voyager-deploy

Deploy Model Pipeline

Compile and deploy a model pipeline for Axelera AI hardware

Use This Skill When / Not When

• Use when: the user explicitly asks to compile, quantize, or calibrate a model with deploy.py, especially custom or non-prebuilt models.
• Not when: the model is a prebuilt zoo network for a demo -- route to voyager-launch (it uses axdownloadmodel).
• Not when: they only want to run an already-deployed model -- route to voyager-run.

Instructions

Deploy the specified model/pipeline: $ARGUMENTS

{{INCLUDE common/voyager-sdk-setup.md}}

{{INCLUDE common/voyager-task-integration.md}}

Step 1: Pre-Deployment Checks

• Verify Axelera environment is activated:

  echo $AXELERA_FRAMEWORK
  

• If not set, activate with: source venv/bin/activate
• Read .voyager-runtime.json. Verify Axelera hardware only when execution.mode is execute_on_device:

  axdevice
  

  In package_for_linux mode, prepare the deployment commands and caveat rather than claiming hardware compilation or runtime validation.

Step 2: Model/Pipeline Identification

• Parse model name from arguments
• If no model specified, list available models:

  ./deploy.py --help
  

• Locate the YAML configuration in ax_models/
• Verify YAML file exists and is valid

Step 3: Deployment Mode Selection

For prebuilt Voyager SDK model-zoo networks, prefer axdownloadmodel and cached payload checks. Use deploy.py only when the user explicitly asks for compilation/deployment or when integrating a custom/non-prebuilt model.

Choose appropriate deployment mode:

• --mode PREQUANTIZED (default): Compile from a pre-quantized model
• --mode QUANTCOMPILE: Quantize and compile the model
• --mode QUANTIZE: Quantize the model (will NOT deploy pipeline)
• --mode QUANTIZE_DEBUG: Quantize with debug outputs
• --export: Export quantized/compiled model to zip (separate flag, not a mode)

Step 4: Target Configuration

• --metis {auto,none,pcie,m2}: Target Axelera device type (default: auto/detect)
• --aipu-cores <n>: Number of AIPU cores to use (1-4)