[Concept Entry] Created the export entry

[dakshdeepHERE]

Description

Issue Solved

Type of Change

• Adding a new entry
• Editing an existing entry (fixing a typo, bug, issues, etc)
• Updating the documentation

Checklist

• All writings are my own.
• My entry follows the Codecademy Docs style guide.
• My changes generate no new warnings.
• I have performed a self-review of my own writing and code.
• I have checked my entry and corrected any misspellings.
• I have made corresponding changes to the documentation if needed.
• I have confirmed my changes are not being pushed from my forked main branch.
• I have confirmed that I'm pushing from a new branch named after the changes I'm making.
• I have linked any issues that are relevant to this PR in the Issues Solved section.

[PragatiVerma18]

Hey @dakshdeepHERE, I have a few suggestions, please check the comments and make the necessary changes.

[PragatiVerma18]

The headers Title, Description, Subject, and Tags should be in title case, and the sentences should be inside ''.

Suggested change

	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'
	Title: 'Model Export with TorchScript'
	Description: 'Convert PyTorch models to TorchScript for optimized deployment in non-Python environments like mobile and embedded systems.'
	Subject:
	- 'Computer Science'
	- 'Data Science'
	- 'Machine Learning'
	Tags:
	- 'Deployment'
	- 'TorchScript'
	- 'Model Export'
	- 'PyTorch'
	CatalogContent:
	- 'learn-pytorch'
	- 'paths/data-science'

[PragatiVerma18]

The example should give some tangible output that helps the reader understand what's happening -

Suggested change

	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")
	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")
	# Testing the scripted model
	x1 = torch.tensor([1.0, -0.5, 3.0])
	x2 = torch.tensor([-2.0, -1.5, -0.5])
	print(scripted_model(x1))
	print(scripted_model(x2))

Add output below this -

tensor([ 2., -1.,  6.])
tensor([-3.0000, -2.5000, -1.5000])

[PragatiVerma18]

Same here. The example should have an output -

Suggested change

	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")
	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")
	# Running inference with traced model
	output = traced_model(dummy_input)
	print(output.shape)

Add output below that:

torch.Size([1, 1000])

This confirms that the traced ResNet model processes an image and produces 1000 output logits (corresponding to ImageNet classes).