Skip to content

Neat Library v0.1.0

Choose a tag to compare

View all tags
@github-actions github-actions released this 13 Jun 04:24
v0.1.0
f2b2c52
This commit was created on GitHub.com and signed with GitHub’s verified signature.
GPG key ID: B5690EEEBB952194
Verified
Learn about vigilant mode.

This first Neat Library release introduces the application framework for building AI pipelines on SiMa.ai Modalix. Neat gives developers a typed C++ and Python programming model for turning compiled model packages, media sources, preprocessing, inference, postprocessing, and application logic into repeatable edge AI pipelines.

Instead of wiring low-level runtime pieces by hand, developers can work with higher-level primitives such as Model, Graph, Run, Tensor, and Sample, then move from a single model invocation to a reusable production pipeline using the same API family.

Platform compatibility: Neat Library v0.1.0 is compatible with the SiMa.ai platform release 2.0.0. Use this release with the matching 2.0.0 SDK, runtime, and DevKit software line.

Installation

Neat Library v0.1.0 is included in the Neat Development Environment, also known as the Neat SDK, for the 2.0.0 release.

To install Neat Library v0.1.0 on any Modalix DevKit running the 2.0.0 platform release, run:

sima-cli neat install core@v0.1.0

Why This Release Matters

Neat v0.1.0 establishes the foundation for application development on Modalix:

  • A composable graph API for constructing deterministic AI and media pipelines.
  • C++20 APIs for production applications and Python bindings for fast development, testing, and automation.
  • A Model workflow for loading compiled model archives and reusing model routes for preprocessing, inference, postprocessing, or full end-to-end execution.
  • Typed tensor and sample contracts for shape, layout, dtype, storage, routing, timestamps, and stream metadata.
  • Runtime diagnostics and measurement APIs for latency, throughput, plugin timing, counters, and optional board power reporting.
  • Documentation and tutorials that cover first inference, asynchronous execution, benchmarking, preprocessing, multi-input models, RTSP ingest, graph composition, and GenAI workflows.

What Is Included

Core Application APIs

  • Public C++ and Python API surfaces for models, graphs, nodes, runs, tensors, samples, and runtime options.
  • One-shot run(...) execution and reusable build(...) / push(...) / pull(...) execution for synchronous and asynchronous application flows.
  • Model route composition so compiled model packages can be used as complete routes or as reusable graph fragments.
  • Improved graph build behavior, stage execution, strict sync behavior, backpressure handling, timeout handling, metadata reporting, and deterministic compilation paths.

PyNeat Python Bindings

  • A production-oriented pyneat package backed by nanobind.
  • Python access to graph, model, runtime, tensor, sample, benchmark, and GenAI types.
  • NumPy and PyTorch interoperability through DLPack-based tensor conversion.
  • Release fixes for Python enum exposure, including FormatSpec binding behavior.

Runtime, Media, and Preprocessing

  • Typed input/output nodes, model execution nodes, preprocessing nodes, media source and output paths, and transport-oriented pipeline components.
  • RTSP and still-image input coverage for application and tutorial workflows.
  • Multi-ROI preprocessing support and expanded validation for preprocessing and ROI contracts.
  • Static contract handling for runtime nodes, including model output and detection decode paths.
  • GStreamer initialization, plugin diagnostics, TensorBuffer behavior, and runtime bridge integration improvements.

GenAI and LLiMa Integration

  • Public GenAI headers, types, server integration paths, and tutorials for LLM, VLM, ASR, and graph-composed GenAI workflows.
  • Release metadata that tracks LLiMa as a Neat dependency for the same release line.
  • Documentation updates that use external-facing GenAI terminology consistently.

Documentation and Tutorials

  • A redesigned documentation structure covering Getting Started, model compilation, app development, API reference, tools, tutorials, and compatibility guidance.
  • C++ and Python tutorials that are self-contained and easier to copy into real projects.
  • Tutorial coverage for model execution, async inference, benchmarking, image preprocessing, detection outputs, multi-input models, RTSP streams, production graph design, and GenAI usage.
  • Generated API reference and search integration for the Developer Center experience.

Packaging and Release Infrastructure

  • Manifest-driven package metadata for Neat Library 0.1.0 and platform compatibility 2.0.0.
  • Package metadata support for platform compatibility so downstream installs can avoid mixing artifacts from different platform lines.
  • Release artifact publishing, install routing, docs deployment, and tag-aware latest package resolution.

Notable Fixes and Refinements

  • Fixed Python FormatSpec enum bindings before the release cutoff.
  • Improved benchmark support for multi-input models and Python benchmark access.
  • Fixed GenAI server teardown behavior so model registry cleanup releases MLA memory before shutdown.
  • Improved graph direct-source and preprocessing sizing behavior.
  • Fixed docs links, generated documentation routing, autodoc imports, and mobile documentation navigation.
  • Added tag-release handling so release artifacts can publish the expected latest.tag metadata for install tools.

Known Issues

Issue Impact Workaround Status
#396 Neat Library v0.1.0 install bundle does not include the LLiMa CLI package. Installations from core@v0.1.0 include the LLiMa runtime and development packages without the matching CLI package. Upgrade to Neat Library v0.2.0 with Neat SDK 2.1.2. Being considered for the next 0.1.x patch release.

Getting Started

Install Neat Library with the package set that matches platform release 2.0.0, then use the documentation tutorials as the recommended path from first model run to production graph composition.

For most applications, start with the Model API, then move to Graph when you need to combine model routes with media ingest, preprocessing, custom nodes, multi-stream execution, diagnostics, or GenAI components.

Release Scope

This is the first Neat Library release. It defines the initial public API and packaging baseline for external developers building on SiMa.ai Modalix with platform release 2.0.0.

Selected change references: #340, #339, #336, #333, #331, #330, #325, #317, #315, #313, #304, #299, #295, #264, #263, #256.

Assets 2
Loading