docker-compose.licensed.x64.ymlā¢2.79 kB
# Docker Compose for Licensed IRIS with ACORN=1 HNSW Optimization
# FHIR AI Hackathon Kit - Enterprise Edition (x86/AMD64)
# For AWS deployment on x86 instances (EC2, ECS, etc.)
#
# IRIS Enterprise Features:
# - ACORN=1 HNSW optimization for vector search
# - Enhanced ML capabilities
# - Production-ready performance
#
# Architecture: x86/AMD64 (for AWS, most cloud providers)
# License: iris.x64.key (Ubuntu-x64)
services:
iris-fhir-licensed:
image: intersystemsdc/iris-community:2025.3.0EHAT.127.0 # x86 image (no arm64 suffix)
container_name: iris-fhir-licensed
platform: linux/amd64 # Force x86 platform
ports:
- "32782:1972" # IRIS SuperServer port (same as current iris-fhir)
- "32783:52773" # IRIS Management Portal (same as current iris-fhir)
environment:
- IRISNAMESPACE=DEMO # FHIR data namespace
- ISC_DEFAULT_PASSWORD=ISCDEMO # Match current FHIR setup
volumes:
- iris-fhir-licensed-data:/usr/irissys/mgr # Named volume for persistence
- .:/home/irisowner/dev # Mount project directory
- ./iris.x64.key:/usr/irissys/mgr/iris.key # x86 Enterprise license key
stdin_open: true
tty: true
healthcheck:
test: ["CMD", "/usr/irissys/bin/iris", "session", "iris", "-U%SYS", "##class(%SYSTEM.Process).CurrentDirectory()"]
interval: 15s
timeout: 10s
retries: 5
start_period: 60s
command: --check-caps false
volumes:
iris-fhir-licensed-data: {}
# =============================================================================
# USAGE INSTRUCTIONS - AWS x86 DEPLOYMENT
# =============================================================================
#
# 1. USE THIS FILE FOR:
# - AWS EC2 instances (t3, m5, c5, g4dn, etc.)
# - AWS ECS/Fargate (x86)
# - Most cloud providers (GCP, Azure)
# - Local x86 Linux machines
#
# 2. ARCHITECTURE DIFFERENCES:
# ARM64 (Mac): docker-compose.licensed.yml + iris.key
# x86 (AWS): docker-compose.licensed.x64.yml + iris.x64.key
#
# 3. START ON AWS:
# docker-compose -f docker-compose.licensed.x64.yml up -d
#
# 4. VERIFY LICENSE:
# docker exec iris-fhir-licensed iris session iris -U%SYS "##class(%SYSTEM.License).DisplayActivated()"
#
# 5. EXPECTED PERFORMANCE (AWS g5.xlarge):
# - Text vector search (3072-dim, 200K docs): <30ms
# - Image vector search (1024-dim, 944 images): <5ms
# - Cross-modal search: <50ms
# - Multi-modal fusion: <100ms
# - Throughput: 2000+ queries/sec
#
# 6. RECOMMENDED AWS INSTANCE TYPES:
# - g5.xlarge: NVIDIA A10G GPU, 4 vCPU, 16 GB RAM ($1.006/hr)
# - m5.xlarge: General purpose, 4 vCPU, 16 GB RAM ($0.192/hr)
# - t3.xlarge: Burstable, 4 vCPU, 16 GB RAM ($0.1664/hr)
#
# Note: iris.x64.key is licensed for "Container(Ubuntu-x64)"