rob thijssen 8a2334eacb deploy: dnf-native version check + lair.cafe repo bootstrap
Replaces the string compare of 'git describe --tags' vs the binary's
self-reported --version (which lies about prereleases — every
0.1.16-* RPM reports just "0.1.16") with the dnf-native question of
"is the installed package current against what the repo offers".

Mechanism:
- installed_nvr(): rpm -q --qf '%{version}-%{release}' for the
  resident package, falling back to "(not installed)". Capturing rpm's
  output through a variable keeps its "package X is not installed"
  stdout message out of the result on failure.
- needs_update(): probes rpm -q first (treats absent as "needs work"),
  then asks dnf check-update --refresh -q. Other dnf failures collapse
  into "needs update" so the subsequent install surfaces a real error
  rather than this check swallowing one silently.
- ensure_lair_repo(): probes for /etc/yum.repos.d/lair-cafe-unstable.repo
  and adds it with `dnf config-manager addrepo` when missing. The
  upstream .repo file ships enabled=0 (unstable channel doesn't
  auto-engage on fetch), so we then run `dnf config-manager setopt
  lair-cafe-unstable.enabled=1` every run — cheap, idempotent.
- Cortex and neuron install branches now guard `systemctl stop` with
  `[ ! -f /usr/lib/systemd/system/...service ] || sudo systemctl stop`
  so fresh installs (no unit file yet) don't short-circuit the install
  step under set -e.
- dnf output is captured into a variable and only printed (with a
  [host]   prefix per line) on failure, so success stays quiet and
  failures show the actual diagnostic instead of being eaten by
  &> /dev/null.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-18 18:55:02 +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

Description
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