rob thijssen da068ded6d
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Stage 7a-ii: real NCCL handshake behind the worker pool
Wires cudarc::nccl into the TP worker lifecycle introduced in 7a-i.
With --features cuda the leader and its workers now establish a live
NCCL communicator end-to-end; without the feature the same code paths
return Error{kind="cuda_feature_not_enabled"} so a misconfigured
build is obvious instead of silently no-op.

NCCL state machine (harness/tp/nccl_state.rs) is shared between the
worker process and the leader's pool:
- generate_comm_id_hex() mints an Id::new() on the leader.
- NcclState::init parses 256 hex chars → [c_char; 128] → Id::uninit,
  opens a CudaContext on the configured device, calls Comm::from_rank
  with the supplied (rank, world_size, id). NCCL blocks until every
  rank has joined.
- NcclState::sanity_check runs one all_reduce(1u32, Sum); the leader
  asserts every rank reports observed_sum == world_size.
- NCCL handles serialised under Mutex; unsafe impl Send/Sync gates
  the Comm across spawn_blocking boundaries (NCCL is move-safe; only
  concurrent op issuance is unsafe).

WorkerPool::init_nccl orchestrates the rendezvous:
1. Write Init { comm_id } to every worker's stdin (no await yet).
2. Leader rank 0 calls its own Comm::from_rank in spawn_blocking,
   concurrently with workers.
3. NCCL handshake completes for all ranks simultaneously.
4. Leader collects InitOk responses.
WorkerPool::nccl_sanity_check follows the same pattern over
all_reduce, validating world_size == observed_sum on every rank.

Worker.send_only / Worker.recv_only split out from the previous
monolithic Worker.request so the leader can interleave its own NCCL
work with the worker calls — required because NCCL blocks during
init.

Tests:
- 4 hex roundtrip unit tests for the wire encoding.
- The 7a-i "not implemented" expectation now reads
  "cuda_feature_not_enabled" on the local dev box (no CUDA), or
  accepts InitOk on a cuda-built test binary.
- New cuda-integration test in tp_worker_lifecycle_cuda.rs covers
  the real init + sanity round-trip; gated on the cuda-integration
  feature so default CI doesn't try to NCCL.

Verifiable on beast (2× RTX 5090):
  cargo test -p neuron --features cuda-integration \
        --test tp_worker_lifecycle_cuda

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-19 16:40:01 +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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