added example page to the pydantic_plugin

Signed-off-by: sumana sree <[email protected]>

examples/pydantic_plugin/README.md

@@ -18,7 +18,7 @@ pip install flytekitplugins-pydantic
 ```
 ## Example usage
 
-For a usage example, see {doc}`Pydantic example usage <example>`.
+For a usage example, see {doc}`Pydantic example usage <pydantic_integration_example>`.
 
 ```{toctree}
 :maxdepth: -1

examples/pydantic_plugin/pydantic_plugin/pydantic_integration_example.py

@@ -0,0 +1,45 @@
+# %% [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 pydantic.v1 import BaseModel
+from flytekit import task, workflow
+from flytekit.types.file import FlyteFile
+from typing import List
+
+# %% [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")])