[Concept Entry] Created the export entry

content/pytorch/concepts/model-export-with-torchscript/model-export-with-torchscript.md

@@ -0,0 +1,94 @@
+---
+title: Model Export with TorchScript
+description: Convert PyTorch models to TorchScript for production deployment in non-Python environments.
+subject: 
+  - 'Computer Science'
+  - 'Data Science'
+  - 'Machine Learning'
+tags:
+  - 'Deployment'
+  - 'TorchScript'
+  - 'Model Export'
+  - 'PyTorch'
+catalog_content:
+  - 'learn-pytorch'
+  - 'paths/data-science'
+---
+
+**TorchScript** is a PyTorch intermediate representation that allows models to be serialized and optimized for execution in resource-constrained environments like mobile devices, embedded systems, or C++ applications. It decouples models from Python dependencies while preserving their logic.
+
+## Syntax
+
+- **Scripting** (for models with dynamic control flow):
+
+   ```pseudo
+   scripted_model = torch.jit.script(model)  # Converts model to TorchScript
+   scripted_model.save("model.pt")
+   ```
+
+- **Tracing** (for static models):
+
+   ```pseudo
+   traced_model = torch.jit.trace(model, example_input)  # Records operations via example input
+   traced_model.save("model.pt")
+   ```
+
+- **Hybrid Approach** (script/trace specific submodules):
+
+   ```pseudo
+   @torch.jit.script
+   def custom_function(x: torch.Tensor) -> torch.Tensor:
+       return x * 2
+   ```
+
+## Example
+
+### Scripting a Model with Conditional Logic
+
+Export a model that uses `if` statements (unsupported by tracing):
+
+```python
+import torch
+
+class DynamicModel(torch.nn.Module):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if x.sum() > 0:
+            return x * 2
+        else:
+            return x - 1
+
+model = DynamicModel()
+scripted_model = torch.jit.script(model)  # Handles dynamic control flow
+scripted_model.save("dynamic_model.pt")
+```
+
+### Tracing a ResNet for Mobile Deployment
+
+Convert a pretrained **ResNet** using tracing:
+
+```python
+import torch
+import torchvision
+
+model = torchvision.models.resnet18(weights="IMAGENET1K_V1").eval()
+
+# Trace with example input
+dummy_input = torch.rand(1, 3, 224, 224)
+traced_model = torch.jit.trace(model, dummy_input)
+traced_model.save("resnet18_traced.pt")
+```
+
+## Key Considerations to Make
+
+- **Scripting vs Tracing**:
+
+  - Use `torch.jit.script` for models with:
+    - Loops/conditionals
+    - Variable-length inputs
+    - Non-tensor data dependencies
+  - Use `torch.jit.trace` for static models (faster execution).
+
+- **Limitations**:
+
+  - TorchScript supports a subset of Python/PyTorch operations.
+  - Avoid `**kwargs` or dynamic tensor shapes in traced models.

documentation/tags.md

@@ -223,6 +223,7 @@ Matrices
 Media Queries
 Memory
 Metadata
+Model Export
 Methods
 Middleware
 Models
@@ -358,6 +359,7 @@ Trigonometry
 Try
 Tuples
 Types
+TorchScript
 Type Guard
 Type Narrowing
 Typography