Docker Deployment

kdeps bundle build packages your workflow into a Docker image that starts an API server when run. No Dockerfile needed -- kdeps generates one from your workflow.yaml.

Overview

# Package workflow into .kdeps file
kdeps bundle package workflow.yaml

# Build Docker image
kdeps bundle build myagent-1.0.0.kdeps --tag myregistry/myagent:latest

# Or with GPU support
kdeps bundle build myagent-1.0.0.kdeps --gpu cuda --tag myregistry/myagent:latest-gpu

Packaging

The package command creates a .kdeps archive containing your workflow and resources:

kdeps bundle package path/to/workflow.yaml

This creates myagent-1.0.0.kdeps (name and version from workflow metadata).

What's Included

myagent-1.0.0.kdeps
├── workflow.yaml          # workflow entry point
├── resources/             # all resource YAML files
├── data/                  # data files and scripts
├── requirements.txt       # Python dependencies (if present)
└── public/                # static files (if present)

Building Docker Images

Basic Build

kdeps bundle build myagent-1.0.0.kdeps

Creates image: kdeps-myagent:1.0.0

Custom Tag

kdeps bundle build myagent-1.0.0.kdeps --tag myregistry/myagent:latest

Show Dockerfile

View the generated Dockerfile without building:

kdeps bundle build myagent-1.0.0.kdeps --show-dockerfile

GPU Support

Build images with GPU acceleration:

# NVIDIA CUDA
kdeps bundle build myagent-1.0.0.kdeps --gpu cuda

# AMD ROCm
kdeps bundle build myagent-1.0.0.kdeps --gpu rocm

# Intel oneAPI
kdeps bundle build myagent-1.0.0.kdeps --gpu intel

# Vulkan (cross-platform)
kdeps bundle build myagent-1.0.0.kdeps --gpu vulkan

GPU Runtime

When running GPU-enabled images:

# NVIDIA
docker run --gpus all myregistry/myagent:latest

# AMD
docker run --device=/dev/kfd --device=/dev/dri myregistry/myagent:latest

Base OS Auto-Selection

KDeps automatically selects the base OS based on GPU requirements:

• No --gpu flag → Alpine (CPU-only, smallest images ~300MB)
• --gpu specified → Ubuntu (GPU support, glibc-based)

The OS is automatically chosen to ensure compatibility:

# CPU-only: Uses Alpine (smallest)
kdeps bundle build myagent-1.0.0.kdeps

# GPU: Uses Ubuntu (required for GPU drivers)
kdeps bundle build myagent-1.0.0.kdeps --gpu cuda

Why Auto-Selection?

• Alpine uses musl libc and cannot run GPU workloads (NVIDIA CUDA, AMD ROCm require glibc)
• Ubuntu uses glibc and supports all GPU types
• Auto-selection prevents invalid combinations (e.g., Alpine + CUDA)

Configuration	Base OS	Image Size	Use Case
kdeps bundle build .	Alpine	~300MB	CPU-only, edge deployment
kdeps bundle build . --gpu cuda	Ubuntu	~800MB+	NVIDIA GPU inference
kdeps bundle build . --gpu rocm	Ubuntu	~800MB+	AMD GPU inference
kdeps bundle build . --gpu intel	Ubuntu	~600MB+	Intel GPU inference
kdeps bundle build . --gpu vulkan	Ubuntu	~600MB+	Cross-platform GPU

Override the auto-selected distro in workflow.yaml:

settings:
  agentSettings:
    baseOS: ubuntu  # alpine (default) or ubuntu

--gpu always forces Ubuntu. debian is not supported.

LLM Backend in Images

By default no LLM server is installed: chat resources run on the file backend, and the llamafile model for each chat resource is downloaded into the image at build time (/app/.kdeps/models/). The container is a vanilla alpine:latest or ubuntu:latest base plus kdeps and the baked models - the llamafile self-serves inside the container, no extra process to manage.

default (file backend)     → FROM alpine:latest or ubuntu:latest
                             + pre-baked llamafile per chat model (~1.1 GB for llama3.2:1b)
no chat resources          → FROM alpine:latest or ubuntu:latest (vanilla)

Ollama Docker Images (opt-in)

Ollama is bundled only when installOllama: true, KDEPS_DEFAULT_BACKEND=ollama is set with chat resources, or KDEPS_LLM_ROUTER routes to Ollama.

needs Ollama + alpine CPU  → FROM alpine/ollama:<tag>   (~70MB third-party CPU image)
needs Ollama + ubuntu CPU  → FROM ollama/ollama:<tag>   (official image)
needs Ollama + --gpu cuda  → FROM ollama/ollama:<tag>   (runtime: docker run --gpus all)
needs Ollama + --gpu rocm  → FROM ollama/ollama:rocm    (runtime: --device /dev/kfd --device /dev/dri)
no Ollama                  → FROM alpine:latest or ubuntu:latest

Image	Size	GPU	Source
alpine/ollama	~70MB	CPU only	alpine-docker/ollama
ollama/ollama	~4GB	CPU + NVIDIA	Official Ollama Docker
ollama/ollama:rocm	varies	AMD	Official :rocm variant

When the base image already includes Ollama, kdeps does not COPY --from a second Ollama layer.

Offline Mode

Bake models into the image for air-gapped deployments:

# workflow.yaml
settings:
  agentSettings:
    offlineMode: true
    models:
      - llama3.2:1b
      - llama3.2-vision

Build with models included:

kdeps bundle build myagent-1.0.0.kdeps

The resulting image contains all models and doesn't require internet access.

Python Dependencies

Using requirements.txt

# workflow.yaml
settings:
  agentSettings:
    requirementsFile: "requirements.txt"

KDeps uses uv for fast Python package management (97% smaller than Anaconda).

Inline Packages

# workflow.yaml
settings:
  agentSettings:
    pythonVersion: "3.12"
    pythonPackages:
      - pandas>=2.0
      - numpy
      - scikit-learn

System Packages

Install OS-level packages:

# workflow.yaml
settings:
  agentSettings:
    osPackages:
      - ffmpeg
      - imagemagick
      - tesseract-ocr
      - poppler-utils
    repositories:
      - ppa:alex-p/tesseract-ocr-devel

Package Version Pinning

On every bundle build, kdeps resolves package versions before generating the Dockerfile:

• kdeps — latest GitHub release; install.sh is fetched from that tag and the same tag is passed to the installer (never main + floating latest)
• ollama — latest ollama/ollama release as the Docker image tag for ollama/ollama and alpine/ollama (never :latest). --gpu rocm uses the fixed ollama/ollama:rocm tag instead.
• uv — latest astral-sh/uv release as the ghcr.io/astral-sh/uv tag

Override any field with an explicit semver (v1.2.3 or 1.2.3). Use latest or omit a field to accept the resolved value at build time.

# workflow.yaml
settings:
  agentSettings:
    versions:
      kdeps: v2.0.0    # optional — default: newest GitHub release when bundle build runs
      ollama: 0.5.4    # optional — default: newest ollama/ollama release
      uv: 0.6.3        # optional — default: newest astral-sh/uv release

Preview resolved pins:

kdeps bundle build myagent-1.0.0.kdeps --show-dockerfile

When no Ollama base image is selected, kdeps uses alpine:latest or ubuntu:latest. Python defaults to 3.12 when pythonVersion is omitted.

Environment Variables

Build-time Args

# workflow.yaml
settings:
  agentSettings:
    args:
      BUILD_VERSION: ""

Pass during build:

docker build --build-arg BUILD_VERSION=1.0.0 ...

Runtime Environment

# workflow.yaml
settings:
  agentSettings:
    env:
      LOG_LEVEL: "info"
      API_TIMEOUT: "30"

Override at runtime:

docker run -e LOG_LEVEL=debug myregistry/myagent:latest

Docker Compose

KDeps generates a docker-compose.yml:

# docker-compose.yml
version: '3.8'

services:
  myagent:
    image: kdeps-myagent:1.0.0
    ports:
      - "16395:16395"      # API server
      - "16395:16395"      # Web server (if enabled)
    environment:
      - LOG_LEVEL=info
    volumes:
      - ollama:/root/.ollama
      - kdeps_data:/agent/volume
    restart: on-failure
    # For GPU:
    # deploy:
    #   resources:
    #     reservations:
    #       devices:
    #         - driver: nvidia
    #           count: 1
    #           capabilities: [gpu]

volumes:
  ollama:
  kdeps_data:

Run with:

docker-compose up -d

Optimized Build Process

KDeps uses a streamlined build process that leverages the official installation script. This ensures the smallest possible image size and maximum compatibility.

# Example of generated Dockerfile logic
FROM alpine:3.18

# Upgrade base packages (security patches), then install dependencies
RUN apk upgrade --no-cache && \
    apk add --no-cache curl bash python3 py3-pip

# Install kdeps via official install script. Released CLIs pin both the
# script ref and the binary tag to their own version; dev builds use main.
RUN curl -LsSf https://raw.githubusercontent.com/kdeps/kdeps/v2.1.0/install.sh | sh -s -- -b /usr/local/bin v2.1.0

# Copy agent files
COPY workflow.yaml /app/workflow.yaml
COPY resources/ /app/resources/

WORKDIR /app
ENTRYPOINT ["kdeps"]
CMD ["run", "workflow.yaml"]

The build process also automatically handles:

• Python environments: Integrated uv for 97% smaller virtual environments.
• Model management: Pre-pulling models for offline readiness.
• Service orchestration: Lightweight supervisor to manage API and LLM processes.

Health Checks

Add a health endpoint:

# workflow.yaml
settings:
  apiServer:
    routes:
      - path: /health
        methods: [GET]

In Docker Compose:

# docker-compose.yml
services:
  myagent:
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:16395/health"]
      interval: 30s
      timeout: 10s
      retries: 3

Kubernetes Deployment

KDeps generates Kubernetes manifests directly from your workflow.yaml using kdeps export k8s. No manual YAML authoring needed.

# Build and push the Docker image first
kdeps bundle build . --tag myregistry/myagent:1.0.0
docker push myregistry/myagent:1.0.0

# Generate manifests
kdeps export k8s . \
  --image myregistry/myagent:1.0.0 \
  --output k8s.yaml

# Apply to cluster
kubectl apply -f k8s.yaml

Configure Kubernetes settings in workflow.yaml:

# workflow.yaml
settings:
  portNum: 16395
  agentSettings:
    replicas: 3
    resources:
      cpuLimit: "2000m"
      memoryLimit: "4Gi"
      cpuRequest: "500m"
      memoryRequest: "1Gi"
    env:
      LOG_LEVEL: info

The generated manifest includes a Deployment with readiness/liveness probes and a ClusterIP Service, both derived from your workflow settings.

See the Kubernetes Deployment guide for the full reference.

See Also

• Docker Reference - Production best practices, security hardening, troubleshooting
• Workflow Configuration - Agent settings
• WebServer Mode - Serve frontends
• LLM Backends - Backend configuration
• Management API - Live workflow updates without rebuilding

LLM server appliance (not an agent image)

This page packages agents (workflow.yaml + kdeps). For a standalone OpenAI-compatible model server:

kdeps llm build --engine ollama --model llama3.2 --tag myorg/llm:1
kdeps llm build --engine vllm --model facebook/opt-125m --gpu cuda --tag myorg/vllm:1

See LLM Server Appliance.

HTTPS / custom domain

For automatic Let's Encrypt certificates on a custom domain, set settings.letsEncrypt in the workflow and publish ports 80 and 443. Persist the certificate cache:

docker run -p 80:80 -p 443:443 \
  -v kdeps-le:/var/lib/kdeps/letsencrypt \
  myagent:latest

settings:
  letsEncrypt:
    domain: api.example.com
    email: ops@example.com
    cacheDir: /var/lib/kdeps/letsencrypt
  apiServer:
    hostIP: "0.0.0.0"
    portNum: 443

See TLS and HTTPS (Custom Domains). Static PEM mounts via certFile/keyFile remain supported.