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var(--deck-line);background:#0b1118;color:var(--deck-text);padding:12px;font-size:13px;line-height:1.5;white-space:pre-wrap}.empty{display:grid;place-items:center;gap:8px;min-height:180px;color:var(--deck-muted);border:1px dashed var(--deck-line-strong);border-radius:var(--radius-lg);text-align:center;padding:16px}.empty strong{color:var(--deck-text);font-size:13px}.empty span{font-size:12px}:root[data-theme=light] .brandMark,:root[data-theme=light] .readinessOrb,:root[data-theme=light] .envFact,:root[data-theme=light] .envFact>span,:root[data-theme=light] .checkItem,:root[data-theme=light] .depPill,:root[data-theme=light] .timelineItem,:root[data-theme=light] .chart,:root[data-theme=light] .sampleCard,:root[data-theme=light] .sampleGrid>div,:root[data-theme=light] .sampleEmpty,:root[data-theme=light] .timePerf,:root[data-theme=light] .codeCard,:root[data-theme=light] .codeTitle,:root[data-theme=light] input,:root[data-theme=light] textarea,:root[data-theme=light] select,:root[data-theme=light] .checkField,:root[data-theme=light] .agentOutput,:root[data-theme=light] .chatComposer,:root[data-theme=light] .timeTrack,:root[data-theme=light] .tabs{background:var(--deck-panel)}:root[data-theme=light] .chartControls select,:root[data-theme=light] .configItem{background:#fff;color:var(--deck-ink)}:root[data-theme=light] .dangerButton{color:#a51d2d;background:#fff1f3;border-color:#c7344647}:root[data-theme=light] .primaryButton{color:#fff;background:#007ea7;border-color:#007ea7;box-shadow:0 8px 18px #007ea72e}:root[data-theme=light] .launchButton{background:#006d94;border-color:#006d94}:root[data-theme=light] .jobIcon{background:#f3f7fb}:root[data-theme=light] .chatBubble.user{background:#1683ff;color:#fff}:root[data-theme=light] .chatMessages{background:transparent}:root[data-theme=light] .toolCallCard{box-shadow:0 6px 18px #1420330f}:root[data-theme=light] .rawLine{stroke:#55657aa6}:root[data-theme=light] pre{color:#25344a}@media(max-width:980px){.shell{grid-template-columns:1fr}.rail{position:sticky;top:0;z-index:20;border-right:0;border-bottom:1px solid var(--deck-line);padding:10px}.brand,.railFooter{display:none}.nav{display:flex;gap:6px;overflow-x:auto}.navItem{width:auto;flex:0 0 auto}.main{padding:12px}.topbar,.panelHeader,.timePerfHeader{flex-direction:column;align-items:stretch}.runtimeDeck,.jobMetricsGrid,.metricsGrid,.split,.sampleGrid,.formGrid{grid-template-columns:1fr}.field,.field.compact{grid-column:span 1}.checkMatrix,.envFacts{grid-template-columns:repeat(2,minmax(0,1fr))}}@media(max-width:560px){.checkMatrix,.envFacts{grid-template-columns:1fr}.jobRow.large{grid-template-columns:34px minmax(0,1fr)}.jobRow.large .statusDot{display:none}.timeRow{grid-template-columns:52px minmax(0,1fr)}.timeRow strong{grid-column:2;text-align:left}} +:root{color-scheme:dark;--deck-canvas: #0b0f14;--deck-rail: #0f151c;--deck-panel: #121923;--deck-panel-2: #151f2b;--deck-line: rgba(167, 186, 210, .14);--deck-line-strong: rgba(167, 186, 210, .24);--deck-ink: #edf4ff;--deck-text: #b8c6d8;--deck-muted: #728196;--deck-cyan: #38d7ff;--deck-green: #57e39b;--deck-amber: #ffc857;--deck-red: #ff6b7a;--deck-violet: #a993ff;--radius-sm: 7px;--radius-md: 10px;--radius-lg: 14px;--shadow-panel: 0 0 0 1px rgba(167, 186, 210, .08), 0 18px 40px rgba(0, 0, 0, .26);font-family:Inter,ui-sans-serif,system-ui,-apple-system,BlinkMacSystemFont,Segoe UI,sans-serif;background:var(--deck-canvas);color:var(--deck-ink);-webkit-font-smoothing:antialiased}:root[data-theme=light]{color-scheme:light;--deck-canvas: #f5f7fb;--deck-rail: #ffffff;--deck-panel: #ffffff;--deck-panel-2: #eef4fb;--deck-line: rgba(25, 44, 68, .13);--deck-line-strong: rgba(25, 44, 68, .24);--deck-ink: #142033;--deck-text: #43566f;--deck-muted: #728096;--deck-cyan: #007ea7;--deck-green: #168a57;--deck-amber: #a56a00;--deck-red: #c73446;--deck-violet: #6e56cf;--shadow-panel: 0 0 0 1px rgba(25, 44, 68, .06), 0 16px 36px rgba(34, 56, 83, .1)}*{box-sizing:border-box}body{margin:0;width:100%;overflow-x:hidden;background:var(--deck-canvas)}button,input,select,textarea{font:inherit}button{cursor:pointer}.shell{display:grid;grid-template-columns:272px minmax(0,1fr);min-height:100vh;width:100%}.rail{display:flex;flex-direction:column;border-right:1px solid var(--deck-line);background:var(--deck-rail);padding:18px 14px}.brand{display:flex;align-items:center;gap:12px;padding:8px 8px 20px}.brandMark{display:grid;place-items:center;width:34px;height:34px;border-radius:9px;background:#101f2c;border:1px solid rgba(56,215,255,.3);color:var(--deck-cyan);font-weight:700}.brandName{font-size:14px;font-weight:650;letter-spacing:0}.brandMeta,.tinyLabel{color:var(--deck-muted);font-size:11px;font-weight:600;text-transform:uppercase;letter-spacing:.06em}.nav{display:grid;gap:5px}.navItem{display:flex;align-items:center;gap:10px;width:100%;height:36px;border:0;padding:0 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var(--deck-line);border-radius:var(--radius-lg);box-shadow:var(--shadow-panel)}.workspace{min-height:560px}.tabGrid{display:grid;gap:12px}.metricsGrid{display:grid;grid-template-columns:minmax(0,1.05fr) minmax(0,.95fr);gap:12px;align-items:start}.jobMetricsGrid{display:grid;grid-template-columns:minmax(0,1.05fr) minmax(0,.95fr);gap:12px;margin-bottom:12px}.insetPanel{box-shadow:none;background:#a7bad208}.runtimeDeck{display:grid;grid-template-columns:minmax(0,1.15fr) minmax(0,1fr);gap:12px;margin-bottom:12px}.gpuDeck{display:grid;grid-template-columns:repeat(auto-fit,minmax(260px,1fr));gap:12px}.gpuCard{background:var(--deck-panel);border:1px solid var(--deck-line);border-radius:var(--radius-lg);box-shadow:var(--shadow-panel);padding:14px}.gpuHead{display:flex;align-items:baseline;justify-content:space-between;gap:10px;margin-bottom:14px}.gpuHead strong{color:var(--deck-ink);font-size:13px}.gpuHead span{color:var(--deck-muted);font-size:12px;text-align:right;overflow-wrap:anywhere}.gpuMeter{display:grid;gap:7px;margin-top:10px}.gpuMeterLabel{display:flex;justify-content:space-between;gap:12px;color:var(--deck-muted);font-size:11px;font-weight:700}.gpuMeterLabel b{color:var(--deck-text);font-variant-numeric:tabular-nums}.meterTrack{height:9px;border-radius:999px;background:#a7bad21f;overflow:hidden}.meterTrack i{display:block;height:100%;border-radius:inherit;background:var(--deck-violet)}.meterTrack.util i{background:var(--deck-cyan)}.runtimeHero,.envFacts,.checkMatrix,.dependencyStrip{background:var(--deck-panel);border:1px solid var(--deck-line);border-radius:var(--radius-lg);box-shadow:var(--shadow-panel)}.runtimeHero{display:flex;align-items:center;gap:16px;min-height:116px;padding:16px}.readinessOrb{display:grid;place-items:center;width:58px;height:58px;border-radius:18px;font-weight:800;font-size:17px;border:1px solid var(--deck-line);background:#0b1118}.readinessOrb.ready{color:var(--deck-green);border-color:#57e39b59;box-shadow:inset 0 0 24px #57e39b0f}.readinessOrb.blocked{color:var(--deck-amber);border-color:#ffc85759;box-shadow:inset 0 0 24px #ffc8570f}.envFacts{display:grid;grid-template-columns:repeat(2,minmax(0,1fr));gap:1px;overflow:hidden;background:var(--deck-line)}.envFact{display:flex;align-items:center;gap:10px;min-height:57px;padding:12px;background:var(--deck-panel)}.envFact>span{display:grid;place-items:center;width:28px;height:28px;color:var(--deck-cyan);border:1px solid var(--deck-line);border-radius:8px;background:#0b1118}.envFact label{display:block;color:var(--deck-muted);font-size:11px;font-weight:650;margin-bottom:3px}.envFact strong{display:block;color:var(--deck-ink);font-size:13px;font-weight:650;overflow-wrap:anywhere}.checkMatrix{grid-column:1 / -1;display:grid;grid-template-columns:repeat(4,minmax(0,1fr));gap:1px;overflow:hidden;background:var(--deck-line)}.checkItem{min-height:76px;padding:11px;background:var(--deck-panel)}.checkItem span{display:inline-flex;align-items:center;height:20px;border-radius:999px;padding:0 7px;font-size:10px;font-weight:800;margin-bottom:8px}.checkItem.ok span{color:var(--deck-green);background:#57e39b1a}.checkItem.warn span{color:var(--deck-amber);background:#ffc8571a}.checkItem.fail span{color:var(--deck-red);background:#ff6b7a1a}.checkItem strong,.checkItem small{display:block}.checkItem strong{color:var(--deck-ink);font-size:12px;font-weight:650}.checkItem small{margin-top:5px;color:var(--deck-muted);font-size:11px;line-height:1.35;overflow-wrap:anywhere}.dependencyStrip{grid-column:1 / -1;display:flex;flex-wrap:wrap;gap:8px;padding:10px}.depPill{display:inline-flex;align-items:center;min-height:26px;padding:0 9px;border-radius:999px;border:1px solid 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b/areno/dashboard/dist/index.html @@ -4,8 +4,8 @@ AReno Dashboard - - + +
diff --git a/areno/dashboard/server.py b/areno/dashboard/server.py index 9bb8b4d4..58f0a0cb 100644 --- a/areno/dashboard/server.py +++ b/areno/dashboard/server.py @@ -52,6 +52,35 @@ ENV_CACHE_LOCK = threading.Lock() FILE_BROWSER = AgentFileBrowser(ROOT) +# OOM error patterns copied from areno/cli/auto_tune.py:_is_oom_error to avoid +# a dashboard→CLI cross-module dependency. Keep in sync if auto_tune changes. +OOM_KEYWORDS = ( + "out of memory", + "cuda error: out of memory", +) + +OOM_COMPOSITE_PATTERNS = ( + # ("worker exited without reporting result", ("exitcode -9", "during op.train", "during op.probe_rollout_cache")), + # ("failed during op.probe_rollout_cache", ("nccl", "device error", "external library call failed", "system call")), +) + + +def _looks_like_oom(line: str) -> bool: + """Return True if a log line matches the OOM patterns.""" + + lowered = line.lower() + for keyword in OOM_KEYWORDS: + if keyword in lowered: + return True + for prefix, suffixes in OOM_COMPOSITE_PATTERNS: + if prefix in lowered and any(s in lowered for s in suffixes): + return True + return False + + +# Event kind constants. +EVENT_KINDS = ("non_finite", "constant_reward", "invalid_batch", "oom") + def now() -> str: return dt.datetime.now(dt.timezone.utc).replace(microsecond=0).isoformat() @@ -93,6 +122,10 @@ def __init__( self._metric_keys: set[tuple[str, int, float]] = set() self._timeperf_keys: set[int] = set() self._sample_keys: set[tuple[int, int, int]] = set() + # --- training-event derivation state (read-only, not persisted) --- + self._events: list[dict[str, Any]] = [] + self._events_step_max: int = -1 + self._nonfinite_seen: list[tuple[int, str, str]] = [] # (step, tag, value_str) @classmethod def from_json(cls, item: dict[str, Any]) -> Job: @@ -284,6 +317,8 @@ def _load_metric_files(self, job: Job) -> None: self._add_metric(job, str(item["name"]), float(item["value"]), int(item.get("step", job.step))) except Exception: continue + # Derive training events from loaded metrics + logs (read-only, no mutation). + self._detect_training_events(job) def _load_dashboard_state(self, job: Job, path: Path) -> None: state_file = dashboard_state_source(path, job_pid(job)) @@ -336,7 +371,11 @@ def _load_tensorboard_scalars(self, job: Job, path: Path) -> None: step = int(event.step) value = float(event.value) if math.isnan(value): + job._nonfinite_seen.append((step, tag, "NaN")) continue + if math.isinf(value): + job._nonfinite_seen.append((step, tag, "Inf")) + # Inf still enters metric series to preserve current behaviour. self._add_metric(job, tag, value, step) time_name = tensorboard_time_segment_name(tag) if time_name: @@ -515,6 +554,143 @@ def metric_series(self, job_id: str | None, metric_name: str, *, limit: int = 50 points.sort(key=lambda point: int(point.get("step") or 0)) return points[-max(1, min(limit, 5000)) :] + # ------------------------------------------------------------------ + # Training-event derivation (read-only, no mutation of job.metrics) + # ------------------------------------------------------------------ + + def _detect_training_events(self, job: Job) -> None: + """Derive training events from loaded metrics + logs. + + Called at the end of ``_load_metric_files`` so that every call to + ``get_job`` (which feeds the metrics/metric polling routes) refreshes + the cached ``job._events``. The dedicated ``/api/jobs//events`` + route reads ``job._events`` directly without another ``get_job`` call. + """ + + events: list[dict[str, Any]] = [] + + # --- non_finite (NaN / ±Inf) ---------------------------------- + for step, tag, value_str in job._nonfinite_seen: + events.append({ + "kind": "non_finite", + "step": step, + "time": job.updated_at, + "severity": "warn", + "detail": f"{tag}={value_str} at step {step}", + "fields": {"tag": tag, "value": value_str}, + "log_hint": {"kind": "metric_context", "ref": tag}, + }) + + # --- constant_reward ----------------------------------------- + rewards_std = _series_by_step(job, "rollout/rewards_std") + rewards_max = _series_by_step(job, "rollout/rewards_max") + rewards_min = _series_by_step(job, "rollout/rewards_min") + for step in sorted(rewards_std): + std = rewards_std[step] + if std is None or std > 1e-9: + continue + mx = rewards_max.get(step) + mn = rewards_min.get(step) + if mx is not None and mn is not None and mx == mn: + events.append({ + "kind": "constant_reward", + "step": step, + "time": job.updated_at, + "severity": "info", + "detail": f"rewards constant (std={std:.2e}, max=min={mx}) at step {step}", + "fields": {"rewards_std": std, "rewards_max": mx, "rewards_min": mn}, + "log_hint": {"kind": "metric_context", "ref": "rollout/rewards_std"}, + }) + + # --- invalid_batch (consecutive streaks) --------------------- + adv_std = _series_by_step(job, "rollout/advantages_std") + sorted_steps = sorted(adv_std) + streak = 0 + streak_start: int | None = None + for step in sorted_steps: + val = adv_std[step] + if val is not None and val <= 1e-6: + if streak == 0: + streak_start = step + streak += 1 + else: + if streak > 0: + assert streak_start is not None + severity = "warn" if streak >= 3 else "info" + events.append({ + "kind": "invalid_batch", + "step": streak_start, + "time": job.updated_at, + "severity": severity, + "detail": ( + f"advantages_std<=1e-6 for {streak} consecutive " + f"step(s) from step {streak_start}" + ), + "fields": {"streak": streak, "start_step": streak_start}, + "log_hint": {"kind": "metric_context", "ref": "rollout/advantages_std"}, + }) + streak = 0 + streak_start = None + # Trailing streak (job may still be running). + if streak > 0: + assert streak_start is not None + severity = "warn" if streak >= 3 else "info" + events.append({ + "kind": "invalid_batch", + "step": streak_start, + "time": job.updated_at, + "severity": severity, + "detail": ( + f"advantages_std<=1e-6 for {streak} consecutive " + f"step(s) from step {streak_start}" + ), + "fields": {"streak": streak, "start_step": streak_start}, + "log_hint": {"kind": "metric_context", "ref": "rollout/advantages_std"}, + }) + + # --- OOM (from captured logs) -------------------------------- + for idx, line in enumerate(job.logs): + if _looks_like_oom(line): + events.append({ + "kind": "oom", + "step": job.step, + "time": job.updated_at, + "severity": "error", + "detail": f"OOM pattern in log line {idx}", + "fields": {"log_index": idx, "preview": line[:200]}, + "log_hint": {"kind": "keyword", "ref": str(idx)}, + }) + + # Full rebuild – cheap for ≤500 points per tag. + # Sort by step then severity for stable ordering. + events.sort(key=lambda e: (e.get("step", 0), EVENT_KINDS.index(e["kind"]) if e["kind"] in EVENT_KINDS else 99)) + job._events = events + job._events_step_max = job.step + + def get_job_events( + self, + job_id: str | None, + *, + types: list[str] | None = None, + limit: int = 500, + ) -> list[dict[str, Any]]: + """Return cached training events for *job_id*. + + Reads ``job._events`` directly without triggering ``get_job`` / + ``_load_metric_files`` / ``_save_state``. The cache is refreshed by + whichever polling route last called ``get_job`` for this job. + """ + + with self.lock: + job = self.jobs.get(job_id) if job_id else next(iter(self.jobs.values()), None) + if job is None: + return [] + events = job._events + if types: + type_set = set(types) + events = [e for e in events if e.get("kind") in type_set] + return list(events[-max(1, min(limit, 5000)) :]) + def scan_registered_jobs(self) -> None: registry_jobs = registered_job_items() if not registry_jobs: @@ -803,6 +979,26 @@ def number_like(value: Any) -> bool: return False +def _series_by_step(job: Job, name: str) -> dict[int, float | None]: + """Extract ``{step: value}`` for one metric tag from ``job.metrics``. + + Returns ``None`` for values that cannot be coerced to float so callers can + skip them without a separate exception handler. + """ + + result: dict[int, float | None] = {} + for point in job.metrics: + if point.get("name") != name: + continue + step = int(point.get("step") or 0) + value = point.get("value") + try: + result[step] = float(value) + except (TypeError, ValueError): + result[step] = None + return result + + def tensorboard_event_sources(path: Path, pid: int | None) -> list[Path]: event_files = sorted(path.rglob("events.out.tfevents.*"), key=lambda item: item.stat().st_mtime) if not event_files: @@ -1301,6 +1497,26 @@ def agent_tool_schemas() -> list[dict[str, Any]]: }, }, }, + { + "type": "function", + "function": { + "name": "fetch_events", + "description": "Fetch derived training events (non_finite, constant_reward, invalid_batch, oom) for a job.", + "parameters": { + "type": "object", + "properties": { + "job_id": {"type": "string"}, + "types": { + "type": "array", + "items": {"type": "string", "enum": list(EVENT_KINDS)}, + }, + "limit": {"type": "integer", "minimum": 1, "maximum": 5000}, + }, + "required": ["job_id"], + "additionalProperties": False, + }, + }, + }, { "type": "function", "function": { @@ -1430,6 +1646,15 @@ def execute_agent_tool(tool_call: dict[str, Any]) -> dict[str, Any]: "metric": metric_name, "points": STATE.metric_series(args.get("job_id"), metric_name, limit=limit), } + if name == "fetch_events": + types_raw = args.get("types") + types = list(types_raw) if isinstance(types_raw, list) else None + limit = int(args.get("limit") or 500) + return { + "name": name, + "ok": True, + "events": STATE.get_job_events(args.get("job_id"), types=types, limit=limit), + } if name == "stop_job": return {"name": name, "ok": STATE.stop(str(args.get("job_id") or ""))} if name == "start_train": @@ -1500,6 +1725,13 @@ def do_GET(self) -> None: metric_name = query.get("name", [""])[0] limit = int(query.get("limit", ["500"])[0] or 500) self.json({"metric": metric_name, "points": STATE.metric_series(job_id, metric_name, limit=limit)}) + elif path.startswith("/api/jobs/") and path.endswith("/events"): + job_id = path.split("/")[-2] + query = urllib.parse.parse_qs(urllib.parse.urlsplit(self.path).query) + types_raw = query.get("types", [""])[0] + types = [t.strip() for t in types_raw.split(",") if t.strip()] if types_raw else None + limit = int(query.get("limit", ["500"])[0] or 500) + self.json({"events": STATE.get_job_events(job_id, types=types, limit=limit)}) elif path.startswith("/api/jobs/"): job = STATE.get_job(path.split("/")[-1]) if not job: diff --git a/dashboard/src/eventUtils.js b/dashboard/src/eventUtils.js new file mode 100644 index 00000000..a1f3c54e --- /dev/null +++ b/dashboard/src/eventUtils.js @@ -0,0 +1,84 @@ +/** + * Pure utility functions for training-event overlay on metric charts. + * + * This file contains NO JSX and uses ES module exports so it can be loaded + * via `node --input-type=module` for smoke testing without a build step. + */ + +// --- Event kind metadata ------------------------------------------------- + +export const EVENT_KIND_META = { + non_finite: { label: "Non-finite", color: "#ef4444", shape: "cross" }, + constant_reward: { label: "Constant reward", color: "#f59e0b", shape: "diamond" }, + invalid_batch: { label: "Invalid batch", color: "#8b5cf6", shape: "square" }, + oom: { label: "OOM", color: "#dc2626", shape: "triangle" }, +}; + +export const ALL_EVENT_KINDS = Object.keys(EVENT_KIND_META); + +// --- Pure utility functions ---------------------------------------------- + +/** + * Map an event's step to an x-coordinate using the same projection as + * buildMetricPlot in main.jsx. + * + * @param {object} event - Event object with a `step` field. + * @param {object} plotRange - { stepMin, stepSpan, width } from the active chart. + * @returns {number} x position in chart pixels. + */ +export function eventStepToX(event, plotRange) { + if (!event || !plotRange) return 0; + const step = Number(event.step || 0); + const { stepMin = 0, stepSpan = 1, width = 700 } = plotRange; + return ((step - stepMin) / Math.max(stepSpan, 1)) * width + 10; +} + +/** + * Filter events by enabled kinds. + * + * @param {Array} events - List of event objects. + * @param {object} enabledTypes - { [kind]: boolean } map. + * @returns {Array} Filtered events. + */ +export function filterEvents(events, enabledTypes) { + if (!Array.isArray(events)) return []; + return events.filter((e) => { + const kind = e && e.kind; + return kind && enabledTypes[kind] !== false; + }); +} + +/** + * Extract a bounded window of log lines around a given index for OOM + * click-through. + * + * @param {Array} logs - Array of log line strings. + * @param {number} index - Centre line index. + * @param {number} radius - Lines before/after (default 20). + * @returns {Array} Sliced log lines with relative offsets. + */ +export function extractLogWindow(logs, index, radius = 20) { + if (!Array.isArray(logs) || index < 0 || index >= logs.length) return []; + const start = Math.max(0, index - radius); + const end = Math.min(logs.length, index + radius + 1); + const result = []; + for (let i = start; i < end; i++) { + result.push({ lineIndex: i, offset: i - index, text: logs[i] }); + } + return result; +} + +/** + * Extract metric values around a given step for metric-context click-through. + * + * @param {Array} points - Metric series points [{ step, value }, ...]. + * @param {number} step - Centre step. + * @param {number} radius - Steps before/after (default 3). + * @returns {Array} Nearby points sorted by step. + */ +export function extractMetricContext(points, step, radius = 3) { + if (!Array.isArray(points)) return []; + return points + .filter((p) => Math.abs(Number(p.step || 0) - step) <= radius) + .sort((a, b) => Number(a.step) - Number(b.step)); +} diff --git a/dashboard/src/main.jsx b/dashboard/src/main.jsx index e5c110fd..e3be8956 100644 --- a/dashboard/src/main.jsx +++ b/dashboard/src/main.jsx @@ -24,6 +24,14 @@ import { Timer, } from "lucide-react"; import "./styles.css"; +import { + EVENT_KIND_META, + ALL_EVENT_KINDS, + eventStepToX, + filterEvents, + extractLogWindow, + extractMetricContext, +} from "./eventUtils.js"; const API_BASE = new URL(".", window.location.href).pathname; @@ -782,7 +790,7 @@ function JobMetricsView({ job, refreshNonce }) { return (
- +
@@ -1120,13 +1128,16 @@ function resolveActiveMetricName(names, selectedName) { return selectedName && names.includes(selectedName) ? selectedName : names[0] || ""; } -function MetricChart({ jobId, metricsDir, refreshNonce }) { +function MetricChart({ jobId, metricsDir, logs, refreshNonce }) { const [selectedName, setSelectedName] = useState(""); const [smooth, setSmooth] = useState(0.6); const [metricList, setMetricList] = useState([]); const [points, setPoints] = useState([]); const [metricLoading, setMetricLoading] = useState(false); const [prevJobId, setPrevJobId] = useState(jobId); + const [events, setEvents] = useState([]); + const [eventTypes, setEventTypes] = useState(() => Object.fromEntries(ALL_EVENT_KINDS.map((k) => [k, true]))); + const [activeEvent, setActiveEvent] = useState(null); // Reset the selection during render (not in an effect) when the job changes so // the reset happens before any effect runs. This avoids a stale-name fetch and // a stuck loading state on job switch, while live polls (refreshNonce) still @@ -1137,6 +1148,8 @@ function MetricChart({ jobId, metricsDir, refreshNonce }) { setMetricList([]); setPoints([]); setMetricLoading(false); + setEvents([]); + setActiveEvent(null); } const names = metricNamesFrom(metricList); const effectiveName = resolveActiveMetricName(names, selectedName); @@ -1184,10 +1197,32 @@ function MetricChart({ jobId, metricsDir, refreshNonce }) { cancelled = true; }; }, [jobId, effectiveName, refreshNonce]); + // Poll training events (independent of metric curve). + useEffect(() => { + let cancelled = false; + if (!jobId) return undefined; + api(`/api/jobs/${jobId}/events?limit=500`) + .then((data) => { + if (cancelled) return; + setEvents(data.events || []); + }) + .catch(() => { + if (!cancelled) setEvents([]); + }); + return () => { + cancelled = true; + }; + }, [jobId, refreshNonce]); const activeName = effectiveName; const visiblePoints = points.slice(-240); const smoothed = smoothTensorboard(visiblePoints, smooth); const plot = buildMetricPlot(visiblePoints, smoothed); + const plotRange = visiblePoints.length > 0 ? { + stepMin: visiblePoints[0].step, + stepSpan: Math.max(visiblePoints[visiblePoints.length - 1].step - visiblePoints[0].step, 1), + width: 700, + } : null; + const visibleEvents = filterEvents(events, eventTypes); return (
@@ -1205,23 +1240,177 @@ function MetricChart({ jobId, metricsDir, refreshNonce }) { {visiblePoints.length === 0 ? (
{metricLoading ? "Loading selected metric..." : "No TensorBoard scalar points loaded yet."}
) : ( - - - {[0, 1, 2, 3].map((item) => )} +
+ + + {[0, 1, 2, 3].map((item) => )} + + + + {visiblePoints.slice(-24).map((point, index) => ( + + {`${activeName} step ${point.step}: ${point.value}`} + + ))} + {plotRange && } + +
+ )} + + {visiblePoints.length > 0 && ( +
+ {activeName || "metric"} · {points.length} points + {metricsDir || "no metrics dir"} · {plot.minLabel} to {plot.maxLabel} +
+ )} + {activeEvent && ( + setActiveEvent(null)} + /> + )} +
+ ); +} + +// --- Event overlay (SVG markers) ---------------------------------------- + +function EventOverlay({ events, plotRange, onSelect }) { + if (!events || !events.length) return null; + return ( + + {events.map((event, index) => { + const x = eventStepToX(event, plotRange); + const meta = EVENT_KIND_META[event.kind] || { color: "#666", shape: "circle" }; + const y = 168; + const common = { + key: `${event.kind}-${event.step}-${index}`, + transform: `translate(${x.toFixed(1)}, ${y})`, + onClick: () => onSelect(event), + style: { cursor: "pointer" }, + }; + if (meta.shape === "cross") { + return ( + + + + {`${meta.label} @ step ${event.step}: ${event.detail}`} + + ); + } + if (meta.shape === "diamond") { + return ( + + + {`${meta.label} @ step ${event.step}: ${event.detail}`} + + ); + } + if (meta.shape === "square") { + return ( + + + {`${meta.label} @ step ${event.step}: ${event.detail}`} + + ); + } + if (meta.shape === "triangle") { + return ( + + + {`${meta.label} @ step ${event.step}: ${event.detail}`} + + ); + } + return ( + + + {`${meta.label} @ step ${event.step}: ${event.detail}`} - - - {visiblePoints.slice(-24).map((point, index) => ( - - {`${activeName} step ${point.step}: ${point.value}`} - + ); + })} + + ); +} + +// --- Event filters (independent of curve) -------------------------------- + +function EventFilters({ eventTypes, setEventTypes, events }) { + if (!events || !events.length) return null; + return ( +
+ {ALL_EVENT_KINDS.map((kind) => { + const meta = EVENT_KIND_META[kind]; + const count = events.filter((e) => e.kind === kind).length; + if (count === 0) return null; + return ( + + ); + })} +
+ ); +} + +// --- Event context popover (click-through) ------------------------------- + +function EventContextPopover({ event, logs, points, onClose }) { + const meta = EVENT_KIND_META[event.kind] || { label: event.kind, color: "#666" }; + let contextBody = null; + if (event.log_hint?.kind === "keyword" && event.log_hint.ref != null) { + const lineIndex = parseInt(event.log_hint.ref, 10); + const window = extractLogWindow(logs, lineIndex, 20); + contextBody = window.length ? ( + 
+        {window.map((line) => (
+          

+            {line.lineIndex}
+            {line.text}
+          

+        ))}
+
+ ) : Log line {lineIndex} no longer available (logs may have rotated).; + } else if (event.log_hint?.kind === "metric_context" && event.log_hint.ref) { + const context = extractMetricContext(points, event.step, 3); + contextBody = context.length ? ( + + + + {context.map((p) => ( + + + ))} - - )} -
- {activeName || "metric"} · {points.length} points - {metricsDir || "no metrics dir"} · {plot.minLabel} to {plot.maxLabel} +
+
step{event.log_hint.ref}
{p.step}{Number(p.value).toFixed(6)}
+ ) : No metric context available for this tag.; + } else { + contextBody = No additional context.; + } + return ( +
+
+ {meta.label} + {event.detail} + +
+
+ step: {event.step} + severity: {event.severity} + {event.fields && Object.entries(event.fields).map(([k, v]) => ( + {k}: {typeof v === "number" ? (Number.isFinite(v) ? v.toExponential(3) : String(v)) : String(v)} + ))}
+
{contextBody}
); } diff --git a/dashboard/src/styles.css b/dashboard/src/styles.css index 6511695d..d96e9d2e 100644 --- a/dashboard/src/styles.css +++ b/dashboard/src/styles.css @@ -2094,3 +2094,139 @@ textarea:focus { text-align: left; } } + +/* --- Training event overlay --- */ + +.chartPlotWrap { + position: relative; +} + +.eventMarkers { + pointer-events: all; +} + +.eventFilters { + display: flex; + flex-wrap: wrap; + gap: 8px 14px; + padding: 4px 0 0; + font-size: 11px; +} + +.eventFilterLabel { + display: inline-flex; + align-items: center; + gap: 4px; + cursor: pointer; + color: var(--text-dim, #94a3b8); +} + +.eventFilterLabel input { + margin: 0; +} + +.eventFilterSwatch { + display: inline-block; + width: 10px; + height: 10px; + border-radius: 2px; +} + +.eventPopover { + margin-top: 8px; + border: 1px solid var(--border, #334155); + border-radius: 6px; + background: var(--panel-bg, #1e293b); + padding: 10px 12px; + font-size: 12px; + max-height: 320px; + overflow-y: auto; +} + +.eventPopoverHeader { + display: flex; + align-items: center; + gap: 8px; + margin-bottom: 6px; +} + +.eventPopoverTag { + display: inline-block; + padding: 1px 7px; + border-radius: 3px; + color: #fff; + font-size: 10px; + font-weight: 600; + text-transform: uppercase; + letter-spacing: 0.5px; +} + +.eventPopoverDetail { + flex: 1; + color: var(--text, #e2e8f0); +} + +.eventPopoverClose { + background: none; + border: none; + color: var(--text-dim, #94a3b8); + cursor: pointer; + font-size: 14px; + padding: 0 2px; +} + +.eventPopoverMeta { + display: flex; + flex-wrap: wrap; + gap: 4px 12px; + color: var(--text-dim, #94a3b8); + margin-bottom: 8px; + font-size: 11px; +} + +.eventLogContext { + margin: 0; + font-family: monospace; + font-size: 11px; + max-height: 200px; + overflow-y: auto; +} + +.eventLogLine { + display: flex; + gap: 8px; + padding: 1px 0; +} + +.eventLogLineHit { + background: rgba(220, 38, 38, 0.15); + border-radius: 2px; +} + +.eventLogLineNum { + color: var(--text-dim, #64748b); + min-width: 30px; + text-align: right; +} + +.eventLogLineText { + white-space: pre-wrap; + word-break: break-all; +} + +.eventMetricContext { + width: 100%; + border-collapse: collapse; + font-size: 11px; +} + +.eventMetricContext th, +.eventMetricContext td { + text-align: left; + padding: 2px 8px; + border-bottom: 1px solid var(--border, #334155); +} + +.eventMetricRowHit { + background: rgba(245, 158, 11, 0.15); +} diff --git a/docs/reference/dashboard-training-events.rst b/docs/reference/dashboard-training-events.rst new file mode 100644 index 00000000..145b0f5a --- /dev/null +++ b/docs/reference/dashboard-training-events.rst @@ -0,0 +1,186 @@ +Training Event Overlay +====================== + +The AReno dashboard overlays **structured training events** on TensorBoard +metric charts, making it easier to spot anomalies during and after training +runs. + +Overview +-------- + +Four event kinds are derived **read-only** from existing metrics and +captured logs — no new artifact files are produced: + +================== ============== =========================================== +Kind Severity Signal source +================== ============== =========================================== +``non_finite`` ``warn`` NaN or ±Inf in TensorBoard scalars +``constant_reward`` ``info`` ``rollout/rewards_std`` ≈ 0 with + ``rewards_max == rewards_min`` +``invalid_batch`` ``info``/``warn`` ``rollout/advantages_std`` ≤ 1e-6 + for consecutive steps (streak ≥ 3 → + ``warn``, streak 1–2 → ``info``) +``oom`` ``error`` OOM pattern in captured subprocess logs +================== ============== =========================================== + +Events appear as **clickable markers** at the bottom of each metric chart. +Filters (checkboxes per kind) are independent of the selected metric curve. + +API +--- + +``GET /api/jobs//events`` + Returns a list of derived training events. + + Query parameters: + - ``types``: comma-separated kind filter (e.g. ``?types=non_finite,oom``) + - ``limit``: maximum number of events (default 500, max 5000) + + Response: + + .. code-block:: json + + { + "events": [ + { + "kind": "non_finite", + "step": 12, + "time": "2026-07-31T01:00:00Z", + "severity": "warn", + "detail": "rollout/rewards_mean=NaN at step 12", + "fields": {"tag": "rollout/rewards_mean", "value": "NaN"}, + "log_hint": {"kind": "metric_context", "ref": "rollout/rewards_mean"} + } + ] + } + +Event fields: + +``kind`` + One of ``non_finite``, ``constant_reward``, ``invalid_batch``, ``oom``. +``step`` + Training step at which the event was detected. +``time`` + Job ``updated_at`` at detection time (not the original TensorBoard write + time). +``severity`` + ``info``, ``warn``, or ``error``. +``detail`` + Human-readable description. +``fields`` + Structured supplementary data (varies by kind). +``log_hint`` + Click-through anchor. ``kind`` is ``keyword`` (OOM: log line index + for ±20-line window), ``metric_context`` (non-OOM: metric tag for + step ± 3 context), or ``none``. + +Agent tool +~~~~~~~~~~ + +``fetch_events(job_id, types?, limit?)`` + Returns the same event list as the HTTP endpoint, available to the + dashboard's built-in agent. + +Click-through context +--------------------- + +- **OOM events** (``log_hint.kind = "keyword"``): popover shows ±20 log + lines around the matching OOM line. +- **Other events** (``log_hint.kind = "metric_context"``): popover shows + the relevant metric values for steps ± 3 around the event. No log + context is shown — captured logs do not carry step/timestamp metadata. +- ``log_hint.kind = "none"``: only event metadata is displayed. + +Limitations +----------- + +- **500-step truncation**: ``_load_tensorboard_scalars`` loads only the + last 500 events per TensorBoard tag. Event detection therefore covers + the most recent ~500 steps. After a dashboard restart, ``job.metrics`` + is rebuilt from scratch and earlier events are not recoverable. +- **OOM semantics**: OOM causes the training subprocess to crash (job + status → ``failed``). The dashboard does **not** track OOM recovery — + ``recovered`` is never set. Auto-tune OOM retries create separate jobs + and are not linked. +- **NaN behaviour change**: NaN scalars were previously silently + discarded; they now appear as ``non_finite`` events. NaN values still + do not enter the metric series (``metric_series`` output is unchanged). +- **Events are not persisted**: Events are derived in memory on each + ``_load_metric_files`` call and cached on the job object. They are not + written to ``to_json``/``from_json`` or any artifact file. + +Default behaviour +----------------- + +When no events are detected (e.g. legacy runs without TensorBoard data), +the chart and filters are absent — identical to pre-feature behaviour. + +Minimal example +--------------- + +The following examples run without external databases, GPU, or network +services. They demonstrate the successful path and one boundary input. + +**1. Query events via curl** (requires a running dashboard): + +.. code-block:: bash + + # All events for a job + curl -s http://localhost:8007/api/jobs//events | python3 -m json.tool + + # Filter to OOM and non_finite only + curl -s "http://localhost:8007/api/jobs//events?types=oom,non_finite" | python3 -m json.tool + +**2. Python fixture** (no dashboard process required): + +.. code-block:: python + + import json, tempfile, threading + from pathlib import Path + from areno.dashboard.server import DashboardState, Job + + mdir = Path(tempfile.mkdtemp()) / "metrics" + mdir.mkdir() + + # Write a .jsonl metric file: step 1 has constant reward, steps 3-5 + # have a 3-step invalid-batch streak (advantages_std=0). + rows = [] + for step, rwd_std, rwd_max, rwd_min, adv_std in [ + (1, 0.0, 1.0, 1.0, 0.5), # constant reward + (2, 0.5, 2.0, 0.0, 0.5), # normal + (3, 0.5, 2.0, 0.0, 0.0), # invalid batch (streak starts) + (4, 0.5, 2.0, 0.0, 0.0), + (5, 0.5, 2.0, 0.0, 0.0), # streak = 3 -> warn + (6, 0.5, 2.0, 0.0, 1.0), # recovery + ]: + rows.append({"name": "rollout/rewards_std", "value": rwd_std, "step": step}) + rows.append({"name": "rollout/rewards_max", "value": rwd_max, "step": step}) + rows.append({"name": "rollout/rewards_min", "value": rwd_min, "step": step}) + rows.append({"name": "rollout/advantages_std", "value": adv_std, "step": step}) + (mdir / "custom_metrics.jsonl").write_text( + "\n".join(json.dumps(r) for r in rows) + "\n" + ) + + # Use an absolute path for metrics_dir so it works regardless of ROOT. + state = DashboardState.__new__(DashboardState) + state.jobs = {} + state.lock = threading.RLock() + job = Job(kind="train", name="example", command=[], config={}, metrics_dir=str(mdir)) + state.jobs[job.id] = job + state._load_metric_files(job) + + # Successful path: constant_reward + invalid_batch detected + kinds = {e["kind"] for e in job._events} + assert "constant_reward" in kinds + assert "invalid_batch" in kinds + ib = [e for e in job._events if e["kind"] == "invalid_batch"][0] + assert ib["severity"] == "warn" and ib["fields"]["streak"] == 3 + + # Boundary input: a single advantages_std=0 (streak=1) is info, not warn + job2 = Job(kind="train", name="boundary", command=[], config={}, metrics_dir=None) + job2.metrics.append({"name": "rollout/advantages_std", "value": 0.0, "step": 1, "time": "t"}) + job2.metrics.append({"name": "rollout/advantages_std", "value": 1.0, "step": 2, "time": "t"}) + state._detect_training_events(job2) + ib2 = [e for e in job2._events if e["kind"] == "invalid_batch"][0] + assert ib2["severity"] == "info" and ib2["fields"]["streak"] == 1 + print("All assertions passed.") diff --git a/docs/reference/index.rst b/docs/reference/index.rst index 32f1540e..0968fd73 100644 --- a/docs/reference/index.rst +++ b/docs/reference/index.rst @@ -15,3 +15,4 @@ Reference pages: * :doc:`cli` * :doc:`/sdk/trainer` * :doc:`/models/supported` +* :doc:`dashboard-training-events` diff --git a/tests/test_dashboard_events_cpu.py b/tests/test_dashboard_events_cpu.py new file mode 100644 index 00000000..2b714116 --- /dev/null +++ b/tests/test_dashboard_events_cpu.py @@ -0,0 +1,467 @@ +"""CPU tests for training-event derivation in the dashboard server. + +Covers ``detect_training_events``, ``get_job_events``, route ordering, and +the ``eventUtils.js`` pure functions via a Node smoke test. +""" + +from __future__ import annotations + +import json +import os +import tempfile +import unittest +from pathlib import Path +from unittest.mock import patch + +# Set ROOT before importing so DashboardState picks a temp state file. +_TMP_ROOT = tempfile.mkdtemp(prefix="areno-test-") +os.environ["ARENO_DASHBOARD_ROOT"] = _TMP_ROOT + +from areno.dashboard.server import ( # noqa: E402 + DashboardState, + EVENT_KINDS, + Job, + _looks_like_oom, + _series_by_step, +) + + +def _make_job(**overrides): + """Create a minimal Job for testing without starting a process.""" + + defaults = dict( + kind="train", + name="test", + command=["areno", "train"], + config={}, + metrics_dir=None, + ) + defaults.update(overrides) + return Job(**defaults) + + +def _add_metric(job, name, value, step): + """Shortcut to add a metric point directly to job.metrics.""" + + job._metric_keys.add((name, step, value)) + job.metrics.append({"name": name, "value": value, "step": step, "time": "2026-07-31T00:00:00Z"}) + job.step = max(job.step, step) + + +class NonFiniteDetectionTest(unittest.TestCase): + """non_finite events derived from NaN/Inf in TensorBoard scalars.""" + + def test_nan_produces_non_finite_event(self): + job = _make_job() + job._nonfinite_seen.append((5, "rollout/rewards_mean", "NaN")) + job.updated_at = "2026-07-31T01:00:00Z" + job.step = 5 + state = DashboardState.__new__(DashboardState) + state._detect_training_events(job) + events = [e for e in job._events if e["kind"] == "non_finite"] + self.assertEqual(len(events), 1) + self.assertEqual(events[0]["step"], 5) + self.assertEqual(events[0]["fields"]["tag"], "rollout/rewards_mean") + self.assertEqual(events[0]["fields"]["value"], "NaN") + self.assertEqual(events[0]["log_hint"]["kind"], "metric_context") + + def test_inf_also_triggers_event(self): + job = _make_job() + job._nonfinite_seen.append((3, "train/loss", "Inf")) + job.step = 3 + DashboardState._detect_training_events(DashboardState.__new__(DashboardState), job) + events = [e for e in job._events if e["kind"] == "non_finite"] + self.assertEqual(len(events), 1) + self.assertEqual(events[0]["fields"]["value"], "Inf") + + def test_nan_does_not_enter_metric_series(self): + """Metric series should remain NaN-free even after NaN detection.""" + job = _make_job() + # Simulate what _load_tensorboard_scalars does: skip metric, record NaN. + job._nonfinite_seen.append((1, "loss", "NaN")) + # Normal metric still gets added. + _add_metric(job, "loss", 0.5, 2) + DashboardState._detect_training_events(DashboardState.__new__(DashboardState), job) + # No NaN values in metrics list. + for m in job.metrics: + self.assertTrue( + isinstance(m["value"], (int, float)) and m["value"] == m["value"], + f"NaN found in metrics: {m}", + ) + + +class ConstantRewardDetectionTest(unittest.TestCase): + + def test_zero_std_with_equal_max_min(self): + job = _make_job() + _add_metric(job, "rollout/rewards_std", 0.0, 1) + _add_metric(job, "rollout/rewards_max", 1.0, 1) + _add_metric(job, "rollout/rewards_min", 1.0, 1) + _add_metric(job, "rollout/rewards_std", 0.5, 2) # normal step + _add_metric(job, "rollout/rewards_max", 2.0, 2) + _add_metric(job, "rollout/rewards_min", 0.0, 2) + DashboardState._detect_training_events(DashboardState.__new__(DashboardState), job) + cr_events = [e for e in job._events if e["kind"] == "constant_reward"] + self.assertEqual(len(cr_events), 1) + self.assertEqual(cr_events[0]["step"], 1) + self.assertEqual(cr_events[0]["severity"], "info") + + def test_zero_std_but_max_ne_min_no_event(self): + """std≈0 but max!=min should NOT trigger (data inconsistency guard).""" + job = _make_job() + _add_metric(job, "rollout/rewards_std", 0.0, 1) + _add_metric(job, "rollout/rewards_max", 1.0, 1) + _add_metric(job, "rollout/rewards_min", 0.5, 1) + DashboardState._detect_training_events(DashboardState.__new__(DashboardState), job) + cr_events = [e for e in job._events if e["kind"] == "constant_reward"] + self.assertEqual(len(cr_events), 0) + + +class InvalidBatchStreakTest(unittest.TestCase): + + def test_streak_below_3_is_info(self): + job = _make_job() + for step in range(1, 3): # 2 consecutive + _add_metric(job, "rollout/advantages_std", 0.0, step) + _add_metric(job, "rollout/advantages_std", 1.0, 3) # recovery + DashboardState._detect_training_events(DashboardState.__new__(DashboardState), job) + ib_events = [e for e in job._events if e["kind"] == "invalid_batch"] + self.assertEqual(len(ib_events), 1) + self.assertEqual(ib_events[0]["severity"], "info") + self.assertEqual(ib_events[0]["fields"]["streak"], 2) + + def test_streak_3_or_more_is_warn(self): + job = _make_job() + for step in range(1, 5): # 4 consecutive + _add_metric(job, "rollout/advantages_std", 0.0, step) + _add_metric(job, "rollout/advantages_std", 1.0, 5) # recovery + DashboardState._detect_training_events(DashboardState.__new__(DashboardState), job) + ib_events = [e for e in job._events if e["kind"] == "invalid_batch"] + self.assertEqual(len(ib_events), 1) + self.assertEqual(ib_events[0]["severity"], "warn") + self.assertEqual(ib_events[0]["fields"]["streak"], 4) + + def test_trailing_streak_in_active_run(self): + """A streak at the end (job still running) should still produce an event.""" + job = _make_job() + for step in range(1, 4): # 3 consecutive, no recovery step + _add_metric(job, "rollout/advantages_std", 0.0, step) + DashboardState._detect_training_events(DashboardState.__new__(DashboardState), job) + ib_events = [e for e in job._events if e["kind"] == "invalid_batch"] + self.assertEqual(len(ib_events), 1) + self.assertEqual(ib_events[0]["severity"], "warn") + + +class OOMDetectionTest(unittest.TestCase): + + def test_oom_log_line_produces_error_event(self): + job = _make_job() + job.logs = [ + "step 5 training started", + "CUDA error: out of memory", + "process exited", + ] + job.step = 5 + DashboardState._detect_training_events(DashboardState.__new__(DashboardState), job) + oom_events = [e for e in job._events if e["kind"] == "oom"] + self.assertEqual(len(oom_events), 1) + self.assertEqual(oom_events[0]["severity"], "error") + self.assertNotIn("recovered", oom_events[0]["fields"]) + self.assertEqual(oom_events[0]["log_hint"]["kind"], "keyword") + + def test_no_oom_when_logs_clean(self): + job = _make_job() + job.logs = ["step 1 done", "step 2 done"] + DashboardState._detect_training_events(DashboardState.__new__(DashboardState), job) + self.assertEqual([e for e in job._events if e["kind"] == "oom"], []) + + def test_looks_like_oom_patterns(self): + self.assertTrue(_looks_like_oom("RuntimeError: out of memory")) + self.assertTrue(_looks_like_oom("CUDA error: out of memory")) + self.assertFalse(_looks_like_oom("step 3 completed")) + + def test_multiple_oom_lines_produce_multiple_events(self): + job = _make_job() + job.logs = ["out of memory at step 1", "retry", "out of memory at step 2"] + DashboardState._detect_training_events(DashboardState.__new__(DashboardState), job) + oom_events = [e for e in job._events if e["kind"] == "oom"] + self.assertEqual(len(oom_events), 2) + + +class OverlappingEventsTest(unittest.TestCase): + + def test_same_step_multiple_kinds(self): + """Multiple event kinds at the same step should all be emitted.""" + job = _make_job() + # non_finite at step 3 + job._nonfinite_seen.append((3, "loss", "NaN")) + # constant_reward at step 3 + _add_metric(job, "rollout/rewards_std", 0.0, 3) + _add_metric(job, "rollout/rewards_max", 1.0, 3) + _add_metric(job, "rollout/rewards_min", 1.0, 3) + # invalid_batch at step 3 + _add_metric(job, "rollout/advantages_std", 0.0, 3) + _add_metric(job, "rollout/advantages_std", 1.0, 4) # recovery + job.step = 4 + DashboardState._detect_training_events(DashboardState.__new__(DashboardState), job) + step3_kinds = {e["kind"] for e in job._events if e["step"] == 3} + self.assertIn("non_finite", step3_kinds) + self.assertIn("constant_reward", step3_kinds) + self.assertIn("invalid_batch", step3_kinds) + + +class LegacyRunTest(unittest.TestCase): + + def test_empty_job_produces_no_events(self): + """A job with no metrics, no logs, no nonfinite should return empty events.""" + job = _make_job() + DashboardState._detect_training_events(DashboardState.__new__(DashboardState), job) + self.assertEqual(job._events, []) + + def test_get_job_events_empty_for_unknown_job(self): + state = DashboardState.__new__(DashboardState) + state.jobs = {} + state.lock = __import__("threading").RLock() + events = state.get_job_events("nonexistent") + self.assertEqual(events, []) + + +class GetJobEventsApiTest(unittest.TestCase): + + def _setup_state_with_job(self): + import threading + state = DashboardState.__new__(DashboardState) + state.jobs = {} + state.lock = threading.RLock() + job = _make_job() + job._nonfinite_seen.append((1, "loss", "NaN")) + job.logs = ["out of memory"] + job.step = 1 + _add_metric(job, "rollout/rewards_std", 0.0, 1) + _add_metric(job, "rollout/rewards_max", 1.0, 1) + _add_metric(job, "rollout/rewards_min", 1.0, 1) + _add_metric(job, "rollout/advantages_std", 0.0, 1) + _add_metric(job, "rollout/advantages_std", 0.0, 2) + _add_metric(job, "rollout/advantages_std", 0.0, 3) + _add_metric(job, "rollout/advantages_std", 1.0, 4) + state.jobs[job.id] = job + state._detect_training_events(job) + return state, job + + def test_returns_all_events(self): + state, job = self._setup_state_with_job() + events = state.get_job_events(job.id) + self.assertTrue(len(events) > 0) + kinds = {e["kind"] for e in events} + self.assertIn("non_finite", kinds) + self.assertIn("constant_reward", kinds) + self.assertIn("invalid_batch", kinds) + self.assertIn("oom", kinds) + + def test_type_filter(self): + state, job = self._setup_state_with_job() + events = state.get_job_events(job.id, types=["oom"]) + self.assertTrue(len(events) > 0) + self.assertTrue(all(e["kind"] == "oom" for e in events)) + + def test_limit(self): + state, job = self._setup_state_with_job() + all_events = state.get_job_events(job.id) + limited = state.get_job_events(job.id, limit=1) + self.assertEqual(len(limited), min(1, len(all_events))) + + +class RouteOrderingTest(unittest.TestCase): + """Verify /events is matched before the /api/jobs/ catch-all.""" + + def test_events_route_not_swallowed_by_catchall(self): + """The Handler.route_path and do_GET logic should route /events + to get_job_events, not treat 'events' as a job_id.""" + from areno.dashboard.server import Handler + # Simulate what do_GET does: check if path ends with /events + path = "/api/jobs/abc123/events" + self.assertTrue(path.startswith("/api/jobs/") and path.endswith("/events")) + # The catch-all path would be path.split("/")[-1] == "events" which is NOT a job_id. + # Verify the events branch matches before the catch-all. + job_id_from_events = path.split("/")[-2] + self.assertEqual(job_id_from_events, "abc123") + + +class SeriesByStepTest(unittest.TestCase): + + def test_extracts_step_value_dict(self): + job = _make_job() + _add_metric(job, "loss", 0.5, 1) + _add_metric(job, "loss", 0.3, 2) + _add_metric(job, "other", 1.0, 1) + result = _series_by_step(job, "loss") + self.assertEqual(result, {1: 0.5, 2: 0.3}) + + def test_returns_empty_for_missing_tag(self): + job = _make_job() + self.assertEqual(_series_by_step(job, "nonexistent"), {}) + + +class EventStructureTest(unittest.TestCase): + """Verify event objects have all required fields per the data model.""" + + def test_event_fields_complete(self): + job = _make_job() + job._nonfinite_seen.append((1, "loss", "NaN")) + DashboardState._detect_training_events(DashboardState.__new__(DashboardState), job) + event = job._events[0] + for field in ("kind", "step", "time", "severity", "detail", "fields", "log_hint"): + self.assertIn(field, event, f"Missing field: {field}") + self.assertIn("kind", event["log_hint"]) + self.assertIn("ref", event["log_hint"]) + + +class EventKindsConstantTest(unittest.TestCase): + + def test_event_kinds_order(self): + self.assertEqual(EVENT_KINDS, ("non_finite", "constant_reward", "invalid_batch", "oom")) + + +# ---------------------------------------------------------------------- +# Malformed input tests +# ---------------------------------------------------------------------- + +class MalformedInputTest(unittest.TestCase): + """Verify detection does not crash on non-numeric / missing metric values.""" + + def test_series_by_step_with_none_value(self): + """A metric point with value=None should yield None in the series, not raise.""" + job = _make_job() + # Manually inject a malformed metric point. + job.metrics.append({"name": "rollout/rewards_std", "value": None, "step": 1, "time": "t"}) + result = _series_by_step(job, "rollout/rewards_std") + self.assertEqual(result, {1: None}) + + def test_series_by_step_with_non_numeric_string(self): + """A metric point with a non-numeric string value should yield None.""" + job = _make_job() + job.metrics.append({"name": "loss", "value": "not_a_number", "step": 1, "time": "t"}) + result = _series_by_step(job, "loss") + self.assertEqual(result, {1: None}) + + def test_detect_events_survives_none_metric_values(self): + """detect_training_events should not crash when metric values are None.""" + import threading + job = _make_job() + # Inject a None-valued rewards_std — _series_by_step returns None, + # and the constant-reward check should skip it (None is not <= 1e-9). + job.metrics.append({"name": "rollout/rewards_std", "value": None, "step": 1, "time": "t"}) + job.metrics.append({"name": "rollout/advantages_std", "value": None, "step": 1, "time": "t"}) + # Should not raise. + state = DashboardState.__new__(DashboardState) + state._detect_training_events(job) + # No events from None values. + self.assertEqual(job._events, []) + + +# ---------------------------------------------------------------------- +# Integration test: temp metrics_dir → _load_metric_files → events +# ---------------------------------------------------------------------- + +class IntegrationLoadAndDetectTest(unittest.TestCase): + """End-to-end: write fixture .jsonl files, load metrics, verify events.""" + + def setUp(self): + import threading + self._fixture_dir = Path(_TMP_ROOT) / "integration-fixture" + self._fixture_dir.mkdir(parents=True, exist_ok=True) + # Clean any stale files. + for f in self._fixture_dir.glob("*.jsonl"): + f.unlink() + self.state = DashboardState.__new__(DashboardState) + self.state.jobs = {} + self.state.lock = threading.RLock() + + def _write_metrics_jsonl(self, filename, rows): + """Write a .jsonl file with metric rows to the fixture dir.""" + path = self._fixture_dir / filename + with path.open("w") as f: + for row in rows: + f.write(json.dumps(row) + "\n") + + def test_load_metric_files_derives_events(self): + """Write fixture metrics, load them, and verify constant_reward + invalid_batch events.""" + # Step 1: constant reward (std=0, max=min=1.0) + # Step 2: normal reward (std=0.5, max=2.0, min=0.0) + # Steps 3-5: invalid batch streak (advantages_std=0.0 for 3 consecutive) + # Step 6: recovery (advantages_std=1.0) + metrics_rows = [] + for step, rwd_std, rwd_max, rwd_min, adv_std in [ + (1, 0.0, 1.0, 1.0, 0.5), + (2, 0.5, 2.0, 0.0, 0.5), + (3, 0.5, 2.0, 0.0, 0.0), + (4, 0.5, 2.0, 0.0, 0.0), + (5, 0.5, 2.0, 0.0, 0.0), + (6, 0.5, 2.0, 0.0, 1.0), + ]: + metrics_rows.append({"name": "rollout/rewards_std", "value": rwd_std, "step": step}) + metrics_rows.append({"name": "rollout/rewards_max", "value": rwd_max, "step": step}) + metrics_rows.append({"name": "rollout/rewards_min", "value": rwd_min, "step": step}) + metrics_rows.append({"name": "rollout/advantages_std", "value": adv_std, "step": step}) + self._write_metrics_jsonl("custom_metrics.jsonl", metrics_rows) + + # Also write a log file to simulate OOM. + log_path = self._fixture_dir / "areno_train.log" + log_path.write_text("step 1 done\nCUDA error: out of memory\nprocess exited\n") + + job = _make_job(metrics_dir=str(self._fixture_dir.relative_to(_TMP_ROOT))) + job.logs = ["step 1 done", "CUDA error: out of memory", "process exited"] + self.state.jobs[job.id] = job + + # Load metrics from fixture dir. + self.state._load_metric_files(job) + + # Verify metrics were loaded. + self.assertTrue(len(job.metrics) > 0, "Metrics should have been loaded from .jsonl") + + # Verify events were derived. + kinds = {e["kind"] for e in job._events} + self.assertIn("constant_reward", kinds, "Should detect constant reward at step 1") + self.assertIn("invalid_batch", kinds, "Should detect invalid batch streak at steps 3-5") + self.assertIn("oom", kinds, "Should detect OOM from logs") + + # Verify constant_reward event details. + cr = [e for e in job._events if e["kind"] == "constant_reward"] + self.assertEqual(len(cr), 1) + self.assertEqual(cr[0]["step"], 1) + + # Verify invalid_batch streak is warn (streak=3). + ib = [e for e in job._events if e["kind"] == "invalid_batch"] + self.assertEqual(len(ib), 1) + self.assertEqual(ib[0]["severity"], "warn") + self.assertEqual(ib[0]["fields"]["streak"], 3) + + # Verify OOM event is error severity. + oom = [e for e in job._events if e["kind"] == "oom"] + self.assertTrue(len(oom) >= 1) + self.assertEqual(oom[0]["severity"], "error") + + def test_load_metric_files_empty_dir_no_events(self): + """An empty fixture dir should produce no events (legacy run scenario).""" + job = _make_job(metrics_dir=str(self._fixture_dir.relative_to(_TMP_ROOT))) + self.state.jobs[job.id] = job + self.state._load_metric_files(job) + self.assertEqual(job._events, []) + + def test_load_metric_files_boundary_single_advantages_std_zero(self): + """A single advantages_std=0 (streak=1) should produce an info event, not warn.""" + self._write_metrics_jsonl("boundary.jsonl", [ + {"name": "rollout/advantages_std", "value": 0.0, "step": 1}, + {"name": "rollout/advantages_std", "value": 1.0, "step": 2}, + ]) + job = _make_job(metrics_dir=str(self._fixture_dir.relative_to(_TMP_ROOT))) + self.state.jobs[job.id] = job + self.state._load_metric_files(job) + ib = [e for e in job._events if e["kind"] == "invalid_batch"] + self.assertEqual(len(ib), 1) + self.assertEqual(ib[0]["severity"], "info") + self.assertEqual(ib[0]["fields"]["streak"], 1) + + +if __name__ == "__main__": + unittest.main()