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dora/apis/python/node/Cargo.toml

Cargo.toml 487 B
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init `dora-node-api-python`
3 years ago
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Refactoring `python-node-api`
3 years ago
init `dora-node-api-python`
3 years ago
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adding `abi3-py38` to pyo3 for best target support
3 years ago
Adding `next`, `send_output` for Python node API Adding `next` and `send_output` requires an async threadpool as the communication Layer defined by the Middleware Layer returns an async Future stream. I solve this issue by adding a tokio runtime on a separater thread that is connected with two channels. One for sending data and one for receiving data. Those channel are then exposed synchronously to Python. This should not be cause for concern as channel are really fast. Looking at Zenoh Python client, they are heavily using `pyo3-asyncio` implementation of futures to pass Rust futures into Python. This can be a solution as well, but, from previous experiment, I'm concerned about performance on such solution. I have experienced that putting futures from Rust into the `asyncio` queue to be slow. I'm concerned also by mixing `async` and `sync` code in Python, as it might be blocking. This might requires 2 threadpool in Python. This might seem as heavy overhead for some operations.
3 years ago
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  1. [package]

  2. name = "dora-node-api-python"

  3. version = "0.1.0"

  4. edition = "2021"

  5. license = "Apache-2.0"

  6. 

  7. # See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html

  8. 

  9. [dependencies]

  10. dora-node-api = { path = "../../rust/node" }

  11. pyo3 = { version = "0.16", features = ["eyre", "abi3-py38"] }

  12. eyre = "0.6"

  13. futures = "0.3.21"

  14. tokio = { version = "1.17.0", features = ["rt", "sync", "macros"] }

  15. serde_yaml = "0.8.23"

  16. 

  17. 

  18. [lib]

  19. name = "dora"

  20. crate-type = ["cdylib"]

DORA (Dataflow-Oriented Robotic Architecture) is middleware designed to streamline and simplify the creation of AI-based robotic applications. It offers low latency, composable, and distributed datafl