Mobilint NPU Python

Shared runtime support for applications that run MXQ models on Mobilint NPUs or ONNX models through ONNX Runtime. mblt-npu-python provides the common backend, device-selection rules, Hugging Face artifact resolution, and model-detail logging used by Mobilint Python packages. It is a library dependency, rather than an end-user model catalog.

Version 0.0.0 is the initial standalone release.

logging ships here rather than with its only caller because the two are mutually dependent — npu_backend imports log_model_details, and log_model_details reads a MobilintNPUBackend's fields.

Installation

pip install mblt-npu-python

This package requires a supported Linux environment with mobilint-qb-runtime available and Python 3.10 through 3.12.

Public API

Import the backend from mblt_npu:

from mblt_npu import MobilintNPUBackend

backend = MobilintNPUBackend(
    mxq_path="model.mxq",
    core_mode="single",
)
backend.create()
try:
    backend.launch()
    outputs = backend.mxq_model.infer([input_tensor])
finally:
    backend.dispose()

MobilintNPUBackend selects the appropriate implementation from target_device (default: "aries-rb"). "aries-rb" selects MobilintAriesBackend; "regulus-ra" and "regulus-rb" select MobilintRegulusBackend. The former generic values "aries" and "regulus" remain accepted when loading older configurations. backend_class_for() and BACKEND_CLASSES are available for integrations that need to inspect the supported targets.

Multi-slot MXQ execution

max_batch_size is aggregate capacity. At create(), the backend probes the compiled per-model capacity K and loads ceil(max_batch_size / K) model slots. Slots are distributed round-robin over the devices named by canonical target strings and reuse one accelerator per device. mxq_model and acc continue to refer to slot zero for compatibility; concurrent callers can use infer_slot(slot_index, inputs). Allocation failures dispose all created slots and raise MobilintBackendAllocError with the failed slot and device.

Hub-backed configurations retain name_or_path, revision, and commit_hash through to_dict() / from_dict(). Artifact lookup never substitutes an unpinned revision or an unrelated cached MXQ.

For ONNX inference, install the optional runtime extra and use ONNXBackend:

pip install "mblt-npu-python[onnxruntime]"

from mblt_npu import ONNXBackend

backend = ONNXBackend("model.onnx")
backend.create()
outputs = backend({"images": input_array})
backend.dispose()

ONNXBackend imports onnxruntime only when it creates a session.

Most users should access the backend through a model package such as mblt-vision-python, which owns model configuration, preprocessing, and postprocessing.

Testing helpers

The optional test extra provides a shared pytest plugin with NPU options and the npu_params fixture used by Mobilint package test suites:

pip install "mblt-npu-python[test]"

Import mblt_npu.pytest_plugin from a repository's root tests/conftest.py to register its options. The plugin is intentionally not auto-registered, so projects control when those command-line options are exposed.

Support and issues

For installation, runtime, or integration support, visit the Mobilint forum. Report reproducible package issues in the mblt-npu-python issue tracker.

License

Distributed under the BSD 3-Clause License.