live scene: per-model lanes (A100 inference), not per-host
The scene's framing was wrong. It's about the A100 doing live model predictions, not about per-host telemetry collection. Lanes now key on `model` instead of `host_id`; the callout leads with model name + A100 latency, demoting host/profile to secondary metadata. Stats line reads "N models · X infer/sec · last window from <host>" instead of "N hosts · model: X". Demo synthesis updated to match: 5 trained models cycle through predictions on rotating fleet windows, each model with its own accuracy + latency profile (KNN fast/loose, BERT slow/precise) so the lanes visually differ. Article prose reframes the scene as side-by-side model agreement, the natural read of per-model lanes. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
Max Gorog
parent
804220d7f6
commit
5533043b02
2 changed files with 53 additions and 45 deletions
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@ -2121,19 +2121,20 @@ def train_nn(*, model, X_train, y_train, X_val, y_val,
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rebuildLegend();
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})();
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// ── Live detections (scene: live) ─────────────────────────────
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// Per-host swim lanes of recent prediction cells, plus a "latest
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// detection" callout. New cells push in from the right; lane caps
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// at MAX_CELLS so memory stays bounded across long sessions. When
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// ── A100 live inference (scene: live) ─────────────────────────
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// Per-MODEL swim lanes of recent prediction cells. Each row is a
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// model the A100 is running inference with; each cell is one
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// ten-second-window prediction colored by predicted phase. When
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// the producer also sends `actual`, mismatch cells get a hatched
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// overlay and the running hit-rate updates.
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// overlay and the running hit-rate updates. The "latest" callout
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// below the lanes leads with the model name + predicted phase.
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(function () {
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const lanesEl = document.getElementById('live-lanes');
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const latestEl = document.getElementById('live-latest');
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const statsHosts = document.getElementById('live-stats-hosts');
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const statsRate = document.getElementById('live-stats-rate');
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const statsModel = document.getElementById('live-stats-model');
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const statsAcc = document.getElementById('live-stats-acc');
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const statsModels = document.getElementById('live-stats-hosts'); // id kept for HTML compat
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const statsRate = document.getElementById('live-stats-rate');
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const statsHost = document.getElementById('live-stats-model'); // id kept; meaning flipped
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const statsAcc = document.getElementById('live-stats-acc');
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if (!lanesEl) return;
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const MAX_CELLS = 60;
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@ -2142,27 +2143,27 @@ def train_nn(*, model, X_train, y_train, X_val, y_val,
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const lanes = new Map();
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const eventTimes = [];
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let totalCorrect = 0, totalLabeled = 0;
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let lastModel = null;
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let lastHost = null;
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function ensureLane(hostId) {
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if (lanes.has(hostId)) return lanes.get(hostId);
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function ensureLane(modelName) {
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if (lanes.has(modelName)) return lanes.get(modelName);
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const row = document.createElement('div');
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row.className = 'live-lane';
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row.innerHTML = `
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<div class="live-lane-host" title="${hostId}">${hostId}</div>
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<div class="live-lane-host" title="${modelName}">${modelName}</div>
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<div class="live-lane-cells"></div>`;
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lanesEl.appendChild(row);
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const lane = { row, cellsEl: row.querySelector('.live-lane-cells'), cells: [] };
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lanes.set(hostId, lane);
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lanes.set(modelName, lane);
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return lane;
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}
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function paintCell(d) {
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const lane = ensureLane(d.host_id);
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const lane = ensureLane(d.model || 'unknown');
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const cell = document.createElement('div');
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cell.className = `live-cell ${d.predicted}`;
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if (d.actual && d.actual !== d.predicted) cell.classList.add('miss');
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cell.title = `${d.host_id} · w${d.window_idx ?? '?'} · ${d.predicted}` +
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cell.title = `${d.model ?? '?'} · ${d.host_id ?? '?'} w${d.window_idx ?? '?'} · ${d.predicted}` +
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(d.actual ? ` (truth: ${d.actual})` : '') +
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(d.confidence != null ? ` · conf ${d.confidence.toFixed(2)}` : '');
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lane.cellsEl.appendChild(cell);
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@ -2187,10 +2188,11 @@ def train_nn(*, model, X_train, y_train, X_val, y_val,
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latestEl.innerHTML = `
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<div class="live-phase-block ${d.predicted}">${phaseLabel}</div>
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<div class="live-meta">
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<div class="live-meta-host">${d.host_id}</div>
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<div class="live-meta-line">profile: <code>${d.profile ?? '—'}</code></div>
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<div class="live-meta-line">model: <code>${d.model ?? '—'}</code>${
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d.latency_ms != null ? ` · ${d.latency_ms.toFixed(0)} ms` : ''
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<div class="live-meta-host">model: ${d.model ?? '—'}${
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d.latency_ms != null ? ` · A100 ${d.latency_ms.toFixed(1)} ms` : ''
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}</div>
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<div class="live-meta-line">window from: <code>${d.host_id ?? '—'}</code>${
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d.profile ? ` · profile <code>${d.profile}</code>` : ''
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}</div>
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<div class="live-meta-line">${truthLine}</div>
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</div>
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@ -2204,10 +2206,10 @@ def train_nn(*, model, X_train, y_train, X_val, y_val,
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const now = Date.now();
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while (eventTimes.length && now - eventTimes[0] > RATE_WINDOW_MS) eventTimes.shift();
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const rate = (eventTimes.length / (RATE_WINDOW_MS / 1000)).toFixed(1);
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statsHosts.textContent = `${lanes.size} host${lanes.size === 1 ? '' : 's'}`;
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statsRate.textContent = `${rate} / sec`;
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statsModel.textContent = `model: ${lastModel ?? '—'}`;
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statsAcc.textContent = totalLabeled > 0
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statsModels.textContent = `${lanes.size} model${lanes.size === 1 ? '' : 's'}`;
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statsRate.textContent = `${rate} infer / sec`;
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statsHost.textContent = `last window: ${lastHost ?? '—'}`;
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statsAcc.textContent = totalLabeled > 0
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? `hit-rate: ${(100 * totalCorrect / totalLabeled).toFixed(0)}% (${totalCorrect}/${totalLabeled})`
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: `hit-rate: —`;
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}
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@ -2224,13 +2226,13 @@ def train_nn(*, model, X_train, y_train, X_val, y_val,
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lanes.clear();
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eventTimes.length = 0;
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totalCorrect = 0; totalLabeled = 0;
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lastModel = null;
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latestEl.innerHTML = '<div class="live-latest-empty">awaiting <code>live_detection</code> events from the inference loop</div>';
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lastHost = null;
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latestEl.innerHTML = '<div class="live-latest-empty">awaiting <code>live_detection</code> events from the A100 inference loop</div>';
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updateStats();
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}
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function paintDetection(d) {
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eventTimes.push(Date.now());
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if (d.model) lastModel = d.model;
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if (d.host_id) lastHost = d.host_id;
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if (d.actual) {
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totalLabeled++;
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if (d.actual === d.predicted) totalCorrect++;
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@ -2239,47 +2241,53 @@ def train_nn(*, model, X_train, y_train, X_val, y_val,
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paintLatest(d);
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}
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function handleDetection(d) {
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if (!d.host_id || !d.predicted) return;
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if (!d.model || !d.predicted) return;
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paintDetection(d);
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}
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on('live_detection', m => {
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if (!m.host_id || !m.predicted) return;
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if (!m.model || !m.predicted) return;
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cachedReal.push(m);
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if (cachedReal.length > REAL_CACHE_CAP) cachedReal.shift();
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if (!demoActive) paintDetection(m);
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});
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// Synthetic demo: 5 hosts, walk through phases, ~92% accuracy.
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// Synthetic demo: A100 cycling through 5 trained models, scoring
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// windows from rotating fleet hosts. Each model has its own
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// accuracy + latency profile (KNN faster but less accurate;
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// BERT slower but more accurate) so the lanes look distinct.
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let demoTimer = null;
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function demoStart() {
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demoActive = true;
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clearLanes();
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if (demoTimer) clearInterval(demoTimer);
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const HOSTS = [
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{ id: 'elliott-lab', profile: 'cpu-saturate', phaseIdx: 0 },
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{ id: 'elliott-thinkpad', profile: 'ransomware-lite', phaseIdx: 0 },
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{ id: 'k-gamingcom', profile: 'bursty-c2', phaseIdx: 1 },
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{ id: 'smelliott', profile: 'fork-bomb', phaseIdx: 0 },
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{ id: 'gosling', profile: 'crypto-miner', phaseIdx: 2 },
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const MODELS = [
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{ name: 'knn', acc: 0.78, latMs: 0.4, phaseIdx: 0 },
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{ name: 'rnn', acc: 0.86, latMs: 1.8, phaseIdx: 0 },
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{ name: 'gru', acc: 0.91, latMs: 2.4, phaseIdx: 1 },
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{ name: 'lstm', acc: 0.93, latMs: 3.2, phaseIdx: 2 },
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{ name: 'bert', acc: 0.95, latMs: 8.6, phaseIdx: 0 },
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];
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const HOSTS = ['elliott-lab', 'elliott-thinkpad', 'k-gamingcom'];
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const PROFILES = ['cpu-saturate', 'ransomware-lite', 'bursty-c2', 'fork-bomb', 'crypto-miner'];
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const PHASES = ['clean', 'armed', 'infecting', 'infected_running', 'dormant'];
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let counter = 0;
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demoTimer = setInterval(() => {
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const host = HOSTS[counter % HOSTS.length];
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const m = MODELS[counter % MODELS.length];
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counter++;
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if (Math.random() < 0.18) host.phaseIdx = (host.phaseIdx + 1) % PHASES.length;
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const truth = PHASES[host.phaseIdx];
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const right = Math.random() < 0.92;
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if (Math.random() < 0.18) m.phaseIdx = (m.phaseIdx + 1) % PHASES.length;
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const truth = PHASES[m.phaseIdx];
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const right = Math.random() < m.acc;
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const predicted = right
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? truth
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: PHASES[(host.phaseIdx + 1 + Math.floor(Math.random() * 4)) % PHASES.length];
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: PHASES[(m.phaseIdx + 1 + Math.floor(Math.random() * 4)) % PHASES.length];
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handleDetection({
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host_id: host.id, profile: host.profile,
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host_id: HOSTS[counter % HOSTS.length],
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profile: PROFILES[counter % PROFILES.length],
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predicted, actual: truth,
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confidence: 0.62 + Math.random() * 0.36,
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model: 'lstm',
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latency_ms: 3 + Math.random() * 4,
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model: m.name,
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latency_ms: m.latMs + (Math.random() - 0.5) * m.latMs * 0.3,
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episode_id: 'demo',
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window_idx: counter,
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t_wall: Date.now() / 1000,
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@ -1314,6 +1314,6 @@
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</article>
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</div>
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<script src="/static/dashboard.js?v=2a8b8c03"></script>
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<script src="/static/dashboard.js?v=48576a1a"></script>
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</body>
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</html>
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