Limitations - Amazon SageMaker
Function argument limitationsFunction importsReferencing child members of function return valueExisting pipeline features that are not supported
Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions, see Getting Started with Amazon Web Services in China (PDF).

Limitations

Be aware of the following limitations when you use the @step decorator for your pipeline steps.

Function argument limitations

When you pass an input argument to the @step-decorated function, the following limitations apply:

  • You can pass the DelayedReturn, Properties (of steps of other types), Parameter, and ExecutionVariable objects to @step-decorated functions as arguments. But @step-decorated functions do not support JsonGet and Join objects as arguments.

  • You cannot directly access a pipeline variable from a @step function. The following example produces an error:

    param = ParameterInteger(name="<parameter-name>", default_value=10)

@step
def func():
    print(param)

func() # this raises a SerializationError

  • You cannot nest a pipeline variable in another object and pass it to a @step function. The following example produces an error:

    param = ParameterInteger(name="<parameter-name>", default_value=10)

@step
def func(arg):
    print(arg)

func(arg=(param,)) # this raises a SerializationError because param is nested in a tuple

  • Since inputs and outputs of a function are serialized, there are restrictions on the type of data that can be passed as input or output from a function. See the Data serialization and deserialization section of Invoking a function for more details. The same restrictions apply to @step-decorated functions.

  • Any object that has a boto client cannot be serialized, hence you cannot pass such objects as input to or output from a @step-decorated function. For example, SageMaker Python SDK client classes such as Estimator, Predictor, and Processor can't be serialized.

Function imports

You should import the libraries required by the step inside rather than outside the function. If you import them at global scope, you risk an import collision while serializing the function. For example, sklearn.pipeline.Pipeline could be overridden by sagemaker.workflow.pipeline.Pipeline.

Referencing child members of function return value

If you reference child members of a @step-decorated function's return value, the following limitations apply:

  • You can reference the child members with [] if the DelayedReturn object represents a tuple, list or dict, as shown in the following example:

    delayed_return[0]
delayed_return["a_key"]
delayed_return[1]["a_key"]

  • You cannot unpack a tuple or list output because the exact length of the underlying tuple or list can't be known when you invoke the function. The following example produces an error:

    a, b, c = func() # this raises ValueError

  • You cannot iterate over a DelayedReturn object. The following example raises an error:

    for item in func(): # this raises a NotImplementedError

  • You cannot reference arbitrary child members with '.'. The following example produces an error:

    delayed_return.a_child # raises AttributeError

Existing pipeline features that are not supported

You cannot use the @step decorator with the following pipeline features: