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Stage 7a-i: TP worker lifecycle scaffolding
Leader → worker process plumbing for tensor parallelism. The neuron
binary picks up two modes: default (the existing daemon, axum + HTTP)
and `--worker` (a bare RPC loop driven over stdin/stdout). The leader
spawns one worker per non-zero NCCL rank via tokio::process::Command
on the same binary path (production: /proc/self/exe; tests:
env!("CARGO_BIN_EXE_neuron")) and talks to each over newline-
delimited JSON.

Protocol (harness/tp/rpc.rs) is serde-tagged from the start —
WorkerRequest::{Ping, Init, NcclSanityCheck, Shutdown} and
WorkerResponse::{Pong, InitOk, NcclSanityResult, Bye, Error}, both
`#[serde(tag = "op", rename_all = "snake_case")]`. Adding ops in 7b/7c
is purely additive; unknown ops on the wire fail to parse (verified
in unit tests).

7a-i scope:
- WorkerPool::spawn(binary, world_size, devices) forks ranks 1..N as
  subprocesses, captures stdin/stdout, kills on drop.
- ping_all() round-trips a Ping to every worker and validates the
  returned rank.
- shutdown() sends Shutdown to each worker, awaits Bye, reaps.
- Worker mode: parse Ping/Shutdown, return Pong/Bye; Init and
  NcclSanityCheck return Error{kind="not_implemented_7a_i"} so a 7a-ii
  binary speaking the same wire is a drop-in replacement (the kind
  field signals "real NCCL lands in the next commit").
- CandleHarness::load_model refuses tensor_parallel > 1 with a clear
  message until 7b is in.

Three integration tests in tests/tp_worker_lifecycle.rs cover spawn/
ping/shutdown for 2- and 3-worker pools, plus the
not_implemented_7a_i contract test for Init. Seven rpc serde unit
tests assert the wire shape (op tags, field names, unknown-op
rejection). All pass on the dev host; no CUDA required.

Stage 7a-ii (next): the real NCCL Comm::from_rank wiring behind the
existing Init/NcclSanityCheck op surface, CUDA-gated. Verifiable on
beast's 2×5090.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-19 15:53:00 +03:00
2026-05-18 17:50:35 +03:00

cortex

A Rust reverse-proxy and fleet management layer for multi-node GPU inference clusters. Cortex sits in front of one or more neuron daemons (each running candle-based inference on a local GPU host) and presents a unified OpenAI + Anthropic compatible API surface.

Problem

Running local LLMs across multiple GPU nodes (different VRAM tiers, different model affinities) requires a unified API surface that:

  • Presents a single /v1/models catalogue merging every model that can be served by any neuron in the fleet.
  • Routes requests to the correct node based on where a model is loaded (or can be loaded), handling cold-load and eviction transparently.
  • Manages model lifecycle — load on demand, unload cold models, pin critical ones — by calling each neuron's /models/{load,unload} API.
  • Translates between OpenAI and Anthropic request/response envelopes so every client speaks whichever dialect it prefers.
  • Captures per-request metrics (tokens, tok/s, TTFT, latency) and exposes them as Prometheus counters/histograms.

Architecture

┌──────────────┐  ┌──────────┐  ┌────────────┐  ┌────────────┐
│ Claude Code  │  │ Zed/IDE  │  │ Tidal / mm │  │ curl / etc │
└──────┬───────┘  └─────┬────┘  └──────┬─────┘  └──────┬─────┘
       │                │              │               │
       └────────────────┴──────┬───────┴───────────────┘
                               │
                    ┌──────────▼──────────┐
                    │      cortex         │
                    │  (cortex-gateway)   │
                    │                     │
                    │  Router · Metrics   │
                    │  Evictor · Translate│
                    └──┬──────┬────────┬──┘
                       │      │        │
            ┌──────────▼┐  ┌──▼─────┐  ┌▼──────────┐
            │  neuron   │  │ neuron │  │  neuron   │
            │  :13131   │  │ :13131 │  │  :13131   │
            │  candle   │  │ candle │  │  candle   │
            └───────────┘  └────────┘  └───────────┘
                  private network (.internal)

Crates

Crate Purpose
cortex-core Shared types: config, node/model state, metrics, OpenAI/Anthropic envelopes, harness trait, discovery types
cortex-gateway Axum HTTP server: proxy, router, evictor, poller, metrics exporter
neuron Per-node daemon: GPU discovery, in-process candle inference, model lifecycle API
cortex-cli CLI entrypoint (cortex serve, cortex status, etc.)

Node setup

Each GPU node runs neuron (listening on :13131). Neuron uses huggingface/candle for in-process inference — there is no external inference subprocess to manage.

The neuron RPM (helexa-neuron) ships a systemd unit:

dnf copr enable helexa/helexa
dnf install helexa-neuron
systemctl enable --now neuron

Gateway config

# /etc/cortex/cortex.toml
[gateway]
listen = "0.0.0.0:31313"
metrics_listen = "0.0.0.0:31314"

[eviction]
strategy = "lru"        # lru | priority
defrag_after_cycles = 50

[[neurons]]
name = "beast"
endpoint = "http://beast.internal:13131"

[[neurons]]
name = "benjy"
endpoint = "http://benjy.internal:13131"

Model placement profiles live in models.toml — see models.example.toml.

Building

cargo build --release

CI

Every push triggers format, lint, and test checks. Ensure these pass locally before pushing:

cargo fmt --check --all                    # must be clean
cargo clippy --workspace -- -D warnings   # warnings are errors
cargo test --workspace                     # all tests must pass

Tagged releases (v*) additionally build SRPMs for both cortex and helexa-neuron and publish to COPR.

Running

# start the gateway
cortex serve --config /etc/cortex/cortex.toml

# check fleet status
cortex status

# list all models across nodes
curl http://localhost:31313/v1/models

License

GPL-3.0

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