Karti Tripathi
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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. A model starts only if committed + requested + margin fits the budget. The refusal is the feature. - A 1 Hz watchdog on MemAvailable that stops the newest model before thrash. - 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, six direct dependencies, no async runtime outside the MCP surface. Published from the internal monorepo with a fresh history. The private development tree keeps its own history; nothing here carries it.
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Contributing to Lumbridge Compute
Contributions are welcome. Changes land as small, reviewable commits.
Development
cargo test
cargo fmt --check
cargo clippy --all-targets
cargo build --release
Changes to the lumbridge/v1 scene or eval contracts require fixtures, backward-
compatibility notes, and documentation. Never add secrets, private model tokens,
personal voice samples, internal hostnames, or arbitrary commands to shareable
scene/eval manifests.
Performance changes should include before/after JSON artifacts and the complete hardware/model/runtime configuration needed to reproduce them.