From d86625e22038e45f25fdbaa65a228e98fc163e06 Mon Sep 17 00:00:00 2001 From: Brandon Schneider Date: Thu, 28 May 2026 01:13:54 -0500 Subject: [PATCH] feat(infra): cluster dashboard + VCN shim indentation fix - Add LyteNyte Grid cluster dashboard (React + FastAPI + k3s) - Real-time telemetry: GPU util/VRAM/temp, CPU/memory, pod counts, Tailscale connectivity, OS/kernel info for all 5 cluster nodes - WebSocket live updates every 3s, dark theme, virtualized grid - k3s deployment with hostNetwork, NodePort 30820, SSH-based collectors - Backend uses /proc/stat + /proc/meminfo for portable NixOS metrics - Tailscale status via SSH to host (container doesn't run tailscaled) - Fix vcn_compute_substrate.py indentation (lines 45-269 had 1 extra leading space). Script now compiles and runs encode/decode/receipt. Build: py_compile OK, npm build OK, podman build OK --- .../shim/vcn_compute_substrate.py | 624 ++++++ 5-Applications/cluster-dashboard/.gitignore | 4 + .../cluster-dashboard/Containerfile | 45 + .../cluster-dashboard/backend/entrypoint.sh | 7 + .../cluster-dashboard/backend/main.py | 448 ++++ .../backend/requirements.txt | 3 + .../cluster-dashboard/frontend/index.html | 23 + .../frontend/package-lock.json | 1910 +++++++++++++++++ .../cluster-dashboard/frontend/package.json | 23 + .../cluster-dashboard/frontend/src/App.tsx | 410 ++++ .../cluster-dashboard/frontend/src/main.tsx | 9 + .../cluster-dashboard/frontend/tsconfig.json | 20 + .../cluster-dashboard/frontend/vite.config.ts | 18 + .../cluster-dashboard/k8s/dashboard.yaml | 107 + 14 files changed, 3651 insertions(+) create mode 100644 4-Infrastructure/shim/vcn_compute_substrate.py create mode 100644 5-Applications/cluster-dashboard/.gitignore create mode 100644 5-Applications/cluster-dashboard/Containerfile create mode 100755 5-Applications/cluster-dashboard/backend/entrypoint.sh create mode 100644 5-Applications/cluster-dashboard/backend/main.py create mode 100644 5-Applications/cluster-dashboard/backend/requirements.txt create mode 100644 5-Applications/cluster-dashboard/frontend/index.html create mode 100644 5-Applications/cluster-dashboard/frontend/package-lock.json create mode 100644 5-Applications/cluster-dashboard/frontend/package.json create mode 100644 5-Applications/cluster-dashboard/frontend/src/App.tsx create mode 100644 5-Applications/cluster-dashboard/frontend/src/main.tsx create mode 100644 5-Applications/cluster-dashboard/frontend/tsconfig.json create mode 100644 5-Applications/cluster-dashboard/frontend/vite.config.ts create mode 100644 5-Applications/cluster-dashboard/k8s/dashboard.yaml diff --git a/4-Infrastructure/shim/vcn_compute_substrate.py b/4-Infrastructure/shim/vcn_compute_substrate.py new file mode 100644 index 00000000..60ec5c62 --- /dev/null +++ b/4-Infrastructure/shim/vcn_compute_substrate.py @@ -0,0 +1,624 @@ +#!/usr/bin/env python3 +""" +VCN Compute Substrate - Use AMD VCN H.264 hardware encoder as computation device. + +This shim implements the "Steam Deck MKV trick" from UNIFIED_TRANSPORT_ENCODING_SPEC.md: +- Pack computation data into 1920×1080 YUV420 video frames +- Use AMD AMF H.264 hardware encoder for computation +- Extract results from encoded bitstream (CRC32, transform coefficients, motion vectors) + +Shim boundary: I/O only. No decision logic. All computation mapping decisions +belong in Lean (Semantics.MeshRouting and future VCN computation modules). + +Usage: + python3 vcn_compute_substrate.py encode + python3 vcn_compute_substrate.py decode + python3 vcn_compute_substrate.py extract_receipt +""" + +import struct +import subprocess +import json +import zlib +from pathlib import Path +from typing import Tuple, List, Optional +from dataclasses import dataclass, field, asdict + +# Frame constants from UNIFIED_TRANSPORT_ENCODING_SPEC.md +FRAME_WIDTH = 1920 +FRAME_HEIGHT = 1080 +YUV420_FRAME_SIZE = 3_110_400 # 1920*1080 + 960*540 + 960*540 +SIGNATURE_HEADER = b"RDMAVCN\0" +SIGNATURE_SIZE = 24 + +# Encoder settings for computation mode (software encoding for initial testing) +ENCODER_PROFILE = "main" +ENCODER_LEVEL = "4" +QP_MIN = 2 # Minimal quantization for precise computation +QP_MAX = 4 +TRANSFORM_SKIP = True +DEBLOCKING = False +SAO = False +# ── Resolution / Frame Rate Catalog ────────────────────────────────────────── + +VCN_RESOLUTIONS = { + "240p": (320, 240), + "360p": (640, 360), + "480p": (854, 480), + "720p": (1280, 720), + "1080p": (1920, 1080), + "1440p": (2560, 1440), + "4K": (3840, 2160), + "5K": (5120, 2880), + "8K": (7680, 4320), + "16K": (15360, 8640), +} + +VCN_FRAME_RATES = [30, 60, 120, 144, 240] + +RESOLUTION_ORDER = list(VCN_RESOLUTIONS.keys()) + + +@dataclass +class VCNHardwareCapabilities: + """Detected hardware encoder/decoder capabilities.""" + supported_resolutions: List[Tuple[int, int]] = field(default_factory=lambda: [(1920, 1080)]) + supported_frame_rates: List[int] = field(default_factory=lambda: [30, 60]) + available_encoders: List[str] = field(default_factory=lambda: ["libx264"]) + max_memory_mb: int = 512 + max_bandwidth_mbps: int = 100 + gpu_vendor: str = "unknown" + gpu_name: str = "unknown" + + +@dataclass +class VCNComputeFrameSpec: + """Dynamic frame specification for VCN computation.""" + width: int = 1920 + height: int = 1080 + format: str = "yuv420p" + bytes_per_frame: int = 3_110_400 + frame_rate: int = 60 + encoder: str = "libx264" + + +def probe_vcn_capabilities() -> VCNHardwareCapabilities: + """Probe hardware VCN capabilities using FFmpeg, VAAPI, and vendor tools.""" + caps = VCNHardwareCapabilities() + encoders_found = [] + try: + result = subprocess.run( + ["ffmpeg", "-encoders"], + capture_output=True, text=True, timeout=10 + ) + output = result.stdout + for enc in ["h264_vaapi", "hevc_vaapi", "h264_amf", "hevc_amf", + "h264_nvenc", "hevc_nvenc", "libx264", "libx265"]: + if enc in output: + encoders_found.append(enc) + except (FileNotFoundError, subprocess.TimeoutExpired): + encoders_found = ["libx264"] + caps.available_encoders = encoders_found if encoders_found else ["libx264"] + # Detect GPU vendor + try: + result = subprocess.run( + ["nvidia-smi", "--query-gpu=name,memory.total", "--format=csv,noheader,nounits"], + capture_output=True, text=True, timeout=5 + ) + if result.returncode == 0 and result.stdout.strip(): + parts = result.stdout.strip().split(",") + caps.gpu_vendor = "nvidia" + caps.gpu_name = parts[0].strip() + caps.max_memory_mb = int(parts[1].strip()) + except (FileNotFoundError, subprocess.TimeoutExpired): + pass + if caps.gpu_vendor == "unknown": + try: + result = subprocess.run(["vainfo"], capture_output=True, text=True, timeout=5) + output = result.stdout + result.stderr + if "AMD" in output or "RADV" in output or "radeon" in output.lower(): + caps.gpu_vendor = "amd" + caps.gpu_name = "AMD Radeon (VAAPI)" + elif "Intel" in output or "iHD" in output or "i965" in output: + caps.gpu_vendor = "intel" + caps.gpu_name = "Intel Graphics (VAAPI)" + except (FileNotFoundError, subprocess.TimeoutExpired): + pass + if caps.gpu_vendor == "unknown": + try: + vendor_path = Path("/sys/class/drm/card1/device/vendor") + if vendor_path.exists(): + vendor_id = vendor_path.read_text().strip() + if vendor_id == "0x1002": + caps.gpu_vendor = "amd" + caps.gpu_name = "AMD Radeon (DRM)" + elif vendor_id == "0x8086": + caps.gpu_vendor = "intel" + caps.gpu_name = "Intel Graphics (DRM)" + except Exception: + pass + preferred_encoder = _select_preferred_encoder(encoders_found, caps.gpu_vendor) + caps.available_encoders = [preferred_encoder] + [e for e in encoders_found if e != preferred_encoder] + supported = [] + for name, (w, h) in VCN_RESOLUTIONS.items(): + if w > 3840 and (3840, 2160) in supported: + supported.append((w, h)) + continue + if _test_resolution(w, h, preferred_encoder): + supported.append((w, h)) + caps.supported_resolutions = supported if supported else [(1920, 1080)] + caps.supported_frame_rates = [fps for fps in VCN_FRAME_RATES if fps <= 240] + max_w, max_h = caps.supported_resolutions[-1] if caps.supported_resolutions else (1920, 1080) + max_pixels = max_w * max_h + caps.max_bandwidth_mbps = (max_pixels * 3 // 2 * 60) // (1024 * 1024) + return caps + + +def _select_preferred_encoder(encoders: List[str], vendor: str) -> str: + """Select the best encoder for the detected GPU vendor.""" + vendor_prefs = { + "nvidia": ["hevc_nvenc", "h264_nvenc"], + "amd": ["hevc_vaapi", "h264_vaapi", "hevc_amf", "h264_amf"], + "intel": ["hevc_vaapi", "h264_vaapi"], + } + for pref in vendor_prefs.get(vendor, []): + if pref in encoders: + return pref + return "libx265" if "libx265" in encoders else "libx264" + + +def _test_resolution(width: int, height: int, encoder: str) -> bool: + """Test if the encoder can handle a given resolution.""" + try: + test_size = width * height * 3 // 2 + if test_size > 100_000_000: + return False + cmd = ["ffmpeg", "-y", "-f", "rawvideo", "-pix_fmt", "yuv420p", + "-s", f"{width}x{height}", "-frames:v", "1", "-i", "/dev/zero", + "-c:v", encoder, "-f", "null", "-"] + if "vaapi" in encoder: + cmd.insert(1, "-vaapi_device") + cmd.insert(2, "/dev/dri/renderD128") + result = subprocess.run(cmd, capture_output=True, text=True, timeout=10) + return result.returncode == 0 + except (FileNotFoundError, subprocess.TimeoutExpired): + return False + + +def compute_frame_size(width: int, height: int, fmt: str = "yuv420p") -> int: + """Compute frame size in bytes for given resolution and format.""" + if fmt == "yuv420p": + return width * height * 3 // 2 + elif fmt == "rgb24": + return width * height * 3 + raise ValueError(f"Unsupported format: {fmt}") + + +def select_optimal_resolution( + caps: VCNHardwareCapabilities, + target_data_size: int, + preferred_format: str = "yuv420p" +) -> VCNComputeFrameSpec: + """Select the smallest resolution that can hold target_data_size.""" + required_size = target_data_size + SIGNATURE_SIZE + for w, h in caps.supported_resolutions: + frame_bytes = compute_frame_size(w, h, preferred_format) + if frame_bytes >= required_size: + max_fps = 60 + for fps in reversed(caps.supported_frame_rates): + bandwidth_needed = (frame_bytes * fps) // (1024 * 1024) + if bandwidth_needed <= caps.max_bandwidth_mbps: + max_fps = fps + break + encoder = caps.available_encoders[0] if caps.available_encoders else "libx264" + return VCNComputeFrameSpec(width=w, height=h, format=preferred_format, + bytes_per_frame=frame_bytes, frame_rate=max_fps, encoder=encoder) + w, h = caps.supported_resolutions[-1] if caps.supported_resolutions else (1920, 1080) + encoder = caps.available_encoders[0] if caps.available_encoders else "libx264" + return VCNComputeFrameSpec(width=w, height=h, format=preferred_format, + bytes_per_frame=compute_frame_size(w, h, preferred_format), frame_rate=60, encoder=encoder) + + +def create_frame_dynamic(data: bytes, seq: int, spec: VCNComputeFrameSpec) -> bytes: + """Create a frame at dynamic resolution from computation data.""" + frame_size = spec.bytes_per_frame + if len(data) > frame_size - SIGNATURE_SIZE: + raise ValueError(f"Data too large: {len(data)} > {frame_size - SIGNATURE_SIZE}") + frame = bytearray(frame_size) + version = 1 + length = len(data) + header = SIGNATURE_HEADER + struct.pack(" subprocess.CompletedProcess: + """Encode frames using detected hardware encoder with software fallback.""" + raw_path = output_path.with_suffix(".raw") + with open(raw_path, "wb") as f: + for frame in input_frames: + f.write(frame) + encoder = spec.encoder + cmd = ["ffmpeg", "-y", "-f", "rawvideo", "-pix_fmt", spec.format, + "-s", f"{spec.width}x{spec.height}", "-r", str(spec.frame_rate), + "-i", str(raw_path), "-c:v", encoder, "-qp", str(QP_MIN), + "-f", "matroska", str(output_path)] + if "vaapi" in encoder: + cmd.insert(1, "-vaapi_device") + cmd.insert(2, "/dev/dri/renderD128") + cmd.insert(3, "-vf") + cmd.insert(4, "format=nv12,hwupload") + elif "nvenc" in encoder: + cmd.extend(["-preset", "p1", "-tune", "ull"]) + elif "amf" in encoder: + cmd.extend(["-usage", "ultralowlatency"]) + elif encoder == "libx264": + cmd.extend(["-profile:v", ENCODER_PROFILE, "-preset", "ultrafast", "-tune", "zerolatency"]) + result = subprocess.run(cmd, capture_output=True, text=True) + if result.returncode != 0 and encoder != "libx264": + cmd[cmd.index("-c:v") + 1] = "libx264" + result = subprocess.run(cmd, capture_output=True, text=True) + raw_path.unlink(missing_ok=True) + return result + + +def pack_q16_16_to_yuv(value: int) -> Tuple[int, int, int]: + """ + Pack a Q16_16 scalar into YUV pixel values. + + Mapping strategy: Split 32-bit Q16_16 into three 8-bit components + with error-diffusion dithering for precision preservation. + + Args: + value: Q16_16 scalar as integer (0x00010000 = 1.0) + + Returns: + (Y, U, V) tuple of pixel values (0-255) + """ + # Extract bytes from Q16_16 (little-endian) + y_val = (value >> 16) & 0xFF + u_val = (value >> 8) & 0xFF + v_val = value & 0xFF + + # Clamp to valid YUV range + y_val = max(16, min(235, y_val)) # Y: 16-235 (limited range) + u_val = max(16, min(240, u_val)) # U: 16-240 + v_val = max(16, min(240, v_val)) # V: 16-240 + + return (y_val, u_val, v_val) + + +def unpack_yuv_to_q16_16(y: int, u: int, v: int) -> int: + """ + Unpack YUV pixel values back to Q16_16 scalar. + + Args: + y, u, v: YUV pixel values (0-255) + + Returns: + Q16_16 scalar as integer + """ + # Reconstruct Q16_16 from YUV components + value = (y << 16) | (u << 8) | v + return value + + +def create_yuv420_frame(data: bytes, seq: int) -> bytes: + """ + Create a 1920×1080 YUV420 frame from computation data. + + Frame layout: + - Bytes 0-23: Signature header (RDMAVCN\0 + version + seq + length) + - Bytes 24+: Computation data packed into YUV macroblocks + + Args: + data: Raw computation data bytes + seq: Frame sequence number + + Returns: + Complete YUV420 frame bytes (3,110,400 bytes) + """ + if len(data) > YUV420_FRAME_SIZE - SIGNATURE_SIZE: + raise ValueError(f"Data too large: {len(data)} > {YUV420_FRAME_SIZE - SIGNATURE_SIZE}") + + # Create frame buffer + frame = bytearray(YUV420_FRAME_SIZE) + + # Write signature header (exactly 24 bytes) + version = 1 + length = len(data) + header = SIGNATURE_HEADER + struct.pack(" subprocess.CompletedProcess: + """ + Encode raw YUV420 frames using H.264 encoder. + + Args: + input_frames: List of YUV420 frame bytes + output_path: Output MKV file path + + Returns: + FFmpeg subprocess result + """ + # Write raw YUV420 file + raw_path = output_path.with_suffix(".yuv") + with open(raw_path, "wb") as f: + for frame in input_frames: + f.write(frame) + + # FFmpeg command for software encoding (libx264) for initial testing + cmd = [ + "ffmpeg", + "-y", # Overwrite output file + "-f", "rawvideo", + "-pix_fmt", "yuv420p", + "-s", f"{FRAME_WIDTH}x{FRAME_HEIGHT}", + "-r", "60", # 60 fps as per spec + "-i", str(raw_path), + "-c:v", "libx264", + "-profile:v", ENCODER_PROFILE, + "-level", ENCODER_LEVEL, + "-qp", str(QP_MIN), + "-preset", "ultrafast", + "-tune", "zerolatency", + "-f", "matroska", + str(output_path) + ] + + result = subprocess.run(cmd, capture_output=True, text=True) + + # Clean up raw file + raw_path.unlink() + + return result + + +def decode_frames(input_path: Path) -> List[bytes]: + """ + Decode MKV file back to raw YUV420 frames using H.264 decoder. + + Note: This is lossy - the encoding process modifies pixel values. + For computation extraction, use extract_receipt() instead. + + Args: + input_path: Input MKV file path + + Returns: + List of decoded YUV420 frame bytes + """ + raw_path = input_path.with_suffix(".decoded.yuv") + + cmd = [ + "ffmpeg", + "-i", str(input_path), + "-c:v", "rawvideo", # Decode to raw, don't re-encode + "-f", "rawvideo", + "-pix_fmt", "yuv420p", + str(raw_path) + ] + + result = subprocess.run(cmd, capture_output=True, text=True) + + if result.returncode != 0: + raise RuntimeError(f"FFmpeg decode failed: {result.stderr}") + + # Read decoded frames + with open(raw_path, "rb") as f: + data = f.read() + + # Split into frames + frames = [] + for i in range(0, len(data), YUV420_FRAME_SIZE): + frame = data[i:i + YUV420_FRAME_SIZE] + if len(frame) == YUV420_FRAME_SIZE: + frames.append(frame) + + # Clean up + raw_path.unlink() + + return frames + + +def extract_receipt(input_path: Path) -> dict: + """ + Extract computation receipt from encoded MKV file. + + Receipt includes: + - CRC32 of encoded file (proves encoding occurred) + - Frame size statistics + - Encoding parameters used + - Bitstream analysis metadata + - Compression ratio (input vs output size) + + Args: + input_path: Input MKV file path + + Returns: + Receipt dictionary + """ + # Use ffprobe to get stream information + cmd = [ + "ffprobe", + "-v", "quiet", + "-print_format", "json", + "-show_streams", + "-show_format", + str(input_path) + ] + + result = subprocess.run(cmd, capture_output=True, text=True) + + if result.returncode != 0: + raise RuntimeError(f"FFprobe failed: {result.stderr}") + + probe_info = json.loads(result.stdout) + + # Calculate file CRC32 + with open(input_path, "rb") as f: + file_data = f.read() + file_crc32 = zlib.crc32(file_data) & 0xFFFFFFFF + + # Extract compression metrics + original_size = YUV420_FRAME_SIZE # Single frame + compressed_size = len(file_data) + compression_ratio = original_size / compressed_size if compressed_size > 0 else 0 + + receipt = { + "schema": "vcn_computation_receipt_v1", + "input_file": str(input_path), + "file_size_bytes": len(file_data), + "file_crc32": file_crc32, + "encoding_params": { + "codec": "libx264", + "profile": ENCODER_PROFILE, + "qp_min": QP_MIN, + "qp_max": QP_MAX, + "transform_skip": TRANSFORM_SKIP, + "deblocking": DEBLOCKING, + "sao": SAO + }, + "stream_info": probe_info.get("streams", []), + "format_info": probe_info.get("format", {}), + "frame_spec": { + "width": FRAME_WIDTH, + "height": FRAME_HEIGHT, + "format": "yuv420p", + "bytes_per_frame": YUV420_FRAME_SIZE + }, + "compression_metrics": { + "original_size": original_size, + "compressed_size": compressed_size, + "compression_ratio": compression_ratio, + "space_saving": (1 - (compressed_size / original_size)) * 100 if original_size > 0 else 0 + } + } + + return receipt + + +def main(): + import sys + + if len(sys.argv) < 2: + print("Usage: vcn_compute_substrate.py ") + sys.exit(1) + + command = sys.argv[1] + input_path = Path(sys.argv[2]) + + if command == "encode": + output_path = Path(sys.argv[3]) + + # Read input data + with open(input_path, "rb") as f: + data = f.read() + + # Create frame + frame = create_yuv420_frame(data, seq=0) + + # Encode + result = encode_frames([frame], output_path) + + if result.returncode != 0: + print(f"Encoding failed: {result.stderr}", file=sys.stderr) + sys.exit(1) + + print(f"Encoded to {output_path}") + + elif command == "decode": + output_path = Path(sys.argv[3]) + + # Decode + frames = decode_frames(input_path) + + # Extract first frame's data + if frames: + frame = frames[0] + # Extract signature header + header = frame[:SIGNATURE_SIZE] + signature, version, seq, length, _ = struct.unpack("<8sIIII", header) + + if signature != SIGNATURE_HEADER: + print(f"Invalid signature: {signature}", file=sys.stderr) + sys.exit(1) + + # Extract data payload + data = frame[SIGNATURE_SIZE:SIGNATURE_SIZE + length] + + with open(output_path, "wb") as f: + f.write(data) + + print(f"Decoded to {output_path}") + else: + print("No frames decoded", file=sys.stderr) + sys.exit(1) + + elif command == "extract_receipt": + receipt = extract_receipt(input_path) + output_path = Path(sys.argv[3]) + + with open(output_path, "w") as f: + json.dump(receipt, f, indent=2) + + print(f"Receipt written to {output_path}") + + else: + print(f"Unknown command: {command}", file=sys.stderr) + sys.exit(1) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/5-Applications/cluster-dashboard/.gitignore b/5-Applications/cluster-dashboard/.gitignore new file mode 100644 index 00000000..e428b76f --- /dev/null +++ b/5-Applications/cluster-dashboard/.gitignore @@ -0,0 +1,4 @@ +frontend/node_modules/ +frontend/dist/ +__pycache__/ +*.pyc diff --git a/5-Applications/cluster-dashboard/Containerfile b/5-Applications/cluster-dashboard/Containerfile new file mode 100644 index 00000000..aef92e6d --- /dev/null +++ b/5-Applications/cluster-dashboard/Containerfile @@ -0,0 +1,45 @@ +# Multi-stage: build frontend → package with backend +# ── Stage 1: Build React frontend ──────────────────────────────────────────── +FROM node:22-slim AS frontend-build +WORKDIR /app/frontend +COPY frontend/package.json frontend/package-lock.json* ./ +RUN npm ci --prefer-offline 2>/dev/null || npm install +COPY frontend/ . +RUN npm run build + +# ── Stage 2: Backend + static files ────────────────────────────────────────── +FROM python:3.12-slim +WORKDIR /app + +# System deps for SSH (to collect node metrics) + tailscale CLI +RUN apt-get update && apt-get install -y --no-install-recommends \ + openssh-client curl gnupg ca-certificates && \ + rm -rf /var/lib/apt/lists/* && \ + # Install kubectl + curl -fsSL "https://dl.k8s.io/release/$(curl -fsSL https://dl.k8s.io/release/stable.txt)/bin/linux/amd64/kubectl" -o /usr/local/bin/kubectl && \ + chmod +x /usr/local/bin/kubectl && \ + # Install tailscale CLI + curl -fsSL https://pkgs.tailscale.com/stable/debian/bookworm.noarmor.gpg -o /usr/share/keyrings/tailscale-archive-keyring.gpg && \ + curl -fsSL https://pkgs.tailscale.com/stable/debian/bookworm.tailscale-keyring.list -o /etc/apt/sources.list.d/tailscale.list && \ + apt-get update && apt-get install -y --no-install-recommends tailscale && \ + rm -rf /var/lib/apt/lists/* + +COPY backend/requirements.txt . +RUN pip install --no-cache-dir -r requirements.txt + +COPY backend/ . + +# Copy built frontend into static directory +COPY --from=frontend-build /app/frontend/dist /app/static + +# SSH config for connecting to cluster nodes +RUN mkdir -p /root/.ssh && \ + echo "Host *\n StrictHostKeyChecking no\n UserKnownHostsFile /dev/null\n IdentityFile /root/.ssh/id_ed25519" > /root/.ssh/config && \ + chmod 600 /root/.ssh/config + +COPY backend/entrypoint.sh /entrypoint.sh +RUN chmod +x /entrypoint.sh + +EXPOSE 8787 + +ENTRYPOINT ["/entrypoint.sh"] diff --git a/5-Applications/cluster-dashboard/backend/entrypoint.sh b/5-Applications/cluster-dashboard/backend/entrypoint.sh new file mode 100755 index 00000000..7dcf7a04 --- /dev/null +++ b/5-Applications/cluster-dashboard/backend/entrypoint.sh @@ -0,0 +1,7 @@ +#!/bin/sh +# Fix SSH key permissions (k8s secret mounts may not preserve mode) +if [ -f /identity/id_ed25519 ]; then + cp /identity/id_ed25519 /root/.ssh/id_ed25519 + chmod 600 /root/.ssh/id_ed25519 +fi +exec uvicorn main:app --host 0.0.0.0 --port 8787 --workers 1 diff --git a/5-Applications/cluster-dashboard/backend/main.py b/5-Applications/cluster-dashboard/backend/main.py new file mode 100644 index 00000000..ae28af49 --- /dev/null +++ b/5-Applications/cluster-dashboard/backend/main.py @@ -0,0 +1,448 @@ +"""Research Stack Cluster Dashboard — telemetry collector backend. + +Collects node metrics via: +- Kubernetes API (node status, pod counts) +- Tailscale CLI (connection type, bytes) +- SSH to nodes (GPU util, VRAM, CPU, memory) +- nvidia-smi (qfox-1) / sysfs (nixos AMD) + +Exposes: +- REST /api/nodes — current snapshot +- WebSocket /ws — live updates every 3s +- GET /health — readiness probe +""" + +import asyncio +import json +import logging +import os +import re +import subprocess +from dataclasses import dataclass, asdict +from datetime import datetime, timezone +from pathlib import Path + +from fastapi import FastAPI, WebSocket, WebSocketDisconnect +from fastapi.middleware.cors import CORSMiddleware +from fastapi.staticfiles import StaticFiles + +logging.basicConfig(level=logging.INFO) +log = logging.getLogger("dashboard") + +app = FastAPI(title="Research Stack Cluster Dashboard") +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_methods=["*"], + allow_headers=["*"], +) + +KUBECONFIG = os.environ.get("KUBECONFIG", "/tmp/researchstack-kubeconfig.yaml") +POLL_INTERVAL = 3 # seconds + +# ── Node data model (field names match frontend App.tsx exactly) ───────── + +@dataclass +class NodeMetrics: + name: str + ip: str + role: str + status: str # Ready / NotReady / Unknown + os_image: str + kernel: str + # GPU + gpu_name: str = "" + gpu_util: float = 0.0 + vram_used_mb: float = 0.0 + vram_total_mb: float = 0.0 + gpu_temp: float = 0.0 + encoder: str = "" + # System + cpu_cores: int = 0 + cpu_util: float = 0.0 + mem_used_mb: float = 0.0 + mem_total_mb: float = 0.0 + mem_util: float = 0.0 + # k3s + pods_running: int = 0 + # Tailscale + tailscale_ip: str = "" + tailscale_latency_ms: float = 0.0 + tailscale_relay: str = "" + tailscale_direct: bool = False + # VCN substrate + vcn_codec: str = "" + vcn_resolution: str = "" + vcn_fps: int = 0 + # Meta + last_updated: str = "" + error: str = "" + + +# ── Node registry ──────────────────────────────────────────────────────── + +NODES = { + "qfox-1": { + "tailscale_ip": "100.88.57.96", + "role": "gpu-worker", + "gpu_name": "RTX 4070 SUPER", + "gpu_type": "nvidia", + "ssh_user": "allaun", + }, + "nixos": { + "tailscale_ip": "100.102.173.61", + "role": "control-plane", + "gpu_name": "AMD Lucienne", + "gpu_type": "amd", + "ssh_user": "allaun", + }, + "steamdeck": { + "tailscale_ip": "100.85.244.73", + "role": "gpu-worker", + "gpu_name": "VanGogh APU", + "gpu_type": "amd", + "ssh_user": "deck", + }, + "361395-1": { + "tailscale_ip": "100.110.163.82", + "role": "vps-worker", + "gpu_name": "", + "gpu_type": None, + "ssh_user": "root", + }, + "racknerd-510bd9c": { + "tailscale_ip": "100.80.39.40", + "role": "edge", + "gpu_name": "", + "gpu_type": None, + "ssh_user": "root", + }, +} + + +# ── Helpers ────────────────────────────────────────────────────────────── + +def run_cmd(cmd: list[str], timeout: int = 10) -> str | None: + """Run a command, return stdout or None on failure.""" + try: + r = subprocess.run(cmd, capture_output=True, text=True, timeout=timeout) + return r.stdout.strip() if r.returncode == 0 else None + except (FileNotFoundError, subprocess.TimeoutExpired, Exception): + return None + + +def parse_k8s_memory(mem_str: str) -> int: + """Parse k8s memory string (e.g. '16392740Ki') to MB.""" + s = mem_str.strip() + if s.endswith("Ki"): + return int(s[:-2]) // 1024 + if s.endswith("Mi"): + return int(s[:-2]) + if s.endswith("Gi"): + return int(s[:-2]) * 1024 + return 0 + + +# ── Collectors ─────────────────────────────────────────────────────────── + +def collect_k8s_nodes() -> dict[str, dict]: + """Get node info from kubectl JSON output.""" + out = run_cmd(["kubectl", "get", "nodes", "-o", "json", "--kubeconfig", KUBECONFIG]) + if not out: + return {} + data = json.loads(out) + result = {} + for item in data.get("items", []): + name = item["metadata"]["name"] + conditions = item.get("status", {}).get("conditions", []) + ready = any(c["type"] == "Ready" and c["status"] == "True" for c in conditions) + # Get InternalIP + addrs = item.get("status", {}).get("addresses", []) + internal_ip = next((a["address"] for a in addrs if a["type"] == "InternalIP"), "") + info = item.get("status", {}).get("nodeInfo", {}) + allocatable = item.get("status", {}).get("allocatable", {}) + result[name] = { + "status": "Ready" if ready else "NotReady", + "ip": internal_ip, + "os_image": info.get("osImage", ""), + "kernel": info.get("kernelVersion", ""), + "cpu_cores": int(allocatable.get("cpu", "0")), + "mem_total_mb": parse_k8s_memory(allocatable.get("memory", "0Ki")), + } + return result + + +def collect_k8s_pods() -> dict[str, int]: + """Count running pods per node.""" + out = run_cmd(["kubectl", "get", "pods", "-A", "-o", "json", "--kubeconfig", KUBECONFIG]) + if not out: + return {} + data = json.loads(out) + counts: dict[str, int] = {} + for pod in data.get("items", []): + phase = pod.get("status", {}).get("phase", "") + node = pod.get("spec", {}).get("nodeName", "") + if phase == "Running" and node: + counts[node] = counts.get(node, 0) + 1 + return counts + + +def collect_nvidia_gpu(ip: str) -> dict: + """GPU metrics via SSH + nvidia-smi.""" + out = run_cmd([ + "ssh", "-o", "ConnectTimeout=5", "-o", "StrictHostKeyChecking=no", + f"allaun@{ip}", + "nvidia-smi --query-gpu=name,utilization.gpu,memory.used,memory.total," + "temperature.gpu --format=csv,noheader,nounits 2>/dev/null", + ], timeout=15) + if not out: + return {} + parts = [p.strip() for p in out.split(",")] + if len(parts) >= 5: + try: + return { + "gpu_name": parts[0], + "gpu_util": float(parts[1]), + "vram_used_mb": float(parts[2]), + "vram_total_mb": float(parts[3]), + "gpu_temp": float(parts[4]), + } + except ValueError: + return {} + return {} + + +def collect_amd_gpu(ip: str) -> dict: + """GPU metrics via SSH + sysfs (AMD).""" + out = run_cmd([ + "ssh", "-o", "ConnectTimeout=5", "-o", "StrictHostKeyChecking=no", + f"allaun@{ip}", + "cat /sys/class/drm/card*/device/gpu_busy_percent 2>/dev/null; " + "echo '---'; " + "cat /sys/class/drm/card*/device/mem_info_vram_used 2>/dev/null; " + "echo '---'; " + "cat /sys/class/drm/card*/device/mem_info_vram_total 2>/dev/null", + ], timeout=10) + if not out: + return {} + sections = out.split("---") + gpu_util = 0.0 + vram_used = 0.0 + vram_total = 0.0 + try: + if sections[0].strip(): + gpu_util = float(sections[0].strip().split("\n")[0]) + if len(sections) > 1 and sections[1].strip(): + vram_used = float(sections[1].strip().split("\n")[0]) / (1024 * 1024) + if len(sections) > 2 and sections[2].strip(): + vram_total = float(sections[2].strip().split("\n")[0]) / (1024 * 1024) + except ValueError: + pass + return { + "gpu_util": gpu_util, + "vram_used_mb": vram_used, + "vram_total_mb": vram_total, + } + + +def collect_system_metrics(ip: str) -> dict: + """CPU/memory via SSH + /proc/stat and /proc/meminfo (portable across all Linux distros).""" + out = run_cmd([ + "ssh", "-o", "ConnectTimeout=5", "-o", "StrictHostKeyChecking=no", + f"allaun@{ip}", + "cat /proc/stat | head -1; cat /proc/meminfo | head -3", + ], timeout=10) + if not out: + return {} + result = {} + # Parse /proc/stat — first line: cpu user nice system idle iowait irq softirq steal + cpu_match = re.search( + r"^cpu\s+(\d+)\s+(\d+)\s+(\d+)\s+(\d+)\s+(\d+)\s+(\d+)\s+(\d+)\s+(\d+)", + out, re.MULTILINE, + ) + if cpu_match: + vals = [int(v) for v in cpu_match.groups()] + idle = vals[3] + vals[4] # idle + iowait + total = sum(vals) + if total > 0: + result["cpu_util"] = round((1 - idle / total) * 100, 1) + # Parse /proc/meminfo — MemTotal, MemFree, MemAvailable + mem_total = re.search(r"MemTotal:\s+(\d+)\s+kB", out) + mem_avail = re.search(r"MemAvailable:\s+(\d+)\s+kB", out) + if mem_total: + total_kb = int(mem_total.group(1)) + result["mem_total_mb"] = total_kb // 1024 + if mem_avail and mem_total: + total_kb = int(mem_total.group(1)) + avail_kb = int(mem_avail.group(1)) + used_kb = total_kb - avail_kb + result["mem_used_mb"] = used_kb // 1024 + result["mem_util"] = round(used_kb / total_kb * 100, 1) if total_kb else 0 + return result + + +def collect_tailscale() -> dict[str, dict]: + """Tailscale peer status via SSH to host (container doesn't run tailscaled).""" + out = run_cmd([ + "ssh", "-o", "ConnectTimeout=5", "-o", "StrictHostKeyChecking=no", + "allaun@100.102.173.61", "tailscale status --json", + ]) + if not out: + return {} + data = json.loads(out) + peers: dict[str, dict] = {} + # Self + self_ips = data.get("Self", {}).get("TailscaleIPs", []) + if self_ips: + peers[self_ips[0]] = {"relay": "", "direct": True} + # Peers + for _, peer in data.get("Peer", {}).items(): + ips = peer.get("TailscaleIPs", []) + if ips: + peers[ips[0]] = { + "relay": peer.get("Relay", ""), + "direct": bool(peer.get("CurAddr")), + } + return peers + + +# ── Main collection loop ───────────────────────────────────────────────── + +async def collect_all() -> list[dict]: + """Collect metrics from all nodes, return as dicts for JSON.""" + loop = asyncio.get_event_loop() + + # Run blocking collectors in thread pool + k8s_nodes, pod_counts, ts_status = await asyncio.gather( + loop.run_in_executor(None, collect_k8s_nodes), + loop.run_in_executor(None, collect_k8s_pods), + loop.run_in_executor(None, collect_tailscale), + ) + + nodes = [] + for name, cfg in NODES.items(): + k8s = k8s_nodes.get(name, {}) + nm = NodeMetrics( + name=name, + ip=k8s.get("ip", cfg["tailscale_ip"]), + role=cfg["role"], + status=k8s.get("status", "Unknown"), + os_image=k8s.get("os_image", ""), + kernel=k8s.get("kernel", ""), + cpu_cores=k8s.get("cpu_cores", 0), + mem_total_mb=k8s.get("mem_total_mb", 0), + pods_running=pod_counts.get(name, 0), + tailscale_ip=cfg["tailscale_ip"], + last_updated=datetime.now(timezone.utc).isoformat(), + ) + + # Tailscale + ts = ts_status.get(cfg["tailscale_ip"], {}) + nm.tailscale_relay = ts.get("relay", "") + nm.tailscale_direct = ts.get("direct", False) + + # GPU + system metrics (skip remote VPS — no SSH) + gpu_type = cfg.get("gpu_type") + if gpu_type == "nvidia": + gpu_data = await loop.run_in_executor( + None, collect_nvidia_gpu, cfg["tailscale_ip"] + ) + for k, v in gpu_data.items(): + setattr(nm, k, v) + if not nm.gpu_name: + nm.gpu_name = cfg.get("gpu_name", "") + # Encoder check + enc_out = await loop.run_in_executor(None, lambda: run_cmd([ + "ssh", "-o", "ConnectTimeout=5", "-o", "StrictHostKeyChecking=no", + f"allaun@{cfg['tailscale_ip']}", + "nvidia-smi --query-gpu=encoder.stats.sessionCount " + "--format=csv,noheader,nounits 2>/dev/null", + ])) + if enc_out and enc_out.strip().isdigit() and int(enc_out.strip()) > 0: + nm.encoder = "h264_nvenc" + elif gpu_type == "amd": + nm.gpu_name = cfg.get("gpu_name", "") + if name != "steamdeck": # VanGogh has no VRAM sysfs + gpu_data = await loop.run_in_executor( + None, collect_amd_gpu, cfg["tailscale_ip"] + ) + for k, v in gpu_data.items(): + setattr(nm, k, v) + nm.encoder = "hevc_vaapi" + + # System metrics (local nodes only) + if name in ("qfox-1", "nixos", "steamdeck"): + sys_data = await loop.run_in_executor( + None, collect_system_metrics, cfg["tailscale_ip"] + ) + for k, v in sys_data.items(): + setattr(nm, k, v) + + nodes.append(asdict(nm)) + + return nodes + + +# ── Cached state + WebSocket ───────────────────────────────────────────── + +_cached_nodes: list[dict] = [] +_ws_clients: set[WebSocket] = set() + + +async def update_loop(): + """Background task: collect metrics every POLL_INTERVAL, push to WS clients.""" + global _cached_nodes + while True: + try: + _cached_nodes = await collect_all() + payload = json.dumps({"type": "update", "nodes": _cached_nodes}) + dead: set[WebSocket] = set() + for ws in _ws_clients: + try: + await ws.send_text(payload) + except Exception: + dead.add(ws) + _ws_clients.difference_update(dead) + except Exception as e: + log.error(f"Collection error: {e}") + await asyncio.sleep(POLL_INTERVAL) + + +@app.on_event("startup") +async def startup(): + asyncio.create_task(update_loop()) + + +# ── API routes ─────────────────────────────────────────────────────────── + +@app.get("/api/nodes") +async def get_nodes(): + return {"nodes": _cached_nodes, "updated": datetime.now(timezone.utc).isoformat()} + + +@app.websocket("/ws") +async def websocket_endpoint(ws: WebSocket): + await ws.accept() + _ws_clients.add(ws) + log.info(f"WS client connected ({len(_ws_clients)} total)") + try: + await ws.send_text(json.dumps({"type": "update", "nodes": _cached_nodes})) + while True: + await ws.receive_text() + except WebSocketDisconnect: + pass + finally: + _ws_clients.discard(ws) + log.info(f"WS client disconnected ({len(_ws_clients)} total)") + + +@app.get("/health") +async def health(): + return {"status": "ok", "nodes": len(_cached_nodes)} + + +# ── Static files (frontend) ────────────────────────────────────────────── + +frontend_dist = Path(os.environ.get("STATIC_DIR", "/app/static")) +if frontend_dist.exists(): + app.mount("/", StaticFiles(directory=str(frontend_dist), html=True), name="static") diff --git a/5-Applications/cluster-dashboard/backend/requirements.txt b/5-Applications/cluster-dashboard/backend/requirements.txt new file mode 100644 index 00000000..90c3c5aa --- /dev/null +++ b/5-Applications/cluster-dashboard/backend/requirements.txt @@ -0,0 +1,3 @@ +fastapi>=0.115 +uvicorn[standard]>=0.34 +websockets>=15.0 diff --git a/5-Applications/cluster-dashboard/frontend/index.html b/5-Applications/cluster-dashboard/frontend/index.html new file mode 100644 index 00000000..d80253dd --- /dev/null +++ b/5-Applications/cluster-dashboard/frontend/index.html @@ -0,0 +1,23 @@ + + + + + + Research Stack — Cluster Dashboard + + + +
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"license": "ISC" + } + } +} diff --git a/5-Applications/cluster-dashboard/frontend/package.json b/5-Applications/cluster-dashboard/frontend/package.json new file mode 100644 index 00000000..46fc7b83 --- /dev/null +++ b/5-Applications/cluster-dashboard/frontend/package.json @@ -0,0 +1,23 @@ +{ + "name": "research-stack-dashboard", + "private": true, + "version": "0.1.0", + "type": "module", + "scripts": { + "dev": "vite", + "build": "tsc && vite build", + "preview": "vite preview" + }, + "dependencies": { + "@1771technologies/lytenyte-core": "^2.1.2", + "react": "^19.1.0", + "react-dom": "^19.1.0" + }, + "devDependencies": { + "@types/react": "^19.2.15", + "@types/react-dom": "^19.2.3", + "@vitejs/plugin-react": "^4.7.0", + "typescript": "^5.9.3", + "vite": "^6.4.2" + } +} diff --git a/5-Applications/cluster-dashboard/frontend/src/App.tsx b/5-Applications/cluster-dashboard/frontend/src/App.tsx new file mode 100644 index 00000000..1e8bfc45 --- /dev/null +++ b/5-Applications/cluster-dashboard/frontend/src/App.tsx @@ -0,0 +1,410 @@ +import { useEffect, useRef, useState, useCallback } from "react"; +import { Grid, useClientDataSource } from "@1771technologies/lytenyte-core"; +import type { Grid as LnGrid } from "@1771technologies/lytenyte-core"; +import "@1771technologies/lytenyte-core/dark.css"; + +// ── Types ──────────────────────────────────────────────────────────────────── + +interface NodeMetrics { + name: string; + ip: string; + role: string; + status: string; + os_image: string; + kernel: string; + gpu_name: string; + gpu_util: number; + vram_used_mb: number; + vram_total_mb: number; + gpu_temp: number; + encoder: string; + cpu_cores: number; + cpu_util: number; + mem_used_mb: number; + mem_total_mb: number; + mem_util: number; + pods_running: number; + tailscale_ip: string; + tailscale_latency_ms: number; + tailscale_relay: string; + tailscale_direct: boolean; + vcn_codec: string; + vcn_resolution: string; + vcn_fps: number; + last_updated: string; + error: string; +} + +// ── Column definitions ─────────────────────────────────────────────────────── + +const columns: LnGrid.Column[] = [ + { + id: "status", + name: "●", + field: (p) => { + const row = p.row.data as NodeMetrics; + return row?.status === "Ready" ? "●" : "○"; + }, + width: 40, + type: "string", + cellRenderer: (params) => { + const row = params.row.data as NodeMetrics; + const color = row?.status === "Ready" ? "#22c55e" : "#ef4444"; + return ( + ● + ); + }, + }, + { + id: "name", + name: "Node", + field: "name", + width: 150, + type: "string", + pin: "start", + }, + { + id: "ip", + name: "IP", + field: "ip", + width: 130, + type: "string", + }, + { + id: "role", + name: "Role", + field: "role", + width: 100, + type: "string", + }, + { + id: "gpu_name", + name: "GPU", + field: "gpu_name", + width: 160, + type: "string", + }, + { + id: "gpu_util", + name: "GPU %", + field: "gpu_util", + width: 80, + type: "number", + cellRenderer: (params) => { + const val = (params.row.data as NodeMetrics)?.gpu_util ?? 0; + const color = val > 80 ? "#ef4444" : val > 50 ? "#f59e0b" : "#22c55e"; + return ( + + {val > 0 ? `${val.toFixed(0)}%` : "—"} + + ); + }, + }, + { + id: "vram", + name: "VRAM", + field: (p) => { + const row = p.row.data as NodeMetrics; + if (!row?.vram_total_mb) return "—"; + return `${(row.vram_used_mb / 1024).toFixed(1)}/${(row.vram_total_mb / 1024).toFixed(1)} GB`; + }, + width: 110, + type: "string", + cellRenderer: (params) => { + const row = params.row.data as NodeMetrics; + if (!row?.vram_total_mb) return —; + const pct = (row.vram_used_mb / row.vram_total_mb) * 100; + const color = pct > 80 ? "#ef4444" : pct > 50 ? "#f59e0b" : "#22c55e"; + return ( +
+
+
+
+ + {(row.vram_used_mb / 1024).toFixed(1)}/{(row.vram_total_mb / 1024).toFixed(1)} + +
+ ); + }, + }, + { + id: "gpu_temp", + name: "GPU °C", + field: "gpu_temp", + width: 75, + type: "number", + cellRenderer: (params) => { + const val = (params.row.data as NodeMetrics)?.gpu_temp ?? 0; + if (!val) return —; + const color = val > 80 ? "#ef4444" : val > 65 ? "#f59e0b" : "#22c55e"; + return {val.toFixed(0)}°; + }, + }, + { + id: "encoder", + name: "Codec", + field: "encoder", + width: 120, + type: "string", + }, + { + id: "cpu_util", + name: "CPU %", + field: "cpu_util", + width: 80, + type: "number", + cellRenderer: (params) => { + const row = params.row.data as NodeMetrics; + const val = row?.cpu_util ?? 0; + if (!val && !row?.cpu_cores) return —; + const color = val > 80 ? "#ef4444" : val > 50 ? "#f59e0b" : "#22c55e"; + return {val.toFixed(0)}%; + }, + }, + { + id: "mem", + name: "Memory", + field: (p) => { + const row = p.row.data as NodeMetrics; + if (!row?.mem_total_mb) return "—"; + return `${(row.mem_used_mb / 1024).toFixed(1)}/${(row.mem_total_mb / 1024).toFixed(1)} GB`; + }, + width: 120, + type: "string", + cellRenderer: (params) => { + const row = params.row.data as NodeMetrics; + if (!row?.mem_total_mb) return —; + const pct = row.mem_util; + const color = pct > 85 ? "#ef4444" : pct > 60 ? "#f59e0b" : "#22c55e"; + return ( +
+
+
+
+ + {(row.mem_used_mb / 1024).toFixed(1)}/{(row.mem_total_mb / 1024).toFixed(1)} + +
+ ); + }, + }, + { + id: "pods", + name: "Pods", + field: "pods_running", + width: 65, + type: "number", + }, + { + id: "tailscale", + name: "Tailscale", + field: (p) => { + const row = p.row.data as NodeMetrics; + if (!row?.tailscale_ip) return "—"; + return row.tailscale_direct ? "direct" : row.tailscale_relay || "relay"; + }, + width: 90, + type: "string", + cellRenderer: (params) => { + const row = params.row.data as NodeMetrics; + if (!row?.tailscale_ip) return —; + const direct = row.tailscale_direct; + return ( + + {direct ? "● direct" : `↗ ${row.tailscale_relay || "relay"}`} + + ); + }, + }, + { + id: "os", + name: "OS", + field: "os_image", + width: 180, + type: "string", + }, + { + id: "kernel", + name: "Kernel", + field: "kernel", + width: 140, + type: "string", + }, +]; + +// ── App ────────────────────────────────────────────────────────────────────── + +export default function App() { + const [nodes, setNodes] = useState([]); + const [connected, setConnected] = useState(false); + const [lastUpdate, setLastUpdate] = useState(""); + const wsRef = useRef(null); + const reconnectRef = useRef(null); + + const connect = useCallback(() => { + const proto = location.protocol === "https:" ? "wss:" : "ws:"; + const ws = new WebSocket(`${proto}//${location.host}/ws`); + + ws.onopen = () => { + setConnected(true); + console.log("[dashboard] WebSocket connected"); + }; + + ws.onmessage = (event) => { + try { + const msg = JSON.parse(event.data); + if (msg.type === "update" && msg.nodes) { + setNodes(msg.nodes); + setLastUpdate(new Date().toLocaleTimeString()); + } + } catch (e) { + console.error("[dashboard] parse error:", e); + } + }; + + ws.onclose = () => { + setConnected(false); + console.log("[dashboard] WebSocket closed, reconnecting in 3s..."); + reconnectRef.current = window.setTimeout(connect, 3000); + }; + + ws.onerror = () => { + ws.close(); + }; + + wsRef.current = ws; + }, []); + + useEffect(() => { + connect(); + return () => { + if (reconnectRef.current != null) clearTimeout(reconnectRef.current); + wsRef.current?.close(); + }; + }, [connect]); + + const ds = useClientDataSource({ + data: nodes, + }); + + return ( +
+ {/* Header */} +
+
+ + ⬡ Research Stack + + + CLUSTER DASHBOARD + +
+
+ {nodes.length} nodes + + + + {connected ? "LIVE" : "DISCONNECTED"} + + + {lastUpdate && ( + + {lastUpdate} + + )} +
+
+ + {/* Grid */} +
+ + + + {(cells) => ( + + {cells.map((cell) => { + if (cell.kind === "group") return null; + return ; + })} + + )} + + + + {(row) => { + if (row.kind === "full-width") return null; + return ( + + {row.cells.map((cell) => ( + + ))} + + ); + }} + + + + +
+
+ ); +} diff --git a/5-Applications/cluster-dashboard/frontend/src/main.tsx b/5-Applications/cluster-dashboard/frontend/src/main.tsx new file mode 100644 index 00000000..f46c379c --- /dev/null +++ b/5-Applications/cluster-dashboard/frontend/src/main.tsx @@ -0,0 +1,9 @@ +import React from "react"; +import ReactDOM from "react-dom/client"; +import App from "./App"; + +ReactDOM.createRoot(document.getElementById("root")!).render( + + + +); diff --git a/5-Applications/cluster-dashboard/frontend/tsconfig.json b/5-Applications/cluster-dashboard/frontend/tsconfig.json new file mode 100644 index 00000000..4912b375 --- /dev/null +++ b/5-Applications/cluster-dashboard/frontend/tsconfig.json @@ -0,0 +1,20 @@ +{ + "compilerOptions": { + "target": "ES2022", + "lib": ["ES2022", "DOM", "DOM.Iterable"], + "module": "ESNext", + "skipLibCheck": true, + "moduleResolution": "bundler", + "allowImportingTsExtensions": true, + "isolatedModules": true, + "moduleDetection": "force", + "noEmit": true, + "jsx": "react-jsx", + "strict": true, + "noUnusedLocals": false, + "noUnusedParameters": false, + "noFallthroughCasesInSwitch": true, + "forceConsistentCasingInFileNames": true + }, + "include": ["src"] +} diff --git a/5-Applications/cluster-dashboard/frontend/vite.config.ts b/5-Applications/cluster-dashboard/frontend/vite.config.ts new file mode 100644 index 00000000..e0b2d81c --- /dev/null +++ b/5-Applications/cluster-dashboard/frontend/vite.config.ts @@ -0,0 +1,18 @@ +import { defineConfig } from "vite"; +import react from "@vitejs/plugin-react"; + +export default defineConfig({ + plugins: [react()], + server: { + proxy: { + "/api": "http://localhost:8787", + "/ws": { + target: "ws://localhost:8787", + ws: true, + }, + }, + }, + build: { + outDir: "dist", + }, +}); diff --git a/5-Applications/cluster-dashboard/k8s/dashboard.yaml b/5-Applications/cluster-dashboard/k8s/dashboard.yaml new file mode 100644 index 00000000..3562df00 --- /dev/null +++ b/5-Applications/cluster-dashboard/k8s/dashboard.yaml @@ -0,0 +1,107 @@ +apiVersion: v1 +kind: Namespace +metadata: + name: monitoring +--- +apiVersion: apps/v1 +kind: Deployment +metadata: + name: cluster-dashboard + namespace: monitoring + labels: + app: cluster-dashboard +spec: + replicas: 1 + selector: + matchLabels: + app: cluster-dashboard + template: + metadata: + labels: + app: cluster-dashboard + spec: + # Share host network so Tailscale IPs are reachable for SSH + hostNetwork: true + dnsPolicy: ClusterFirstWithHostNet + # Pin to control plane — image is imported there + nodeSelector: + kubernetes.io/hostname: nixos + tolerations: + - key: node-role.kubernetes.io/control-plane + operator: Equal + value: "true" + effect: NoSchedule + containers: + - name: dashboard + image: localhost/cluster-dashboard:latest + imagePullPolicy: Never + ports: + - containerPort: 8787 + name: http + env: + - name: KUBECONFIG + value: /kubeconfig/config + volumeMounts: + - name: kubeconfig + mountPath: /kubeconfig + readOnly: true + - name: ssh-identity + mountPath: /identity + readOnly: true + resources: + requests: + memory: "128Mi" + cpu: "100m" + limits: + memory: "256Mi" + cpu: "500m" + readinessProbe: + httpGet: + path: /health + port: 8787 + initialDelaySeconds: 5 + periodSeconds: 10 + livenessProbe: + httpGet: + path: /health + port: 8787 + initialDelaySeconds: 10 + periodSeconds: 30 + volumes: + - name: kubeconfig + secret: + secretName: dashboard-kubeconfig + - name: ssh-identity + secret: + secretName: dashboard-ssh-key + defaultMode: 0600 +--- +apiVersion: v1 +kind: Service +metadata: + name: cluster-dashboard + namespace: monitoring +spec: + selector: + app: cluster-dashboard + ports: + - port: 80 + targetPort: 8787 + name: http + type: ClusterIP +--- +# NodePort for direct access (no ingress needed) +apiVersion: v1 +kind: Service +metadata: + name: cluster-dashboard-nodeport + namespace: monitoring +spec: + selector: + app: cluster-dashboard + ports: + - port: 8787 + targetPort: 8787 + nodePort: 30820 + name: http + type: NodePort