aeondave/pytorch
ai/pytorch/SKILL.md
PyTorch model inspection and checkpoint workflow for loading tensors, `state_dict` data, modules, and parameters. Use when working with `.pt` or `.pth` artifacts, auditing model structure, extracting weights, or scripting inference-oriented inspection of deep-learning checkpoints.
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SKILL.md
- name:
- pytorch
- description:
- PyTorch model inspection and checkpoint workflow for loading tensors, `state_dict` data, modules, and parameters. Use when working with `.pt` or `.pth` artifacts, auditing model structure, extracting weights, or scripting inference-oriented inspection of deep-learning checkpoints.
- compatibility:
- Linux, Windows, macOS; Python 3; PyTorch installed; GPU optional but often useful
- author:
- AeonDave
- version:
- 1.0
PyTorch
Use PyTorch when model artifacts are tensors first and everything else second.
When to use PyTorch
Use PyTorch when you need to:
- load
.ptor.pthcheckpoints safely onto CPU or GPU - inspect
state_dictkeys, 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=Truefor safer tensor-only loads from trusted workflows. eval()andtorch.no_grad()belong together for stable inspection and reduced memory noise.
Caveats
torch.loaduses 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.
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