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Quick-Start Checklist

Deploy the HCLS AI Factory in 30 minutes. This checklist extracts the critical steps from the full Deployment Guide.

Prerequisites

  • Hardware: NVIDIA DGX Spark (or equivalent with GB10 GPU, 128GB unified memory)
  • Storage: 500GB+ free space for genomics data and models
  • Network: Internet access for pulling containers and reference data
  • Software: Docker 24+, Docker Compose 2.20+, Git

Step 1: Clone the Repository

git clone https://github.com/ajones1923/hcls-ai-factory.git
cd hcls-ai-factory

Step 2: Environment Setup

# Copy environment template
cp .env.example .env

# Edit with your API keys
nano .env

Required variables:

Variable Description
ANTHROPIC_API_KEY Claude API key for RAG chat
NGC_API_KEY NVIDIA NGC key for BioNeMo models

Step 3: Start Core Services

# Start all services
docker compose up -d

# Verify services are running
docker compose ps

Expected services:

  • genomics-pipeline — Parabricks + DeepVariant
  • rag-service — Milvus + Claude integration
  • drug-discovery — BioNeMo MolMIM + DiffDock
  • grafana — Monitoring dashboard (port 3000)
  • streamlit — Chat UI (port 8501)

Step 4: Verify GPU Access

# Check GPU visibility
docker compose exec genomics-pipeline nvidia-smi

You should see your GPU(s) listed with available memory.

Step 5: Load Reference Data

# Download reference genome (one-time, ~15GB)
./scripts/download_reference.sh

# Load ClinVar and AlphaMissense annotations
./scripts/load_annotations.sh

Step 6: Run a Test Pipeline

# Run with synthetic test data
./scripts/run_demo.sh --mock

# Expected output: variant calls in output/demo/

Step 7: Access the UI

Open your browser:

Service URL Purpose
Streamlit Chat http://localhost:8501 Query variants with Claude
Grafana http://localhost:3000 Monitor pipeline metrics
API Docs http://localhost:8080/docs REST API reference

Troubleshooting

Services won't start

# Check logs
docker compose logs -f

# Restart specific service
docker compose restart rag-service

GPU not detected

# Verify NVIDIA runtime
docker run --rm --gpus all nvidia/cuda:12.0-base nvidia-smi

Out of memory

Reduce batch sizes in .env:

PARABRICKS_BATCH_SIZE=1
MOLMIM_BATCH_SIZE=10

Next Steps

Success Criteria

You're ready when:

  • All Docker services show Up status
  • GPU is visible in containers
  • Streamlit chat responds to queries
  • Grafana shows pipeline metrics

Total time: ~30 minutes (excluding reference data download)

Need help? Open an issue on GitHub.