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 integration of data validation within Flyte tasks and workflows.
+
+## 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