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An extremely fast and simple dataflow oriented robotic framework to manage your projects and run complex apps, written in Rust.
Latency benchmark with Python API for both framework, sending 40M of random bytes.
- π A single CLI to launch your Python and Rust robotic projects.
- β‘οΈ 10-17x faster than
ros2
. - π Easy and Fast prototyping with a clean Python API.
- π₯οΈ Supports macOS, Linux, and Windows.
- β¬ Installable without Rust via
curl
orpowershell
. - βοΈ Includes a large set of pre-packaged nodes for fast prototyping.
- π οΈ Build and Run applications without compilation step beyond the native compiler of your favourite language.
- π€ Simplifies building robotic applications by integrating hardware, algorithms, and AI models to facilitate seamless communication.
- βοΈ Eases integration of hardware and software by supporting Python, C, C++, and ROS2, while ensuring low-latency communication with zero-copy Arrow messages.
Install dora with our standalone installers, or from crates.io:
curl --proto '=https' --tlsv1.2 -sSf https://raw.githubusercontent.com/dora-rs/dora/main/install.sh | bash
powershell -c "irm https://raw.githubusercontent.com/dora-rs/dora/main/install.ps1 | iex"
cargo install dora-cli
The full documentation is available on our website. A lot of guides are available on this section of our website.
- Run the benchmark example (Rust must be installed):
git clone https://github.com/dora-rs/dora
cd dora
cargo run --example benchmark --release
- Run some Python examples (A venv must be activated):
# activate your venv here
cd dora/examples/python-dataflow
dora up
dora build dataflow.yml
dora start dataflow.yml
Make sure to have a webcam
To stop your dataflow, you can use ctrl+c
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.
Each node defines its inputs and outputs to connect with other nodes.
nodes:
- id: camera
path: opencv-video-capture
inputs:
tick: dora/timer/millis/20
outputs:
- image
-
- id: plot
path: opencv-plot
inputs:
image: camera/image
The dataflow paradigm has the advantage of creating an abstraction layer that makes robotic applications modular and easily configurable.
It offers several features, such as:
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.
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.
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.
dora-rs implements Hot-Reloading for python which means you can change code at runtime in Python while keeping your state intact.
Using the feature flag: --attach --hot-reload
, dora-rs watch for code change and reload nodes that has been modified.
You can check fail-safe mechanism at: #239.
See this demo.
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
You can easily create a self-coding robot, by combining Hot-reloading with a Retrieval Augmented Generation (RAG) that is going to generate code modification from your prompt. See:examples/python-operator-dataflow
Self-Coding Robot is just the tip of the iceberg of robotics combined with llm, that we hope to power. There is so much more that we haven't explored yet like:
Cool hardware that we think might be good fit to try out dora-rs π We are not sponsored by manufacturers:
Price | Open Source | Github | type | Dora Project | |
---|---|---|---|---|---|
DJI Robomaster S1 | 550$ | SDK | https://github.com/dji-sdk/RoboMaster-SDK | Rover | https://huggingface.co/datasets/dora-rs/dora-robomaster |
DJI Robomaster EP Core | 950$ | SDK | https://github.com/dji-sdk/RoboMaster-SDK | Rover, Arm | |
DJI Tello | 100$ | Drone | |||
BitCraze Crazyflies | 225$ | Firmware, Lib, SDK | https://github.com/bitcraze | Drone | |
AlexanderKoch-Koch/low_cost_robot | 250$ | Everything | https://github.com/AlexanderKoch-Koch/low_cost_robot | Arm | |
xArm 1S | 200$ | Arm | |||
Wavego | 250$ | Quadruplet | |||
AINex | 800$ | Humanoid |
For more: https://docs.google.com/spreadsheets/d/1YYeW2jfOIWDVgdEgqnMvltonHquQ7K8OZCrnJRELL6o/edit#gid=0
dora-rs | Hoped for | |
---|---|---|
Tier 1 Support | Python, Rust | C, C++, ROS 2 |
Tier 2 Support | C, C++, ROS2 | |
Hot-reloading | Python | Rust (https://github.com/orgs/dora-rs/discussions/360) |
Message Format | Arrow | Native |
Local Communication | Shared Memory | Custom Middleware, zero-copy GPU IPC, intra-process tokio::channel communication |
Remote Communication | TCP | Custom Middleware, Zenoh |
Metrics, Tracing, and Logging | Opentelemetry | Native logging libraries into Opentelemetry |
Data archives | Parquet (dora-record) | |
Visualization and annotation | OpenCV | rerun.io |
Supported Platforms (x86) | Windows, macOS, Linux | |
Supported Platforms (ARM) | macOS, Linux | |
Configuration | YAML |
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.
Our main communication channels are:
Feel free to reach out on any topic, issues or ideas.
We also have a contributing guide.
This project is licensed under Apache-2.0. Check out NOTICE.md for more information.