> ## Documentation Index
> Fetch the complete documentation index at: https://docs.loom.aayanmishra.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Platform Overview

> Complete architecture guide, installation, configuration, and operational best practices for LoomOS distributed AI platform.

# LoomOS Platform Architecture & Operations Guide

LoomOS is a comprehensive distributed AI runtime and orchestration platform designed for enterprise-scale machine learning workloads. It provides unified infrastructure for training, verifying, and deploying AI models with built-in safety, auditability, and horizontal scalability.

## Executive Summary

LoomOS addresses critical challenges in production AI systems:

* **Scale**: Distribute training across hundreds of nodes with automatic resource management
* **Safety**: Built-in verification (Prism) for model safety, factuality, and compliance
* **Auditability**: Complete event sourcing and lineage tracking via LoomDB
* **Flexibility**: Modular architecture supporting multiple ML frameworks and cloud providers
* **Reliability**: Fault-tolerant design with automatic failover and recovery

<CardGroup cols={3}>
  <Card title="Core Architecture" href="#core-architecture">
    Master/worker design, event sourcing, distributed scheduler, and verification systems.
  </Card>

  <Card title="Installation Guide" href="#installation-guide">
    Complete setup instructions from development to production deployment.
  </Card>

  <Card title="Security & Compliance" href="#security-compliance">
    mTLS, TEE attestation, RBAC, audit logging, and compliance frameworks.
  </Card>
</CardGroup>

## Core Architecture

LoomOS follows a distributed, microservices-based architecture with five primary subsystems that work together to provide a complete AI operations platform.

### 1. Nexus - Distributed Coordination Layer

The Nexus system provides cluster-wide coordination and resource management:

**Master Nodes** handle:

* Job scheduling and resource allocation
* Cluster state management and coordination
* API endpoints for client interactions
* Health monitoring and automatic failover

**Worker Nodes** execute:

* Training and inference workloads
* Model deployment and serving
* Data preprocessing and validation
* Distributed computation tasks

**Coordination Protocol**:

```python theme={null}
# Nexus Master Configuration
from core.nexus import MasterCoordinator, ClusterConfig, ConsensusConfig

cluster_config = ClusterConfig(
    cluster_id="production-cluster",
    region="us-west-2",
    availability_zones=["us-west-2a", "us-west-2b", "us-west-2c"]
)

consensus_config = ConsensusConfig(
    algorithm="raft",
    election_timeout_ms=5000,
    heartbeat_interval_ms=1000,
    log_replication_batch_size=100
)

master = MasterCoordinator(
    config=cluster_config,
    consensus=consensus_config,
    bind_address="0.0.0.0:8080"
)

# Start master with high availability
await master.start(enable_ha=True)
```

### 2. LoomDB - Event Sourcing & Audit System

LoomDB provides comprehensive event sourcing and audit capabilities for complete system observability:

**Event Store Features**:

* Immutable append-only event log
* Horizontal partitioning for scale
* Real-time event streaming
* Complex event processing (CEP)

**Audit & Compliance**:

* Complete model lineage tracking
* User action auditing
* Data access logging
* Compliance reporting (SOC2, HIPAA, GDPR)

```python theme={null}
# Comprehensive Event Logging Example
from core.loomdb import LoomDB, EventType, AuditContext, ComplianceLevel

db = LoomDB(
    connection_string="postgresql://user:pass@localhost/loomdb",
    event_store_config={
        "partition_strategy": "time_based",
        "retention_days": 2555,  # 7 years for compliance
        "compression": "lz4",
        "replication_factor": 3
    }
)

# Create audit context for traceability
context = AuditContext(
    user_id="data_scientist_123",
    session_id="sess_456",
    request_id="req_789",
    compliance_level=ComplianceLevel.SOC2_TYPE2
)

# Log training lifecycle events
await db.log_event(
    event_type=EventType.MODEL_TRAINING_START,
    data={
        "model_name": "gpt-custom-v2",
        "dataset_id": "healthcare_data_2024",
        "dataset_size_gb": 150.5,
        "hyperparameters": {
            "learning_rate": 0.0001,
            "batch_size": 64,
            "num_epochs": 10,
            "optimizer": "adamw",
            "warmup_steps": 1000
        },
        "hardware_config": {
            "node_count": 8,
            "gpu_per_node": 4,
            "gpu_type": "A100",
            "total_gpu_memory_gb": 320
        }
    },
    context=context,
    tags=["healthcare", "phi_data", "production"]
)

# Query events for compliance audits
compliance_events = await db.query_events(
    event_types=[
        EventType.DATA_ACCESS,
        EventType.MODEL_TRAINING_START,
        EventType.MODEL_DEPLOYMENT
    ],
    time_range=(
        datetime(2024, 1, 1),
        datetime(2024, 12, 31)
    ),
    filters={
        "tags": ["phi_data"],
        "compliance_level": ComplianceLevel.SOC2_TYPE2
    },
    include_metadata=True
)

# Generate compliance report
report = await db.generate_compliance_report(
    report_type="SOC2_TYPE2",
    time_period="2024-Q1",
    include_user_actions=True,
    include_data_lineage=True
)
```

### 3. Distributed Scheduler - Resource & Job Management

Advanced job orchestration with intelligent resource allocation:

**Scheduling Features**:

* Multi-tenant resource isolation
* Gang scheduling for distributed jobs
* Preemption and priority handling
* Auto-scaling based on queue depth

**Resource Management**:

* GPU topology awareness
* Memory-optimized placement
* Network bandwidth allocation
* Storage I/O optimization

```python theme={null}
# Advanced Scheduling Configuration
from core.scheduler import (
    Scheduler, JobSpec, ResourceRequirements, 
    SchedulingPolicy, AutoScalingConfig, QoSLevel
)

# Configure advanced scheduler
scheduler = Scheduler(
    scheduling_policy=SchedulingPolicy.FAIR_SHARE,
    preemption_enabled=True,
    gang_scheduling=True,
    resource_overcommit_ratio=1.2
)

# Auto-scaling configuration
autoscaling = AutoScalingConfig(
    min_nodes=5,
    max_nodes=100,
    scale_up_threshold=0.8,   # CPU/GPU utilization
    scale_down_threshold=0.3,
    scale_up_delay_seconds=300,
    scale_down_delay_seconds=600
)

await scheduler.configure_autoscaling(autoscaling)

# Define complex resource requirements
resources = ResourceRequirements(
    # Compute resources
    cpu_cores=32,
    memory_gb=256,
    gpu_count=8,
    gpu_type="A100",
    gpu_memory_gb=80,
    
    # Storage requirements
    local_ssd_gb=1000,
    shared_storage_gb=5000,
    io_operations_per_sec=10000,
    
    # Network requirements
    network_bandwidth_gbps=25,
    infiniband_required=True,
    
    # Placement constraints
    node_affinity={"datacenter": "us-west"},
    node_anti_affinity={"maintenance": "scheduled"},
    
    # QoS and priority
    qos_level=QoSLevel.GUARANTEED,
    priority=85,
    preemptible=False
)

# Create sophisticated job specification
job_spec = JobSpec(
    name="large_language_model_training",
    algorithm="weave_distributed",
    
    # Container configuration
    container_image="loomos/training:v1.2.3",
    command=["python", "train_llm.py"],
    working_directory="/workspace",
    
    # Resource allocation
    resources=resources,
    
    # Job dependencies and workflow
    dependencies=[
        JobDependency(
            job_name="data_preprocessing",
            type=DependencyType.COMPLETION,
            output_artifacts=["preprocessed_data"]
        ),
        JobDependency(
            job_name="checkpoint_validation",
            type=DependencyType.SUCCESS,
            timeout_seconds=1800
        )
    ],
    
    # Environment and configuration
    environment_variables={
        "CUDA_VISIBLE_DEVICES": "0,1,2,3,4,5,6,7",
        "NCCL_DEBUG": "INFO",
        "PYTORCH_CUDA_ALLOC_CONF": "max_split_size_mb:512"
    },
    
    # Checkpoint and recovery
    checkpoint_config=CheckpointConfig(
        enabled=True,
        interval_minutes=30,
        max_checkpoints=10,
        storage_backend="s3://checkpoints/llm-training/"
    ),
    
    # Monitoring and notifications
    monitoring_config=MonitoringConfig(
        metrics_interval_seconds=60,
        log_level="INFO",
        alert_on_failure=True,
        notification_channels=["slack://ai-team", "email://team@company.com"]
    ),
    
    # Execution constraints
    max_runtime_hours=72,
    retry_limit=3,
    failure_tolerance=0.1  # Allow 10% task failures
)

# Submit job with advanced options
job_handle = await scheduler.submit_job(
    job_spec=job_spec,
    dry_run=False,
    wait_for_resources=True,
    priority_boost=True
)

# Monitor job execution with detailed metrics
async for update in scheduler.stream_job_updates(job_handle.job_id):
    print(f"Job {update.phase}: {update.progress}% complete")
    print(f"Resource utilization: CPU {update.cpu_usage}%, GPU {update.gpu_usage}%")
    print(f"Estimated completion: {update.eta}")
    
    # Handle job state transitions
    if update.phase == JobPhase.FAILED:
        # Analyze failure and potentially retry
        failure_analysis = await scheduler.analyze_job_failure(job_handle.job_id)
        if failure_analysis.retryable:
            await scheduler.retry_job(job_handle.job_id)
```

### 4. Reinforcement Learning Infrastructure

Comprehensive RL training system with advanced algorithms:

**Supported Algorithms**:

* Proximal Policy Optimization (PPO)
* Deep Q-Networks (DQN) and variants
* WEAVE (proprietary multi-agent algorithm)
* Custom algorithm integration

**Training Features**:

* Distributed experience collection
* Asynchronous policy updates
* Multi-environment training
* Hierarchical reinforcement learning

### 5. Blocks & Adapters - Integration Ecosystem

Modular integration system supporting diverse ML ecosystems:

**Model Adapters**:

* OpenAI API integration
* Hugging Face model hub
* Custom model backends
* Multi-modal model support

**Infrastructure Adapters**:

* Kubernetes orchestration
* Cloud provider integration (AWS, GCP, Azure)
* On-premises deployment
* Hybrid cloud configurations

## System Requirements & Sizing

### Development Environment

**Minimum Requirements**:

* CPU: 4 cores (Intel/AMD x86\_64)
* Memory: 8GB RAM
* Storage: 50GB SSD
* Network: 100Mbps connection
* OS: Linux (Ubuntu 20.04+), macOS 11+, Windows 10 with WSL2

**Recommended Development Setup**:

* CPU: 8-16 cores
* Memory: 32GB RAM
* Storage: 500GB NVMe SSD
* GPU: NVIDIA RTX 3080 or better (for local training)
* Network: 1Gbps connection

### Production Environment

**Small Production Cluster (10-50 nodes)**:

* CPU: 32+ cores per node (Intel Xeon or AMD EPYC)
* Memory: 256GB+ RAM per node
* Storage: 2TB+ NVMe SSD per node
* GPU: 4-8x NVIDIA A100 or H100 per training node
* Network: 25Gbps with RDMA support
* Redundancy: 3x master nodes, N+2 worker redundancy

**Large Production Cluster (100+ nodes)**:

* CPU: 64+ cores per node
* Memory: 512GB+ RAM per node
* Storage: 10TB+ NVMe SSD with 100K+ IOPS
* GPU: 8x NVIDIA H100 per training node
* Network: 100Gbps InfiniBand fabric
* Redundancy: 5x master nodes across availability zones

## Installation Guide

### Quick Start (Development)

For evaluation and development purposes:

```bash theme={null}
# 1. Clone repository and setup environment
git clone https://github.com/loomos/loomos.git
cd loomos

# Create and activate virtual environment
python3.11 -m venv venv
source venv/bin/activate  # Linux/macOS
# OR: venv\Scripts\activate  # Windows

# 2. Install dependencies
pip install --upgrade pip
pip install -r requirements.txt
pip install -r requirements-dev.txt
pip install -e .

# 3. Start local development cluster
docker-compose -f docker-compose.dev.yml up -d

# 4. Initialize database and run migrations
python scripts/init_database.py
python scripts/run_migrations.py

# 5. Run comprehensive demo
python examples/quick_demo.py

# Expected output:
# [INFO] LoomOS cluster started successfully
# [INFO] Submitting test job...
# [INFO] Job 'test_training' accepted with ID: job_12345
# [INFO] Training progress: 100% complete
# [INFO] Model artifacts saved to: ./artifacts/model_v1.pt
# [INFO] Demo completed successfully
```

### Verification Steps

```bash theme={null}
# Check service health
curl http://localhost:8080/health
# Response: {"status": "healthy", "version": "1.2.3", "uptime": "5m32s"}

# Test job submission
curl -X POST http://localhost:8080/api/v1/jobs \
  -H "Content-Type: application/json" \
  -d '{"name": "test_job", "algorithm": "ppo", "resources": {"gpu_count": 1}}'

# Monitor cluster status
python scripts/cluster_status.py
```

### Production Installation

#### Infrastructure Prerequisites

```bash theme={null}
# 1. Install Docker and container runtime
curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh get-docker.sh
sudo usermod -aG docker $USER

# 2. Install NVIDIA Container Toolkit (for GPU nodes)
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | \
  sudo tee /etc/apt/sources.list.d/nvidia-docker.list

sudo apt-get update
sudo apt-get install -y nvidia-docker2
sudo systemctl restart docker

# 3. Verify GPU access
docker run --rm --gpus all nvidia/cuda:12.0-base nvidia-smi
```

#### Database Setup (Production)

```bash theme={null}
# PostgreSQL for event store and metadata
sudo apt-get install postgresql-14 postgresql-contrib
sudo -u postgres psql -c "CREATE DATABASE loomdb;"
sudo -u postgres psql -c "CREATE USER loomuser WITH ENCRYPTED PASSWORD 'secure_random_password';"
sudo -u postgres psql -c "GRANT ALL PRIVILEGES ON DATABASE loomdb TO loomuser;"

# Redis for caching and job queues
sudo apt-get install redis-server
sudo systemctl enable redis-server
sudo systemctl start redis-server

# Configure Redis for production
sudo sed -i 's/# maxmemory <bytes>/maxmemory 4gb/' /etc/redis/redis.conf
sudo sed -i 's/# maxmemory-policy noeviction/maxmemory-policy allkeys-lru/' /etc/redis/redis.conf
sudo systemctl restart redis-server
```

#### LoomOS Configuration

```yaml theme={null}
# config/production.yml
cluster:
  name: "production-cluster"
  region: "us-west-2"
  environment: "production"
  
database:
  host: "loomdb.internal"
  port: 5432
  name: "loomdb"
  user: "loomuser"
  password: "${LOOM_DB_PASSWORD}"  # From environment or secrets manager
  ssl_mode: "require"
  connection_pool_size: 50
  max_overflow: 100

redis:
  host: "redis.internal"
  port: 6379
  db: 0
  password: "${REDIS_PASSWORD}"
  ssl: true
  connection_pool_size: 20

security:
  tls:
    enabled: true
    cert_file: "/etc/ssl/certs/loomos.pem"
    key_file: "/etc/ssl/private/loomos.key"
    ca_file: "/etc/ssl/certs/ca.pem"
  
  authentication:
    method: "oauth2"
    provider: "keycloak"
    issuer_url: "https://auth.company.com/realms/loomos"
  
  authorization:
    rbac_enabled: true
    default_role: "user"
    admin_roles: ["admin", "platform-engineer"]

monitoring:
  prometheus:
    enabled: true
    port: 9090
    path: "/metrics"
  
  jaeger:
    enabled: true
    agent_host: "jaeger-agent"
    agent_port: 6831
  
  logging:
    level: "INFO"
    format: "json"
    output: ["stdout", "file"]
    file_path: "/var/log/loomos/loomos.log"
    max_file_size_mb: 100
    max_backup_count: 10

scheduler:
  max_concurrent_jobs: 500
  job_timeout_hours: 168  # 7 days
  resource_overcommit_ratio: 1.1
  
autoscaling:
  enabled: true
  min_workers: 10
  max_workers: 1000
  scale_up_threshold: 0.8
  scale_down_threshold: 0.2
  scale_up_delay_minutes: 5
  scale_down_delay_minutes: 15
```

## Security & Compliance

### Transport Layer Security

```bash theme={null}
# Generate TLS certificates for production
openssl genrsa -out loomos.key 4096
openssl req -new -key loomos.key -out loomos.csr \
  -subj "/C=US/ST=CA/L=San Francisco/O=Company/CN=loomos.company.com"
openssl x509 -req -in loomos.csr -signkey loomos.key -out loomos.crt -days 365
```

### Role-Based Access Control (RBAC)

```python theme={null}
# Define custom roles and permissions
from core.security import RBACManager, Role, Permission

rbac = RBACManager()

# Define permissions
permissions = [
    Permission("jobs:create", "Create new training jobs"),
    Permission("jobs:read", "View job status and logs"),
    Permission("jobs:cancel", "Cancel running jobs"),
    Permission("models:deploy", "Deploy models to production"),
    Permission("cluster:admin", "Administer cluster resources")
]

# Define roles
data_scientist = Role(
    name="data_scientist",
    permissions=["jobs:create", "jobs:read", "jobs:cancel"]
)

ml_engineer = Role(
    name="ml_engineer", 
    permissions=["jobs:create", "jobs:read", "models:deploy"]
)

platform_admin = Role(
    name="platform_admin",
    permissions=["*"]  # All permissions
)

# Assign roles to users
await rbac.assign_role("user123", "data_scientist")
await rbac.assign_role("user456", "ml_engineer")
```

### Audit & Compliance

```python theme={null}
# Generate compliance reports
from core.compliance import ComplianceManager, ReportType

compliance = ComplianceManager()

# SOC 2 Type II compliance report
soc2_report = await compliance.generate_report(
    report_type=ReportType.SOC2_TYPE2,
    time_period="2024-Q1",
    include_controls=[
        "CC6.1",  # Logical access controls
        "CC6.2",  # Authentication and authorization
        "CC7.1",  # System monitoring
    ]
)

# GDPR data processing report
gdpr_report = await compliance.generate_report(
    report_type=ReportType.GDPR,
    time_period="2024-Q1",
    include_data_subjects=True,
    include_processing_activities=True
)
```

## Monitoring & Observability

### Metrics Collection

LoomOS exposes comprehensive metrics via Prometheus:

```python theme={null}
# Custom application metrics
from core.monitoring import MetricsCollector, MetricType

metrics = MetricsCollector()

# Counter metrics
metrics.increment(
    name="jobs_submitted_total",
    tags={"algorithm": "weave", "priority": "high"},
    value=1
)

# Histogram metrics for latency
metrics.record_histogram(
    name="job_execution_duration_seconds",
    value=3600.5,  # 1 hour execution
    tags={"status": "completed", "node_type": "gpu"}
)

# Gauge metrics for current state
metrics.set_gauge(
    name="active_workers_count",
    value=42,
    tags={"datacenter": "us-west-2"}
)
```

### Health Checks & Alerts

```bash theme={null}
# Comprehensive health check endpoints
curl http://localhost:8080/health/detailed

# Response includes:
# {
#   "status": "healthy",
#   "timestamp": "2024-01-15T10:30:00Z",
#   "version": "1.2.3",
#   "uptime_seconds": 86400,
#   "components": {
#     "database": {"status": "healthy", "latency_ms": 5.2},
#     "redis": {"status": "healthy", "memory_usage": "45%"},
#     "scheduler": {"status": "healthy", "active_jobs": 23},
#     "workers": {"status": "healthy", "available": 45, "total": 50}
#   },
#   "resource_usage": {
#     "cpu_percent": 35.2,
#     "memory_percent": 67.8,
#     "disk_percent": 45.1
#   }
# }
```

## Performance Optimization

### Database Tuning

```sql theme={null}
-- PostgreSQL optimization for high-throughput workloads
ALTER SYSTEM SET shared_buffers = '8GB';
ALTER SYSTEM SET effective_cache_size = '24GB';
ALTER SYSTEM SET work_mem = '256MB';
ALTER SYSTEM SET maintenance_work_mem = '2GB';
ALTER SYSTEM SET checkpoint_completion_target = 0.9;
ALTER SYSTEM SET wal_buffers = '64MB';
ALTER SYSTEM SET default_statistics_target = 500;

-- Reload configuration
SELECT pg_reload_conf();

-- Create optimized indexes for LoomDB
CREATE INDEX CONCURRENTLY idx_events_timestamp_type 
ON events (timestamp DESC, event_type) 
INCLUDE (data);

CREATE INDEX CONCURRENTLY idx_events_user_session 
ON events (user_id, session_id, timestamp DESC);
```

### GPU Memory Optimization

```python theme={null}
# Advanced GPU memory management
from core.gpu import GPUManager, MemoryPoolConfig

gpu_manager = GPUManager()

# Configure memory pools for efficient allocation
memory_config = MemoryPoolConfig(
    initial_pool_size_gb=16,
    max_pool_size_gb=64,
    growth_factor=1.5,
    enable_unified_memory=True,
    fragmentation_threshold=0.3
)

await gpu_manager.configure_memory_pools(memory_config)

# Enable memory optimization features
gpu_manager.set_memory_growth(enabled=True)
gpu_manager.set_memory_limit_per_process(limit_gb=32)
gpu_manager.enable_memory_defragmentation(interval_minutes=30)

# Monitor GPU utilization
gpu_stats = await gpu_manager.get_utilization_stats()
print(f"GPU Memory Usage: {gpu_stats.memory_used_gb:.1f}GB / {gpu_stats.memory_total_gb:.1f}GB")
print(f"GPU Utilization: {gpu_stats.gpu_utilization_percent:.1f}%")
```

## Troubleshooting Guide

### Common Issues & Solutions

#### 1. Job Scheduling Failures

**Symptoms**:

* Jobs stuck in "pending" state
* Resource allocation errors
* Scheduling timeout errors

**Diagnosis**:

```bash theme={null}
# Check scheduler logs
docker logs loomos-scheduler

# Check resource availability
curl http://localhost:8080/api/v1/cluster/resources

# Examine job requirements vs available resources
loomos jobs describe job_12345 --verbose
```

**Solutions**:

```python theme={null}
# Adjust resource requirements
resources = ResourceRequirements(
    cpu_cores=16,        # Reduced from 32
    memory_gb=64,        # Reduced from 128
    gpu_count=2,         # Reduced from 4
    priority=50          # Lower priority
)

# Or enable resource overcommit
scheduler_config = SchedulerConfig(
    resource_overcommit_ratio=1.3,  # Allow 30% overcommit
    preemption_enabled=True
)
```

#### 2. GPU Memory Exhaustion

**Symptoms**:

* CUDA out of memory errors
* Training job failures
* GPU utilization drops to zero

**Diagnosis**:

```bash theme={null}
# Monitor GPU memory usage
nvidia-smi -l 1

# Check LoomOS GPU metrics
curl http://localhost:8080/metrics | grep gpu_memory
```

**Solutions**:

```python theme={null}
# Implement gradient accumulation
training_config = TrainingConfig(
    batch_size=32,           # Reduced batch size
    gradient_accumulation_steps=4,  # Accumulate gradients
    mixed_precision=True,    # Use FP16
    memory_efficient_attention=True
)

# Enable memory optimization
from core.optimization import MemoryOptimizer

optimizer = MemoryOptimizer()
optimizer.enable_gradient_checkpointing()
optimizer.enable_cpu_offloading()
optimizer.set_memory_fraction(0.8)  # Use 80% of GPU memory
```

#### 3. Network Connectivity Issues

**Symptoms**:

* Worker nodes disconnecting
* Slow data transfer between nodes
* Training synchronization failures

**Diagnosis**:

```bash theme={null}
# Test network connectivity between nodes
loomos cluster test-connectivity

# Check bandwidth and latency
iperf3 -s  # On one node
iperf3 -c <server_ip> -t 30  # On another node

# Monitor network metrics
curl http://localhost:8080/metrics | grep network
```

**Solutions**:

```yaml theme={null}
# Optimize network configuration
network:
  backend: "nccl"
  interface: "ib0"  # Use InfiniBand if available
  compression: true
  async_error_handling: true
  
  # Tune NCCL parameters
  nccl_settings:
    NCCL_IB_DISABLE: "0"
    NCCL_IB_HCA: "mlx5_0,mlx5_1"
    NCCL_SOCKET_IFNAME: "ib0"
    NCCL_DEBUG: "INFO"
```

## Disaster Recovery & Backup

### Backup Strategy

```bash theme={null}
#!/bin/bash
# Automated backup script for LoomOS

# Backup LoomDB (PostgreSQL)
pg_dump -h localhost -U loomuser -d loomdb | \
  gzip > "loomdb_backup_$(date +%Y%m%d_%H%M%S).sql.gz"

# Backup Redis state
redis-cli --rdb redis_backup_$(date +%Y%m%d_%H%M%S).rdb

# Backup model artifacts and checkpoints
aws s3 sync s3://loomos-artifacts/ ./artifacts_backup/ --delete

# Backup configuration files
tar -czf config_backup_$(date +%Y%m%d_%H%M%S).tar.gz \
  /etc/loomos/ ~/.loomos/ ./config/
```

### Recovery Procedures

```bash theme={null}
# Restore from backup
psql -h localhost -U loomuser -d loomdb < loomdb_backup_20240115_120000.sql.gz
redis-cli --rdb < redis_backup_20240115_120000.rdb
aws s3 sync ./artifacts_backup/ s3://loomos-artifacts/
```

## Next Steps & Advanced Topics

After completing the platform setup, explore these advanced topics:

1. **[Core Modules](/loomlabs/loomos/core-modules)**: Deep dive into LoomDB, Scheduler, and Security
2. **[RL System](/loomlabs/loomos/rl-system)**: Advanced reinforcement learning capabilities
3. **[Nexus System](/loomlabs/loomos/nexus-system)**: Distributed coordination and cluster management
4. **[SDK & CLI](/loomlabs/loomos/sdk-cli)**: Programmatic access and automation
5. **[Deployment Guide](/loomlabs/loomos/deployment-guide)**: Production deployment patterns

<Warning>
  This platform overview provides comprehensive guidance for getting started with LoomOS. For production deployments, ensure you implement proper security measures, monitoring, and backup procedures as outlined in the respective sections.
</Warning>