PyTorch — Industry-Standard Deep Learning Framework¶

Parent: Module 2 — Deep Learning Frameworks

The framework most models are written in. You need fluency here because every model you deploy on hardware starts as PyTorch code.

What to Master¶

Concept	Why it matters for hardware
`torch.Tensor`	The data structure accelerators process. Shape, dtype, layout (contiguous, channels-last).
`nn.Module`	How models are structured. Layers → forward pass → computational graph.
Autograd (`loss.backward()`)	Generates the backward graph that training hardware executes.
Data loading (`DataLoader`)	CPU-GPU pipeline. Bottleneck if not overlapped with compute.
`torch.onnx.export()`	How models leave PyTorch and enter the compiler/runtime stack (Phase 4C).
`torch.compile()`	PyTorch's built-in compiler (Inductor). Generates Triton kernels.
`torch.profiler`	Where is time spent? Kernel launches, memory copies, CPU overhead.
Mixed precision (`torch.cuda.amp`)	FP16/BF16 training — what tensor cores accelerate.
Quantization (`torch.ao.quantization`)	INT8 inference — what L6 PE arrays must support.

Train ResNet-18 on CIFAR-10 from scratch. Profile with torch.profiler. Identify top-3 time-consuming ops.
Export to ONNX. Visualize graph with Netron. Count total ops and parameters.
Post-training quantization to INT8. Measure accuracy drop and inference speedup.
torch.compile() on a transformer block. Compare eager vs compiled execution time.