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Backend.AI Agent

Purpose

The Agent is a compute node component responsible for container lifecycle management. It runs computing sessions as containers, monitors resource usage, and reports status to the Manager.

Key Responsibilities

1. Container Lifecycle Management

  • Create and start containers from session specifications
  • Initialize kernels and execute runtime configuration
  • Monitor container health and status
  • Delete containers upon session termination
  • Handle container restarts and error recovery

2. Resource Monitoring

  • Track CPU, memory, GPU usage per container
  • Monitor disk I/O and network traffic
  • Collect accelerator metrics (CUDA, ROCm, TPU)
  • Report resource availability to Manager
  • Enforce resource limits via cgroups

3. Local Storage Management

  • Manage scratch storage for temporary files
  • Mount virtual folders (vfolders) to containers
  • Handle file permissions and ownership
  • Clean up storage after session termination

4. Service Port Management

  • Expose container service ports (Jupyter, SSH, TensorBoard, etc.)
  • Forward traffic from App Proxy to containers
  • Manage port allocation from configured ranges
  • Handle SSL/TLS termination when needed

5. Agent Status Reporting

  • Periodically report agent status to Manager
  • Send heartbeat signals indicating availability
  • Update occupied and available resource slots
  • Report errors and exceptional conditions

Entry Points

Agent has 4 entry points. The RPC Server handles Manager requests, Event Dispatcher sends and receives events, Background Task Handler performs async tasks, and Internal REST API is used exclusively for metrics exposure.

1. RPC Server (Primary Request Handler)

Framework: Callosum (ZeroMQ-based RPC, Curve authentication)

Port: 6011 (default)

Key Features:

  • Only Manager can send RPC requests to Agent (no direct user access)

2. Event Dispatcher

System: Backend.AI Event Dispatcher

Agent sends Agent and Kernel lifecycle events to Manager.

Published Events: Agent and Kernel lifecycle events

Consumed Events: Plugin integration events

Related Documentation: Event Dispatcher System

3. Background Task Handler

System: Backend.AI Background Task Handler

Handles long-running tasks asynchronously, issues Task IDs, and notifies completion via events.

Usage Examples:

  • Image pulling tasks
  • Large-scale container cleanup tasks

Related Documentation: Background Task Handler System

4. Internal REST API (Metrics Only)

Framework: aiohttp

Port: 6003 (metrics only, separate from RPC port)

Endpoints:

  • GET /metrics - Expose Prometheus metrics

Key Features:

  • Metrics exposure only (no service logic triggering)
  • Prometheus scrapes periodically
  • Auto-registered via Manager's Service Discovery

Entry Point Interactions

Each Entry Point operates independently. However, service logic can coordinate them:

Background Task Triggering:

  • Service logic in RPC Server or Event Dispatcher can trigger long-running tasks as Background Tasks.

Event Publishing:

  • RPC Server or Background Task can publish events via Event Dispatcher after task completion.

Integrated Architecture:

┌──────────────┐  ┌──────────────┐  ┌──────────────┐
│  RPC Server  │  │Event Dispatch│  │  Background  │
│   (Callosum) │  │              │  │     Task     │
│   Port 6011  │  │              │  │              │
└───────┬──────┘  └───────┬──────┘  └───────┬──────┘
        │                 │                 │
        └─────────────────┴─────────────────┘
                          │
                ┌─────────▼──────────┐
                │  Agent Core Logic  │
                │  - Resource Mgmt   │
                │  - Kernel Mgmt     │
                └─────────┬──────────┘
                          │
                ┌─────────▼──────────┐
                │  Container Backend │
                └────────────────────┘

        ┌──────────────────┐
        │ Internal REST API│ (Independent, metrics only)
        │   Port 6003      │
        └──────────────────┘

Architecture

┌────────────────────────────────────────┐
│         Agent Server (agent.py)        │
├────────────────────────────────────────┤
│    Container Backend (docker/)         │  ← Docker
├────────────────────────────────────────┤
│      Resource Monitor (stats.py)       │  ← CPU, GPU, Memory
├────────────────────────────────────────┤
│   Storage Manager (scratch.py fs.py)   │  ← Local and mounted storage
├────────────────────────────────────────┤
│      Plugin System (plugin/)           │  ← Plugin
└────────────────────────────────────────┘

Directory Structure

agent/
├── docker/              # Docker container backend
│   ├── agent.py        # Docker-specific agent implementation
│   └── resources.py    # Docker resource management
├── watcher/             # Agent watcher
├── plugin/              # Plugin system
├── observer/            # Metrics observers
├── cli/                 # CLI commands
├── config/              # Configuration
├── agent.py             # Main agent logic
├── server.py            # RPC server entry point
├── kernel.py            # Kernel (container) management
├── resources.py         # Resource allocation
├── stats.py             # Statistics aggregation
├── scratch.py           # Scratch storage management
└── fs.py                # Filesystem utilities

Core Concepts

Kernels

Kernels represent running containers executing computing sessions:

  • Kernel ID: Unique identifier for the container
  • Session ID: Associated session in Manager
  • Image: Container image (e.g., lablup/python:3.11-ubuntu20.04)
  • Resources: Allocated CPU, memory, GPU slots
  • Status: PREPARING, RUNNING, RESTARTING, TERMINATING, TERMINATED
  • Service Ports: Exposed ports for services (Jupyter, SSH, etc.)

Container Backend

The Agent supports multiple container runtimes:

  • Docker: Standard Docker containers

The Docker backend implements these interfaces:

  • create_kernel(): Create new container
  • destroy_kernel(): Remove container
  • restart_kernel(): Restart container
  • get_kernel_status(): Query container status
  • execute_code(): Execute code in container

Resource Allocation

Resources are allocated from the agent's capacity:

  • CPU Slots: Measured in cores (e.g., 2.0 cores)
  • Memory Slots: Measured in bytes (e.g., 4GB)
  • GPU Slots: Number of GPU devices (e.g., 1 GPU)
  • Accelerator Slots: Custom accelerator resources

The Agent tracks:

  • Total Capacity: Maximum available resources
  • Occupied Slots: Currently allocated resources
  • Available Slots: Remaining resources for new sessions

Scratch Storage

Each kernel receives local scratch storage:

  • Location: /scratches/{kernel_id}
  • Quota: Configurable size limit per kernel
  • Cleanup: Automatically removed after kernel termination

Virtual Folder Mounting

VFolders are mounted to containers at runtime:

  • Mount Point: /home/work/{vfolder_name}
  • Permissions: RO, RW, or RW-DELETE
  • Backend: Storage Proxy manages actual storage

Resource Monitoring

CPU Monitoring

  • Track per-container CPU usage via cgroups
  • Measure CPU time in user and system modes
  • Calculate CPU utilization percentages
  • Enforce CPU quotas and limits

Memory Monitoring

  • Track RSS (Resident Set Size) per container
  • Measure cache and swap usage
  • Detect OOM (Out-of-Memory) conditions
  • Enforce memory limits via cgroups

Shared Memory (shmem)

Containers can request shared memory (/dev/shm) for inter-process communication.

Docker Memory Architecture:

  • shm (tmpfs) and app memory share the Memory cgroup space
  • shm has an additional ShmSize limit (tmpfs maximum size)
  • Effective shm limit = min(ShmSize, Memory cgroup available space)

OOM Conditions:

Signal Exit Code Condition
SIGKILL 137 shm + app > Memory cgroup limit
SIGBUS 135 shm > ShmSize

Configuration:

  • Set via resource_opts.shmem in session specification
  • Docker HostConfig: ShmSize parameter

References:

GPU Monitoring

  • Query NVIDIA GPUs via NVML (nvidia-ml-py)
  • Query AMD GPUs via ROCm SMI
  • Track GPU utilization and memory usage
  • Measure GPU temperature and power consumption

Disk I/O Monitoring

  • Track read/write operations per container
  • Measure I/O bandwidth usage
  • Monitor disk space consumption
  • Enforce I/O throttling when configured

Plugin System

Agent can uses plugin system for accelerator support:

CUDA Plugin

  • Detect NVIDIA GPUs via nvidia-smi
  • Allocate GPU devices to containers
  • Set CUDA_VISIBLE_DEVICES environment variable
  • Monitor GPU metrics via NVML

ROCm Plugin

  • Detect AMD GPUs via rocm-smi
  • Allocate GPU devices to containers
  • Set HIP_VISIBLE_DEVICES environment variable
  • Monitor GPU metrics via ROCm

TPU Plugin

  • Detect Google TPUs
  • Configure TPU access for TensorFlow
  • Monitor TPU utilization

Communication Protocols

Manager → Agent (ZeroMQ RPC)

  • Port: 6011 (default)
  • Protocol: ZeroMQ request-response
  • Operations:
    • create_kernel: Create new container
    • destroy_kernel: Terminate container
    • restart_kernel: Restart container
    • execute_code: Execute code in container
    • get_status: Query agent and kernel status

Agent → Manager (HTTP Watcher API)

  • Port: 6009 (default)
  • Protocol: HTTP
  • Operations:
    • Heartbeat signals
    • Resource usage reporting
    • Kernel status updates
    • Error notifications

Agent → Storage Proxy

  • Protocol: HTTP
  • Operations:
    • Mount vfolder
    • Unmount vfolder
    • Query vfolder metadata

Container Execution Flow

1. Manager sends create_kernel RPC
   ↓
2. Agent validates resource availability
   ↓
3. Agent pulls container image (if needed)
   ↓
4. Agent creates scratch directory
   ↓
5. Agent mounts vfolders via Storage Proxy
   ↓
6. Agent creates container with resources
   ↓
7. Agent starts container and runs init script
   ↓
8. Agent registers service ports
   ↓
9. Agent reports kernel status to Manager
   ↓
10. Container runs until termination
   ↓
11. Agent cleans up resources upon termination

Service Ports

Containers can expose service ports:

  • Jupyter Notebook: Port 8080 (HTTP)
  • Jupyter Lab: Port 8090 (HTTP)
  • SSH: Port 2200 (TCP)
  • TensorBoard: Port 6006 (HTTP)
  • Custom Services: User-defined ports

Service ports are:

  • Allocated from configured range (default 30000-31000)
  • Registered with App Proxy for external access
  • Forwarded from agent host to container
  • Cleaned up upon container termination

Configuration

See configs/agent/halfstack.toml for configuration file examples.

Key Configuration Items

Agent Basic Settings:

  • Agent ID and region information
  • Resource slot definitions
  • Container runtime configuration (Docker)

Storage Settings:

  • Scratch storage root path
  • Per-session quota

Service Port Settings:

  • Container port range

Resource Monitoring:

  • Watcher interval settings

Infrastructure Dependencies

Required Infrastructure

Container Runtime

  • Purpose: Container creation and management
  • Supported Runtimes: Docker

Redis (Event and State Management)

  • Purpose:
    • Send heartbeat events (for Manager registration)
    • Update agent status
    • Manage background tasks
  • Note: Agent registers with Manager via Redis and receives RPC commands from Manager.

Manager Connection

  • Protocol: ZeroMQ RPC (6011), HTTP Watcher API (6009)
  • Purpose: Receive session commands from Manager, report status
  • Note: Agent only receives commands from Manager, not directly from users or other components.

etcd (Global Configuration)

  • Purpose:
    • Retrieve global configuration (storage volumes, etc.)
    • Auto-discover Manager address

Optional Infrastructure (Observability)

Optional infrastructure for Agent monitoring.

Prometheus (Metrics Collection)

  • Purpose:
    • Monitor agent resource utilization
    • Collect kernel (container) metrics
    • Track heartbeat and health status
  • HTTP Service Port: 6003 (separate from RPC port 6011)
  • Exposed Endpoint: http://localhost:6003/metrics
  • Key Metrics:
    • backendai_agent_heartbeat - Last heartbeat timestamp
    • backendai_agent_cpu_usage - CPU usage percentage
    • backendai_agent_mem_usage - Memory usage (bytes)
    • backendai_agent_gpu_usage - GPU usage percentage
    • backendai_kernel_count - Number of running kernels
  • Service Discovery: Automatically registered via Manager's HTTP SD
    • Agent auto-registers with Manager at startup
    • Manager provides agent information to Prometheus SD

Loki (Log Aggregation)

  • Purpose:
    • Centralized agent log collection
    • Aggregate kernel execution logs
    • Track errors and debugging information
  • Log Transmission Methods:
    • Direct HTTP API usage
    • Use Promtail or Fluent Bit
  • Log Labels:
    • agent_id - Agent identifier
    • kernel_id - Kernel identifier
    • level - Log level

Container Runtime Metrics

  • Docker: Container metrics via cAdvisor or Docker API
  • cgroups: Direct cgroups filesystem reading

Container Runtime Requirements

Docker

  • Minimum Version: Docker 20.10+
  • Required Features:
    • cgroups v2 support (resource limits)
    • GPU support (NVIDIA Docker Runtime or CDI)

Storage Requirements

Local Scratch Storage

  • Purpose: Temporary files, build cache
  • Recommended Specifications:
    • Fast SSD (NVMe recommended)
    • Minimum 100GB free space
    • Configurable per-session quota

VFolder Mount

  • Dependencies: Storage Proxy or direct storage access
  • Recommendations:
    • High-performance network (10GbE or higher)
    • Low-latency storage

Halfstack Configuration

Recommended: Use the ./scripts/install-dev.sh script for development environment setup.

Starting Development Environment

# Setup development environment via script (recommended)
./scripts/install-dev.sh

# Start Agent
./backend.ai ag start-server

Observability Integration

Agent automatically registers with Prometheus via Manager's Service Discovery.

Metrics and Monitoring

Prometheus Metrics

Agent exposes metrics:

RPC Related Metrics

Metrics related to processing RPC commands received from Manager.

backendai_rpc_requests (Counter)

  • Description: Total RPC requests processed by the agent
  • Labels:
    • method: RPC method name (e.g., "create_kernel", "destroy_kernel", "execute_code")
  • Tracks processing frequency by Manager command type

backendai_rpc_failure_requests (Counter)

  • Description: Failed RPC request count
  • Labels:
    • method: RPC method name that failed
    • exception: Exception class name (e.g., "ContainerNotFound", "ResourceExhausted")
  • Analyzes RPC command processing failure causes

backendai_rpc_request_duration_seconds (Histogram)

  • Description: RPC request processing time in seconds
  • Labels:
    • method: RPC method name
  • Buckets: [0.001, 0.01, 0.1, 0.5, 1, 2, 5, 10, 30, 60] seconds
  • Measures performance of RPC commands like kernel creation/deletion

Resource Utilization Metrics

Metrics related to container and hardware resource usage.

backendai_container_utilization (Gauge)

  • Description: Container resource utilization per kernel
  • Labels:
    • container_metric_name: Metric type (cpu_used, cpu_util, mem, cuda_mem, cuda_util, net_rx, net_tx)
    • agent_id: Agent identifier
    • kernel_id: Kernel identifier (Backend.AI's container wrapper, not the actual container ID)
    • session_id: Session identifier
    • user_id: User UUID who owns the session
    • project_id: Project UUID that the session belongs to
    • value_type: Value interpretation (current, capacity, pct)
  • Tracks resource usage per kernel (CPU, memory, GPU, network, etc.)

backendai_device_utilization (Gauge)

  • Description: Physical device utilization on the agent node
  • Labels:
    • device_metric_name: Metric type (cpu_util, mem, cuda_mem, cuda_util, cuda_temp, cuda_power)
    • agent_id: Agent identifier
    • device_id: Hardware device identifier (e.g., GPU index)
    • value_type: Value interpretation (current, capacity, pct)
  • Tracks hardware device usage (GPU, CPU, memory) on the agent node

Container Lifecycle Synchronization Metrics

Metrics related to container state synchronization operations between Manager and Agent.

backendai_sync_container_lifecycle_trigger_count (Counter)

  • Description: Number of times container lifecycle sync was triggered
  • Labels:
    • agent_id: Agent identifier
  • Frequency of container state synchronization between Manager and Agent

backendai_sync_container_lifecycle_success_count (Counter)

  • Description: Number of successfully synchronized containers
  • Labels:
    • agent_id: Agent identifier
  • Number of successfully synchronized kernels

backendai_sync_container_lifecycle_failure_count (Counter)

  • Description: Number of failed container synchronization attempts
  • Labels:
    • agent_id: Agent identifier
    • exception: Exception class name causing the failure
  • Tracks synchronization failure causes

Statistics Collection Task Metrics

Metrics related to node, container, and process level statistics collection.

backendai_stat_task_trigger_count (Counter)

  • Description: Number of times statistics collection was triggered
  • Labels:
    • agent_id: Agent identifier
    • stat_scope: Statistics scope (node, container, process)
  • Collection frequency by Node/Container/Process level

backendai_stat_task_success_count (Counter)

  • Description: Number of successful statistics collection operations
  • Labels:
    • agent_id: Agent identifier
    • stat_scope: Statistics scope (node, container, process)
  • Tracks statistics collection success rate

backendai_stat_task_failure_count (Counter)

  • Description: Number of failed statistics collection operations
  • Labels:
    • agent_id: Agent identifier
    • stat_scope: Statistics scope (node, container, process)
    • exception: Exception class name causing the failure
  • Analyzes statistics collection failure causes

Prometheus Query Examples

The following examples demonstrate common Prometheus queries for Agent metrics. Note that Counter metrics use the _total suffix and Histogram metrics use _bucket, _sum, _count suffixes in actual queries.

Important Notes:

  • When using increase() or rate() functions, the time range must be at least 2-4x longer than your Prometheus scrape interval to get reliable data. If the time range is too short, metrics may not appear or show incomplete data.
  • Default Prometheus scrape interval is typically 15s-30s
  • Time range selection trade-offs:
    • Shorter ranges (e.g., [1m]): Detect changes faster with more granular data, but more sensitive to noise and short-term fluctuations
    • Longer ranges (e.g., [5m]): Smoother graphs with reduced noise, better for identifying trends, but slower to detect sudden changes
    • For real-time alerting: Use shorter ranges like [1m] or [2m]
    • For dashboards and trend analysis: Use longer ranges like [5m] or [10m]

RPC Request Monitoring

RPC Request Rate by Method

Monitor RPC request rate by method. This shows how frequently the Manager sends commands to agents. Use this to understand agent workload and command distribution.

sum(rate(backendai_rpc_requests_total{service_group="$service_groups"}[5m])) by (method)

RPC Failure Rate by Method

Track RPC failure rate by method and exception type. This helps identify which RPC operations are failing and why.

sum(rate(backendai_rpc_failure_requests_total{service_group="$service_groups"}[5m])) by (method, exception)

P95 RPC Request Duration

Calculate P95 RPC request duration by method. This shows how long critical operations like kernel creation take. Use this to identify performance bottlenecks in agent operations.

histogram_quantile(0.95,
  sum(rate(backendai_rpc_request_duration_seconds_bucket{service_group="$service_groups"}[5m])) by (le, method)
)

Container Resource Utilization

Top 5 GPU Memory Usage by Kernel

Monitor GPU memory usage by kernel (top 5). This identifies kernels consuming the most GPU memory. Note: topk returns the top N items at each evaluation time. Over multiple time points, you may see more than 5 unique kernels if different kernels appear in the top 5 at different times.

topk(5, sum(backendai_container_utilization{container_metric_name="cuda_mem", value_type="current"}) by (kernel_id))

Top 5 GPU Utilization by Kernel

Monitor GPU utilization by kernel (top 5). This shows which kernels are actively using GPU compute. Note: topk returns the top N items at each evaluation time. Over multiple time points, you may see more than 5 unique kernels if different kernels appear in the top 5 at different times.

topk(5, sum(backendai_container_utilization{container_metric_name="cuda_util", value_type="current"}) by (kernel_id))

Top 5 CPU Utilization Increase by Kernel

Track CPU utilization increase rate by kernel (top 5). This shows CPU consumption trends over time. Note: Divided by 1000 to convert from per-mille to percentage. topk returns the top N items at each evaluation time. Over multiple time points, you may see more than 5 unique kernels if different kernels appear in the top 5 at different times.

topk(5, sum(increase(backendai_container_utilization{container_metric_name="cpu_util", value_type="current"}[5m])) by (kernel_id)) / 1000

Network RX Traffic by Kernel

Monitor network RX (receive) traffic by kernel. This tracks inbound network bandwidth usage per container.

sum(increase(backendai_container_utilization{container_metric_name="net_rx", value_type="current"}[5m])) by (kernel_id)

Network TX Traffic by Kernel

Monitor network TX (transmit) traffic by kernel. This tracks outbound network bandwidth usage per container.

sum(increase(backendai_container_utilization{container_metric_name="net_tx", value_type="current"}[5m])) by (kernel_id)

Device Utilization (Agent-level)

GPU Memory Usage by Agent

Monitor GPU memory usage at the agent level. This shows total GPU memory consumption on each agent node.

sum(backendai_device_utilization{device_metric_name="cuda_mem", value_type="current"}) by (agent_id)

GPU Memory Capacity by Agent

Monitor GPU memory capacity. This shows the maximum available GPU memory on each agent.

sum(backendai_device_utilization{device_metric_name="cuda_mem", value_type="capacity"}) by (agent_id)

GPU Utilization Percentage by Agent

Monitor GPU utilization percentage at the agent level. This shows overall GPU usage across all kernels on the agent.

sum(backendai_device_utilization{device_metric_name="cuda_util", value_type="current"}) by (agent_id)

CPU Utilization Increase by Agent

Track CPU utilization increase rate at the agent level. This shows agent-wide CPU consumption trends.

sum(increase(backendai_device_utilization{device_metric_name="cpu_util", value_type="current"}[5m])) by (agent_id) / 1000

Network RX Traffic by Agent

Monitor network RX (receive) traffic at the agent level. This tracks total inbound network bandwidth usage on each agent node.

sum(increase(backendai_device_utilization{device_metric_name="net_rx", value_type="current"}[5m])) by (agent_id)

Network TX Traffic by Agent

Monitor network TX (transmit) traffic at the agent level. This tracks total outbound network bandwidth usage on each agent node.

sum(increase(backendai_device_utilization{device_metric_name="net_tx", value_type="current"}[5m])) by (agent_id)

Container Lifecycle Synchronization

Container Sync Trigger Frequency

Monitor container sync trigger frequency. This shows how often the agent synchronizes container state with the Manager.

sum(rate(backendai_sync_container_lifecycle_trigger_count_total[5m])) by (agent_id)

Container Sync Success Rate

Track container sync success rate. This helps ensure containers are properly synchronized.

sum(rate(backendai_sync_container_lifecycle_success_count_total[5m])) by (agent_id)

Container Sync Failure Rate

Monitor container sync failures. This identifies issues with container state synchronization.

sum(rate(backendai_sync_container_lifecycle_failure_count_total[5m])) by (agent_id, exception)

Statistics Collection

Statistics Collection Trigger Rate

Monitor statistics collection trigger rate by scope. This shows how frequently statistics are collected at different levels.

sum(rate(backendai_stat_task_trigger_count_total[5m])) by (agent_id, stat_scope)

Statistics Collection Success Rate

Track statistics collection success rate. This ensures statistics collection is working properly.

sum(rate(backendai_stat_task_success_count_total[5m])) by (agent_id, stat_scope)

Statistics Collection Failure Rate

Monitor statistics collection failures. This identifies issues with resource monitoring.

sum(rate(backendai_stat_task_failure_count_total[5m])) by (agent_id, stat_scope, exception)

Logs

Agent logs include:

  • Kernel creation and termination events
  • Resource allocation and release
  • Error situations and recovery operations
  • RPC request/response tracking

Development

See README.md for development setup instructions.

Related Documentation