Encrypting Models & Resources

Encrypt AI models and other binary assets, and read them back at runtime without ever writing plaintext to disk.

magiclock protect-model encrypts a model, weights file, or any other resource into a .enc envelope. Your app decrypts it in memory at runtime with bootstrap() + open_model() — the plaintext bytes never touch disk.

Encrypting a model

shell

magiclock protect-model models/face.onnx                       # -> models/face.onnx.enc
magiclock protect-model weights/net.pt --feature ai_model -o net.enc

--feature names the capability your license must grant to unlock it — it defaults to ai_model. The same expiry (--trial / --expires-in / --expires-at), portable (--no-bind-machine, --passphrase, --emit-key), and --lock-passphrase flags from Encrypting Code all apply here too.

Reading it back at runtime

python

import magiclock
import onnxruntime

magiclock.bootstrap()  # brings the gate up against this machine's license
model_bytes = magiclock.open_model("models/face.onnx.enc")  # plaintext, in memory only
session = onnxruntime.InferenceSession(model_bytes)

open_model() works the same way regardless of what's inside the envelope — ONNX, PyTorch state dicts, raw weight arrays, or arbitrary binary resources like configuration bundles or datasets. Anything bytes-shaped in, and back out.

Function reference

	Signature	Notes
`magiclock.bootstrap()`	`bootstrap(vault_dir=None, *, app_version="1.0.0") -> MagicLockRuntime`	Call once, before any `open_model()` (or gated code) runs. `magiclock build` inserts this call for you at the compiled entry point — you only call it by hand in plain (non-compiled) scripts. Raises if this machine hasn't been activated.
`magiclock.open_model()`	`open_model(path, *, vault_dir=None, passphrase=None, key=None) -> bytes`	Auto-detects machine-locked vs. portable envelopes. Pass `passphrase`/`key` for portable or two-factor artifacts instead of prompting.

See the full Python API Reference for exceptions and edge cases.

Memory-only, every time

Decrypted model bytes exist only in the process's memory for as long as you hold a reference to them — open_model() never writes a plaintext copy to disk, a temp directory, or anywhere else. If your inference framework needs a file path rather than bytes, keep it in a memory-backed location (e.g. an in-memory file object) rather than writing it out yourself, or the plaintext ends up on disk outside MagicLock's protection.

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