Deployment Options

Overview

The memories-dev library supports multiple deployment configurations to meet various operational requirements. This guide covers the available deployment types, configuration options, and best practices.

Deployment Types

Local Deployment

Run models and data processing locally on your machine:

from memories.models.load_model import LoadModel

# Initialize a local model
model = LoadModel(
    deployment_type="local",
    model_provider="deepseek-ai",
    model_name="deepseek-coder-small",
    use_gpu=True
)

API Deployment

Connect to provider APIs for model inference:

from memories.models.load_model import LoadModel

# Initialize an API-based model
model = LoadModel(
    deployment_type="api",
    model_provider="openai",
    model_name="gpt-4",
    api_key="your-api-key-here"
)

Cloud Deployment

Standalone Deployment

Configure a single-instance deployment on cloud platforms:

from memories.deployment.cloud import CloudDeployment

# Configure GCP deployment
deployment = CloudDeployment(
    provider="gcp",
    config={
        "machine_type": "n1-standard-8",
        "accelerator": "nvidia-tesla-t4",
        "accelerator_count": 1,
        "region": "us-central1",
        "zone": "us-central1-a"
    }
)

# Deploy the application
deployment.deploy()

Distributed Deployment

Configure a high-reliability distributed deployment:

from memories.deployment.distributed import DistributedDeployment

# Configure AWS distributed deployment
deployment = DistributedDeployment(
    provider="aws",
    config={
        "instance_type": "p3.2xlarge",
        "node_count": 3,
        "quorum_size": 2,
        "region": "us-east-1",
        "availability_zones": ["us-east-1a", "us-east-1b", "us-east-1c"]
    }
)

# Deploy the distributed system
deployment.deploy()

Container Deployment

Deploy using container orchestration:

from memories.deployment.container import ContainerDeployment

# Configure Azure container deployment
deployment = ContainerDeployment(
    provider="azure",
    config={
        "cluster_name": "memories-cluster",
        "manager_count": 3,
        "worker_count": 5,
        "worker_vm_size": "Standard_NC6s_v3",
        "region": "eastus"
    }
)

# Deploy the container cluster
deployment.deploy()

Configuration Options

Common Configuration Parameters

Parameter	Type	Description
`provider`	string	Cloud provider (gcp, aws, azure)
`region`	string	Deployment region
`instance_type`	string	VM/instance type
`use_gpu`	boolean	Whether to use GPU acceleration
`storage_size`	integer	Storage size in GB

Security Configuration

# Configure security settings
deployment.configure_security({
    "enable_encryption": True,
    "encryption_key": "your-encryption-key",
    "network_policy": "private",
    "firewall_rules": [
        {"port": 443, "source": "0.0.0.0/0", "protocol": "tcp"}
    ]
})

Scaling Configuration

# Configure auto-scaling
deployment.configure_scaling({
    "min_instances": 1,
    "max_instances": 10,
    "target_cpu_utilization": 0.7,
    "cooldown_period": 300
})

Monitoring and Logging

Enable monitoring and logging:

# Configure monitoring
deployment.configure_monitoring({
    "enable_metrics": True,
    "metrics_interval": 60,
    "log_level": "INFO",
    "alert_email": "admin@example.com",
    "alert_thresholds": {
        "cpu_utilization": 0.9,
        "memory_utilization": 0.85,
        "error_rate": 0.01
    }
})

Best Practices

Resource Sizing: Choose appropriate instance types based on your workload requirements.
High Availability: Use distributed deployments for critical applications.
Cost Optimization: Configure auto-scaling to optimize resource usage.
Security: Always enable encryption and restrict network access.
Monitoring: Set up comprehensive monitoring and alerting.
Backup: Implement regular backup strategies for persistent data.
Testing: Test your deployment configuration in a staging environment before production.

Example: Complete Deployment

from memories.deployment.cloud import CloudDeployment
from memories.models.load_model import LoadModel

# Configure cloud deployment
deployment = CloudDeployment(
    provider="gcp",
    config={
        "machine_type": "n1-standard-8",
        "accelerator": "nvidia-tesla-t4",
        "accelerator_count": 1,
        "region": "us-central1",
        "zone": "us-central1-a",
        "storage_size": 100
    }
)

# Configure security
deployment.configure_security({
    "enable_encryption": True,
    "network_policy": "private"
})

# Configure scaling
deployment.configure_scaling({
    "min_instances": 1,
    "max_instances": 5
})

# Configure monitoring
deployment.configure_monitoring({
    "enable_metrics": True,
    "log_level": "INFO"
})

# Deploy the application
deployment_info = deployment.deploy()

print(f"Deployed at: {deployment_info['endpoint']}")

# Connect to the deployed instance
model = LoadModel(
    deployment_type="remote",
    endpoint=deployment_info['endpoint'],
    api_key=deployment_info['api_key']
)

# Use the model
response = model.get_response("How does distributed deployment work?")
print(response)