Karti Tripathi
75aa63d737
Governed compute for unified-memory AI hardware — the machines where CPU and GPU share one pool and there is no separate VRAM allocation to bounce off. Over-commit that pool and the box thrashes and wedges, SSH and ping included, before the OOM killer gets a turn. Compute does not run inference. It supervises the servers that do: - Admission control against two ceilings: a declared budget, and what the machine actually has free. The refusal is the feature. - A 1 Hz watchdog on MemAvailable that stops the newest model before thrash, and defers to a Scene transition rather than racing it. - Scenes: named sets of models activated as one transactional unit, with pre-flight validation and rollback to the previously active Scene on failure. Scenes reference model ids, never weight paths or commands, so a Scene obtained from elsewhere cannot introduce code. - Process ownership bound to (boot_id, pid, start_time_ticks, pgid == pid), so a reused PID can never be group-killed. - A protocol-transparent TCP gateway, so clients keep one address while model runtimes move behind it. - An MCP server, so agents drive the node as tools rather than as a CLI. One binary, no async runtime outside the MCP surface. Apache-2.0. Generated from the internal monorepo by scripts/publish-compute.sh, which refuses to publish a tree it cannot prove clean.
1.8 KiB
Lumbridge Compute evaluations (lumbridge/v1)
Lumbridge Compute evaluates the model configuration that is actually serving: weights, quantization, context, runtime, parsers, and speculative decoder. A model name without its serving configuration is not a reproducible benchmark target.
lumbridge-compute eval ls
lumbridge-compute eval run smoke
lumbridge-compute eval run performance --model brain --repeat 5
lumbridge-compute eval run finance-core --base-url http://your-node:8001/v1
Suites live in evals/*.eval.yaml. Each case has a stable id, prompt, category,
generation limit, and deterministic assertions. Runs produce append-only JSON in
eval-results/ with raw outputs and per-sample metrics.
Metrics
- TTFT: wall time until the first streamed content or reasoning token.
- Prefill tok/s (approximate): API-reported prompt tokens divided by TTFT. This is client-observed and includes queueing/scheduling; server-native prefill metrics should be added as a separate source rather than conflated with it.
- Decode tok/s: completion tokens divided by time after the first token.
- Score: share of samples satisfying every declared assertion.
Performance runs should include warmups in automation and record hardware, Lumbridge Compute scene, runtime version, model revision, and cold/warm cache state. The v1 artifact is deliberately local and portable; a future registry can ingest the same JSON.
Eval-to-training bridge
Capability suites should graduate into environment packages containing a dataset,
harness, and reward function. That common contract can be adapted to Prime
Intellect verifiers for evaluation, synthetic-data generation, SFT, or RL with
prime-rl. Lumbridge Compute owns scene scheduling, memory admission, checkpoints, and process
lifecycle; the training framework owns optimization and distributed execution.