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doc: add instruction for bentoml.IODescriptor #4975

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Sep 17, 2024
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doc: add instruction for bentoml.IODescriptor #4975

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9 changes: 9 additions & 0 deletions docs/source/guides/iotypes.rst
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Expand Up @@ -141,6 +141,15 @@ You can also use a Pydantic model directly at the top level for a BentoML Servic

In the above code snippet, all the validated and parsed fields from the incoming request are passed into the ``generate`` method as keyword arguments stored in the ``params`` dictionary. You can access these parameters directly by their field names defined in ``AdsGenerationParams`` as keys in the dictionary.

Pydantic's ``BaseModel`` only supports built-in types in Python as field types. You can use ``bentoml.IODescriptor`` instead of ``pydantic.BaseModel`` to gain support for types such as ``numpy.ndarray``, ``pandas.DataFrame``, and ``torch.Tensor``.

.. code-block:: python

import bentoml

class MyInputParams(bentoml.IODescriptor):
data: np.ndarray[tuple[int], np.dtype[np.float16]]

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^^^^^

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