aeondave/pytorch

PyTorch

Use PyTorch when model artifacts are tensors first and everything else second.

When to use PyTorch

Use PyTorch when you need to:

• load .pt or .pth checkpoints safely onto CPU or GPU
• inspect state_dict keys, module hierarchy, and parameter shapes
• switch a model into stable inference mode for probing
• script quick tensor statistics or output checks against a checkpoint

Quick Start

import torch

checkpoint = torch.load("model.pt", map_location="cpu")
print(type(checkpoint))
print(checkpoint.keys() if isinstance(checkpoint, dict) else "non-dict checkpoint")

High-Value Workflows

Inspect a state dict

state = torch.load("weights.pth", map_location="cpu")
for name, tensor in state.items():
    print(name, tuple(tensor.shape), tensor.dtype)

Inspect a loaded model

model.eval()
for name, module in model.named_modules():
    print(name, module.__class__.__name__)

for name, param in model.named_parameters():
    print(name, tuple(param.shape))

Safe inference baseline

model.eval()
with torch.no_grad():
    output = model(sample_input)

Practical Notes

• Use map_location="cpu" first unless you explicitly need GPU execution.
• Prefer state_dict-style loading and inspection over whole-model pickle blobs when possible.
• If your version supports it, prefer weights_only=True for safer tensor-only loads from trusted workflows.
• eval() and torch.no_grad() belong together for stable inspection and reduced memory noise.

Caveats

• torch.load uses pickle under the hood; do not trust untrusted checkpoint files.
• Whole-model checkpoints require the original class code to be importable.
• Version drift can break deserialization or subtly alter behavior.

Resources

No bundled scripts/, references/, or assets/. Use the official PyTorch docs for torch.load, module introspection, and checkpoint best practices.