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ci: add build-prerelease workflow for CUDA RPMs on rpm.lair.cafe
Adds a manually-triggered workflow that builds CUDA-flavoured neuron
binaries and a CPU cortex binary, packages them as Fedora RPMs, signs
them, and rsyncs to the unstable channel at
https://rpm.lair.cafe/fedora/43/x86_64/unstable/. Mirrors the build
pipeline used by grenade/mistralrs-package.

Pipeline:
- prepare: derive {version,short_sha,commit_date} from the checkout;
  the prerelease Release stamp "0.1.YYYYMMDDgitSHORTSHA" sorts below
  the eventual "1" stable release.
- build-cortex: cargo build --release -p cortex-cli on a rust runner.
- build-neuron: matrix over ada (sm_89) and blackwell (sm_120) on
  cuda-13.0 runners; cargo build with features "cuda cudnn flash-attn"
  and CUDA_COMPUTE_CAP set per flavour.
- package-{cortex,neuron}: rpmbuild on the rpm runner against the new
  prebuilt-binary specs in rpm/.
- publish: import signing key, sign RPMs, rsync to oolon, createrepo_c
  --update, then regenerate packages.json for the UI.

New specs are prebuilt-binary variants — they consume the artifact
from the build job rather than running cargo at rpmbuild time. Each
helexa-neuron-{flavour} package Conflicts with the other flavours and
with helexa-neuron (the future source-build stable package) so one
flavour is installed at a time on a given host.

neuron crate gains cudnn and flash-attn feature flags forwarding to
the corresponding candle features, so the CI build command compiles
those kernels into the binary.

sccache is intentionally NOT used in the prerelease jobs — CUDA
compute cap isn't in its cache key, so flavours would mis-hit each
other. Each prerelease build is a clean cargo build.

Required Gitea secrets (already in place for cortex.spec / COPR
workflow):
- RPM_SIGNING_KEY, RPM_SIGNING_KEY_ID
- RSYNC_SSH_KEY

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-18 17:01:35 +03:00
2026-04-16 17:45:25 +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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