Code Lab 01 - Raw PyTorch Classifier

A small CNN image classifier trained end-to-end in raw PyTorch - no Trainer class, no wrapper library. This lab exists to make every mechanic from the Notes files (Dataset/DataLoader, the hand-written training loop, checkpointing, mixed precision) concrete in one runnable script.

← Back to Overview: PyTorch Fundamentals · Back to Concepts: Training Loop From Scratch · Checkpointing & Mixed Precision

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What's In This Lab

Property	Detail
Task	Image classification (MNIST digits, via torchvision.datasets)
Model	A small hand-written CNN (SimpleCNN in model.py)
Training	Hand-written loop - custom Dataset usage via torchvision, DataLoader, forward/backward/step
Checkpointing	Saves model + optimizer state every N epochs to checkpoints/
Mixed Precision	torch.cuda.amp.autocast() + GradScaler (auto-disabled gracefully on CPU-only machines)
Output	Final train/val accuracy printed at the end of the run
Complexity	Beginner-Intermediate
Files	01-Raw-PyTorch-Classifier/{train.py, model.py, requirements.txt, README.mdx}

Architecture

flowchart LR
    D["📦 torchvision.datasets.MNIST"] --> DL["🔄 DataLoader\nbatch_size=64, shuffle=True"]
    DL --> M["🧠 SimpleCNN\nconv → pool → conv → pool → fc"]
    M --> L["📉 CrossEntropyLoss"]
    L --> BW["⬅️ backward() + GradScaler"]
    BW --> OPT["🔧 optimizer.step()"]
    OPT -->|every N epochs| CKPT["💾 checkpoints/model_epochN.pt"]
    OPT --> Next["🔁 next epoch"]
    Next -->|after final epoch| Eval["📊 Final train/val accuracy"]

    style D fill:#d8dfe8,stroke:#b0bac8
    style DL fill:#e8e0d4,stroke:#c8b89a
    style M fill:#dde4dc,stroke:#b0c4b0
    style L fill:#dde4dc,stroke:#b0c4b0
    style BW fill:#ddd8e4,stroke:#b8b0c8
    style OPT fill:#ddd8e4,stroke:#b8b0c8
    style CKPT fill:#d8dfe8,stroke:#b0bac8
    style Eval fill:#e8e0d4,stroke:#c8b89a

Running

cd 11-Prog-Langs/PyTorch/CodeLabs/01-Raw-PyTorch-Classifier
pip install -r requirements.txt
python train.py

Full details, code walkthrough, and what each part demonstrates: see README.