Created a new page in Integrations section for Pydantic plugin in Flyte documentation

examples/pydantic_plugin/Dockerfile

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+FROM python:3.11-slim-buster
+LABEL org.opencontainers.image.source=https://github.com/flyteorg/flytesnacks
+
+WORKDIR /root
+ENV VENV /opt/venv
+ENV LANG C.UTF-8
+ENV LC_ALL C.UTF-8
+ENV PYTHONPATH /root
+
+# Install Python dependencies
+COPY requirements.in /root
+RUN pip install -r /root/requirements.in
+
+# Copy the actual code
+COPY . /root/
+
+# This tag is supplied by the build script and will be used to determine the version
+# when registering tasks, workflows, and launch plans
+ARG tag
+ENV FLYTE_INTERNAL_IMAGE $tag

examples/pydantic_plugin/README.md

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+(pydantic_integration)=
+
+# Pydantic
+
+```{eval-rst}
+.. tags:: Pydantic, Flytekit
+```
+[Pydantic](https://pydantic.dev/) is a data validation and settings management library that leverages Python type annotations to enforce type hints at runtime. It provides user-friendly errors when data is invalid, making it easier to ensure data integrity.
+
+The Flytekit Pydantic plugin adds type support for Pydantic models, enabling seamless integration of data validation and settings management within Flyte tasks and workflows. This documentation demonstrates how to integrate Pydantic with Flytekit using the Flytekit Pydantic plugin.
+
+## Installation
+
+To install the Flytekit Pydantic plugin, run the following command:
+
+```
+pip install flytekitplugins-pydantic
+```
+## Example usage
+
+For a usage example, see {doc}`Pydantic example usage <pydantic_integration_example>`.
+
+```{toctree}
+:maxdepth: -1
+:hidden:
+
+example
+```

examples/pydantic_plugin/pydantic_plugin/pydantic_integration_example.py

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+# %% [markdown]
+# (pydantic_integration_example)=
+#
+# # Pydantic Integration Example
+#
+# Pydantic is a data validation and settings management library for Python, enabling the creation of data models with type annotations.
+#
+# Flyte leverages Pydantic for robust input validation and serialization, ensuring that task inputs are correctly structured.
+
+# %%
+from typing import List
+
+from flytekit import task, workflow
+from flytekit.types.file import FlyteFile
+from pydantic.v1 import BaseModel
+
+
+# %% [markdown]
+# Let's first define a Pydantic model for training configuration.
+# %%
+class TrainConfig(BaseModel):
+    lr: float = 1e-3  # Learning rate
+    batch_size: int = 32  # Batch size for training
+    files: List[FlyteFile]  # List of file inputs for training
+
+
+# %% [markdown]
+# Next, we use the Pydantic model in a Flyte task to train a model.
+# %%
+@task
+def train(cfg: TrainConfig):
+    print(f"Training with learning rate: {cfg.lr} and batch size: {cfg.batch_size}")
+    for file in cfg.files:
+        print(f"Processing file: {file}")
+
+
+# %% [markdown]
+# Now we define a Flyte workflow that utilizes the training task.
+# %%
+@workflow
+def training_workflow(lr: float = 1e-3, batch_size: int = 32, files: List[FlyteFile] = []):
+    cfg = TrainConfig(lr=lr, batch_size=batch_size, files=files)
+    train(cfg=cfg)
+
+
+# %% [markdown]
+# Finally, we execute the workflow with sample parameters.
+# %%
+if __name__ == "__main__":
+    training_workflow(lr=1e-3, batch_size=32, files=[FlyteFile(path="path/to/your/file")])

examples/pydantic_plugin/requirements.in

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+flytekitplugins-pydantic