Developer Tools Portal — tools.gantman.biz

Developer Tools Portal — tools.gantman.biz

Challenge

DevSecOps practitioners juggle six to ten web tools daily: JSON formatters, JWT decoders, hash generators, API clients, network calculators. These tools live at different URLs, require separate bookmarks, and offer inconsistent UX. No single public interface provides all common developer utilities in one place with immediate access — no signup walls, no cookie consent popups blocking tool use.

Build a unified developer tool suite that:

DevSecOps-5090 — GPU Training Pipeline on Kubernetes

DevSecOps-5090 — GPU Training Pipeline on Kubernetes

Challenge

Fine-tuning Large Language Models typically requires cloud GPU instances (expensive) or complex local setups. Need a production-ready, self-hosted training pipeline that:

• Runs on local RTX 5090 (32GB VRAM)
• Deploys via Kubernetes (k3s homelab)
• Uses pre-built images (no runtime pip installs)
• Supports QLoRA for memory efficiency

Solution Architecture

Pipeline Overview

┌─────────────────────────────────────────────────────────────┐
│                    K3s Cluster (Homelab)                     │
├─────────────────────────────────────────────────────────────┤
│  ┌─────────────────────────────────────────────────────┐   │
│  │              Training Pod (GPU)                      │   │
│  │  ┌─────────────────┐  ┌─────────────────────────┐   │   │
│  │  │ Init Containers │  │    Main Container       │   │   │
│  │  │ - Verify GPU    │  │ - qwen_qlora_trainer.py │   │   │
│  │  │ - Check deps    │  │ - HuggingFace ecosystem │   │   │
│  │  │ - Mount PVCs    │  │ - bitsandbytes (4-bit)  │   │   │
│  │  └─────────────────┘  └─────────────────────────┘   │   │
│  │                                                      │   │
│  │  ┌─────────────────────────────────────────────┐    │   │
│  │  │              Sidecar                         │    │   │
│  │  │         metrics-exporter                     │    │   │
│  │  └─────────────────────────────────────────────┘    │   │
│  └─────────────────────────────────────────────────────┘   │
│                                                             │
│  Volumes:                                                   │
│  ├── /mnt/models      (RO) - Model cache                   │
│  ├── /mnt/data        (RO) - Training data                 │
│  ├── /mnt/checkpoints (RW) - Output checkpoints            │
│  └── /mnt/training-logs (RW) - Logs + TensorBoard          │
└─────────────────────────────────────────────────────────────┘

Pre-Built Training Image

FROM pytorch/pytorch:2.6.0-cuda12.4-cudnn9-runtime

# Pre-install all dependencies (no runtime downloads)
RUN pip install --no-cache-dir \
    transformers==4.47.0 \
    peft==0.14.0 \
    trl==0.13.0 \
    bitsandbytes==0.45.0 \
    datasets==3.2.0 \
    accelerate==1.2.1 \
    safetensors \
    sentencepiece \
    protobuf

Result: 11.4 GB image, ~2 min boot (vs. 15+ min with runtime pip)