Files
RuView/tools/ruview-cli/src/commands/train.ts
rUv 3f462a254d feat(tools): scaffold ruview MCP server + CLI + ADR-104 (#705)
Adds two new npm packages that expose RuView's WiFi-DensePose
sensing capabilities outside the Cognitum appliance ecosystem:

- tools/ruview-mcp/ (@ruv/ruview-mcp) — MCP server with 6 tools:
  ruview_csi_latest, ruview_pose_infer, ruview_count_infer,
  ruview_registry_list, ruview_train_count, ruview_job_status.
  Uses @modelcontextprotocol/sdk with stdio transport.
  6/6 smoke tests pass. TypeScript strict mode, Node 20.

- tools/ruview-cli/ (@ruv/ruview-cli) — Yargs CLI with matching
  subcommands: csi tail, pose infer, count infer, cogs list,
  train count, job status. Same fail-open pattern as the cog
  binaries (WARN to stderr, exit 0 on unavailable sensing-server).

- docs/adr/ADR-104-ruview-mcp-cli-distribution.md — design rationale,
  6-row threat table, packaging plan, acceptance gates, failure modes.

- docs/research/sota-2026-05-22/HORIZON.md — 12-hour horizon plan
  with 7 milestones tracked (M1 complete in this commit).

Both packages are private:true pending the user's publish decision.
Inference is via subprocess to the signed cog binaries (ADR-100/101/103)
— no JS/WASM ML engine bundled.
2026-05-21 23:33:18 -04:00

120 lines
3.2 KiB
TypeScript

/**
* ruview train — Training commands.
*
* train count --paired <jsonl> — kick off a count-cog training run.
*/
import type { Argv } from "yargs";
import { randomUUID } from "node:crypto";
import { mkdirSync, appendFileSync, openSync } from "node:fs";
import path from "node:path";
import os from "node:os";
import { spawn } from "node:child_process";
import { loadConfig } from "../config.js";
export function trainCommand(cli: Argv): void {
cli.command(
"train <task>",
"Training commands",
(y) =>
y
.positional("task", {
choices: ["count"] as const,
description: "Which cog to train",
})
.option("paired", {
type: "string",
demandOption: true,
description:
"Path to the paired JSONL training file (produced by scripts/align-ground-truth.js)",
})
.option("epochs", {
type: "number",
default: 400,
description: "Training epochs (default: 400)",
})
.option("lr", {
type: "number",
default: 1e-3,
description: "Initial learning rate (default: 0.001)",
})
.option("output-dir", {
type: "string",
description: "Output directory for model artifacts",
}),
async (args) => {
const config = loadConfig();
const jobId = randomUUID();
const logDir = config.jobsDir;
mkdirSync(logDir, { recursive: true });
const logPath = path.join(logDir, `${jobId}.log`);
const queuedAt = Date.now() / 1000;
const outputDir =
(args["output-dir"] as string | undefined) ??
"v2/crates/cog-person-count/cog/artifacts";
const header = [
`# RuView training job ${jobId}`,
`# started: ${new Date().toISOString()}`,
`# task: ${args.task}`,
`# paired: ${args.paired}`,
`# epochs: ${args.epochs}`,
`# lr: ${args.lr}`,
`# output-dir: ${outputDir}`,
"",
].join("\n");
appendFileSync(logPath, header);
const logFdOut = openSync(logPath, "a");
const logFdErr = openSync(logPath, "a");
const cargoArgs = [
"run",
"--release",
"-p",
"wifi-densepose-train",
"--",
"--task",
"count",
"--paired",
args.paired as string,
"--epochs",
String(args.epochs),
"--lr",
String(args.lr),
"--output-dir",
outputDir,
];
const child = spawn("cargo", cargoArgs, {
detached: true,
stdio: ["ignore", logFdOut, logFdErr],
});
child.unref();
child.on("error", (e) => {
appendFileSync(logPath, `\n# ERROR: ${e.message}\n`);
});
child.on("close", (code) => {
appendFileSync(logPath, `\n# exit code: ${code}\n`);
});
process.stdout.write(
JSON.stringify(
{
ok: true,
job_id: jobId,
status: "running",
log_path: logPath,
queued_at: queuedAt,
note: `Poll with: ruview job status --id ${jobId}`,
},
null,
2
) + "\n"
);
}
);
}