DEEPBOM Python launcher

This package is a zero-analysis-logic launcher for the same platform-specific DEEPBOM engine used by the npm CLI release. Each wheel binds its operating system, architecture, engine SHA-256, and canonical TFLite WASM SHA-256 in an installed manifest. No parser or numerical rule is reimplemented in Python.

An experimental typed facade invokes that same verified engine and converts its JSON and exit-code contracts into Python values and exceptions:

from deepbom import (
    DeepBomPolicyBlocked, audit, capabilities, capture_contract, diff,
    tensor_inventory, tensors, verify_bom, verify_contract,
)

caps = capabilities()
envelope = audit("model.gguf")
selected = audit("model.gguf", sections=["summary", "findings"])
try:
    gated = audit("model.safetensors", gate="defects")
except DeepBomPolicyBlocked as blocked:
    gated = blocked.document
rows = tensors("model.gguf")
inventory = tensor_inventory("model.gguf")
comparison = diff("baseline.gguf", "candidate.gguf", tensors=True)
baseline = capture_contract("model.onnx")
contract_result = verify_contract("candidate.onnx", "baseline.interface-contract.json")
bom_result = verify_bom("model.onnx", "supplied.cdx.json")

audit() defaults to the canonical envelope, or to analysis selection when sections are supplied without an explicit output. It accepts the CLI-equivalent gate="defects" and policy="engineering"|"regulatory" controls plus explicit timeout and maximum-output-byte bounds. gate and policy are mutually exclusive. Exit 1 raises DeepBomInvocationError, exit 2 raises DeepBomPolicyBlocked, and exit 3 raises DeepBomIncompleteBinding; exit 4 raises DeepBomIdentityMismatch. Policy exceptions retain any completed JSON result as .document. tensors() converts exact counts to Python int and decimal ratios to decimal.Decimal; tensor_inventory() preserves the raw JSON document. The facade is experimental in 1.96.x, and the CLI schemas remain the compatibility contract.

deepbom audit model.tflite --compact
deepbom audit model.onnx --format cyclonedx
deepbom audit model.onnx --format sarif --output deepbom.sarif --fail-on high
deepbom capabilities --compact
deepbom audit Model.mlpackage --compact
deepbom audit safetensors-repository/ --compact
deepbom audit model.pte --executorch-build deepbom.executorch-build.json --compact
deepbom verify model.onnx --bom supplied.cdx.json --render markdown
deepbom contract capture model.onnx -o baseline.interface-contract.json
deepbom capabilities --format agent-text

ONNX external data next to the model is discovered only from safe serialized references. Use --external-data-dir to bind a different explicit root. ExecuTorch PTE audits can additionally bind a selected source/build and binary inventory with --executorch-build; backend execution remains unobserved.

Set DEEPBOM_ENGINE only for a deliberately supplied engine build and bind it with DEEPBOM_ENGINE_SHA256. TensorRT options import configuration or observed parser evidence; NVIDIA runtime libraries are not bundled.

For the installed version, deepbom --help and deepbom capabilities --compact are authoritative. The current source inventory is maintained in the generated CLI reference.

This public channel package is licensed under Apache-2.0. Protected analyzers and private rulepack-generation sources are not included.

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  • Released:
Newer release available (1.103.0)

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Credits

Author: Jun-Hwan Kwon

GitHub Statistics

Data verified by PyPI on Sep 16, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

Apache-2.0
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Requires

Python >=3.9

Tags

tflite onnx gguf safetensors coreml executorch tensorrt quantization on-device ml-bom cyclonedx sarif mcp model-context-protocol chatgpt

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