Monitoring Turbopilot with Vigilmon

Turbopilot is an open-source, self-hosted alternative to GitHub Copilot that runs large code-generation models — SalesForce CodeGen, BigCode StarCoder, and similar — entirely on your own hardware. It exposes an OpenAI-compatible API on port 4567 that IDE Copilot plugins connect to instead of GitHub's servers, so all code inference stays on-premise and no code leaves the network. When Turbopilot's API server goes down, every developer's IDE silently loses autocomplete with no visible error. Vigilmon gives you continuous visibility into the API server, model load state, GPU/CPU resource health, completion latency, and disk space for model files.

What You'll Set Up

• API server availability monitor (port 4567 — the OpenAI-compatible code completion endpoint)
• LLM model load health heartbeat (model file integrity and load success)
• GPU/CPU inference health heartbeat (VRAM, CPU usage, thermal throttling)
• Code completion latency heartbeat (P50/P95 milliseconds from request to first token)
• Request queue depth heartbeat (queue buildup under concurrent IDE connections)
• Context token usage heartbeat (alert when context approaches model maximum)
• API compatibility health check (response format validation)
• Disk space monitor for model files (alert at 90% full)

Prerequisites

• Turbopilot installed and running (Go binary + LLM backend)
• A code LLM downloaded (StarCoder, CodeGen, or similar — typically 4–15 GB)
• Port 4567 accessible from your monitoring probe host
• A free Vigilmon account

Step 1: Monitor the API Server (Port 4567)

Turbopilot's entire value is the OpenAI-compatible API on port 4567. If this server crashes — due to an OOM kill from the LLM consuming all VRAM, a Go panic, or a misconfigured model path — every IDE Copilot plugin falls back to no-op silence. There is no user-facing error; autocomplete simply stops working. Continuous HTTP monitoring catches crashes within the check interval.

1. Log in to vigilmon.online and click Add Monitor.
2. Set Type to HTTP / HTTPS.
3. Enter: http://localhost:4567/v1/engines (or your LAN IP if Turbopilot runs on a separate host, e.g. http://192.168.1.x:4567/v1/engines).
4. Set Check interval to 1 minute.
5. Set Expected HTTP status to 200.
6. Click Save.

The /v1/engines endpoint is the OpenAI-compatible engines list that Copilot plugins query on connection. A non-200 or connection refused means the API server is not serving requests.

Tip: If you expose Turbopilot through a reverse proxy (nginx, Caddy), monitor the HTTPS proxy URL instead and enable SSL certificate monitoring in Step 8.

Step 2: Heartbeat Monitor for LLM Model Load Health

Turbopilot loads the code LLM from disk on startup. If the model file is corrupt, if the quantized GGUF file is truncated due to an interrupted download, or if the model fails to load into CPU/GPU memory, Turbopilot will start its HTTP server but return errors on every completion request. The API appearing up is not sufficient — the model itself must be loaded and ready.

1. Click Add Monitor → Cron Heartbeat.
2. Set Expected ping interval to 10 minutes.
3. Copy the heartbeat URL (e.g. https://vigilmon.online/heartbeat/abc123).

Create a probe script check_turbopilot_model.sh:

#!/bin/bash
# Verify Turbopilot returns a valid completion (model is loaded and serving)
RESPONSE=$(curl -s -X POST http://localhost:4567/v1/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"turbopilot","prompt":"// hello","max_tokens":1,"temperature":0}')

# A loaded model returns a choices array; an unloaded model returns an error object
if echo "$RESPONSE" | grep -q '"choices"'; then
  curl -s "https://vigilmon.online/heartbeat/YOUR_MODEL_HEARTBEAT_ID"
else
  echo "Model not loaded — response: $RESPONSE"
fi

Schedule every 10 minutes:

*/10 * * * * /path/to/check_turbopilot_model.sh

If the model file is corrupt or the LLM backend fails to initialize, the heartbeat stops and Vigilmon alerts before developers notice the silent failure.

Step 3: Heartbeat Monitor for GPU/CPU Inference Health

Running StarCoder or CodeGen requires significant hardware resources. A GPU VRAM exhaustion event causes the model to crash back to CPU-only inference with dramatically higher latency. Thermal throttling under sustained load can degrade inference speed without an outright failure. Monitoring GPU VRAM utilization and CPU load catches resource exhaustion before it becomes a latency or availability problem.

1. Click Add Monitor → Cron Heartbeat.
2. Set Expected ping interval to 5 minutes.
3. Copy the heartbeat URL.

Create check_turbopilot_resources.sh:

#!/bin/bash
HEARTBEAT_URL="https://vigilmon.online/heartbeat/YOUR_RESOURCE_HEARTBEAT_ID"
VRAM_THRESHOLD=90  # Alert if GPU VRAM exceeds 90%
CPU_THRESHOLD=95   # Alert if CPU exceeds 95%

# Check GPU VRAM utilization (requires nvidia-smi for NVIDIA GPUs)
if command -v nvidia-smi &> /dev/null; then
  VRAM_USED=$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits | head -1)
  VRAM_TOTAL=$(nvidia-smi --query-gpu=memory.total --format=csv,noheader,nounits | head -1)
  VRAM_PCT=$(( VRAM_USED * 100 / VRAM_TOTAL ))
  if [ "$VRAM_PCT" -gt "$VRAM_THRESHOLD" ]; then
    echo "GPU VRAM at ${VRAM_PCT}% — above threshold"
    exit 1
  fi
fi

# Check CPU utilization (1-minute average)
CPU_LOAD=$(awk '{print int($1)}' /proc/loadavg)
CPU_CORES=$(nproc)
CPU_PCT=$(( CPU_LOAD * 100 / CPU_CORES ))
if [ "$CPU_PCT" -gt "$CPU_THRESHOLD" ]; then
  echo "CPU load at ${CPU_PCT}% — above threshold"
  exit 1
fi

curl -s "$HEARTBEAT_URL"

Schedule every 5 minutes:

*/5 * * * * /path/to/check_turbopilot_resources.sh

Step 4: Heartbeat Monitor for Code Completion Latency

Local LLMs are slower than cloud services. Developers using Turbopilot expect inline completions within 2 seconds; if latency rises above that threshold, the IDE extension times out and shows no suggestion. Latency can spike due to thermal throttling, model quantization level, or a suddenly larger context window from an open file.

1. Click Add Monitor → Cron Heartbeat.
2. Set Expected ping interval to 5 minutes.
3. Copy the heartbeat URL.

Create check_turbopilot_latency.sh:

#!/bin/bash
HEARTBEAT_URL="https://vigilmon.online/heartbeat/YOUR_LATENCY_HEARTBEAT_ID"
LATENCY_THRESHOLD_MS=2000  # Alert if P95 exceeds 2 seconds

START_MS=$(date +%s%3N)

RESPONSE=$(curl -s -X POST http://localhost:4567/v1/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"turbopilot","prompt":"def fibonacci(n):","max_tokens":16,"temperature":0}')

END_MS=$(date +%s%3N)
ELAPSED_MS=$(( END_MS - START_MS ))

if echo "$RESPONSE" | grep -q '"choices"' && [ "$ELAPSED_MS" -lt "$LATENCY_THRESHOLD_MS" ]; then
  curl -s "$HEARTBEAT_URL"
else
  echo "Completion latency ${ELAPSED_MS}ms exceeds threshold or request failed"
fi

Schedule every 5 minutes:

*/5 * * * * /path/to/check_turbopilot_latency.sh

Step 5: Heartbeat Monitor for Request Queue Depth

Turbopilot processes one completion request at a time. When multiple developers have their IDEs open and all trigger completions simultaneously, requests queue. A growing queue means some developers wait several seconds for completions — or the IDE extension's own timeout fires first and the request is abandoned. Monitor queue depth to detect demand that exceeds single-instance capacity.

1. Click Add Monitor → Cron Heartbeat.
2. Set Expected ping interval to 5 minutes.
3. Copy the heartbeat URL.

Create check_turbopilot_queue.sh:

#!/bin/bash
HEARTBEAT_URL="https://vigilmon.online/heartbeat/YOUR_QUEUE_HEARTBEAT_ID"
MAX_QUEUE_WAIT_MS=5000  # Alert if any queued request waits more than 5 seconds

# Fire two concurrent requests and measure total wall-clock time
START_MS=$(date +%s%3N)

curl -s -X POST http://localhost:4567/v1/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"turbopilot","prompt":"// ping 1","max_tokens":1}' > /dev/null &

curl -s -X POST http://localhost:4567/v1/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"turbopilot","prompt":"// ping 2","max_tokens":1}' > /dev/null &

wait
END_MS=$(date +%s%3N)
TOTAL_MS=$(( END_MS - START_MS ))

# If both finished within threshold, queue is not building
if [ "$TOTAL_MS" -lt "$MAX_QUEUE_WAIT_MS" ]; then
  curl -s "$HEARTBEAT_URL"
else
  echo "Concurrent request wall-clock ${TOTAL_MS}ms — queue may be building"
fi

Schedule every 5 minutes:

*/5 * * * * /path/to/check_turbopilot_queue.sh

Step 6: Heartbeat Monitor for Context Token Usage

Turbopilot passes the surrounding code context to the LLM with each completion request. An open file with thousands of lines of code can push the context window close to or past the model's maximum — typically 2,048 to 8,192 tokens depending on the model. A context overflow causes the request to silently fail or return a truncated completion. Alert when context sizes consistently approach the limit.

1. Click Add Monitor → Cron Heartbeat.
2. Set Expected ping interval to 10 minutes.
3. Copy the heartbeat URL.

Create check_turbopilot_context.sh:

#!/bin/bash
HEARTBEAT_URL="https://vigilmon.online/heartbeat/YOUR_CONTEXT_HEARTBEAT_ID"
# Generate a large-context test payload (~1500 tokens of code)
LARGE_CONTEXT=$(python3 -c "print('// ' + 'a' * 100 + '\n' * 50)")

RESPONSE=$(curl -s -X POST http://localhost:4567/v1/completions \
  -H "Content-Type: application/json" \
  -d "{\"model\":\"turbopilot\",\"prompt\":\"$LARGE_CONTEXT\",\"max_tokens\":4,\"temperature\":0}")

if echo "$RESPONSE" | grep -q '"choices"'; then
  curl -s "$HEARTBEAT_URL"
else
  echo "Large-context request failed: $RESPONSE"
fi

Schedule every 10 minutes:

*/10 * * * * /path/to/check_turbopilot_context.sh

Step 7: Heartbeat Monitor for API Compatibility

Turbopilot mimics the OpenAI Completions API that Copilot plugins expect. A misconfiguration, a version update, or a partial startup can cause responses with missing or malformed fields — e.g., a missing "choices" array or an incorrect content type. The IDE plugin may silently fail if the response does not exactly match expected format.

1. Click Add Monitor → Cron Heartbeat.
2. Set Expected ping interval to 15 minutes.
3. Copy the heartbeat URL.

Create check_turbopilot_compat.sh:

#!/bin/bash
HEARTBEAT_URL="https://vigilmon.online/heartbeat/YOUR_COMPAT_HEARTBEAT_ID"

RESPONSE=$(curl -s -X POST http://localhost:4567/v1/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"turbopilot","prompt":"x=","max_tokens":4}')

# Validate required OpenAI response fields
if echo "$RESPONSE" | grep -q '"choices"' && \
   echo "$RESPONSE" | grep -q '"text"' && \
   echo "$RESPONSE" | grep -q '"finish_reason"'; then
  curl -s "$HEARTBEAT_URL"
else
  echo "API compatibility check failed — missing required fields: $RESPONSE"
fi

Schedule every 15 minutes:

*/15 * * * * /path/to/check_turbopilot_compat.sh

Step 8: Monitor Disk Space for Model Files

Code LLMs are 1–15 GB each. If you maintain multiple quantization variants or switch models frequently, the disk hosting them can fill up. A full disk prevents model downloads, causes logging failures, and can crash the Turbopilot process if it writes to disk during inference.

1. Click Add Monitor → Cron Heartbeat.
2. Set Expected ping interval to 30 minutes.
3. Copy the heartbeat URL.

Create check_turbopilot_disk.sh:

#!/bin/bash
HEARTBEAT_URL="https://vigilmon.online/heartbeat/YOUR_DISK_HEARTBEAT_ID"
MODEL_DIR="${TURBOPILOT_MODEL_DIR:-/opt/turbopilot/models}"
THRESHOLD_PCT=90  # Alert when disk is 90% full

DISK_PCT=$(df "$MODEL_DIR" | awk 'NR==2{gsub(/%/,"",$5); print $5}')

if [ "$DISK_PCT" -lt "$THRESHOLD_PCT" ]; then
  curl -s "$HEARTBEAT_URL"
else
  echo "Disk at ${DISK_PCT}% full on $MODEL_DIR — above ${THRESHOLD_PCT}% threshold"
fi

Schedule every 30 minutes:

*/30 * * * * /path/to/check_turbopilot_disk.sh

Step 9: SSL Certificate Monitoring

If Turbopilot is exposed through a reverse proxy with HTTPS (nginx, Caddy, Traefik) so that IDE plugins outside the local network can reach it, monitor the TLS certificate expiry alongside the HTTP endpoint.

1. Open the HTTP monitor for port 4567 (created in Step 1).
2. Enable Monitor SSL certificate.
3. Set Alert when certificate expires in less than 21 days.
4. Click Save.

Step 10: Configure Alert Channels

1. Go to Alert Channels in Vigilmon and add Slack, email, Discord, or a webhook.
2. On the API server monitor (port 4567): set Consecutive failures before alert to 1 — an API server crash is immediate and hard.
3. On the latency heartbeat: the default single-missed-interval threshold is appropriate.
4. On disk space: consider a longer window (2 missed intervals) since disk fills gradually.

Example: Slack Alert

1. Create a Slack incoming webhook in your workspace.
2. In Vigilmon → Alert Channels → Add → Slack.
3. Paste the webhook URL and assign it to all monitors in this tutorial.

Summary

| Monitor | Type | Target | Interval | |---------|------|--------|----------| | API server (port 4567) | HTTP | http://localhost:4567/v1/engines | 1 min | | LLM model load health | Heartbeat | probe script → Vigilmon | 10 min | | GPU/CPU resource health | Heartbeat | probe script → Vigilmon | 5 min | | Completion latency | Heartbeat | probe script → Vigilmon | 5 min | | Request queue depth | Heartbeat | probe script → Vigilmon | 5 min | | Context token usage | Heartbeat | probe script → Vigilmon | 10 min | | API compatibility | Heartbeat | probe script → Vigilmon | 15 min | | Disk space (model files) | Heartbeat | probe script → Vigilmon | 30 min | | SSL certificate | SSL (on HTTP monitor) | reverse proxy domain | — |

Turbopilot is the invisible backbone of AI-assisted development for teams who keep their code off the cloud. When the API server crashes or the model silently fails to respond, every developer loses autocomplete with no indication of what went wrong. With Vigilmon watching every layer — from the API endpoint to GPU VRAM and disk space — your team knows within minutes, not hours.

Get started for free at vigilmon.online.