Pre-release

This release is a pre-release and may not be stable for production use.

Amesa Inference

A lightweight inference package for running Amesa agents using ONNX models without Ray or PyTorch dependencies.

Overview

composabl_inference provides a standalone inference engine for running trained Amesa agents. It uses ONNX Runtime for model inference, making it suitable for deployment scenarios where you want to avoid heavy dependencies like Ray and PyTorch.

Features

ONNX-based inference: Uses ONNX Runtime for efficient model inference
No Ray or PyTorch dependencies: Lightweight package suitable for production deployment
Network management: Supports both local and remote objects (agents, perceptors, controllers)
Compatible API: Similar interface to Trainer.package() for easy migration

Installation

pip install amesa-inference

Usage

Basic Inference

from composabl_inference import InferenceEngine
from composabl_core import Agent

# Create inference engine (only license needed for license validation)
engine = InferenceEngine(license="your-license-key")

# Load agent
agent = Agent.load("path/to/agent")
await engine.load_agent(agent)

# Package agent for inference (similar to Trainer.package())
await engine.package()

# Run inference
observation = {...}  # Your observation from the simulator
action = engine.execute(observation)

With Remote Objects

The inference engine supports remote agents, perceptors, and controllers, just like the Trainer:

from composabl_inference import InferenceEngine

# Optional: provide custom config for NetworkMgr (e.g., for remote targets)
config = {
    "target": {
        "local": {
            "address": "localhost:1337",
        },
    },
}

engine = InferenceEngine(license="your-license-key", config=config)
await engine.load_agent("path/to/agent")
await engine.package()

# The agent processor will automatically handle remote objects
action = engine.execute(observation)

Cleanup

# Clean up resources
await engine.close()

Architecture

Components

InferenceEngine: Main entry point for inference operations
NetworkMgr: Manages network connections (non-Ray version)
ONNXInferenceEngine: Handles ONNX model loading and inference
ONNXAgentProcessor: Processes agents using ONNX models instead of PyTorch

Differences from Trainer

Uses ONNX Runtime instead of PyTorch for model inference
NetworkMgr is not a Ray actor (runs in the same process)
No Ray initialization required
Lighter weight, suitable for production deployment

Requirements

Python >= 3.10
composabl-core
composabl-api
onnxruntime
numpy

License

Proprietary and confidential - Copyright (C) Amesa, Inc

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