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# Dynamo

<h4> A Datacenter Scale Distributed Inference Serving Framework </h4>

[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![GitHub Release](https://img.shields.io/github/v/release/ai-dynamo/dynamo)](https://github.com/ai-dynamo/dynamo/releases/latest)


Dynamo is a flexible, component based, data center scale inference
serving framework designed to meet the demands of complex use cases
including those of Generative AI. It is designed to enable developers
to implement and customize routing, load balancing, scaling and
workflow definitions at the data center scale without sacrificing
performance or ease of use.

> [!NOTE]
> This project is currently in the alpha / experimental /
> rapid-prototyping stage and we are actively looking for feedback and
> collaborators.

## Building Dynamo

### Requirements
Dynamo development and examples are container based.

* [Docker](https://docs.docker.com/get-started/get-docker/)
* [buildx](https://github.com/docker/buildx)

### Development

You can build the Dynamo container using the build scripts
in `container/` (or directly with `docker build`).

We provide 3 types of builds:

1. `STANDARD` which includes our default set of backends (onnx, openvino...)
2. `TENSORRTLLM` which includes our TRT-LLM backend
3. `VLLM` which includes our VLLM backend using NCCL communication library.
4. `VLLM_NIXL` which includes our VLLM backend using new NIXL communication library.

For example, if you want to build a container for the `STANDARD` backends you can run

<!--pytest.mark.skip-->
```bash
./container/build.sh
```

Please see the instructions in the corresponding example for specific build instructions.

## Running Dynamo for Local Testing and Development

You can run the Dynamo container using the run scripts in
`container/` (or directly with `docker run`).

The run script offers a few common workflows:

1. Running a command in a container and exiting.

<!--pytest.mark.skip-->
```bash
./container/run.sh -- python3 -c "import dynamo.runtime; help(dynamo.runtime)"
```
<!--

# This tests the above the line but from within the container
# using pytest-codeblocks

```bash
python3 -c "import dynamo.runtime; help(dynamo.runtime)"
```
-- >

2. Starting an interactive shell.

<!--pytest.mark.skip-->
```bash
./container/run.sh -it
```

3. Mounting the local workspace and Starting an interactive shell.

<!--pytest.mark.skip-->
```bash
./container/run.sh -it --mount-workspace
```

The last command also passes common environment variables ( `-e
HF_TOKEN` ) and mounts common directories such as `/tmp:/tmp`,
`/mnt:/mnt`.

Please see the instructions in the corresponding example for specific
deployment instructions.

## Rust Based Runtime

Dynamo has a new rust based distributed runtime with
implementation under development. The rust based runtime enables
serving arbitrary python code as well as native rust. Please note the
APIs are subject to change.

### Hello World

[Hello World](./lib/bindings/python/examples/hello_world)

A basic example demonstrating the rust based runtime and python
bindings.

### LLM

[VLLM](./examples/python_rs/llm/vllm)

An intermediate example expanding further on the concepts introduced
in the Hello World example. In this example, we demonstrate
[Disaggregated Serving](https://arxiv.org/abs/2401.09670) as an
application of the components defined in Dynamo.

# Disclaimers

> [!NOTE]
> This project is currently in the alpha / experimental /
> rapid-prototyping stage and we will be adding new features incrementally.

1. The `TENSORRTLLM` and `VLLM` containers are WIP and not expected to
   work out of the box.

2. Testing has primarily been on single node systems with processes
   launched within a single container.
