motidivya-dlogs 0.1.5

dLogs - one command installer/bootstrapper for your logging & monitoring stack

dLogs: The "1-Click" Observability Stack

Monitor Everything. Windows Host, Docker Containers, Nvidia GPUs, and App Logs. All in one place.

dLogs is a pre-configured, high-performance observability stack built on top of the industry-standard LGTM stack (Loki, Grafana, Telegraf/Promtail, Monitoring/Prometheus), enhanced with automatic dashboard provisioning and Python integration.

---

🚀 Features

• ⚡ Instant Setup: Install via pip or Docker.
• 🖥️ Full Windows Visibility: CPU, RAM, Disk I/O, Network traffic, and Services.
• 🐳 Docker Stats: CPU/Memory/Network per container (powered by cAdvisor).
• 🎮 Nvidia GPU Monitoring: realtime usage, temperatures, and power draw (WSL2 supported).
• 📜 Centralized Logging: Logs from your Python apps + Docker logs in one queryable UI.
• 🔔 Alerts: Built-in ntfy server for push notifications.
• 🐍 Python SDK: dlogs wrapper to start logging in 2 lines of code.

---

💿 Installation

Option 1: Python (Recommended)

Install using pip (or pipx for isolation):

pip install motidivya-dlogs

Then initialize and start the stack:

# Create a folder for your stack
mkdir my-stack
cd my-stack

# Initialize configuration
dlogs init .

# Start the stack
dlogs up .

Option 2: Docker

Run the CLI container directly:

docker run --rm -it \
  -v /var/run/docker.sock:/var/run/docker.sock \
  -v $(pwd):/app/work \
  divyesh1099/dlogs up /app/work

Option 3: Installer Script (Windows)

iwr -useb https://raw.githubusercontent.com/divyesh1099/dLogs/main/install_dlogs.ps1 | iex

---

🐍 Python Integration (dLogs SDK)

The dlogs package includes a zero-dependency wrapper to send logs directly to this stack.

Usage:

from dlogs import dLogs

# Initialize (Automatically creates C:/Logs/my_app.json)
logger = dLogs("my_super_app")

# Log normal info (Shows in Loki/Grafana)
logger.log("Application started successfully.")

# Log errors (Highlights in Red)
try:
    1 / 0
except Exception as e:
    logger.alert(f"Critical math failure: {e}")

How it works:

• It writes structured JSON logs to C:\Logs\app_name.json (or configured directory).
• Promtail (running in Docker) watches that folder.
• It scrapes the new lines instantly and sends them to Loki.
• You view them in Grafana Explore ({job="varlogs"} |= "my_super_app").

---

📊 Dashboards Guide

Grafana comes pre-provisioned. Go to http://localhost:3000/dashboards (admin/admin) and open the dLogs folder.

1. 🪟 Windows Host

• Real-time CPU/RAM: Total system usage.
• Network I/O: Upload/Download speeds.
• Disk Usage: Space on C: drive.
• Requirement: windows_exporter service running on port 9182.

2. 🐳 Docker Containers

• CPU/Memory per Container: identify resource hogs.
• Network Traffic: See which container is chatting the most.
• Requirement: cadvisor container running.

3. 🟢 Nvidia GPU

• Usage %: Graphics load.
• VRAM: Memory allocation.
• Temp/Power: Thermal monitoring.
• Requirement: dlogs-gpu container + Nvidia Drivers.

4. 🪵 Loki Logs

• Search Engine: Query logs using LogQL.
• Filters: Filter by app name, log level (INFO/ERROR).
• Live Tail: See logs as they happen.

---

🛠️ Architecture

The stack runs completely locally using Docker Compose:

Component	Port	Purpose
Grafana	3000	The Dashboard UI. Access via http://localhost:3000.
Prometheus	9090	Time-series database for metrics.
Loki	3100	Log aggregation system (like Splunk/ELK but lighter).
Promtail	—	Log collector. Watches log folder and sends to Loki.
cAdvisor	8090	Container usage metrics (Google's official tool).
GPU/Nvidia	9835	nvidia_gpu_exporter for monitoring graphics cards.
Windows	9182	windows_exporter MSI running natively on host.
Ntfy	8080	Notification server (publish/subscribe).

---

Moti❤️