PyTorch explained: tensors, automatic differentiation and your model
PyTorch explained: tensors, automatic differentiation and your model
Build a mental model of the framework before downloading pretrained weights.
What you will learn
- Start with tensors
- Understand differentiation
- Know the evidence boundary
Before you start
- Basic Python and calculus
- An isolated environment for later exercises
A learning exercise compares analytical derivatives and framework outputs without hiding failed checks.
Key takeaways
- Tensors carry more than values.
- Autograd computes derivatives.
- A gradient is not an optimizer step.
Start with tensors
PyTorch supplies tensor computation and automatic differentiation for Python programs. A tensor has shape, dtype and device; checking all three is often more useful than guessing why an operation failed.
For a first lesson, use a scalar or a tiny vector whose answer you can calculate. A large pretrained model introduces formats and dependencies before the basic computation is understood.
Understand differentiation
Autograd records differentiable operations and applies the chain rule. A scalar loss can produce gradients for trainable leaves; computing those gradients is separate from updating parameters.
The inspected backward function accumulates into gradient fields, while autograd.grad returns requested derivatives. Confusing those contracts can produce stale gradients or a training loop that never updates anything.
Know the evidence boundary
This series reads commit 4de991c, including Python autograd entry points and the security policy. It does not audit the entire native engine or every accelerator backend.
PyTorch is not installed in the inspected local Python runtime. The examples are unexecuted teaching exercises with analytical expectations, not claims of a successful training or GPU benchmark.
Decision guide
| Criterion | Option A | Option B |
|---|---|---|
| Best when | You need predictable behavior and easy auditing | You need adaptive optimization and have reliable telemetry |
| Main risk | May leave performance on the table | Can become difficult to explain or debug |
Implementation steps
- 1
Inspect shape, dtype and device.
- 2
Choose a calculation with a known derivative.
- 3
Separate gradient computation and parameter update.
Copy-ready example
import torch
x = torch.tensor(3.0, requires_grad=True)
y = x * x
y.backward()
print(x.grad) # Analytical expectation: 6; not executed hereFrequently asked questions
Is PyTorch a pretrained model?
It is a framework; model code and weights are separate artifacts.
Were the examples run here?
No. Expected values are derived mathematically and labeled as such.
Sources
- PyTorch / README.mdSource checked 2026-09-23
- PyTorch / torch/autograd/__init__.pySource checked 2026-09-23