inferential 0.3.2

Robotics-aware inference orchestration on top of Ray Serve

Inferential

Multi-client inference orchestration on top of Ray Serve.

Inferential sits between your clients and your ML models. It receives observations over ZMQ, schedules inference requests using cadence-aware priority scoring, dispatches to Ray Serve, and streams results back — all with sub-millisecond transport overhead. Built for any scenario where multiple clients need concurrent access to shared models: robotics fleets, game agents, IoT devices, real-time ML pipelines.

Features

• ZMQ transport — ROUTER/DEALER sockets with automatic reconnection and zero-copy tensor payloads
• Pluggable schedulers — Deadline-aware (default), batch-optimized, priority-tiered, round-robin
• Cadence learning — EMA-based tracking of each client's request pattern to predict urgency
• Protobuf wire protocol — Typed tensor metadata (dtype, shape, encoding) with binary payload
• Queue management — Request TTL, drop-oldest overflow policy, dispatch retry
• In-memory metrics — Ring-buffer storage with label filtering and percentile stats (p50/p95/p99)
• Lightweight client SDK — No Ray dependency; just pyzmq, protobuf, and numpy

Install

# Client SDK only
pip install inferential

# Server with Ray Serve
pip install inferential[server]

# Development
pip install inferential[dev]

Quick Start

Server

import asyncio
from inferential import Server

server = Server(bind="tcp://*:5555", models=["policy-v2"])

@server.on_metric
def log(name, value, labels):
    if name == "inference_latency_ms":
        print(f"Client {labels.get('client')}: {value:.1f}ms")

asyncio.run(server.run())

Client

import numpy as np
from inferential import Connection

conn = Connection(server="tcp://localhost:5555", client_id="agent-01", client_type="sensor")
model = conn.model("policy-v2", latency_budget_ms=30.0)

state = np.random.randn(7).astype(np.float32)
model.observe(urgency=0.8, state=state)

result = model.get_result(timeout_ms=50)
if result is not None:
    actions = result["actions"]  # np.ndarray

Documentation

• Quick Start — Install, run server + client, get your first result
• Architecture — System design, wire protocol, schedulers, queue management, metrics, configuration
• Examples — Multi-client demos, metric callbacks, extending with custom models and schedulers
• Contributing — Commit conventions, branching, code style, pre-commit hooks, releases

Development

# Generate protobuf code
make proto

# Run tests
make test

# Lint
make lint

License

Apache-2.0