Flamehaven Tensor Canon
"Data is not just numbers; it is a structural covenant."
Flamehaven Tensor Canon is a small library for:
- Structural validation (shapes and optional dtypes)
- Distribution drift detection (MMD-based)
PyTorch is required. NumPy support is optional.
Installation
pip install flamehaven-tensor-canon
Enable NumPy support:
pip install flamehaven-tensor-canon[numpy]
Quick start (validation)
import torch
from tensor_canon import validate
spec = "batch channels 224 224"
data = torch.randn(32, 3, 224, 224)
validate(data, spec, key="input_layer")
Drift detection
import torch
from tensor_canon.validator import engine
torch.manual_seed(42)
golden = torch.randn(100, 5)
engine.check_drift("embeddings", golden, register_golden=True)
current = golden + torch.randn_like(golden) * 0.01
drift = engine.check_drift("embeddings", current)
print("drift:", drift)
Development
pip install -e .[dev,numpy]
ruff check .
pytest -q
Docker
docker build -t tensor-canon .
docker run --rm tensor-canon
Release notes and security
- See
CHANGELOG.mdfor release notes. - See
SECURITY.mdfor vulnerability reporting.
License
MIT. See LICENSE.
1 maintainer
Metadata
1 maintainer
Release files for flamehaven-tensor-canon 0.1.3
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