refactor: cortex talks to neurons instead of mistral.rs directly
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Replace NodeConfig (static vram_mb, pinned) with NeuronEndpoint.
Hardware discovery and model pinning now come from neuron API and
models.toml catalogue respectively.

- config.rs: nodes -> neurons, add models_config path
- catalogue.rs: ModelProfile with pinned_on, ModelCatalogue
- poller.rs: poll neuron GET /models (ModelInfo format)
- router.rs: resolve inference endpoint via neuron GET /models/{id}/endpoint
- evictor.rs: call neuron POST /models/unload
- node.rs: remove vram_mb, pinned fields (come from discovery/catalogue)
- All 22 gateway tests updated to mock neuron API
- Remove MistralModelsResponse, ModelLifecycleRequest (no longer needed)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
2026-04-15 14:42:52 +03:00
parent 26e5e7ead8
commit e42e8ee81f
19 changed files with 385 additions and 437 deletions

View File

@@ -1,29 +1,19 @@
//! Model eviction logic.
//!
//! The evictor runs as a background task. When the router determines that a
//! model needs to be loaded on a node but VRAM is tight, it can request
//! eviction via a channel. The evictor then:
//! 1. Identifies the LRU model on that node (excluding pinned models)
//! 2. Calls `POST /v1/models/unload` on the node
//! 3. Increments the lifecycle cycle counter (for defrag tracking)
//! The evictor identifies the LRU model on a node (excluding pinned models),
//! calls neuron's `POST /models/unload` to free the model, and updates
//! local state.
use crate::state::CortexState;
use cortex_core::node::{ModelLifecycleRequest, ModelStatus};
use cortex_core::node::ModelStatus;
use std::sync::Arc;
use std::time::Duration;
/// Runs forever. Currently a placeholder that periodically checks for
/// eviction opportunities. In the future, this will be driven by a
/// channel from the router when VRAM pressure is detected.
/// Runs forever. Placeholder for future channel-driven eviction.
pub async fn eviction_loop(fleet: Arc<CortexState>) {
// TODO: Replace this polling approach with a channel-driven design
// where the router sends eviction requests when it detects that a
// model load would exceed available VRAM.
loop {
tokio::time::sleep(Duration::from_secs(30)).await;
// Placeholder: the actual eviction logic is in `evict_lru_on_node`,
// called on demand by the router.
let _ = &fleet; // suppress unused warning
let _ = &fleet;
}
}
@@ -33,18 +23,19 @@ pub async fn evict_lru_on_node(
fleet: &CortexState,
node_name: &str,
) -> anyhow::Result<Option<String>> {
let (endpoint, candidate) = {
let (neuron_endpoint, candidate) = {
let nodes = fleet.nodes.read().await;
let Some(node) = nodes.get(node_name) else {
anyhow::bail!("node '{node_name}' not found");
};
// Find the loaded model with the oldest last_accessed, excluding pinned.
// Find the loaded model with the oldest last_accessed,
// excluding models pinned on this neuron (from catalogue).
let candidate = node
.models
.values()
.filter(|m| m.status == ModelStatus::Loaded)
.filter(|m| !node.pinned.contains(&m.id))
.filter(|m| !fleet.catalogue.is_pinned(&m.id, node_name))
.min_by_key(|m| m.last_accessed)
.map(|m| m.id.clone());
@@ -58,18 +49,16 @@ pub async fn evict_lru_on_node(
tracing::info!(node = node_name, model = %model_id, "evicting model");
let url = format!("{endpoint}/v1/models/unload");
// Call neuron's unload endpoint.
let url = format!("{neuron_endpoint}/models/unload");
let resp = fleet
.http_client
.post(&url)
.json(&ModelLifecycleRequest {
model_id: model_id.clone(),
})
.json(&serde_json::json!({ "model_id": model_id }))
.send()
.await?;
if resp.status().is_success() {
// Update local state.
let mut nodes = fleet.nodes.write().await;
if let Some(node) = nodes.get_mut(node_name) {
if let Some(entry) = node.models.get_mut(&model_id) {
@@ -77,14 +66,13 @@ pub async fn evict_lru_on_node(
}
node.lifecycle_cycles += 1;
// Check if we should flag for defrag.
if fleet.eviction.defrag_after_cycles > 0
&& node.lifecycle_cycles >= fleet.eviction.defrag_after_cycles
{
tracing::warn!(
node = node_name,
cycles = node.lifecycle_cycles,
"VRAM fragmentation threshold reached — consider restarting mistralrs"
"VRAM fragmentation threshold reached — consider restarting harness"
);
}
}