Building Dynamo (dynamo build) — Dynamo
Title: Building Dynamo (dynamo build) — Dynamo
URL Source: https://docs.nvidia.com/dynamo/archive/0.3.1/guides/dynamo_build.html?userAgent=PromptingBot%2F1.0.0
Published Time: Wed, 02 Jul 2025 21:58:29 GMT
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Table of Contents
Architecture & Features
Dynamo Command Line Interface
- CLI Overview
- Running Dynamo (dynamo run)
- Serving Inference Graphs (dynamo serve)
- Building Dynamo (dynamo build)
- Deploying Inference Graphs (dynamo deploy)
Usage Guides
- Writing Python Workers in Dynamo
- Disaggregation and Performance Tuning
- KV Cache Router Performance Tuning
- Working with Dynamo Kubernetes Operator
Deployment Guides
- Dynamo Cloud Kubernetes Platform
- Deploying Dynamo Inference Graphs to Kubernetes using the Dynamo Cloud Platform
- Manual Helm Deployment
- Minikube Setup Guide
- Model Caching with Fluid
Benchmarking
API
Examples
Building Dynamo (dynamo build)#
This guide explains how to use the dynamo build command to containerize Dynamo inference graphs (pipelines) for deployment.
dynamo build is a command-line tool that helps containerize inference graphs created with Dynamo SDK. Run dynamo build --containerize to build a stand-alone Docker container that encapsulates your entire inference graph. This image can then be shared and run standalone.
Note
This experimental feature is tested on the examples in the examples/ directory. You need to make some modifications. Pay particular attention if your inference graph introduces custom dependencies.
Building a containerized inference graph#
The basic workflow for using dynamo build includes:
#. Defining your inference graph and testing locally with dynamo serve #. Specifying a base image for your inference graph. More on this below. #. Running dynamo build to build a containerized inference graph
Basic Usage#
dynamo build <graph_definition> --containerize
previous Serving Inference Graphs (dynamo serve)next Deploying Inference Graphs to Kubernetes (dynamo deploy)
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- Support Matrix
- Getting Started
- High Level Architecture
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- Disaggregated Serving
- KV Block Manager
- Motivation
- KVBM Architecture
- Understanding KVBM components
- KVBM Further Reading
- KV Cache Routing
- Planner
- CLI Overview
- Running Dynamo (dynamo run)
- Serving Inference Graphs (dynamo serve)
- Building Dynamo (dynamo build)
- Deploying Inference Graphs (dynamo deploy)
- Writing Python Workers in Dynamo
- Disaggregation and Performance Tuning
- KV Cache Router Performance Tuning
- Working with Dynamo Kubernetes Operator
- Dynamo Cloud Kubernetes Platform
- Deploying Dynamo Inference Graphs to Kubernetes using the Dynamo Cloud Platform
- Manual Helm Deployment
- Minikube Setup Guide
- Model Caching with Fluid
- Planner Benchmark Example
- SDK Reference
- Python API
- Hello World Example: Basic
- Hello World Example: Aggregated and Disaggregated Deployment
- LLM Deployment Examples
- Multinode Examples
- LLM Deployment Examples using TensorRT-LLM
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- Do Not Sell or Share My Data
- Terms of Service
- Accessibility
- Corporate Policies
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