keys specify the output names for which the materializers will be used.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
materializers |
Union[Type[zenml.materializers.base_materializer.BaseMaterializer], Dict[str, Type[zenml.materializers.base_materializer.BaseMaterializer]]] |
The materializers for the outputs of this step. |
required |
Returns:
| Type | Description |
|---|---|
~T |
The object that this method was called on. |
Exceptions:
| Type | Description |
|---|---|
StepInterfaceError |
If a materializer is not a |
Source code in zenml/steps/base_step.py
def with_return_materializers(
self: T,
materializers: Union[
Type[BaseMaterializer], Dict[str, Type[BaseMaterializer]]
],
) -> T:
"""Register materializers for step outputs.
If a single materializer is passed, it will be used for all step
outputs. Otherwise, the dictionary keys specify the output names
for which the materializers will be used.
Args:
materializers: The materializers for the outputs of this step.
Returns:
The object that this method was called on.
Raises:
StepInterfaceError: If a materializer is not a `BaseMaterializer`
subclass or a materializer for a non-existent output is given.
"""
def _is_materializer_class(value: Any) -> bool:
"""Checks whether the given object is a `BaseMaterializer` subclass.
Args:
value: The object to check.
Returns:
True if the object is a `BaseMaterializer` subclass, False otherwise.
"""
is_class = isinstance(value, type)
return is_class and issubclass(value, BaseMaterializer)
if isinstance(materializers, dict):
allowed_output_names = set(self.OUTPUT_SIGNATURE)
for output_name, materializer in materializers.items():
if output_name not in allowed_output_names:
raise StepInterfaceError(
f"Got unexpected materializers for non-existent "
f"output '{output_name}' in step '{self.name}'. "
f"Only materializers for the outputs "
f"{allowed_output_names} of this step can"
f" be registered."
)
if not _is_materializer_class(materializer):
raise StepInterfaceError(
f"Got unexpected object `{materializer}` as "
f"materializer for output '{output_name}' of step "
f"'{self.name}'. Only `BaseMaterializer` "
f"subclasses are allowed."
)
self._explicit_materializers[output_name] = materializer
elif _is_materializer_class(materializers):
# Set the materializer for all outputs of this step
self._explicit_materializers = {
key: materializers for key in self.OUTPUT_SIGNATURE
}
else:
raise StepInterfaceError(
f"Got unexpected object `{materializers}` as output "
f"materializer for step '{self.name}'. Only "
f"`BaseMaterializer` subclasses or dictionaries mapping "
f"output names to `BaseMaterializer` subclasses are allowed "
f"as input when specifying return materializers."
)
return self
BaseStepMeta (type)
Metaclass for BaseStep.
Checks whether everything passed in: * Has a matching materializer, * Is a subclass of the Config class, * Is typed correctly.
Source code in zenml/steps/base_step.py
class BaseStepMeta(type):
"""Metaclass for `BaseStep`.
Checks whether everything passed in:
* Has a matching materializer,
* Is a subclass of the Config class,
* Is typed correctly.
"""
def __new__(
mcs, name: str, bases: Tuple[Type[Any], ...], dct: Dict[str, Any]
) -> "BaseStepMeta":
"""Set up a new class with a qualified spec.
Args:
name: The name of the class.
bases: The base classes of the class.
dct: The attributes of the class.
Returns:
The new class.
Raises:
StepInterfaceError: When unable to create the step.
"""
dct.setdefault("PARAM_SPEC", {})
dct.setdefault("INPUT_SPEC", {})
dct.setdefault("OUTPUT_SPEC", {})
cls = cast(Type["BaseStep"], super().__new__(mcs, name, bases, dct))
cls.INPUT_SIGNATURE = {}
cls.OUTPUT_SIGNATURE = {}
cls.CONFIG_PARAMETER_NAME = None
cls.CONFIG_CLASS = None
cls.CONTEXT_PARAMETER_NAME = None
# Get the signature of the step function
step_function_signature = inspect.getfullargspec(
inspect.unwrap(cls.entrypoint)
)
if bases:
# We're not creating the abstract `BaseStep` class
# but a concrete implementation. Make sure the step function
# signature does not contain variable *args or **kwargs
variable_arguments = None
if step_function_signature.varargs:
variable_arguments = f"*{step_function_signature.varargs}"
elif step_function_signature.varkw:
variable_arguments = f"**{step_function_signature.varkw}"
if variable_arguments:
raise StepInterfaceError(
f"Unable to create step '{name}' with variable arguments "
f"'{variable_arguments}'. Please make sure your step "
f"functions are defined with a fixed amount of arguments."
)
step_function_args = (
step_function_signature.args + step_function_signature.kwonlyargs
)
# Remove 'self' from the signature if it exists
if step_function_args and step_function_args[0] == "self":
step_function_args.pop(0)
# Verify the input arguments of the step function
for arg in step_function_args:
arg_type = step_function_signature.annotations.get(arg, None)
arg_type = resolve_type_annotation(arg_type)
if not arg_type:
raise StepInterfaceError(
f"Missing type annotation for argument '{arg}' when "
f"trying to create step '{name}'. Please make sure to "
f"include type annotations for all your step inputs "
f"and outputs."
)
if issubclass(arg_type, BaseStepConfig):
# Raise an error if we already found a config in the signature
if cls.CONFIG_CLASS is not None:
raise StepInterfaceError(
f"Found multiple configuration arguments "
f"('{cls.CONFIG_PARAMETER_NAME}' and '{arg}') when "
f"trying to create step '{name}'. Please make sure to "
f"only have one `BaseStepConfig` subclass as input "
f"argument for a step."
)
cls.CONFIG_PARAMETER_NAME = arg
cls.CONFIG_CLASS = arg_type
elif issubclass(arg_type, StepContext):
if cls.CONTEXT_PARAMETER_NAME is not None:
raise StepInterfaceError(
f"Found multiple context arguments "
f"('{cls.CONTEXT_PARAMETER_NAME}' and '{arg}') when "
f"trying to create step '{name}'. Please make sure to "
f"only have one `StepContext` as input "
f"argument for a step."
)
cls.CONTEXT_PARAMETER_NAME = arg
else:
# Can't do any check for existing materializers right now
# as they might get be defined later, so we simply store the
# argument name and type for later use.
cls.INPUT_SIGNATURE.update({arg: arg_type})
# Parse the returns of the step function
if "return" not in step_function_signature.annotations:
raise StepInterfaceError(
"Missing return type annotation when trying to create step "
f"'{name}'. Please make sure to include type annotations for "
"all your step inputs and outputs. If your step returns "
"nothing, please annotate it with `-> None`."
)
cls.OUTPUT_SIGNATURE = parse_return_type_annotations(
step_function_signature.annotations,
)
# Raise an exception if input and output names of a step overlap as
# tfx requires them to be unique
# TODO [ENG-155]: Can we prefix inputs and outputs to avoid this
# restriction?
counter: Counter[str] = collections.Counter()
counter.update(list(cls.INPUT_SIGNATURE))
counter.update(list(cls.OUTPUT_SIGNATURE))
if cls.CONFIG_CLASS:
counter.update(list(cls.CONFIG_CLASS.__fields__.keys()))
shared_keys = {k for k in counter.elements() if counter[k] > 1}
if shared_keys:
raise StepInterfaceError(
f"The following keys are overlapping in the input, output and "
f"config parameter names of step '{name}': {shared_keys}. "
f"Please make sure that your input, output and config "
f"parameter names are unique."
)
return cls
__new__(mcs, name, bases, dct)
special
staticmethod
Set up a new class with a qualified spec.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
name |
str |
The name of the class. |
required |
bases |
Tuple[Type[Any], ...] |
The base classes of the class. |
required |
dct |
Dict[str, Any] |
The attributes of the class. |
required |
Returns:
| Type | Description |
|---|---|
BaseStepMeta |
The new class. |
Exceptions:
| Type | Description |
|---|---|
StepInterfaceError |
When unable to create the step. |
Source code in zenml/steps/base_step.py
def __new__(
mcs, name: str, bases: Tuple[Type[Any], ...], dct: Dict[str, Any]
) -> "BaseStepMeta":
"""Set up a new class with a qualified spec.
Args:
name: The name of the class.
bases: The base classes of the class.
dct: The attributes of the class.
Returns:
The new class.
Raises:
StepInterfaceError: When unable to create the step.
"""
dct.setdefault("PARAM_SPEC", {})
dct.setdefault("INPUT_SPEC", {})
dct.setdefault("OUTPUT_SPEC", {})
cls = cast(Type["BaseStep"], super().__new__(mcs, name, bases, dct))
cls.INPUT_SIGNATURE = {}
cls.OUTPUT_SIGNATURE = {}
cls.CONFIG_PARAMETER_NAME = None
cls.CONFIG_CLASS = None
cls.CONTEXT_PARAMETER_NAME = None
# Get the signature of the step function
step_function_signature = inspect.getfullargspec(
inspect.unwrap(cls.entrypoint)
)
if bases:
# We're not creating the abstract `BaseStep` class
# but a concrete implementation. Make sure the step function
# signature does not contain variable *args or **kwargs
variable_arguments = None
if step_function_signature.varargs:
variable_arguments = f"*{step_function_signature.varargs}"
elif step_function_signature.varkw:
variable_arguments = f"**{step_function_signature.varkw}"
if variable_arguments:
raise StepInterfaceError(
f"Unable to create step '{name}' with variable arguments "
f"'{variable_arguments}'. Please make sure your step "
f"functions are defined with a fixed amount of arguments."
)
step_function_args = (
step_function_signature.args + step_function_signature.kwonlyargs
)
# Remove 'self' from the signature if it exists
if step_function_args and step_function_args[0] == "self":
step_function_args.pop(0)
# Verify the input arguments of the step function
for arg in step_function_args:
arg_type = step_function_signature.annotations.get(arg, None)
arg_type = resolve_type_annotation(arg_type)
if not arg_type:
raise StepInterfaceError(
f"Missing type annotation for argument '{arg}' when "
f"trying to create step '{name}'. Please make sure to "
f"include type annotations for all your step inputs "
f"and outputs."
)
if issubclass(arg_type, BaseStepConfig):
# Raise an error if we already found a config in the signature
if cls.CONFIG_CLASS is not None:
raise StepInterfaceError(
f"Found multiple configuration arguments "
f"('{cls.CONFIG_PARAMETER_NAME}' and '{arg}') when "
f"trying to create step '{name}'. Please make sure to "
f"only have one `BaseStepConfig` subclass as input "
f"argument for a step."
)
cls.CONFIG_PARAMETER_NAME = arg
cls.CONFIG_CLASS = arg_type
elif issubclass(arg_type, StepContext):
if cls.CONTEXT_PARAMETER_NAME is not None:
raise StepInterfaceError(
f"Found multiple context arguments "
f"('{cls.CONTEXT_PARAMETER_NAME}' and '{arg}') when "
f"trying to create step '{name}'. Please make sure to "
f"only have one `StepContext` as input "
f"argument for a step."
)
cls.CONTEXT_PARAMETER_NAME = arg
else:
# Can't do any check for existing materializers right now
# as they might get be defined later, so we simply store the
# argument name and type for later use.
cls.INPUT_SIGNATURE.update({arg: arg_type})
# Parse the returns of the step function
if "return" not in step_function_signature.annotations:
raise StepInterfaceError(
"Missing return type annotation when trying to create step "
f"'{name}'. Please make sure to include type annotations for "
"all your step inputs and outputs. If your step returns "
"nothing, please annotate it with `-> None`."
)
cls.OUTPUT_SIGNATURE = parse_return_type_annotations(
step_function_signature.annotations,
)
# Raise an exception if input and output names of a step overlap as
# tfx requires them to be unique
# TODO [ENG-155]: Can we prefix inputs and outputs to avoid this
# restriction?
counter: Counter[str] = collections.Counter()
counter.update(list(cls.INPUT_SIGNATURE))
counter.update(list(cls.OUTPUT_SIGNATURE))
if cls.CONFIG_CLASS:
counter.update(list(cls.CONFIG_CLASS.__fields__.keys()))
shared_keys = {k for k in counter.elements() if counter[k] > 1}
if shared_keys:
raise StepInterfaceError(
f"The following keys are overlapping in the input, output and "
f"config parameter names of step '{name}': {shared_keys}. "
f"Please make sure that your input, output and config "
f"parameter names are unique."
)
return cls
base_step_config
Base step config.
BaseStepConfig (BaseModel)
pydantic-model
Base configuration class to pass execution params into a step.
Source code in zenml/steps/base_step_config.py
class BaseStepConfig(BaseModel):
"""Base configuration class to pass execution params into a step."""
step_context
Step context class.
StepContext
Provides additional context inside a step function.
This class is used to access the metadata store, materializers and
artifacts inside a step function. To use it, add a StepContext object
to the signature of your step function like this:
@step
def my_step(context: StepContext, ...)
context.get_output_materializer(...)
You do not need to create a StepContext object yourself and pass it
when creating the step, as long as you specify it in the signature ZenML
will create the StepContext and automatically pass it when executing your
step.
Note: When using a StepContext inside a step, ZenML disables caching
for this step by default as the context provides access to external
resources which might influence the result of your step execution. To
enable caching anyway, explicitly enable it in the @step decorator or when
initializing your custom step class.
Source code in zenml/steps/step_context.py
class StepContext:
"""Provides additional context inside a step function.
This class is used to access the metadata store, materializers and
artifacts inside a step function. To use it, add a `StepContext` object
to the signature of your step function like this:
```python
@step
def my_step(context: StepContext, ...)
context.get_output_materializer(...)
```
You do not need to create a `StepContext` object yourself and pass it
when creating the step, as long as you specify it in the signature ZenML
will create the `StepContext` and automatically pass it when executing your
step.
**Note**: When using a `StepContext` inside a step, ZenML disables caching
for this step by default as the context provides access to external
resources which might influence the result of your step execution. To
enable caching anyway, explicitly enable it in the `@step` decorator or when
initializing your custom step class.
"""
def __init__(
self,
step_name: str,
output_materializers: Dict[str, Type["BaseMaterializer"]],
output_artifacts: Dict[str, "BaseArtifact"],
):
"""Initializes a StepContext instance.
Args:
step_name: The name of the step that this context is used in.
output_materializers: The output materializers of the step that
this context is used in.
output_artifacts: The output artifacts of the step that this
context is used in.
Raises:
StepContextError: If the keys of the output materializers and
output artifacts do not match.
"""
if output_materializers.keys() != output_artifacts.keys():
raise StepContextError(
f"Mismatched keys in output materializers and output "
f"artifacts for step '{step_name}'. Output materializer "
f"keys: {set(output_materializers)}, output artifact "
f"keys: {set(output_artifacts)}"
)
self.step_name = step_name
self._outputs = {
key: StepContextOutput(
output_materializers[key], output_artifacts[key]
)
for key in output_materializers.keys()
}
self._metadata_store = Repository().active_stack.metadata_store
self._stack = Repository().active_stack
def _get_output(
self, output_name: Optional[str] = None
) -> StepContextOutput:
"""Returns the materializer and artifact URI for a given step output.
Args:
output_name: Optional name of the output for which to get the
materializer and URI.
Returns:
Tuple containing the materializer and artifact URI for the
given output.
Raises:
StepContextError: If the step has no outputs, no output for
the given `output_name` or if no `output_name`
was given but the step has multiple outputs.
"""
output_count = len(self._outputs)
if output_count == 0:
raise StepContextError(
f"Unable to get step output for step '{self.step_name}': "
f"This step does not have any outputs."
)
if not output_name and output_count > 1:
raise StepContextError(
f"Unable to get step output for step '{self.step_name}': "
f"This step has multiple outputs ({set(self._outputs)}), "
f"please specify which output to return."
)
if output_name:
if output_name not in self._outputs:
raise StepContextError(
f"Unable to get step output '{output_name}' for "
f"step '{self.step_name}'. This step does not have an "
f"output with the given name, please specify one of the "
f"available outputs: {set(self._outputs)}."
)
return self._outputs[output_name]
else:
return next(iter(self._outputs.values()))
@property
def metadata_store(self) -> "BaseMetadataStore":
"""Returns the metadata store.
Returns an instance of the metadata store that is used to store
metadata about the step (and the corresponding pipeline) which is
being executed.
Returns:
The metadata store.
"""
return self._metadata_store
@property
def stack(self) -> Optional["Stack"]:
"""Returns the current active stack.
Returns:
The current active stack or None.
"""
return self._stack
def get_output_materializer(
self,
output_name: Optional[str] = None,
custom_materializer_class: Optional[Type["BaseMaterializer"]] = None,
) -> "BaseMaterializer":
"""Returns a materializer for a given step output.
Args:
output_name: Optional name of the output for which to get the
materializer. If no name is given and the step only has a
single output, the materializer of this output will be
returned. If the step has multiple outputs, an exception
will be raised.
custom_materializer_class: If given, this `BaseMaterializer`
subclass will be initialized with the output artifact instead
of the materializer that was registered for this step output.
Returns:
A materializer initialized with the output artifact for
the given output.
"""
materializer_class, artifact = self._get_output(output_name)
# use custom materializer class if provided or fallback to default
# materializer for output
materializer_class = custom_materializer_class or materializer_class
return materializer_class(artifact)
def get_output_artifact_uri(self, output_name: Optional[str] = None) -> str:
"""Returns the artifact URI for a given step output.
Args:
output_name: Optional name of the output for which to get the URI.
If no name is given and the step only has a single output,
the URI of this output will be returned. If the step has
multiple outputs, an exception will be raised.
Returns:
Artifact URI for the given output.
"""
return cast(str, self._get_output(output_name).artifact.uri)
metadata_store: BaseMetadataStore
property
readonly
Returns the metadata store.
Returns an instance of the metadata store that is used to store metadata about the step (and the corresponding pipeline) which is being executed.
Returns:
| Type | Description |
|---|---|
BaseMetadataStore |
The metadata store. |
stack: Optional[Stack]
property
readonly
Returns the current active stack.
Returns:
| Type | Description |
|---|---|
Optional[Stack] |
The current active stack or None. |
__init__(self, step_name, output_materializers, output_artifacts)
special
Initializes a StepContext instance.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
step_name |
str |
The name of the step that this context is used in. |
required |
output_materializers |
Dict[str, Type[BaseMaterializer]] |
The output materializers of the step that this context is used in. |
required |
output_artifacts |
Dict[str, BaseArtifact] |
The output artifacts of the step that this context is used in. |
required |
Exceptions:
| Type | Description |
|---|---|
StepContextError |
If the keys of the output materializers and output artifacts do not match. |
Source code in zenml/steps/step_context.py
def __init__(
self,
step_name: str,
output_materializers: Dict[str, Type["BaseMaterializer"]],
output_artifacts: Dict[str, "BaseArtifact"],
):
"""Initializes a StepContext instance.
Args:
step_name: The name of the step that this context is used in.
output_materializers: The output materializers of the step that
this context is used in.
output_artifacts: The output artifacts of the step that this
context is used in.
Raises:
StepContextError: If the keys of the output materializers and
output artifacts do not match.
"""
if output_materializers.keys() != output_artifacts.keys():
raise StepContextError(
f"Mismatched keys in output materializers and output "
f"artifacts for step '{step_name}'. Output materializer "
f"keys: {set(output_materializers)}, output artifact "
f"keys: {set(output_artifacts)}"
)
self.step_name = step_name
self._outputs = {
key: StepContextOutput(
output_materializers[key], output_artifacts[key]
)
for key in output_materializers.keys()
}
self._metadata_store = Repository().active_stack.metadata_store
self._stack = Repository().active_stack
get_output_artifact_uri(self, output_name=None)
Returns the artifact URI for a given step output.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
output_name |
Optional[str] |
Optional name of the output for which to get the URI. If no name is given and the step only has a single output, the URI of this output will be returned. If the step has multiple outputs, an exception will be raised. |
None |
Returns:
| Type | Description |
|---|---|
str |
Artifact URI for the given output. |
Source code in zenml/steps/step_context.py
def get_output_artifact_uri(self, output_name: Optional[str] = None) -> str:
"""Returns the artifact URI for a given step output.
Args:
output_name: Optional name of the output for which to get the URI.
If no name is given and the step only has a single output,
the URI of this output will be returned. If the step has
multiple outputs, an exception will be raised.
Returns:
Artifact URI for the given output.
"""
return cast(str, self._get_output(output_name).artifact.uri)
get_output_materializer(self, output_name=None, custom_materializer_class=None)
Returns a materializer for a given step output.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
output_name |
Optional[str] |
Optional name of the output for which to get the materializer. If no name is given and the step only has a single output, the materializer of this output will be returned. If the step has multiple outputs, an exception will be raised. |
None |
custom_materializer_class |
Optional[Type[BaseMaterializer]] |
If given, this |
None |
Returns:
| Type | Description |
|---|---|
BaseMaterializer |
A materializer initialized with the output artifact for the given output. |
Source code in zenml/steps/step_context.py
def get_output_materializer(
self,
output_name: Optional[str] = None,
custom_materializer_class: Optional[Type["BaseMaterializer"]] = None,
) -> "BaseMaterializer":
"""Returns a materializer for a given step output.
Args:
output_name: Optional name of the output for which to get the
materializer. If no name is given and the step only has a
single output, the materializer of this output will be
returned. If the step has multiple outputs, an exception
will be raised.
custom_materializer_class: If given, this `BaseMaterializer`
subclass will be initialized with the output artifact instead
of the materializer that was registered for this step output.
Returns:
A materializer initialized with the output artifact for
the given output.
"""
materializer_class, artifact = self._get_output(output_name)
# use custom materializer class if provided or fallback to default
# materializer for output
materializer_class = custom_materializer_class or materializer_class
return materializer_class(artifact)
StepContextOutput (tuple)
Tuple containing materializer class and artifact for a step output.
Source code in zenml/steps/step_context.py
class StepContextOutput(NamedTuple):
"""Tuple containing materializer class and artifact for a step output."""
materializer_class: Type["BaseMaterializer"]
artifact: "BaseArtifact"
__getnewargs__(self)
special
Return self as a plain tuple. Used by copy and pickle.
Source code in zenml/steps/step_context.py
def __getnewargs__(self):
'Return self as a plain tuple. Used by copy and pickle.'
return _tuple(self)
__new__(_cls, materializer_class, artifact)
special
staticmethod
Create new instance of StepContextOutput(materializer_class, artifact)
__repr__(self)
special
Return a nicely formatted representation string
Source code in zenml/steps/step_context.py
def __repr__(self):
'Return a nicely formatted representation string'
return self.__class__.__name__ + repr_fmt % self
step_decorator
Step decorator function.
step(_func=None, *, name=None, enable_cache=None, output_types=None, custom_step_operator=None, resource_configuration=None)
Outer decorator function for the creation of a ZenML step.
In order to be able to work with parameters such as name, it features a
nested decorator structure.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
_func |
Optional[~F] |
The decorated function. |
None |
name |
Optional[str] |
The name of the step. If left empty, the name of the decorated function will be used as a fallback. |
None |
enable_cache |
Optional[bool] |
Specify whether caching is enabled for this step. If no
value is passed, caching is enabled by default unless the step
requires a |
None |
output_types |
Optional[Dict[str, Type[BaseArtifact]]] |
A dictionary which sets different outputs to non-default artifact types. |
None |
custom_step_operator |
Optional[str] |
Optional name of a
|
None |
resource_configuration |
Optional[ResourceConfiguration] |
Optional resource configuration for this step. |
None |
Returns:
| Type | Description |
|---|---|
Union[Type[zenml.steps.base_step.BaseStep], Callable[[~F], Type[zenml.steps.base_step.BaseStep]]] |
the inner decorator which creates the step class based on the ZenML BaseStep |
Source code in zenml/steps/step_decorator.py
def step(
_func: Optional[F] = None,
*,
name: Optional[str] = None,
enable_cache: Optional[bool] = None,
output_types: Optional[Dict[str, Type["BaseArtifact"]]] = None,
custom_step_operator: Optional[str] = None,
resource_configuration: Optional["ResourceConfiguration"] = None
) -> Union[Type[BaseStep], Callable[[F], Type[BaseStep]]]:
"""Outer decorator function for the creation of a ZenML step.
In order to be able to work with parameters such as `name`, it features a
nested decorator structure.
Args:
_func: The decorated function.
name: The name of the step. If left empty, the name of the decorated
function will be used as a fallback.
enable_cache: Specify whether caching is enabled for this step. If no
value is passed, caching is enabled by default unless the step
requires a `StepContext` (see
`zenml.steps.step_context.StepContext` for more information).
output_types: A dictionary which sets different outputs to non-default
artifact types.
custom_step_operator: Optional name of a
`zenml.step_operators.BaseStepOperator` to use for this step.
resource_configuration: Optional resource configuration for this step.
Returns:
the inner decorator which creates the step class based on the
ZenML BaseStep
"""
def inner_decorator(func: F) -> Type[BaseStep]:
"""Inner decorator function for the creation of a ZenML Step.
Args:
func: types.FunctionType, this function will be used as the
"process" method of the generated Step.
Returns:
The class of a newly generated ZenML Step.
"""
step_name = name or func.__name__
output_spec = output_types or {}
return type( # noqa
step_name,
(BaseStep,),
{
STEP_INNER_FUNC_NAME: staticmethod(func),
INSTANCE_CONFIGURATION: {
PARAM_ENABLE_CACHE: enable_cache,
PARAM_CREATED_BY_FUNCTIONAL_API: True,
PARAM_CUSTOM_STEP_OPERATOR: custom_step_operator,
PARAM_RESOURCE_CONFIGURATION: resource_configuration,
},
OUTPUT_SPEC: output_spec,
"__module__": func.__module__,
"__doc__": func.__doc__,
},
)
if _func is None:
return inner_decorator
else:
return inner_decorator(_func)
step_environment
Step environment class.
StepEnvironment (BaseEnvironmentComponent)
Added information about a step runtime inside a step function.
This takes the form of an Environment component. This class can be used from within a pipeline step implementation to access additional information about the runtime parameters of a pipeline step, such as the pipeline name, pipeline run ID and other pipeline runtime information. To use it, access it inside your step function like this:
from zenml.environment import Environment
@step
def my_step(...)
env = Environment().step_environment
do_something_with(env.pipeline_name, env.pipeline_run_id, env.step_name)
Source code in zenml/steps/step_environment.py
class StepEnvironment(BaseEnvironmentComponent):
"""Added information about a step runtime inside a step function.
This takes the form of an Environment component. This class can be used from
within a pipeline step implementation to access additional information about
the runtime parameters of a pipeline step, such as the pipeline name,
pipeline run ID and other pipeline runtime information. To use it, access it
inside your step function like this:
```python
from zenml.environment import Environment
@step
def my_step(...)
env = Environment().step_environment
do_something_with(env.pipeline_name, env.pipeline_run_id, env.step_name)
```
"""
NAME = STEP_ENVIRONMENT_NAME
def __init__(
self,
pipeline_name: str,
pipeline_run_id: str,
step_name: str,
cache_enabled: bool,
docker_configuration: "DockerConfiguration",
runtime_configuration: "RuntimeConfiguration",
):
"""Initialize the environment of the currently running step.
Args:
pipeline_name: the name of the currently running pipeline
pipeline_run_id: the ID of the currently running pipeline
step_name: the name of the currently running step
cache_enabled: whether cache is enabled for this step
docker_configuration: The Docker configuration of the currently
running pipeline.
runtime_configuration: The runtime configuration of the currently
running pipeline.
"""
super().__init__()
self._pipeline_name = pipeline_name
self._pipeline_run_id = pipeline_run_id
self._step_name = step_name
self._cache_enabled = cache_enabled
self._docker_configuration = docker_configuration
self._runtime_configuration = runtime_configuration
@property
def pipeline_name(self) -> str:
"""The name of the currently running pipeline.
Returns:
The name of the currently running pipeline.
"""
return self._pipeline_name
@property
def pipeline_run_id(self) -> str:
"""The ID of the current pipeline run.
Returns:
The ID of the current pipeline run.
"""
return self._pipeline_run_id
@property
def step_name(self) -> str:
"""The name of the currently running step.
Returns:
The name of the currently running step.
"""
return self._step_name
@property
def cache_enabled(self) -> bool:
"""Returns whether cache is enabled for the step.
Returns:
True if cache is enabled for the step, otherwise False.
"""
return self._cache_enabled
@property
def docker_configuration(self) -> "DockerConfiguration":
"""The Docker configuration of the currently running pipeline.
Returns:
A Docker configuration object.
"""
return self._docker_configuration
@property
def runtime_configuration(self) -> "RuntimeConfiguration":
"""The Runtime configuration of the currently running pipeline.
Returns:
A Runtime configuration object.
"""
return self._runtime_configuration
cache_enabled: bool
property
readonly
Returns whether cache is enabled for the step.
Returns:
| Type | Description |
|---|---|
bool |
True if cache is enabled for the step, otherwise False. |
docker_configuration: DockerConfiguration
property
readonly
The Docker configuration of the currently running pipeline.
Returns:
| Type | Description |
|---|---|
DockerConfiguration |
A Docker configuration object. |
pipeline_name: str
property
readonly
The name of the currently running pipeline.
Returns:
| Type | Description |
|---|---|
str |
The name of the currently running pipeline. |
pipeline_run_id: str
property
readonly
The ID of the current pipeline run.
Returns:
| Type | Description |
|---|---|
str |
The ID of the current pipeline run. |
runtime_configuration: RuntimeConfiguration
property
readonly
The Runtime configuration of the currently running pipeline.
Returns:
| Type | Description |
|---|---|
RuntimeConfiguration |
A Runtime configuration object. |
step_name: str
property
readonly
The name of the currently running step.
Returns:
| Type | Description |
|---|---|
str |
The name of the currently running step. |
__init__(self, pipeline_name, pipeline_run_id, step_name, cache_enabled, docker_configuration, runtime_configuration)
special
Initialize the environment of the currently running step.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
pipeline_name |
str |
the name of the currently running pipeline |
required |
pipeline_run_id |
str |
the ID of the currently running pipeline |
required |
step_name |
str |
the name of the currently running step |
required |
cache_enabled |
bool |
whether cache is enabled for this step |
required |
docker_configuration |
DockerConfiguration |
The Docker configuration of the currently running pipeline. |
required |
runtime_configuration |
RuntimeConfiguration |
The runtime configuration of the currently running pipeline. |
required |
Source code in zenml/steps/step_environment.py
def __init__(
self,
pipeline_name: str,
pipeline_run_id: str,
step_name: str,
cache_enabled: bool,
docker_configuration: "DockerConfiguration",
runtime_configuration: "RuntimeConfiguration",
):
"""Initialize the environment of the currently running step.
Args:
pipeline_name: the name of the currently running pipeline
pipeline_run_id: the ID of the currently running pipeline
step_name: the name of the currently running step
cache_enabled: whether cache is enabled for this step
docker_configuration: The Docker configuration of the currently
running pipeline.
runtime_configuration: The runtime configuration of the currently
running pipeline.
"""
super().__init__()
self._pipeline_name = pipeline_name
self._pipeline_run_id = pipeline_run_id
self._step_name = step_name
self._cache_enabled = cache_enabled
self._docker_configuration = docker_configuration
self._runtime_configuration = runtime_configuration
step_interfaces
special
Initialization for step interfaces.
base_alerter_step
Base alerter step.
BaseAlerterStep (BaseStep)
Send a message to the configured chat service.
Source code in zenml/steps/step_interfaces/base_alerter_step.py
class BaseAlerterStep(BaseStep):
"""Send a message to the configured chat service."""
@abstractmethod
def entrypoint( # type: ignore[override]
self,
message: str,
config: BaseAlerterStepConfig,
context: StepContext,
) -> bool:
"""Entrypoint for an Alerter step.
Args:
message: The message to send.
config: The configuration for the step.
context: The context for the step.
Returns:
True if the message was sent successfully.
"""
CONFIG_CLASS (BaseStepConfig)
pydantic-model
Step config definition for all alerters.
Source code in zenml/steps/step_interfaces/base_alerter_step.py
class BaseAlerterStepConfig(BaseStepConfig):
"""Step config definition for all alerters."""
entrypoint(self, message, config, context)
Entrypoint for an Alerter step.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
message |
str |
The message to send. |
required |
config |
BaseAlerterStepConfig |
The configuration for the step. |
required |
context |
StepContext |
The context for the step. |
required |
Returns:
| Type | Description |
|---|---|
bool |
True if the message was sent successfully. |
Source code in zenml/steps/step_interfaces/base_alerter_step.py
@abstractmethod
def entrypoint( # type: ignore[override]
self,
message: str,
config: BaseAlerterStepConfig,
context: StepContext,
) -> bool:
"""Entrypoint for an Alerter step.
Args:
message: The message to send.
config: The configuration for the step.
context: The context for the step.
Returns:
True if the message was sent successfully.
"""
BaseAlerterStepConfig (BaseStepConfig)
pydantic-model
Step config definition for all alerters.
Source code in zenml/steps/step_interfaces/base_alerter_step.py
class BaseAlerterStepConfig(BaseStepConfig):
"""Step config definition for all alerters."""
base_analyzer_step
Base analyzer step.
BaseAnalyzerConfig (BaseStepConfig)
pydantic-model
Base class for analyzer step configurations.
Source code in zenml/steps/step_interfaces/base_analyzer_step.py
class BaseAnalyzerConfig(BaseStepConfig):
"""Base class for analyzer step configurations."""
BaseAnalyzerStep (BaseStep)
Base step implementation for any analyzer step implementation.
Source code in zenml/steps/step_interfaces/base_analyzer_step.py
class BaseAnalyzerStep(BaseStep):
"""Base step implementation for any analyzer step implementation."""
@abstractmethod
def entrypoint( # type: ignore[override]
self,
dataset: DataArtifact,
config: BaseAnalyzerConfig,
context: StepContext,
) -> Output( # type:ignore[valid-type]
statistics=StatisticsArtifact, schema=SchemaArtifact
):
"""Base entrypoint for any analyzer implementation.
Args:
dataset: The dataset to analyze.
config: The configuration for the step.
context: The context for the step.
Returns:
The statistics and the schema of the given dataset.
"""
CONFIG_CLASS (BaseStepConfig)
pydantic-model
Base class for analyzer step configurations.
Source code in zenml/steps/step_interfaces/base_analyzer_step.py
class BaseAnalyzerConfig(BaseStepConfig):
"""Base class for analyzer step configurations."""
entrypoint(self, dataset, config, context)
Base entrypoint for any analyzer implementation.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
dataset |
DataArtifact |
The dataset to analyze. |
required |
config |
BaseAnalyzerConfig |
The configuration for the step. |
required |
context |
StepContext |
The context for the step. |
required |
Returns:
| Type | Description |
|---|---|
<zenml.steps.step_output.Output object at 0x7f45771b0910> |
The statistics and the schema of the given dataset. |
Source code in zenml/steps/step_interfaces/base_analyzer_step.py
@abstractmethod
def entrypoint( # type: ignore[override]
self,
dataset: DataArtifact,
config: BaseAnalyzerConfig,
context: StepContext,
) -> Output( # type:ignore[valid-type]
statistics=StatisticsArtifact, schema=SchemaArtifact
):
"""Base entrypoint for any analyzer implementation.
Args:
dataset: The dataset to analyze.
config: The configuration for the step.
context: The context for the step.
Returns:
The statistics and the schema of the given dataset.
"""
base_datasource_step
Base datasource step.
BaseDatasourceConfig (BaseStepConfig)
pydantic-model
Base class for datasource configs to inherit from.
Source code in zenml/steps/step_interfaces/base_datasource_step.py
class BaseDatasourceConfig(BaseStepConfig):
"""Base class for datasource configs to inherit from."""
BaseDatasourceStep (BaseStep)
Base step implementation for any datasource step implementation.
Source code in zenml/steps/step_interfaces/base_datasource_step.py
class BaseDatasourceStep(BaseStep):
"""Base step implementation for any datasource step implementation."""
@abstractmethod
def entrypoint( # type: ignore[override]
self,
config: BaseDatasourceConfig,
context: StepContext,
) -> DataArtifact:
"""Base entrypoint for any datasource implementation.
Args:
config: The configuration for the step.
context: The context for the step.
Returns:
The dataset.
"""
CONFIG_CLASS (BaseStepConfig)
pydantic-model
Base class for datasource configs to inherit from.
Source code in zenml/steps/step_interfaces/base_datasource_step.py
class BaseDatasourceConfig(BaseStepConfig):
"""Base class for datasource configs to inherit from."""
entrypoint(self, config, context)
Base entrypoint for any datasource implementation.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
config |
BaseDatasourceConfig |
The configuration for the step. |
required |
context |
StepContext |
The context for the step. |
required |
Returns:
| Type | Description |
|---|---|
DataArtifact |
The dataset. |
Source code in zenml/steps/step_interfaces/base_datasource_step.py
@abstractmethod
def entrypoint( # type: ignore[override]
self,
config: BaseDatasourceConfig,
context: StepContext,
) -> DataArtifact:
"""Base entrypoint for any datasource implementation.
Args:
config: The configuration for the step.
context: The context for the step.
Returns:
The dataset.
"""
base_drift_detection_step
Base drift detection step.
BaseDriftDetectionConfig (BaseStepConfig)
pydantic-model
Base class for drift detection step configurations.
Source code in zenml/steps/step_interfaces/base_drift_detection_step.py
class BaseDriftDetectionConfig(BaseStepConfig):
"""Base class for drift detection step configurations."""
BaseDriftDetectionStep (BaseStep)
Base step implementation for any drift detection step implementation.
Source code in zenml/steps/step_interfaces/base_drift_detection_step.py
class BaseDriftDetectionStep(BaseStep):
"""Base step implementation for any drift detection step implementation."""
@abstractmethod
def entrypoint( # type: ignore[override]
self,
reference_dataset: DataArtifact,
comparison_dataset: DataArtifact,
config: BaseDriftDetectionConfig,
context: StepContext,
) -> Any:
"""Base entrypoint for any drift detection implementation.
Args:
reference_dataset: The reference dataset.
comparison_dataset: The comparison dataset.
config: The configuration for the step.
context: The context for the step.
Returns:
The result of the drift detection.
"""
CONFIG_CLASS (BaseStepConfig)
pydantic-model
Base class for drift detection step configurations.
Source code in zenml/steps/step_interfaces/base_drift_detection_step.py
class BaseDriftDetectionConfig(BaseStepConfig):
"""Base class for drift detection step configurations."""
entrypoint(self, reference_dataset, comparison_dataset, config, context)
Base entrypoint for any drift detection implementation.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
reference_dataset |
DataArtifact |
The reference dataset. |
required |
comparison_dataset |
DataArtifact |
The comparison dataset. |
required |
config |
BaseDriftDetectionConfig |
The configuration for the step. |
required |
context |
StepContext |
The context for the step. |
required |
Returns:
| Type | Description |
|---|---|
Any |
The result of the drift detection. |
Source code in zenml/steps/step_interfaces/base_drift_detection_step.py
@abstractmethod
def entrypoint( # type: ignore[override]
self,
reference_dataset: DataArtifact,
comparison_dataset: DataArtifact,
config: BaseDriftDetectionConfig,
context: StepContext,
) -> Any:
"""Base entrypoint for any drift detection implementation.
Args:
reference_dataset: The reference dataset.
comparison_dataset: The comparison dataset.
config: The configuration for the step.
context: The context for the step.
Returns:
The result of the drift detection.
"""
step_output
Step output class.
Output
A named tuple with a default name that cannot be overridden.
Source code in zenml/steps/step_output.py
class Output(object):
"""A named tuple with a default name that cannot be overridden."""
def __init__(self, **kwargs: Type[Any]):
"""Initializes the output.
Args:
**kwargs: The output values.
"""
# TODO [ENG-161]: do we even need the named tuple here or is
# a list of tuples (name, Type) sufficient?
self.outputs = NamedTuple("ZenOutput", **kwargs) # type: ignore[misc]
def items(self) -> Iterator[Tuple[str, Type[Any]]]:
"""Yields a tuple of type (output_name, output_type).
Yields:
A tuple of type (output_name, output_type).
"""
yield from self.outputs.__annotations__.items()
__init__(self, **kwargs)
special
Initializes the output.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
**kwargs |
Type[Any] |
The output values. |
{} |
Source code in zenml/steps/step_output.py
def __init__(self, **kwargs: Type[Any]):
"""Initializes the output.
Args:
**kwargs: The output values.
"""
# TODO [ENG-161]: do we even need the named tuple here or is
# a list of tuples (name, Type) sufficient?
self.outputs = NamedTuple("ZenOutput", **kwargs) # type: ignore[misc]
items(self)
Yields a tuple of type (output_name, output_type).
Yields:
| Type | Description |
|---|---|
Iterator[Tuple[str, Type[Any]]] |
A tuple of type (output_name, output_type). |
Source code in zenml/steps/step_output.py
def items(self) -> Iterator[Tuple[str, Type[Any]]]:
"""Yields a tuple of type (output_name, output_type).
Yields:
A tuple of type (output_name, output_type).
"""
yield from self.outputs.__annotations__.items()
utils
Utility functions for Steps.
The collection of utility functions/classes are inspired by their original implementation of the Tensorflow Extended team, which can be found here:
https://github.com/tensorflow/tfx/blob/master/tfx/dsl/component/experimental /decorators.py
This version is heavily adjusted to work with the Pipeline-Step paradigm which is proposed by ZenML.
clone_step(step, step_name)
Returns a clone of the given step.
Call this function when you want to use several copies of the same step in a pipeline.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
step |
Type[BaseStep] |
the step to clone |
required |
step_name |
str |
name to use for the cloned step. This name will be used to uniquely identify the cloned step in the Python module from which this function is called. |
required |
Returns:
| Type | Description |
|---|---|
Type[BaseStep] |
A clone of the given step. |
Exceptions:
| Type | Description |
|---|---|
TypeError |
if the given step is not a subclass of BaseStep. |
ImportError |
if the the calling Python module cannot be determined or if the given step name cannot be used because the calling Python module already contains a symbol with the same name. |
Source code in zenml/steps/utils.py
def clone_step(step: Type["BaseStep"], step_name: str) -> Type["BaseStep"]:
"""Returns a clone of the given step.
Call this function when you want to use several copies of the same step
in a pipeline.
Args:
step: the step to clone
step_name: name to use for the cloned step. This name will be used
to uniquely identify the cloned step in the Python module from
which this function is called.
Returns:
A clone of the given step.
Raises:
TypeError: if the given step is not a subclass of BaseStep.
ImportError: if the the calling Python module cannot be determined
or if the given step name cannot be used because the calling
Python module already contains a symbol with the same name.
"""
from zenml.steps.base_step import BaseStep
if not issubclass(step, BaseStep):
raise TypeError(
f"The step source `{step}` is not a `{BaseStep}` subclass."
)
# get the python module from which this function is called
frm = inspect.stack()[1]
mod = inspect.getmodule(frm[0])
if not mod:
raise ImportError(
f"Cannot clone step `{step}` because the calling Python module "
f"cannot be determined."
)
if step_name in mod.__dict__:
raise ImportError(
f"The step name `{step_name}` cannot be used because the "
f"`{mod.__name__}` Python module already contains an identifier "
f"with the same name. Try using a different step name."
)
config = getattr(step, INSTANCE_CONFIGURATION, {}).copy()
# mark the step as being created by the functional API,
# otherwise the base class will try to use the class source code
# to compute the hash for the cache key (instead of the entrypoint
# source code)
config[PARAM_CREATED_BY_FUNCTIONAL_API] = True
step_clone = cast(
Type[BaseStep],
type( # noqa
step_name,
(step,),
{
INSTANCE_CONFIGURATION: config,
"__module__": mod.__name__,
"__doc__": step.__doc__,
},
),
)
mod.__dict__[step_name] = step_clone
return step_clone
collect_docker_configuration(pipeline_node)
Collects the Docker config of a step.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
pipeline_node |
PipelineNode |
Pipeline node info for a step. |
required |
Returns:
| Type | Description |
|---|---|
DockerConfiguration |
The Docker configuration for that step. |
Exceptions:
| Type | Description |
|---|---|
RuntimeError |
If no Docker configuration was found. |
Source code in zenml/steps/utils.py
def collect_docker_configuration(
pipeline_node: pipeline_pb2.PipelineNode,
) -> DockerConfiguration:
"""Collects the Docker config of a step.
Args:
pipeline_node: Pipeline node info for a step.
Returns:
The Docker configuration for that step.
Raises:
RuntimeError: If no Docker configuration was found.
"""
for context in pipeline_node.contexts.contexts:
if context.type.name == ZENML_MLMD_CONTEXT_TYPE:
config_json = context.properties[
MLMD_CONTEXT_DOCKER_CONFIGURATION_PROPERTY_NAME
].field_value.string_value
return DockerConfiguration.parse_raw(config_json)
else:
raise RuntimeError("Unable to find Docker configuration.")
collect_runtime_config(pipeline_node)
Collects the Runtime config of a step.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
pipeline_node |
PipelineNode |
Pipeline node info for a step. |
required |
Returns:
| Type | Description |
|---|---|
RuntimeConfiguration |
The Runtime configuration for that step. |
Exceptions:
| Type | Description |
|---|---|
RuntimeError |
If no runtime configuration was found. |
Source code in zenml/steps/utils.py
def collect_runtime_config(
pipeline_node: pipeline_pb2.PipelineNode,
) -> RuntimeConfiguration:
"""Collects the Runtime config of a step.
Args:
pipeline_node: Pipeline node info for a step.
Returns:
The Runtime configuration for that step.
Raises:
RuntimeError: If no runtime configuration was found.
"""
for context in pipeline_node.contexts.contexts:
if context.type.name == ZENML_MLMD_CONTEXT_TYPE:
config_json = context.properties[
MLMD_CONTEXT_RUNTIME_CONFIG_PROPERTY_NAME
].field_value.string_value
parsed = json.loads(config_json)
return RuntimeConfiguration(**parsed)
else:
raise RuntimeError("Unable to find resource configuration.")
collect_step_resources(pipeline_node)
Collects the resource config of a step.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
pipeline_node |
PipelineNode |
Pipeline node info for a step. |
required |
Returns:
| Type | Description |
|---|---|
ResourceConfiguration |
The resource configuration for that step. |
Exceptions:
| Type | Description |
|---|---|
RuntimeError |
If no resource configuration was found. |
Source code in zenml/steps/utils.py
def collect_step_resources(
pipeline_node: pipeline_pb2.PipelineNode,
) -> ResourceConfiguration:
"""Collects the resource config of a step.
Args:
pipeline_node: Pipeline node info for a step.
Returns:
The resource configuration for that step.
Raises:
RuntimeError: If no resource configuration was found.
"""
for context in pipeline_node.contexts.contexts:
if context.type.name == ZENML_MLMD_CONTEXT_TYPE:
config_json = context.properties[
MLMD_CONTEXT_STEP_RESOURCES_PROPERTY_NAME
].field_value.string_value
return ResourceConfiguration.parse_raw(config_json)
else:
raise RuntimeError("Unable to find resource configuration.")
generate_component_class(step_name, step_module, input_spec, output_spec, execution_parameter_names, step_function, materializers, enable_cache)
Generates a TFX component class for a ZenML step.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
step_name |
str |
Name of the step for which the component will be created. |
required |
step_module |
str |
Module in which the step class is defined. |
required |
input_spec |
Dict[str, Type[zenml.artifacts.base_artifact.BaseArtifact]] |
Input artifacts of the step. |
required |
output_spec |
Dict[str, Type[zenml.artifacts.base_artifact.BaseArtifact]] |
Output artifacts of the step |
required |
execution_parameter_names |
Set[str] |
Execution parameter names of the step. |
required |
step_function |
Callable[..., Any] |
The actual function to execute when running the step. |
required |
materializers |
Dict[str, Type[zenml.materializers.base_materializer.BaseMaterializer]] |
Materializer classes for all outputs of the step. |
required |
enable_cache |
bool |
Whether cache is enabled for the step. |
required |
Returns:
| Type | Description |
|---|---|
Type[_ZenMLSimpleComponent] |
A TFX component class. |
Source code in zenml/steps/utils.py
def generate_component_class(
step_name: str,
step_module: str,
input_spec: Dict[str, Type[BaseArtifact]],
output_spec: Dict[str, Type[BaseArtifact]],
execution_parameter_names: Set[str],
step_function: Callable[..., Any],
materializers: Dict[str, Type[BaseMaterializer]],
enable_cache: bool,
) -> Type["_ZenMLSimpleComponent"]:
"""Generates a TFX component class for a ZenML step.
Args:
step_name: Name of the step for which the component will be created.
step_module: Module in which the step class is defined.
input_spec: Input artifacts of the step.
output_spec: Output artifacts of the step
execution_parameter_names: Execution parameter names of the step.
step_function: The actual function to execute when running the step.
materializers: Materializer classes for all outputs of the step.
enable_cache: Whether cache is enabled for the step.
Returns:
A TFX component class.
"""
component_spec_class = generate_component_spec_class(
step_name=step_name,
input_spec=input_spec,
output_spec=output_spec,
execution_parameter_names=execution_parameter_names,
)
# Create executor class
executor_class_name = f"{step_name}_Executor"
executor_class = type(
executor_class_name,
(_FunctionExecutor,),
{
"_FUNCTION": staticmethod(step_function),
"__module__": step_module,
"materializers": materializers,
PARAM_STEP_NAME: step_name,
PARAM_ENABLE_CACHE: enable_cache,
},
)
# Add the executor class to the module in which the step was defined
module = sys.modules[step_module]
setattr(module, executor_class_name, executor_class)
return type(
step_name,
(_ZenMLSimpleComponent,),
{
"SPEC_CLASS": component_spec_class,
"EXECUTOR_SPEC": ExecutorClassSpec(executor_class=executor_class),
"__module__": step_module,
},
)
generate_component_spec_class(step_name, input_spec, output_spec, execution_parameter_names)
Generates a TFX component spec class for a ZenML step.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
step_name |
str |
Name of the step for which the component will be created. |
required |
input_spec |
Dict[str, Type[zenml.artifacts.base_artifact.BaseArtifact]] |
Input artifacts of the step. |
required |
output_spec |
Dict[str, Type[zenml.artifacts.base_artifact.BaseArtifact]] |
Output artifacts of the step |
required |
execution_parameter_names |
Set[str] |
Execution parameter names of the step. |
required |
Returns:
| Type | Description |
|---|---|
Type[tfx.types.component_spec.ComponentSpec] |
A TFX component spec class. |
Source code in zenml/steps/utils.py
def generate_component_spec_class(
step_name: str,
input_spec: Dict[str, Type[BaseArtifact]],
output_spec: Dict[str, Type[BaseArtifact]],
execution_parameter_names: Set[str],
) -> Type[component_spec.ComponentSpec]:
"""Generates a TFX component spec class for a ZenML step.
Args:
step_name: Name of the step for which the component will be created.
input_spec: Input artifacts of the step.
output_spec: Output artifacts of the step
execution_parameter_names: Execution parameter names of the step.
Returns:
A TFX component spec class.
"""
inputs = {
key: component_spec.ChannelParameter(type=artifact_type)
for key, artifact_type in input_spec.items()
}
outputs = {
key: component_spec.ChannelParameter(type=artifact_type)
for key, artifact_type in output_spec.items()
}
parameters = {
key: component_spec.ExecutionParameter(type=str) # type: ignore[no-untyped-call] # noqa
for key in execution_parameter_names
}
return type(
f"{step_name}_Spec",
(component_spec.ComponentSpec,),
{
"INPUTS": inputs,
"OUTPUTS": outputs,
"PARAMETERS": parameters,
},
)
parse_return_type_annotations(step_annotations)
Parse the returns of a step function into a dict of resolved types.
Called within BaseStepMeta.__new__() to define cls.OUTPUT_SIGNATURE.
Called within Do() to resolve type annotations.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
step_annotations |
Dict[str, Any] |
Type annotations of the step function. |
required |
Returns:
| Type | Description |
|---|---|
Dict[str, Any] |
Output signature of the new step class. |
Source code in zenml/steps/utils.py
def parse_return_type_annotations(
step_annotations: Dict[str, Any]
) -> Dict[str, Any]:
"""Parse the returns of a step function into a dict of resolved types.
Called within `BaseStepMeta.__new__()` to define `cls.OUTPUT_SIGNATURE`.
Called within `Do()` to resolve type annotations.
Args:
step_annotations: Type annotations of the step function.
Returns:
Output signature of the new step class.
"""
return_type = step_annotations.get("return", None)
if return_type is None:
return {}
# Cast simple output types to `Output`.
if not isinstance(return_type, Output):
return_type = Output(**{SINGLE_RETURN_OUT_NAME: return_type})
# Resolve type annotations of all outputs and save in new dict.
output_signature = {
output_name: resolve_type_annotation(output_type)
for output_name, output_type in return_type.items()
}
return output_signature
resolve_type_annotation(obj)
Returns the non-generic class for generic aliases of the typing module.
If the input is no generic typing alias, the input itself is returned.
Example: if the input object is typing.Dict, this method will return the
concrete class dict.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
obj |
Any |
The object to resolve. |
required |
Returns:
| Type | Description |
|---|---|
Any |
The non-generic class for generic aliases of the typing module. |
Source code in zenml/steps/utils.py
def resolve_type_annotation(obj: Any) -> Any:
"""Returns the non-generic class for generic aliases of the typing module.
If the input is no generic typing alias, the input itself is returned.
Example: if the input object is `typing.Dict`, this method will return the
concrete class `dict`.
Args:
obj: The object to resolve.
Returns:
The non-generic class for generic aliases of the typing module.
"""
from typing import _GenericAlias # type: ignore[attr-defined]
if sys.version_info >= (3, 8):
return typing.get_origin(obj) or obj
else:
# python 3.7
if isinstance(obj, _GenericAlias):
return obj.__origin__
else:
return obj