atomr-infer-runtime

Runtime-agnostic actors on top of atomr-core. Gateway, per-request lifecycle, coordinator, deployment manager, two-tier supervision — none of which knows or cares whether the underlying backend is a GPU or a remote network call.

Actors

Remote-network engine cores live in atomr-infer-remote-core — same actor shapes, different internals (HTTP/2 worker pool instead of CUDA streams).

Two-tier supervision — adopted, not reinvented

[dependencies]
atomr-infer-runtime = { workspace = true, features = ["local-gpu"] }

With the local-gpu feature, WorkerActor::supervisor_strategy() returns atomr_accel_cuda::error::device_supervisor_strategy() verbatim — three retries inside a 60-second window with the upstream ContextPoisoned / OutOfMemory / Unrecoverable decider. When a ModelRunner::execute returns InferenceError::CudaContextPoisoned, the ContextActor panics with the atomr_accel_cuda::error::CONTEXT_POISONED_TAG marker so the upstream supervisor routes the failure to Restart.

Without the feature, the same shape is preserved with an in-crate fallback strategy — useful when you embed the runtime-agnostic actors into a remote-only build that doesn't want atomr-accel in its dependency graph.

Feature flags

A canonical wiring

use inference_runtime::{
    ApiGatewayActor, DeploymentManagerActor, DpCoordinatorActor, GatewayConfig,
    MetricsActor, spawn_gateway,
};
use atomr_core::actor::{ActorSystem, Props};
use atomr_config::Config;

# async fn run() -> anyhow::Result<()> {
let sys = ActorSystem::create("inference", Config::reference()).await?;

let dp = sys.actor_of(Props::create(|| DpCoordinatorActor::new()), "dp")?;
let _mgr = sys.actor_of(Props::create(|| DeploymentManagerActor::new()), "mgr")?;
let _metrics = sys.actor_of(Props::create(|| MetricsActor::new()), "metrics")?;
let _gateway = spawn_gateway(&sys, GatewayConfig::default(), dp)?;
# Ok(())
# }