How to install the shared-memory-server crate // Lib.rs

shared-memory-server is both an executable binary that can be run, and a library that can be used in Rust programs.

Installing the `bench` executable

Assuming you have Rust/Cargo installed, run this command in a terminal:

cargo install shared-memory-server

It will make the bench command available in your PATH if you've allowed the PATH to be modified when installing Rust. cargo uninstall shared-memory-server uninstalls.

Adding `shared_memory_server` library as a dependency

Run this command in a terminal, in your project's directory:

cargo add shared-memory-server

To add it manually, edit your project's Cargo.toml file and add to the [dependencies] section:

shared-memory-server = "0.4.1"

The shared_memory_server library will be automatically available globally. Read the shared_memory_server library documentation.

Back to the crate overview.

Readme

Website | Python API | Rust API | Guide | Discord

Highlights

🚀 dora-rs is a framework to run realtime multi-AI and multi-hardware applications.
🦀 dora-rs internals are 100% Rust making it extremely fast compared to alternative such as being ⚡️ 10-17x faster than ros2.
❇️ Includes a large set of pre-packaged nodes for fast prototyping which simplifies integration of hardware, algorithms, and AI models.

Latency benchmark with Python API for both framework, sending 40M of random bytes.

Latest News 🎉

2025

[08/25] Introduced dora.builder, a new Pythonic API for imperatively defining dora dataflows.
[07/25] Added Kornia rust nodes in the hub for V4L / Gstreamer cameras and Sobel image processing.
[06/25] Add support for git based node, dora-vggt for multi-camera depth estimation, and adding robot_descriptions_py as a default way to get urdfs within dora.
[05/25] Add support for dora-pytorch-kinematics for fk and ik, dora-mediapipe for pose estimation, dora-rustypot for rust serialport read/write, points2d and points3d visualization in rerun.
[04/25] Add support for dora-cotracker to track any point on a frame, dora-rav1e AV1 encoding up to 12bit and dora-dav1d AV1 decoding,
[03/25] Add support for dora async Python.
[03/25] Add support for Microsoft Phi4, Microsoft Magma.
[03/25] dora-rs has been accepted to GSoC 2025 🎉, with the following idea list.
[03/25] Add support for Zenoh for distributed dataflow.
[03/25] Add support for Meta SAM2, Kokoro(TTS), Improved Qwen2.5 Performance using llama.cpp.
[02/25] Add support for Qwen2.5(LLM), Qwen2.5-VL(VLM), outetts(TTS)

Support Matrix

	dora-rs
APIs	Python >= 3.7 including sync ⭐✅ Rust ✅ C/C++ 🆗 ROS2 >= Foxy 🆗
OS	Linux: Arm 32 ⭐✅ Arm 64 ⭐✅ x64_86 ⭐✅ MacOS: Arm 64 ⭐✅ Windows: x64_86 🆗 WSL: x64_86 🆗 Android: 🛠️ (Blocked by: https://github.com/elast0ny/shared_memory/issues/32) IOS: 🛠️
Message Format	Arrow ✅ Standard Specification 🛠️
Local Communication	Shared Memory ✅ Cuda IPC 📐
Remote Communication	Zenoh 📐
RGB-D Streaming	AV1 Encoding (dora-rav1e), AV1 Decoding (dora-dav1d)📐
Metrics, Tracing, and Logging	Opentelemetry 📐
Configuration	YAML ✅
Package Manager	pip: Python Node ✅ Rust Node ✅ C/C++ Node 🛠️ cargo: Rust Node ✅

⭐ = Recommended

✅ = First Class Support

🆗 = Best Effort Support

📐 = Experimental and looking for contributions

🛠️ = Unsupported but hoped for through contributions

Everything is open for contributions 🙋

Node Hub

The node hub is available in the dora-rs/dora-hub repository.

Examples

Type	Title	Description
Vision	YOLO	Use YOLO to detect object within image.
ROS2	C++ ROS2 Example	Example using C++ ROS2
ROS2	Rust ROS2 Example	Example using Rust ROS2
ROS2	Python ROS2 Example	Example using Python ROS2
Benchmark	GPU Benchmark	GPU Benchmark of dora-rs
Benchmark	CPU Benchmark	CPU Benchmark of dora-rs
Tutorial	Rust Example	Example using Rust
Tutorial	Python Example	Example using Python
Tutorial	CMake Example	Example using CMake
Tutorial	C Example	Example with C node
Tutorial	CUDA Example	Example using CUDA Zero Copy
Tutorial	C++ Example	Example with C++ node
Tutorial	Python Dataflow Builder Examples	Examples using the new Pythonic API.

Getting Started

Installation

pip install dora-rs-cli

Additional installation methods

Install dora with our standalone installers, or from crates.io:

With cargo

cargo install dora-cli

With Github release for macOS and Linux

curl --proto '=https' --tlsv1.2 -LsSf https://github.com/dora-rs/dora/releases/latest/download/dora-cli-installer.sh | sh

To install a specific version:

curl --proto '=https' --tlsv1.2 -LsSf https://raw.githubusercontent.com/dora-rs/dora/main/install.sh | sh -s -- --tag v0.3.12

With Github release for Windows

powershell -ExecutionPolicy ByPass -c "irm https://github.com/dora-rs/dora/releases/latest/download/dora-cli-installer.ps1 | iex"

With Source

git clone https://github.com/dora-rs/dora.git
cd dora
cargo build --release -p dora-cli
PATH=$PATH:$(pwd)/target/release

Run

Run the yolo python example:

## Create a virtual environment
uv venv --seed -p 3.11

## Install nodes dependencies of a remote graph
dora build https://raw.githubusercontent.com/dora-rs/dora/refs/heads/main/examples/python-dataflow/dataflow.yml --uv

## Run yolo graph
dora run dataflow.yml --uv

Make sure to have a webcam

To stop your dataflow, you can use ctrl+c

To understand what is happening, you can look at the dataflow with:

cat dataflow.yml

Resulting in:

nodes:
  - id: camera
    build: pip install opencv-video-capture
    path: opencv-video-capture
    inputs:
      tick: dora/timer/millis/20
    outputs:
      - image
    env:
      CAPTURE_PATH: 0
      IMAGE_WIDTH: 640
      IMAGE_HEIGHT: 480

  - id: object-detection
    build: pip install dora-yolo
    path: dora-yolo
    inputs:
      image: camera/image
    outputs:
      - bbox

  - id: plot
    build: pip install dora-rerun
    path: dora-rerun
    inputs:
      image: camera/image
      boxes2d: object-detection/bbox

In the above example, we can understand that the camera is sending image to both the rerun viewer as well as a yolo model that generates bounding box that is visualized within rerun.

Documentation

The full documentation is available on our website. A lot of guides are available on this section of our website.

What is Dora? And what features does Dora offer?

Dataflow-Oriented Robotic Architecture (dora-rs) is a framework that makes creation of robotic applications fast and simple.

dora-rs implements a declarative dataflow paradigm where tasks are split between nodes isolated as individual processes.

The dataflow paradigm has the advantage of creating an abstraction layer that makes robotic applications modular and easily configurable.

TCP Communication and Shared Memory

Communication between nodes is handled with shared memory on a same machine and TCP on distributed machines. Our shared memory implementation tracks messages across processes and discards them when obsolete. Shared memory slots are cached to avoid new memory allocation.

Arrow Message Format

Nodes communicate with Apache Arrow Data Format.

Apache Arrow is a universal memory format for flat and hierarchical data. The Arrow memory format supports zero-copy reads for lightning-fast data access without serialization overhead. It defines a C data interface without any build-time or link-time dependency requirement, that means that dora-rs has no compilation step beyond the native compiler of your favourite language.

Opentelemetry

dora-rs uses Opentelemetry to record all your logs, metrics and traces. This means that the data and telemetry can be linked using a shared abstraction.

Opentelemetry is an open source observability standard that makes dora-rs telemetry collectable by most backends such as elasticsearch, prometheus, Datadog...

Opentelemetry is language independent, backend agnostic, and easily collect distributed data, making it perfect for dora-rs applications.

ROS2 Bridge

Note: this feature is marked as unstable.

Compilation Free Message passing to ROS 2
Automatic conversion ROS 2 Message <-> Arrow Array

import pyarrow as pa

# Configuration Boilerplate...
turtle_twist_writer = ...

## Arrow Based ROS2 Twist Message
## which does not require ROS2 import
message = pa.array([{
            "linear": {
                "x": 1,
            },
            "angular": {
                "z": 1
            },
        }])

turtle_twist_writer.publish(message)

You might want to use ChatGPT to write the Arrow Formatting: https://chat.openai.com/share/4eec1c6d-dbd2-46dc-b6cd-310d2895ba15

Contributing

We are passionate about supporting contributors of all levels of experience and would love to see you get involved in the project. See the contributing guide to get started.

Discussions

Our main communication channels are:

Feel free to reach out on any topic, issues or ideas.

We also have a contributing guide.

License

This project is licensed under Apache-2.0. Check out NOTICE.md for more information.

Installing the bench executable

Adding shared_memory_server library as a dependency

Installing the `bench` executable

Adding `shared_memory_server` library as a dependency