ERDOS is a platform for developing self-driving cars and robotics applications.
The easiest way to get ERDOS running is to use our Docker image:
docker pull erdosproject/erdos
ERDOS is known to work on Ubuntu 16.04, 18.04, and 20.04.
To develop an ERDOS application in Rust, simply include ERDOS in Cargo.toml
.
The latest ERDOS release is published on
Crates.io
and documentation is available on Crates.io.
If you'd like to contribute to ERDOS, first install Rust. Then run the following to clone the repository and build ERDOS:
rustup default nightly # use nightly Rust toolchain
git clone https://github.com/erdos-project/erdos.git && cd erdos
cargo build
To develop an ERDOS application in Python, simply run
pip install erdos
. Documentation is available on
Read the Docs.
If you'd like to contribute to ERDOS, first install Rust. Then run the following to clone the repository and build ERDOS:
rustup default nightly # use nightly Rust toolchain
git clone https://github.com/erdos-project/erdos.git && cd erdos
python3 python/setup.py develop
Python files are available under the python/
directory, and the Python-Rust
bridge interface is developed under src/python/
.
If you'd like to build ERDOS for release (has better performance, but longer
build times), run python3 python/setup.py install
.
python3 python/examples/simple_pipeline.py
By default, ERDOS supports up to 20 read streams and 10 write streams in a stream bundle (used to add callbacks across multiple streams; see callback builder in Rust docs for more details).
Some applications may require bundles that support more read and write streams. This can be configured by setting the ERDOS_BUNDLE_MAX_READ_STREAMS
and ERDOS_BUNDLE_MAX_WRITE_STREAMS
environment variables when building.
Building ERDOS for the first time may be slow because these parameters result in generated code which dominates the build time.
Subsequent builds should be much faster due to caching unless build.rs
or the code generation scripts are modified.
In that case, consider setting ERDOS_BUNDLE_MAX_READ_STREAMS
and ERDOS_BUNDLE_MAX_WRITE_STREAMS
to speed up builds.
ERDOS provides Python and Rust interfaces for developing applications.
The Python interface provides easy integration with popular libraries such as tensorflow, but comes at the cost of performance (e.g. slower serialization and the lack of multithreading).
The Rust interface provides more safety guarantees (e.g. compile-time type checking) and faster performance (e.g. multithreading and zero-copy message passing). High performance, safety critical applications such as self-driving car pipelines deployed in production should use the Rust API to take full advantage of ERDOS.
ERDOS is a streaming dataflow system designed for self-driving car pipelines and robotics applications.
Components of the pipelines are implemented as operators which are connected by data streams. The set of operators and streams forms the dataflow graph, the representation of the pipline that ERDOS processes.
Applications define the dataflow graph by connecting operators to streams in the driver section of the program. Operators are typically implemented elsewhere.
ERDOS is designed for low latency. Self-driving car pipelines require end-to-end deadlines on the order of hundreds of milliseconds for safe driving. Similarly, self-driving cars typically process gigabytes per second of data on small clusters. Therefore, ERDOS is optimized to send small amounts of data (gigabytes as opposed to terabytes) as quickly as possible.
ERDOS provides determinisim through watermarks. Low watermarks are a bound on the age of messages received and operators will ignore any messages older than the most recent watermark received. By processing on watermarks, applications can avoid non-determinism from processing messages out of order.
If you would like to contact us, you can:
- Community on Slack: Join our community on Slack for discussions about development, questions about usage, and feature requests.
- Github Issues: For reporting bugs.
We always welcome contributions to ERDOS. One way to get started is to pick one of the issues tagged with good first issue -- these are usually good issues that help you familiarize yourself with the ERDOS code base. Please submit contributions using pull requests.