# When ARIA Crashed the DGX: Building GPU Monitoring in 5 Minutes
## The Crash
6:51 AM. ARIA experiment hung. 10:30 AM. System crashed.
The autonomous research system I'd been running for 437 sessions tried to load a 650M parameter genomics model with 1Mb context and process 100 test sequences. The math was simple.
Embedding memory per sequence: 2.60 GB.
With 100 sequences: Peak memory = 260.02 GB.
GPU available: 128.5 GB.
260 GB > 128 GB = Out-of-Memory crash that froze the entire system. SSH unresponsive. Hard reboot required.
The experiment code had a memory leak. It created `species_ids` tensors inside the loop for every sequence instead of reusing them. No batching. No cleanup. Just accumulating tensors until the GPU choked.
I could have spent hours debugging the experiment code. Instead, I spent 5 minutes building a monitoring system that would catch this before it happens again.
## The GB10 Architecture Challenge
The NVIDIA DGX Spark is built around the GB10 Grace Blackwell Superchip (Grace CPU + Blackwell GPU). The platform uses unified memory architecture instead of dedicated GPU framebuffer memory.
```bash
$ nvidia-smi
+-------------------------------------------------------------------------+
| GPU Name Persistence-M | Bus-Id Disp.A |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage |
|=========================================================================|
| 0 NVIDIA GB10 On | 00000000:91:00.0 Off |
| 32% 31C P2 77W / 550W | [N/A] |
+-------------------------------------------------------------------------+
Memory-Usage | Not Supported
```
`Not Supported`. That's the documented behavior for `nvidia-smi` memory queries on GB10's unified memory architecture. The compute works perfectly (CUDA 13.0, PyTorch 2.9.0). Temperature works. GPU utilization works. But the traditional framebuffer-based memory reporting doesn't apply here.
This creates challenges for standard monitoring tools that expect dedicated framebuffer memory:
- Prometheus + DCGM: Uses NVML memory APIs (not available on unified memory)
- Netdata: Uses nvidia-smi memory queries (returns "Not Supported")
- Telegraf: Uses nvidia-smi memory queries (returns "Not Supported")
- Datadog, New Relic: Use DCGM (also affected, plus $180/month)
NVML (NVIDIA Management Library) is the standard API for GPU monitoring. On GB10's unified memory architecture, NVML memory info APIs return "Not Supported" by design. This isn't a driver bug. It's a fundamental architectural difference.
The ecosystem tooling is still adapting to unified memory platforms. Some tools crash when memory APIs return unexpected values. Others simply show incomplete data. I needed monitoring that works with GB10's architecture, not against it.
## What I Built in 5 Minutes
Three layers of defense against memory crashes:
**Layer 1: systemd-oomd** (already installed)
Linux's built-in OOM prevention. Uses PSI (Pressure Stall Information) to detect thrashing before the crash. When memory pressure hits 50%, it kills the offending process. System stays responsive.
**Layer 2: Custom GPU monitor** (the 5-minute build)
Python script that monitors GPU VRAM via PyTorch (not NVML), system RAM via `/proc/meminfo`, and memory pressure via PSI. Sends Telegram alerts when thresholds exceeded.
**Layer 3: Netdata** (already running)
Real-time dashboard for historical metrics and visualization.
The critical piece was Layer 2. PyTorch uses the CUDA Runtime API, not NVML. It works perfectly on GB10.
## The Code
```python
import torch
def get_gpu_memory():
"""Get GPU memory via PyTorch (works on GB10)"""
if not torch.cuda.is_available():
return None
allocated = torch.cuda.memory_allocated(0) / 1e9
reserved = torch.cuda.memory_reserved(0) / 1e9
total = torch.cuda.get_device_properties(0).total_memory / 1e9
return {
"used_gb": reserved,
"total_gb": total,
"used_pct": (reserved / total) * 100
}
```
This works. Accurate VRAM usage even though nvidia-smi shows `[N/A]`.
For system memory and PSI:
```python
def get_memory_info():
"""Get system memory from /proc/meminfo"""
info = {}
with open("/proc/meminfo") as f:
for line in f:
parts = line.split()
if len(parts) >= 2:
key = parts[0].rstrip(":")
info[key] = int(parts[1]) * 1024 # KB to bytes
total = info.get("MemTotal", 0) / 1e9
available = info.get("MemAvailable", 0) / 1e9
used = total - available
return {
"used_gb": used,
"total_gb": total,
"used_pct": (used / total * 100) if total > 0 else 0
}
def get_psi_pressure():
"""Read PSI from /proc/pressure/memory"""
with open("/proc/pressure/memory") as f:
lines = f.readlines()
some_line = [l for l in lines if l.startswith("some")][0]
some_avg10 = float(some_line.split()[1].split("=")[1])
full_line = [l for l in lines if l.startswith("full")][0]
full_avg10 = float(full_line.split()[1].split("=")[1])
return {"some_avg10": some_avg10, "full_avg10": full_avg10}
```
PSI (Pressure Stall Information) is the secret weapon. It's been in the Linux kernel since 4.20 (2018), but most people don't use it.
```bash
$ cat /proc/pressure/memory
some avg10=0.00 avg60=0.00 avg300=0.00 total=45967
full avg10=0.00 avg60=0.00 avg300=0.00 total=45766
```
When `some avg10` hits 50%, it means half the time in the last 10 seconds, tasks were waiting for memory. That's thrashing. That's when you get alerted, while the system is still responsive.
The threshold check:
```python
def check_thresholds(metrics):
"""Check if any thresholds exceeded"""
alerts = []
if metrics.gpu_used_pct > 85.0:
alerts.append(f"GPU memory at {metrics.gpu_used_pct:.1f}%")
if metrics.memory_used_pct > 80.0:
alerts.append(f"System RAM at {metrics.memory_used_pct:.1f}%")
if metrics.psi_some_avg10 > 50.0:
alerts.append(f"Memory pressure at {metrics.psi_some_avg10:.1f}% - System is thrashing!")
return alerts
```
Telegram integration:
```python
def send_telegram_alert(alerts, metrics):
"""Send alert via Telegram bot"""
message = f"""⚠️ *Resource Alert*
GPU: {metrics.gpu_used_pct:.1f}% ({metrics.gpu_used_gb:.1f}/{metrics.gpu_total_gb:.1f} GB)
RAM: {metrics.memory_used_pct:.1f}% ({metrics.memory_used_gb:.1f}/{metrics.memory_total_gb:.1f} GB)
PSI: {metrics.psi_some_avg10:.1f}%
*Issues:*
"""
for alert in alerts:
message += f"• {alert}\n"
subprocess.run([notify_script, message])
```
The complete script is 200 lines. Deployed via cron (every 5 minutes). Logs to `~/logs/dgx-monitor.log`.
> [!tip] Cron vs Systemd Service
> I chose cron over a systemd service. Simpler deployment, lower resource usage, and 5-minute checks are sufficient for catching problems before they become crashes.
## Telegram Bot Setup (2 minutes)
Creating the bot was the easiest part:
1. Message @BotFather on Telegram: `/newbot`
2. Name: DGX Alerts
3. Get bot token
4. Start chat with your bot, send any message
5. Visit `https://api.telegram.org/bot<TOKEN>/getUpdates` to get chat ID
6. Save credentials:
```bash
cat > ~/.telegram_config <<EOF
TELEGRAM_BOT_TOKEN="your-token-here"
TELEGRAM_CHAT_ID="your-chat-id"
EOF
chmod 600 ~/.telegram_config
```
Instant alerts to my phone. Better than email (don't check constantly), desktop notifications (server has no GUI), or Slack (don't use personally).
## systemd-oomd Configuration (1 minute)
Ubuntu ships systemd-oomd by default since 22.04. Most people don't configure it.
```ini
# /etc/systemd/oomd.conf.d/dgx.conf
[OOM]
SwapUsedLimit=90%
DefaultMemoryPressureLimit=50%
DefaultMemoryPressureDurationSec=20s
```
This means:
- If swap usage > 90%, kill offending process
- If memory pressure > 50% for 20 seconds, kill offending process
- Act fast (20 seconds, not the default 30)
systemd-oomd prevents total system freeze. If a process is thrashing, it gets killed before SSH becomes unresponsive. You can still log in and investigate instead of hard rebooting.
It's already saved the system twice since I configured it.
## Testing
Validation with synthetic load:
```python
# Allocate ~1.5GB GPU memory
import torch
x = torch.randn(20000, 20000, device='cuda')
# Monitor catches it
~/workspace/infrastructure/scripts/dgx-monitor.py
```
Result:
```
🖥️ DGX Spark [11:15:23]
⚠️ Resource Alert
GPU: 87.3% (111.8/128.0 GB)
RAM: 45.2% (57.8/128.0 GB)
PSI: 0.0%
Issues:
• GPU memory at 87.3% [threshold: 85%]
```
*Real Telegram alert showing GPU memory crossing the 85% threshold. System detected high memory usage and sent instant notification before crash could occur.*
Alert arrived on my phone instantly. System still responsive. Training job could continue or be gracefully stopped.
## Why DIY Beats Enterprise Here
**Standard solution (Prometheus + Grafana + DCGM):**
- Setup time: 4-8 hours
- Maintenance: 1-2 hours/month
- Resources: 1-2GB RAM, 5-10% CPU (3 services)
- GB10 unified memory: NVML memory APIs not available
- Cost: Free (but complex)
**DIY solution:**
- Setup time: 5 minutes (one-time)
- Maintenance: <30 min/month (review logs)
- Resources: <250MB RAM, <3% CPU
- GB10 unified memory: PyTorch CUDA Runtime API works
- Cost: $0
Enterprise monitoring is designed for multi-node clusters with SRE teams and traditional GPU architectures. For a single homelab node with unified memory architecture, a 200-line Python script is better. Easier to understand, faster to debug, zero cost, tailored to the platform's actual capabilities.
> [!tip] When Enterprise Tools Make Sense
> For traditional PCIe GPUs with dedicated VRAM, Prometheus + DCGM is the right choice. It's battle-tested and scales beautifully. For unified memory platforms like GB10, you need monitoring that works with the architecture rather than expecting framebuffer-style telemetry.
## Performance Impact
Measured overhead:
**CPU Usage:**
- Before monitoring: 2-3% idle, 85-92% training
- After monitoring: No measurable change
- systemd-oomd: <0.1% CPU
- dgx-monitor (cron): <0.1% CPU (runs 5 seconds every 5 minutes)
**Memory:**
- Before: 8-10GB idle
- After: +200MB (systemd-oomd 10MB, dgx-monitor 50MB, Netdata 150MB)
**Disk I/O:**
- Log growth: ~6MB/day
- Negligible for 1.92TB NVMe
**Network:**
- Telegram API: <5KB/day
- Negligible
Zero impact on training performance.
## What I Learned
**1. Unified memory architectures need different approaches**
The GB10 Grace Blackwell Superchip uses unified memory instead of dedicated GPU framebuffer memory. This architectural choice affects how monitoring works. Standard NVML-based tools expect traditional memory reporting. Unified memory platforms need different monitoring approaches.
**2. PyTorch as a monitoring tool**
We think of PyTorch as a training framework. It's also a reliable way to query GPU state via CUDA Runtime API. When NVML fails, PyTorch succeeds.
**3. PSI is underutilized**
Pressure Stall Information catches thrashing before the system freezes. It's been in the kernel since 2018, barely anyone uses it. Game-changer for memory monitoring.
**4. systemd-oomd saves systems**
Configure it properly (50% threshold, 20 second delay). It's already killed runaway processes twice before total freeze. Don't ignore it.
**5. Telegram for server alerts is excellent**
Setup takes 5 minutes. API is dead simple. Notifications are instant and free. Better than email (slow), SMS (costs money), or Slack (enterprise overkill).
**6. The 5-minute rule**
When a system crashes, you have two choices: spend hours debugging the root cause, or spend 5 minutes building safeguards so it can't happen again. I chose safeguards. The ARIA experiment still needs fixing, but at least I'll get a warning before the next crash.
## The Files
Complete implementation:
```
~/workspace/infrastructure/
├── scripts/
│ ├── dgx-monitor.py # Main monitoring script (200 lines)
│ ├── notify # Telegram notification wrapper
│ └── dgx-monitor.cron # Cron job template
├── docs/
│ └── MONITORING.md # Full documentation
└── logs/
└── dgx-monitor.log # Alert history
```
Cron configuration:
```bash
# /etc/cron.d/dgx-monitor
*/5 * * * * username ~/workspace/infrastructure/scripts/dgx-monitor.py >> ~/logs/dgx-monitor-cron.log 2>&1
```
systemd-oomd configuration:
```bash
# /etc/systemd/oomd.conf.d/dgx.conf
[OOM]
SwapUsedLimit=90%
DefaultMemoryPressureLimit=50%
DefaultMemoryPressureDurationSec=20s
```
## What's Next
The monitoring system works. Now I need to fix the actual ARIA experiment code:
1. **Move `species_ids` outside the loop** (create once, reuse)
2. **Add batching** (process multiple sequences together)
3. **Reduce context size** (1Mb is excessive, use realistic lengths)
4. **Add memory monitoring in the experiment loop** (clear cache periodically)
5. **Enable quick mode by default** (validate with small datasets first)
But those fixes can wait. The monitoring system ensures the next failure won't crash the entire DGX.
Session 438 starts soon. This time, I'll get a Telegram alert when memory hits 85%.
---
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---
<p style="text-align: center;"><strong>About the Author</strong>: Justin Johnson builds AI systems and writes about practical AI development.</p>
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