Resolves the candle harness's HuggingFace cache directory with the
following precedence (first hit wins):
1. Explicit `hf_cache` in `[harness.candle]` from neuron.toml.
2. `HF_HUB_CACHE` env var — the Python `huggingface_hub` convention.
The Rust hf-hub crate doesn't read this natively, so we bridge here.
3. `HF_HOME` env var (`$HF_HOME/hub` per the canonical layout).
4. None — falls through to hf-hub's own default.
Honouring HF_HUB_CACHE lets a neuron host reuse an existing cache
directory shared with Python tooling or other harnesses on the same
host without per-tool config. The canonical per-host setup is a
systemd drop-in:
/etc/systemd/system/neuron.service.d/local.conf
[Service]
Environment=HF_HUB_CACHE=/archive/hf-cache
neuron.example.toml documents the resolution chain inline.
script/validate-neuron.sh: bump LOAD_TIMEOUT from 600s to 3600s and
expose both load/infer timeouts via env (NEURON_LOAD_TIMEOUT,
NEURON_INFER_TIMEOUT). A Qwen3.6-class dense model is ~54 GB and was
hitting the 10-min ceiling cold-downloading on a residential link.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Stage 5 of the candle-native pivot. Adds first-class support for
auto-loading a configured set of models when the neuron service
activates.
Config:
- NeuronConfig.default_models: Vec<ModelSpec> (defaults to []).
- neuron.example.toml ships a commented [[default_models]] example.
Activation flow (crates/neuron/src/startup.rs::load_default_models):
- Sequential — VRAM contention makes parallel loads risky.
- Per-entry timing logged at info level on success.
- Failures logged as warnings; the next entry is still attempted.
- An empty list short-circuits without log noise.
Called from main.rs after the registry is built and before the axum
listener binds, so /models reflects the loaded state from the very
first request.
data/neuron.service gains TimeoutStartSec=1800s. With activation
blocked on potentially slow first-time HF downloads + GGUF
materialisation, systemd's default 90s would kill larger model loads
mid-flight.
Two non-gated tests in tests/activation.rs cover the
continues-past-failure and empty-list paths using a synthetically
unknown harness name to fail loads fast without touching the network.
The cuda-integration test from earlier stages still exercises the
real load/unload lifecycle.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Stage 2 of the candle-native pivot. Fleshes out CandleHarness with a
LoadedModel registry keyed by model_id, hf-hub-backed GGUF download,
and Qwen3 quantized weight construction via candle-transformers'
quantized_qwen3 module. unload_model drops the entry; Drop on the
candle ModelWeights frees device memory.
Device selection prefers CUDA (gated behind the new `cuda` feature),
falling back to CPU when CUDA is unavailable so default builds work
on non-GPU hosts. The candle CUDA toolchain isn't pulled in unless
`--features cuda` is passed, keeping CI green on CPU runners.
Config gains a [harness.candle] block with an optional hf_cache path.
HarnessRegistry::from_configs now takes HarnessSettings so per-harness
config flows through.
A gated tests/candle_lifecycle.rs exercises real load → list → unload
→ list-empty when run with `--features cuda-integration` against a
host with HF network access. The default-feature test in tests/api.rs
covers the wrong-harness rejection path without needing the network.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Stage 1 of the candle-native pivot. Replaces the external-process
harness model (mistralrs over HTTP, llamacpp placeholder) with an
in-process Harness trait whose sole implementation is candle. The
trait keeps its shape so future engines slot in additively, but
start/stop default to no-ops and HarnessConfig drops endpoint and
systemd_unit since no harness needs external supervision.
Behaviour is unchanged on the wire: load_model returns a "not
implemented yet (Stage 2)" error and list_models is empty. The
gateway-side proxy, poller, and router are untouched.
CLAUDE.md Phase 11 (llama.cpp) and Phase 12 (mistral.rs COPR) are
marked superseded; the staged plan lives in
~/.claude/plans/create-a-more-aggressive-calm-naur.md.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Previous defaults collided with well-trodden infra services and with
the Linux ephemeral port range:
- cortex API 8000 — common dev-server default (Django, minio UI)
- cortex metrics 9100 — Prometheus node_exporter default
- neuron API 9090 — Cockpit default on Fedora, Prometheus self
Move to helexa-themed palindromic ports, all below Linux's
32768-60999 ephemeral range and not registered to any well-known
service:
- cortex API 31313
- cortex metrics 31314
- neuron API 13131
Updated places:
- cortex.example.toml, neuron.example.toml defaults
- default impls in cortex-core and neuron config
- cortex-cli --endpoint default for the status subcommand
- doc comments citing example URLs
- README.md and CLAUDE.md snippets
Consumers already on the old ports need a one-line edit in their
/etc/cortex/cortex.toml or /etc/neuron/neuron.toml to match;
firewall rules and prometheus scrape configs will also need
updating.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The neuron package was shipping its config at /etc/cortex/neuron.toml,
which implied a shared config directory between two independent
packages. Move to /etc/neuron/neuron.toml — neuron owns its own etc
dir, consistent with its own /usr/lib/sysusers.d/neuron.conf and
/usr/lib/systemd/system/neuron.service. Updated the systemd unit's
ExecStart path and the example toml header to match.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- cortex.spec: gateway binary, cortex.service systemd unit,
cortex.toml + models.toml config files
- neuron.spec: neuron binary, neuron.service systemd unit,
neuron.toml config file
- Parallel CI: srpm-cortex and srpm-neuron jobs build SRPMs
concurrently, then publish to separate COPR repos
(helexa/cortex and helexa/neuron)
- Shared cortex user/group across both packages
- Example configs: cortex.example.toml, neuron.example.toml,
models.example.toml
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
