v1.5: Release notes
The Voyager SDK makes it easy to build high-performance inferencing applications with Axelera AI Metis devices. The sections below provide links to code examples, tutorials and reference documentation.
Important
This is a production-ready release of Voyager SDK. Software components and features that are in development are marked "[Beta]" indicating tested functionality that will continue to grow in future releases or "[Experimental]" indicating early-stage feature with limited testing.
| Document | Description |
|---|---|
| Installation guide | Explains how to setup the Voyager SDK repository and toolchain on your development system |
| Quick start guide | Explains how to deploy and run your first model |
| Windows getting started guide | Explains how to install Voyager SDK and run a model in Windows 11 |
| AxDevice manual | AxDevice is a tool that lists all Metis boards connected to your system and can configure their settings |
| Board firmware update guide | Explains how to update your board firmware (for customers with older boards who have received instructions) |
| Document | Description |
|---|---|
| Model zoo | Lists all models supported by this release of the Voyager SDK |
Deployment manual (deploy.py) |
Explains all options provided by the command-line deployment tool |
| Custom weights tutorial | Explains how to deploy a model using your own weights |
| Custom model tutorial | Explains how to deploy a custom model |
| Document | Description |
|---|---|
| Benchmarking guide | Explains how to measure end-to-end performance and accuracy |
Inferencing manual (inference.py) |
Explains all options provided by command-line interencing tool |
| Application integration tutorial (high level) | Explains how to integrate a YAML pipeline within your application |
| Application integration tutorial (low level) | Explains how to integrate an AxInferenceNet model within your application |
The Voyager SDK allows you to develop inferencing pipelines and end-user applications at different levels of abstraction.
| API | Description |
|---|---|
| InferenceStream (high level) | Library for directly reading pipeline image and inference metadata from within your application |
| AxInferenceNet (middle level) | C/C++ API reference for integrating model inferencing and pipeline construction directly within an application |
| AxRuntime (low level) | Python and C/C++ APIs for manually constructing, configuring and executing pipelines |
| GStreamer | Plugins for integrating Metis inferencing within a GStreamer pipeline |
The InferenceStream library is the easiest to use and enables most users to achieve the highest performance. The lower-level APIs enable expert users to integrate Metis within existing video streaming frameworks.
The Voyager SDK makes it easy to construct pipelines that combine multiple models in different ways. A number of end-to-end reference pipelines are provided, which you can use as templates for your own projects.
| Directory | Description |
|---|---|
/ax_models/reference/parallel |
Multiple pipelines running in parallel |
/ax_models/reference/cascade |
Cascaded pipelines in which the output of one model is input to a secondary model |
/ax_models/reference/cascade/with_tracker |
Cascaded pipelines in which the output of the first model is tracked prior to being input to a secondary model |
/ax_models/reference/image_preprocess |
Pipelines in which the camera input is first preprocessed prior to being used for inferencing |
This section provides links to additional documentation available in the Voyager SDK repository.
| Document | Description |
|---|---|
| Advanced deployment tutorials | Advanced deployment options [experimental] |
| AxRunmodel manual | AxRunModel is a tool that can run deployed models on Metis hardware using different features available in the AxRuntime API (such as DMA buffers, double buffering and multiple cores) |
| Compiler CLI | Compiler Command Line Interface [beta] |
| Compiler API | Python Compiler API [experimental] |
| ONNX operator support | List of ONNX operators supported by the Axelera AI compiler |
| Thermal Guide | Document detailing the thermal behavior for Metis and instructions to make changes |
| SLM/LLM inference tutorial | Explains how to run Language Models on Metis devices [experimental] |
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