{ "$schema": "https://uipkge.dev/schema/registry-item.json", "name": "fine-tuning-job-monitor", "title": "Fine Tuning Job Monitor", "type": "registry:block", "files": [ { "path": "packages/registry-angular/blocks/fine-tuning-job-monitor/fine-tuning-job-monitor.component.ts", "content": "import { Component, DestroyRef, Input, inject, signal, ChangeDetectionStrategy } from '@angular/core'\nimport {\n Activity,\n AlertCircle,\n Bot,\n Check,\n CheckCircle2,\n Clock,\n Copy,\n Cpu,\n Database,\n Download,\n Gauge,\n Loader2,\n LucideAngularModule,\n Rocket,\n Save,\n ShieldCheck,\n Sliders,\n Sparkles,\n TrendingDown,\n XCircle,\n} from 'lucide-angular'\nimport { cn } from '@/lib/utils'\nimport { UiBadgeComponent } from '@/ui/badge/badge.component'\nimport { UiButtonComponent } from '@/ui/button/button.component'\nimport {\n UiCardComponent,\n UiCardContentComponent,\n UiCardDescriptionComponent,\n UiCardHeaderComponent,\n UiCardTitleComponent,\n} from '@/ui/card/card.component'\nimport { UiProgressComponent } from '@/ui/progress/progress.component'\nimport { UiSeparatorComponent } from '@/ui/separator/separator.component'\nimport {\n UiTableBodyComponent,\n UiTableCellComponent,\n UiTableComponent,\n UiTableHeadComponent,\n UiTableHeaderComponent,\n UiTableRowComponent,\n} from '@/ui/table/table.component'\n\nexport interface CheckpointRecord {\n id: string\n epoch: number\n step: number\n totalSteps: number\n trainLoss: number\n valLoss: number\n size: string\n filename: string\n createdAgo: string\n isBest?: boolean\n status: 'saved' | 'best' | 'evaluating'\n}\n\nexport interface LossReading {\n step: number\n trainLoss: number\n valLoss?: number\n isProjected?: boolean\n label?: string\n}\n\nexport interface FineTuningJobNotification {\n title: string\n message: string\n type: 'success' | 'info' | 'destructive'\n}\n\nconst defaultLossData: LossReading[] = [\n { step: 0, trainLoss: 1.84, valLoss: 1.92, label: 'Init' },\n { step: 25, trainLoss: 1.32, valLoss: undefined },\n { step: 50, trainLoss: 0.95, valLoss: 1.02, label: 'Warmup Done' },\n { step: 75, trainLoss: 0.915, valLoss: undefined },\n { step: 100, trainLoss: 0.892, valLoss: 0.945, label: 'Epoch 1 (Ckpt #1)' },\n { step: 125, trainLoss: 0.79, valLoss: undefined },\n { step: 150, trainLoss: 0.71, valLoss: 0.748, label: 'Step 150' },\n { step: 175, trainLoss: 0.64, valLoss: undefined },\n { step: 200, trainLoss: 0.584, valLoss: 0.612, label: 'Epoch 2 (Ckpt #2)' },\n { step: 225, trainLoss: 0.535, valLoss: undefined },\n { step: 250, trainLoss: 0.495, valLoss: 0.52, label: 'Step 250' },\n { step: 275, trainLoss: 0.46, valLoss: undefined },\n { step: 300, trainLoss: 0.435, valLoss: 0.468, label: 'Epoch 3 (Ckpt #3)' },\n { step: 320, trainLoss: 0.412, valLoss: 0.458, label: 'Step 320 (Live)' },\n // Projected points\n { step: 360, trainLoss: 0.395, valLoss: undefined, isProjected: true },\n { step: 400, trainLoss: 0.38, valLoss: 0.415, isProjected: true, label: 'Epoch 4 (Proj)' },\n { step: 450, trainLoss: 0.365, valLoss: undefined, isProjected: true },\n { step: 500, trainLoss: 0.355, valLoss: 0.39, isProjected: true, label: 'Epoch 5 (Target)' },\n]\n\nconst defaultCheckpoints: CheckpointRecord[] = [\n {\n id: 'ckpt-3',\n epoch: 3,\n step: 300,\n totalSteps: 500,\n trainLoss: 0.435,\n valLoss: 0.468,\n size: '1.2 GB LoRA',\n filename: 'adapter_model_step300.safetensors',\n createdAgo: '24m ago',\n isBest: true,\n status: 'best',\n },\n {\n id: 'ckpt-2',\n epoch: 2,\n step: 200,\n totalSteps: 500,\n trainLoss: 0.584,\n valLoss: 0.612,\n size: '1.2 GB LoRA',\n filename: 'adapter_model_step200.safetensors',\n createdAgo: '1h 44m ago',\n isBest: false,\n status: 'saved',\n },\n {\n id: 'ckpt-1',\n epoch: 1,\n step: 100,\n totalSteps: 500,\n trainLoss: 0.892,\n valLoss: 0.945,\n size: '1.2 GB LoRA',\n filename: 'adapter_model_step100.safetensors',\n createdAgo: '3h 02m ago',\n isBest: false,\n status: 'saved',\n },\n]\n\n// SVG telemetry loss-curve geometry (viewBox 0 0 740 220). Steps map to x, loss 0..2 to y.\nconst SVG_WIDTH = 740\nconst PAD_LEFT = 45\nconst PAD_RIGHT = 695\nconst USABLE_W = PAD_RIGHT - PAD_LEFT\nconst PAD_TOP = 25\nconst PAD_BOTTOM = 190\nconst USABLE_H = PAD_BOTTOM - PAD_TOP\n\n/**\n * FineTuningJobMonitor -- the React block, 1:1. LLM fine-tuning job telemetry: a header\n * with copyable job id, live status and cancel / download actions, four telemetry cards\n * (training loss, validation loss, learning rate, GPU cluster), a dual-series SVG loss\n * convergence chart with step milestones, a checkpoint history table with deploy /\n * download actions, and a hyperparameters & dataset manifest.\n */\n@Component({\n changeDetection: ChangeDetectionStrategy.Eager,\n selector: 'ui-fine-tuning-job-monitor, [ui-fine-tuning-job-monitor]',\n standalone: true,\n // React renders the root
itself: keep this wrapper out of the layout.\n host: { class: 'contents' },\n imports: [\n LucideAngularModule,\n UiBadgeComponent,\n UiButtonComponent,\n UiCardComponent,\n UiCardContentComponent,\n UiCardDescriptionComponent,\n UiCardHeaderComponent,\n UiCardTitleComponent,\n UiProgressComponent,\n UiSeparatorComponent,\n UiTableComponent,\n UiTableBodyComponent,\n UiTableCellComponent,\n UiTableHeadComponent,\n UiTableHeaderComponent,\n UiTableRowComponent,\n ],\n template: `\n
\n \n
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\n {{ fineTunedModelName }}\n

\n \n {{ jobId }}\n @if (copiedJobId()) {\n \n } @else {\n \n }\n \n
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\n Base Model:\n {{ baseModel }}\n
\n ·\n \n
\n @if (!isJobCancelled()) {\n \n \n \n \n } @else {\n \n }\n \n {{ isJobCancelled() ? 'Job Cancelled by Operator' : status }}\n \n
\n ·\n
\n \n Elapsed: {{ elapsedTime }}\n · ETA {{ etaRemaining }}\n
\n
\n
\n\n \n
\n \n \n \n \n {{ isJobCancelled() ? 'Cancelled' : isCancelling() ? 'Cancelling...' : 'Cancel Job' }}\n \n\n \n \n {{ isDownloadingWeights() ? 'Preparing Archive...' : 'Download Weights / LoRA Adapter' }}\n \n
\n
\n\n \n @if (activeNotification(); as note) {\n
\n
\n @if (note.type === 'success') {\n \n } @else {\n \n }\n
\n {{ note.title }}: \n {{ note.message }}\n
\n
\n \n ✕\n \n
\n }\n\n \n
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Training Loss

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\n \n Step {{ currentStep }}/{{ totalSteps }}\n \n
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\n \n {{ fixed3(trainingLoss) }}\n \n cross-entropy\n
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\n \n -77.6%\n
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\n Initial Loss:\n {{ fixed3(initialLoss) }}\n
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Target convergence corridor: < 0.450

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Validation Loss

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\n Generalizing\n
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\n \n {{ fixed3(validationLoss) }}\n \n eval set\n
\n +0.046 gap\n
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\n Overfitting Status:\n No Overfitting\n
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Evaluated every 50 steps on 4,250 rows

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Learning Rate

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\n Cosine\n
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\n \n {{ learningRate }}\n \n AdamW\n
\n Decay active\n
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\n Schedule:\n Cosine Decay (10% warmup)\n
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Peak: 1.5e-4 · Min: 1.0e-6

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GPU Compute Cluster

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\n \n {{ gpuUtilization }}% Utilized\n \n
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\n {{ gpuCluster }}\n
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\n \n VRAM: {{ vramUsage }}\n \n {{ locale(tokensPerSec) }} tok/s\n
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Training & Validation Loss Convergence

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\n Dual-series loss trajectory across {{ totalSteps }} gradient steps. Solid lines represent observed loss;\n dashed lines denote projected cosine decay trajectory.\n

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\n \n Training Loss ({{ fixed3(trainingLoss) }})\n
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\n \n Validation Loss ({{ fixed3(validationLoss) }})\n
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\n \n Target (< 0.400)\n
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\n \n \n \n \n \n \n \n\n \n \n\n \n \n \n \n \n \n \n\n \n \n 2.00\n \n \n 1.50\n \n \n 1.00\n \n \n 0.50\n \n \n 0.40\n \n \n 0.00\n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n \n \n\n \n @for (pt of valPointsHistoric; track pt.step) {\n \n }\n\n \n @for (pt of historicalPoints; track pt.step; let idx = $index) {\n \n }\n\n \n \n \n \n Step 320 / 500\n \n \n \n\n \n
\n @for (pt of milestones; track $index) {\n
\n

Step {{ pt.step }}

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{{ pt.label }}

\n
\n {{ fixed3(pt.trainLoss) }}\n @if (pt.valLoss) {\n / {{ fixed3(pt.valLoss) }}\n }\n
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\n }\n
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\n Step Progress\n

\n {{ currentStep }} / {{ totalSteps }} ({{ progressPercent }}%)\n

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\n Tokens Processed\n

98.4M / 153.8M

\n 64.0% of corpus\n
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\n Cluster Throughput\n

\n {{ locale(tokensPerSec) }} tok/s\n

\n Zero pipeline bubbles\n
\n\n
\n Current Epoch\n

\n Epoch {{ currentEpoch }} of {{ totalEpochs }}\n

\n 100 steps / epoch\n
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\n Convergence Projection\n

0.355 at Step 500

\n Expected final loss\n
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Checkpoint History & Weight Exporter

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\n Saved LoRA adapter snapshots. Deploy directly to test playground or download safe tensors.\n

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\n \n {{ checkpoints.length }} Saved Snapshots\n \n
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\n \n \n Checkpoint\n Step\n Train Loss\n Val Loss\n Size & Type\n Created\n Actions\n \n \n \n @for (ckpt of checkpoints; track ckpt.id) {\n \n \n
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\n Epoch {{ ckpt.epoch }}.0\n @if (ckpt.isBest) {\n \n Best Val Loss\n \n }\n
\n

{{ ckpt.filename }}

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\n \n \n {{ ckpt.step }} / {{ ckpt.totalSteps }}\n \n \n \n {{ fixed3(ckpt.trainLoss) }}\n \n \n \n \n {{ fixed3(ckpt.valLoss) }}\n \n \n \n {{ ckpt.size }}\n \n \n {{ ckpt.createdAgo }}\n \n \n
\n \n \n @if (deployingCkptId() === ckpt.id) {\n \n } @else {\n \n }\n {{ deployingCkptId() === ckpt.id ? 'Deploying...' : 'Deploy to Playground' }}\n \n \n \n \n
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Hyperparameters & Dataset

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\n LoRA PEFT\n
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Fine-tuning recipe manifest and dataset partition metadata.

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\n \n Training Dataset\n
\n \n @if (copiedDataset()) {\n \n } @else {\n \n }\n \n
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{{ trainingDataset }}

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\n {{ locale(trainingExamples) }} train · {{ locale(validationExamples) }} val\n {{ totalTokens }}\n
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\n\n \n
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\n Effective Batch Size:\n \n {{ batchSize }}\n ({{ microBatchSize }} × {{ gradAccumSteps }} accum)\n \n
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\n Context Length:\n \n {{ locale(contextLength) }} tokens\n \n
\n\n
\n Optimizer:\n {{ optimizer }}\n
\n\n
\n LoRA Rank & Alpha:\n \n r={{ loraRank }}, α={{ loraAlpha }} (dropout={{ loraDropout }})\n \n
\n\n
\n Target Modules:\n All Linear (q, k, v, o, gate, up, down)\n
\n\n
\n Precision & Kernel:\n bfloat16 · FlashAttention-2\n
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\n Parallelism Strategy:\n PyTorch FSDP-2 (Hybrid Sharded)\n
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\n \n HuggingFace Hub & vLLM Ready\n
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\n Checkpoints are stored in standard SafeTensors format with tokenizer configs ready for instant\n serverless vLLM / TensorRT-LLM deployment.\n

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\n \n `,\n})\nexport class UiFineTuningJobMonitorComponent {\n @Input() jobId = '#ft-job-2026-0842'\n @Input() baseModel = 'Meta Llama 3.3 70B Instruct'\n @Input() fineTunedModelName = 'llama-3.3-70b-uipkge-expert-v2'\n @Input() status = 'Training in Progress · Epoch 3 of 5 · 64% Complete'\n @Input() currentEpoch = 3\n @Input() totalEpochs = 5\n @Input() progressPercent = 64\n @Input() currentStep = 320\n @Input() totalSteps = 500\n @Input() trainingLoss = 0.412\n @Input() initialLoss = 1.84\n @Input() validationLoss = 0.458\n @Input() learningRate = '1.5e-5'\n @Input() gpuCluster = '8× NVIDIA H100 80GB SXM5'\n @Input() gpuUtilization = 100\n @Input() vramUsage = '76.4 GB / 80 GB'\n @Input() tokensPerSec = 3480\n @Input() elapsedTime = '4h 18m 22s'\n @Input() etaRemaining = '2h 25m'\n @Input() trainingDataset = 'uipkge_synthetic_sfc_pairs.jsonl'\n @Input() trainingExamples = 42500\n @Input() validationExamples = 4250\n @Input() totalTokens = '153.8M tokens'\n @Input() batchSize = 32\n @Input() microBatchSize = 4\n @Input() gradAccumSteps = 8\n @Input() contextLength = 4096\n @Input() optimizer = 'AdamW (beta1=0.9, beta2=0.95)'\n @Input() loraRank = 64\n @Input() loraAlpha = 128\n @Input() loraDropout = 0.05\n @Input() checkpoints: CheckpointRecord[] = defaultCheckpoints\n @Input() lossData: LossReading[] = defaultLossData\n @Input('class') className?: string\n\n readonly copiedJobId = signal(false)\n readonly copiedDataset = signal(false)\n readonly isDownloadingWeights = signal(false)\n readonly isCancelling = signal(false)\n readonly isJobCancelled = signal(false)\n readonly deployingCkptId = signal(null)\n readonly activeNotification = signal(null)\n /** Index into `lossData` of the highlighted milestone. Defaults to step 320. */\n readonly selectedStepIndex = signal(13)\n\n protected readonly Activity = Activity\n protected readonly AlertCircle = AlertCircle\n protected readonly Bot = Bot\n protected readonly Check = Check\n protected readonly CheckCircle2 = CheckCircle2\n protected readonly Clock = Clock\n protected readonly Copy = Copy\n protected readonly Cpu = Cpu\n protected readonly Database = Database\n protected readonly Download = Download\n protected readonly Gauge = Gauge\n protected readonly Loader2 = Loader2\n protected readonly Rocket = Rocket\n protected readonly Save = Save\n protected readonly ShieldCheck = ShieldCheck\n protected readonly Sliders = Sliders\n protected readonly Sparkles = Sparkles\n protected readonly TrendingDown = TrendingDown\n protected readonly XCircle = XCircle\n\n protected readonly svgWidth = SVG_WIDTH\n protected readonly padLeft = PAD_LEFT\n protected readonly padRight = PAD_RIGHT\n protected readonly usableW = USABLE_W\n protected readonly padBottom = PAD_BOTTOM\n\n private readonly timers = new Set>()\n\n constructor() {\n inject(DestroyRef).onDestroy(() => this.timers.forEach(clearTimeout))\n }\n\n get rootClass(): string {\n return cn('mx-auto w-full max-w-6xl space-y-6', this.className)\n }\n\n // --- formatting -------------------------------------------------------------------------\n\n fixed3(n: number): string {\n return n.toFixed(3)\n }\n\n locale(n: number): string {\n return n.toLocaleString()\n }\n\n // --- classes ----------------------------------------------------------------------------\n\n statusClass(): string {\n return cn('font-medium', this.isJobCancelled() ? 'text-destructive' : 'text-info')\n }\n\n cancelIconClass(): string {\n return cn('size-3.5', this.isCancelling() && 'animate-spin')\n }\n\n downloadIconClass(): string {\n return cn('size-3.5', this.isDownloadingWeights() && 'animate-bounce')\n }\n\n notificationClass(note: FineTuningJobNotification): string {\n return cn(\n 'flex items-center justify-between rounded-lg border p-3 text-xs shadow-xs transition-colors',\n note.type === 'destructive'\n ? 'border-destructive/30 bg-destructive/10 text-destructive'\n : 'border-border bg-card text-card-foreground',\n )\n }\n\n trainMarkerClass(pt: LossReading): string {\n return cn(\n 'stroke-background cursor-pointer stroke-2 transition-transform hover:scale-150',\n pt.step === this.currentStep ? 'fill-info ring-info ring-2' : 'fill-info',\n )\n }\n\n milestoneClass(pt: LossReading): string {\n const isSelected = this.selectedStepIndex() === this.lossData.indexOf(pt)\n return cn(\n 'rounded p-1.5 text-center transition-colors',\n isSelected ? 'border-border/80 bg-muted border shadow-2xs' : 'bg-muted/30 border border-transparent',\n )\n }\n\n // --- chart geometry ---------------------------------------------------------------------\n\n mapX(step: number): number {\n return PAD_LEFT + (step / this.totalSteps) * USABLE_W\n }\n\n mapY(loss: number): number {\n const clamped = Math.max(0, Math.min(2.0, loss))\n return PAD_BOTTOM - (clamped / 2.0) * USABLE_H\n }\n\n get targetLossY(): number {\n return this.mapY(0.4)\n }\n\n get currentStepX(): number {\n return this.mapX(this.currentStep)\n }\n\n get historicalPoints(): LossReading[] {\n return this.lossData.filter((d) => !d.isProjected)\n }\n\n get projectedPoints(): LossReading[] {\n const proj = this.lossData.filter((d) => d.isProjected)\n const hist = this.historicalPoints\n const lastHist = hist[hist.length - 1]\n return lastHist ? [lastHist, ...proj] : proj\n }\n\n get valPointsHistoric(): LossReading[] {\n return this.lossData.filter((d) => !d.isProjected && d.valLoss !== undefined)\n }\n\n get valPointsProjected(): LossReading[] {\n const list = this.lossData.filter((d) => d.valLoss !== undefined && d.isProjected)\n const hist = this.valPointsHistoric\n const lastValHist = hist[hist.length - 1]\n return lastValHist ? [lastValHist, ...list] : list\n }\n\n get milestones(): LossReading[] {\n return this.lossData.filter((d) => d.label)\n }\n\n get trainPathHistoric(): string {\n return this.curve(this.historicalPoints, (p) => p.trainLoss)\n }\n\n get trainAreaHistoric(): string {\n const pts = this.historicalPoints\n if (!pts.length) return ''\n const firstX = this.mapX(pts[0]!.step)\n const lastX = this.mapX(pts[pts.length - 1]!.step)\n return `${this.trainPathHistoric} L ${lastX},${PAD_BOTTOM} L ${firstX},${PAD_BOTTOM} Z`\n }\n\n get trainPathProjected(): string {\n return this.curve(this.projectedPoints, (p) => p.trainLoss)\n }\n\n get valPathHistoric(): string {\n return this.curve(this.valPointsHistoric, (p) => p.valLoss ?? 0)\n }\n\n get valPathProjected(): string {\n return this.curve(this.valPointsProjected, (p) => p.valLoss ?? 0)\n }\n\n /** Smooth cubic path through the points (control points at the horizontal midpoints). */\n private curve(pts: LossReading[], loss: (p: LossReading) => number): string {\n if (!pts.length) return ''\n let path = `M ${this.mapX(pts[0]!.step)},${this.mapY(loss(pts[0]!))}`\n for (let i = 1; i < pts.length; i++) {\n const prev = pts[i - 1]!\n const curr = pts[i]!\n const x0 = this.mapX(prev.step)\n const y0 = this.mapY(loss(prev))\n const x1 = this.mapX(curr.step)\n const y1 = this.mapY(loss(curr))\n const cx = (x0 + x1) / 2\n path += ` C ${cx},${y0} ${cx},${y1} ${x1},${y1}`\n }\n return path\n }\n\n selectStep(step: number): void {\n this.selectedStepIndex.set(this.lossData.findIndex((d) => d.step === step))\n }\n\n // --- actions ----------------------------------------------------------------------------\n\n showNotification(title: string, message: string, type: FineTuningJobNotification['type'] = 'success'): void {\n this.activeNotification.set({ title, message, type })\n this.later(() => {\n this.activeNotification.update((prev) => (prev?.title === title ? null : prev))\n }, 4000)\n }\n\n copyJobId(): void {\n if (typeof navigator !== 'undefined' && navigator.clipboard) {\n void navigator.clipboard.writeText(this.jobId)\n this.copiedJobId.set(true)\n this.later(() => this.copiedJobId.set(false), 2000)\n }\n }\n\n copyDatasetName(): void {\n if (typeof navigator !== 'undefined' && navigator.clipboard) {\n void navigator.clipboard.writeText(this.trainingDataset)\n this.copiedDataset.set(true)\n this.later(() => this.copiedDataset.set(false), 2000)\n }\n }\n\n handleDownloadWeights(): void {\n if (this.isDownloadingWeights()) return\n this.isDownloadingWeights.set(true)\n this.later(() => {\n this.isDownloadingWeights.set(false)\n this.showNotification(\n 'Weights Download Triggered',\n 'LoRA adapter weights (adapter_model.safetensors, 1.2 GB) download initialized with SHA-256 verification.',\n )\n }, 900)\n }\n\n handleCancelJob(): void {\n if (this.isCancelling() || this.isJobCancelled()) return\n this.isCancelling.set(true)\n this.later(() => {\n this.isCancelling.set(false)\n this.isJobCancelled.set(true)\n this.showNotification(\n 'Job Cancellation Dispatched',\n 'SIGTERM gracefully sent to GPU cluster. Last checkpoint #3 preserved.',\n 'destructive',\n )\n }, 700)\n }\n\n handleDeployToPlayground(ckpt: CheckpointRecord): void {\n this.deployingCkptId.set(ckpt.id)\n this.later(() => {\n this.deployingCkptId.set(null)\n this.showNotification(\n 'Checkpoint Deployed to Playground',\n `Checkpoint Epoch ${ckpt.epoch} (Step ${ckpt.step}) has been loaded into the interactive testing playground with zero cold-start latency.`,\n )\n }, 1000)\n }\n\n private later(fn: () => void, ms: number): void {\n const t = setTimeout(() => {\n this.timers.delete(t)\n fn()\n }, ms)\n this.timers.add(t)\n }\n}\n", "type": "registry:block", "target": "~/src/app/components/blocks/fine-tuning-job-monitor/fine-tuning-job-monitor.component.ts" }, { "path": "packages/registry-angular/blocks/fine-tuning-job-monitor/index.ts", "content": "export {\n UiFineTuningJobMonitorComponent,\n type CheckpointRecord,\n type FineTuningJobNotification,\n type LossReading,\n} from './fine-tuning-job-monitor.component'\n", "type": "registry:block", "target": "~/src/app/components/blocks/fine-tuning-job-monitor/index.ts" } ], "dependencies": [ "lucide-angular" ], "devDependencies": [], "registryDependencies": [ "https://uipkge.dev/r/angular/badge.json", "https://uipkge.dev/r/angular/button.json", "https://uipkge.dev/r/angular/card.json", "https://uipkge.dev/r/angular/progress.json", "https://uipkge.dev/r/angular/separator.json", "https://uipkge.dev/r/angular/table.json" ], "description": "LLM fine-tuning job telemetry, loss curve convergence tracking, GPU cluster utilization, hyperparameter manifests, and LoRA checkpoint exporter.", "categories": [ "ai", "dashboard", "app" ] }