TorchScript & torch.compile
Freeze and accelerate a model for serving.
TorchScript & torch.compile is a free Deep Learning Academy lesson on CoddyKit — lesson 1 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Deep Learning Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Why Production Needs a Frozen Model
Your training code is flexible Python, but servers want something fast and stable. TorchScript freezes your model into a portable, optimized form. 🚀
What TorchScript Actually Is
TorchScript is a serializable, optimizable subset of Python that runs without a Python interpreter, so your model can ship to C++ servers and mobile.
Tracing: Record One Real Run
Tracing runs your model on an example input and records every operation it performs, capturing the path the data actually took.
import torch
traced = torch.jit.trace(model, example_input)Tracing Misses Control Flow
Because tracing only follows one run, it silently ignores if-statements and loops that depend on the data. Those branches just vanish.
Scripting: Read the Logic
Scripting compiles your code directly, preserving every loop and condition. Use it when control flow depends on the input values.
scripted = torch.jit.script(model)Save and Load Anywhere
A scripted or traced model saves to a single self-contained file you can load on any machine, no original class definition required.
scripted.save('model.pt')
loaded = torch.jit.load('model.pt')Meet torch.compile
The modern alternative is torch.compile, which speeds up your model with one line while keeping your code plain Python.
model = torch.compile(model)How torch.compile Speeds Things Up
Under the hood torch.compile fuses many small operations into fewer optimized kernels, cutting overhead and boosting throughput on GPU.
The First Call Is Slow
The first forward pass after torch.compile is slow because it compiles in the background. Every call after that runs much faster. ⏳
Always Set Eval Mode First
Before you trace, script, or compile for serving, call model.eval() so dropout and batch norm behave correctly for inference.
model.eval()
traced = torch.jit.trace(model, x)Which One Should You Reach For?
Choose TorchScript when you need a Python-free deployment file, and torch.compile when you want a quick speedup inside Python.
Quick Check
One method captures only the path one example takes. Which is it?
Recap: Freeze and Accelerate
You learned to freeze a model with TorchScript via tracing or scripting, save it anywhere, and accelerate inference in Python with torch.compile. 🎉
Frequently asked questions
Is the “TorchScript & torch.compile” lesson free?
Yes — the full text of “TorchScript & torch.compile” is free to read here on the web, and the Deep Learning Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Deep Learning Academy course, upgrade to CoddyKit PRO.
What will I learn in “TorchScript & torch.compile”?
Freeze and accelerate a model for serving. You practise Deep Learning Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Deep Learning Academy?
No prior experience is required. Deep Learning Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 1 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “TorchScript & torch.compile” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Deep Learning Academy lesson?
Yes. Every Deep Learning Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- TorchScript & torch.compile
- Export to ONNX
- Quantization for Smaller, Faster Models
- Serve with FastAPI