Getting Started#

Development Environment#

This section describes how to set up your development environment.

Alternative Setup: Manual Installation#

If you don’t want to use the dev container, you can set the environment up manually:

  1. Ensure you have:

    • Ubuntu 24.04 (recommended)

    • x86_64 CPU

    • Python 3.x

    • Git

    See Support Matrix for more information.

  2. If you plan to use vLLM or SGLang, you must also install:

    • etcd

    • NATS.io

    Before starting dyanmo, run both etcd and NATS.io in seperate processes.

  3. Install required system packages:

    apt-get update
DEBIAN_FRONTEND=noninteractive apt-get install -yq python3-dev python3-pip python3-venv libucx0

  4. Set up the Python environment:

    python3 -m venv venv
source venv/bin/activate

  5. Install Dynamo:

    pip install "ai-dynamo[all]"

[!Important] To ensure compatibility, use the examples in the release branch or tag that matches the version you installed.

Building the Dynamo Base Image#

Deploying your Dynamo pipelines to Kubernetes requires you to build and push a Dynamo base image to your container registry. You can use any private container registry of your choice, including:

To build it:

./container/build.sh
docker tag dynamo:latest-vllm <your-registry>/dynamo-base:latest-vllm
docker login <your-registry>
docker push <your-registry>/dynamo-base:latest-vllm

This documentation describes these frameworks:

After building, use this image by setting the DYNAMO_IMAGE environment variable to point to your built image:

export DYNAMO_IMAGE=<your-registry>/dynamo-base:latest-vllm

Running and Interacting with an LLM Locally#

To run a model and interact with it locally, call dynamo run with a Hugging Face model. dynamo run supports several backends, including mistralrs, sglang, vllm, and tensorrtllm.

Example Command#

dynamo run out=vllm deepseek-ai/DeepSeek-R1-Distill-Llama-8B

? User › Hello, how are you?
✔ User · Hello, how are you?
Okay, so I'm trying to figure out how to respond to the user's greeting.
They said, "Hello, how are you?" and then followed it with "Hello! I'm just a program, but thanks for asking."
Hmm, I need to come up with a suitable reply. ...

LLM Serving#

Dynamo provides a simple way to spin up a local set of inference components including:

  • OpenAI-compatible Frontend: High-performance OpenAI compatible http api server written in Rust.

  • Basic and Kv Aware Router: Route and load balance traffic to a set of workers.

  • Workers: Set of pre-configured LLM serving engines.

To run a minimal configuration, use a pre-configured example.

Start Dynamo Distributed Runtime Services#

To start the Dynamo Distributed Runtime services the first time:

docker compose -f deploy/docker-compose.yml up -d

Start Dynamo LLM Serving Components#

Next, serve a minimal configuration with an http server, basic round-robin router, and a single worker.

cd examples/llm
dynamo serve graphs.agg:Frontend -f configs/agg.yaml

Send a Request#

curl localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
    "messages": [
    {
        "role": "user",
        "content": "Hello, how are you?"
    }
    ],
    "stream":false,
    "max_tokens": 300
  }' | jq

Local Development#

If you use vscode or cursor, use the .devcontainer folder built on Microsoft’s Extension. For instructions, see the Dynamo repository’s devcontainer README.

Otherwise, to develop locally, we recommend working inside of the container:

./container/build.sh
./container/run.sh -it --mount-workspace

cargo build --release
mkdir -p /workspace/deploy/dynamo/sdk/src/dynamo/sdk/cli/bin
cp /workspace/target/release/http /workspace/deploy/dynamo/sdk/src/dynamo/sdk/cli/bin
cp /workspace/target/release/llmctl /workspace/deploy/dynamo/sdk/src/dynamo/sdk/cli/bin
cp /workspace/target/release/dynamo-run /workspace/deploy/dynamo/sdk/src/dynamo/sdk/cli/bin

uv pip install -e .
export PYTHONPATH=$PYTHONPATH:/workspace/deploy/dynamo/sdk/src:/workspace/components/planner/src

Conda Environment#

Alternatively, use a Conda environment:

conda activate <ENV_NAME>

pip install nixl # Or install https://github.com/ai-dynamo/nixl from source

cargo build --release

# To install ai-dynamo-runtime from source
cd lib/bindings/python
pip install .

cd ../../../
pip install .[all]

# To test
docker compose -f deploy/docker-compose.yml up -d
cd examples/llm
dynamo serve graphs.agg:Frontend -f configs/agg.yaml
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