SparkScope: An Open-Source Real-Time Monitoring Dashboard Solution for NVIDIA DGX Spark Clusters

SparkScope is an open-source real-time monitoring dashboard designed specifically for NVIDIA DGX Spark and Dell Pro Max GB10 clusters. It uses FastAPI, WebSocket, and SQLite tech stack, supports vLLM inference monitoring, and provides a lightweight, efficient solution for AI infrastructure operation and maintenance. This post will break down its background, technical details, features, deployment, and more.

With the explosive growth in demand for large language model inference, NVIDIA DGX Spark (equipped with GB10 Grace Blackwell super chips) has become key infrastructure for developers and research teams. However, monitoring tools for such dedicated hardware are relatively scarce. For teams deploying multiple DGX Spark devices, unified monitoring of node status, performance bottlenecks, and hardware anomalies is a core operational challenge—SparkScope fills this gap.

SparkScope adopts a lightweight design. Backend: Python FastAPI (REST API + WebSocket services). Frontend: Alpine.js + native Canvas (avoids heavy chart library dependencies). Data persistence: SQLite with WAL mode (stable in resource-constrained environments). Data collection: 2-second polling cycle via SSH, collecting comprehensive metrics (CPU load, GPU utilization, etc.) to balance real-time performance and SSH overhead.

Monitoring covers multiple dimensions:

• CPU: Utilization, 1/5/15min load, max temperature.
• GPU: Utilization, memory usage, temperature, power, SM/memory clock, ECC errors, throttling reasons, PCIe gen, persistence mode.
• Storage: NVMe SMART info (temperature, wear level, media errors), disk I/O.
• Network: WiFi/cluster link rates and error rates. Native vLLM support: Auto-detects vLLM instances, collects model name, max context length, token generation rate, active/queued requests, KV cache usage, prefix cache hit rate—critical for optimizing throughput and latency.

• Command Panel: Execute whitelisted commands (system info, GPU status, network diagnosis, logs) via web interface; destructive operations (restart, GPU reset) require confirmation.
• Alert System: Threshold-based monitoring for CPU/GPU temperature, disk usage, memory, GPU power; critical alerts for ECC uncorrectable errors (early hardware failure warning).

Deployment steps:

1. Require Python ≥3.11; use uv package manager.
2. Install dependencies via uv sync.
3. Configure YAML file (SSH aliases, host IPs).
4. Target hosts need passwordless SSH and appropriate sudo permissions for monitoring users.
5. macOS: Use LaunchAgent for auto-start.
6. Initialize database, start service with uvicorn, access via browser.

Design principles: Lightweight (no heavy dependencies), secure (bind to 127.0.0.1 by default, command confirmation, config in .gitignore), modular. Applicable scenarios:

• Research teams with multiple DGX Spark devices needing unified monitoring.
• Edge AI inference services requiring real-time model performance observation.
• Small clusters wanting enterprise-level monitoring without complex Prometheus/Grafana stacks.

SparkScope contributes a practical open-source monitoring tool to the NVIDIA DGX Spark ecosystem, focusing on edge AI core needs: lightweight deployment, real-time monitoring, security. For teams using or planning to use DGX Spark, it's a valuable addition to the toolchain. Future extensions: Expand SSH collection to other host types, adapt vLLM integration to other inference frameworks, add more data source plugins or alert channels via community contributions.