Guide to Dynamo CLI — Dynamo
Title: Guide to Dynamo CLI — Dynamo
URL Source: https://docs.nvidia.com/dynamo/archive/0.3.0/guides/README.html?userAgent=PromptingBot%2F1.0.0
Published Time: Fri, 13 Jun 2025 20:22:49 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
- Planner Benchmark Example
- 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
API
Examples
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Hello World Example: Aggregated and Disaggregated Deployment
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Guide to Dynamo CLI
Guide to Dynamo CLI#
After installing Dynamo with the following command, Dynamo can be used primarily through its CLI.
apt-get update DEBIAN_FRONTEND=noninteractive apt-get install -yq python3-dev python3-pip python3-venv libucx0 python3 -m venv venv source venv/bin/activate
pip install "ai-dynamo[all]"
Dynamo workflow#
Dynamo CLI has the following 4 sub-commands.
-
:runner: dynamo run: quickly spin up a server to experiment with a specified model, input and output target.
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:palm_up_hand: dynamo serve: compose a graph of workers locally and serve.
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:hammer: (Experimental) dynamo build: containerize either the entire graph or parts of graph to multiple containers
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:rocket: (Experimental) dynamo deploy: deploy to K8 with helm charts or custom operators
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:cloud: (Experimental) dynamo cloud: interact with your dynamo cloud server
For more detailed examples on serving LLMs with disaggregated serving, KV aware routing, etc, please refer to LLM deployment examples
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- Support Matrix
- Getting Started
- High Level Architecture
- Distributed Runtime
- 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
- Planner Benchmark Example
- 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
- Dynamo SDK
- Python API
- Hello World Example: Basic
- Hello World Example: Aggregated and Disaggregated Deployment
- LLM Deployment Examples
- Multinode Examples
- LLM Deployment Examples using TensorRT-LLM
- #
- LLM deployment examples
- Privacy Policy
- Manage My Privacy
- Do Not Sell or Share My Data
- Terms of Service
- Accessibility
- Corporate Policies
- Product Security
- Contact