atomr-accel 0.3.2

Actor-shaped face for compute-acceleration backends (NVIDIA CUDA shipping; ROCm / Metal / oneAPI / Vulkan future), on the atomr actor runtime.

atomr-accel (Python)

Python bindings for atomr-accel — drive an actor-supervised NVIDIA CUDA pipeline directly from Python without juggling streams, contexts, or hand-rolled retry loops.

import numpy as np
import atomr_accel

with atomr_accel.System.open("my-app") as sys:
    dev = sys.spawn_device(device_id=0)         # real CUDA device

    # Allocate two N×N f32 buffers on-device.
    n = 256
    a = dev.allocate_f32(n * n)
    b = dev.allocate_f32(n * n)
    c = dev.allocate_f32(n * n)

    # Upload from numpy.
    dev.copy_from_numpy(a, np.ones(n * n, dtype=np.float32))
    dev.copy_from_numpy(b, np.full(n * n, 2.0, dtype=np.float32))

    # Run cuBLAS SGEMM — the call blocks until the kernel finishes.
    dev.sgemm(a, b, c, m=n, n=n, k=n, alpha=1.0, beta=0.0)

    # Pull the result back into a fresh numpy array.
    result = dev.copy_to_numpy(c)
    print(result.reshape(n, n))

For hosts without a GPU, pass mock=True to spawn_device and the device replies Unrecoverable("...mock mode") for any kernel call — useful for testing the surrounding plumbing in CI.

Install

The wheel builds with maturin:

# from this directory
pip install maturin pytest numpy
maturin develop --release       # builds + installs into the active venv
pytest tests/                    # runs the no-GPU smoke suite

For a release wheel:

maturin build --release --no-default-features --features extension-module
# wheel lands in target/wheels/

The pure-Python facade (python/atomr_accel/__init__.py) re-exports the native classes and exception types. Downstream libraries import from atomr_accel and treat atomr_accel._native as private.

Feature flags

The Rust crate matches atomr-accel's feature gating so the wheel can be built minimal or full:

Feature	Adds
(default)	System, Device, GpuBuffer, exceptions
curand	RngGenerator
nvrtc	NvrtcKernel
cudnn / cufft / cusolver / cublaslt / nccl	placeholder; future Python surfaces
core-libs / training-libs / full-cuda	aggregates

maturin develop --features atomr-accel-py/curand,atomr-accel-py/nvrtc

Public API

Class / function	What it wraps
atomr_accel.System.open(name)	A atomr_core::actor::ActorSystem lifetime
system.spawn_device(id, mock=)	A DeviceActor (real or mock)
device.allocate_f32(len)	DeviceMsg::AllocateF32 → GpuBuffer
device.copy_from_numpy(buf, np)	H2D CopyFromHostF32
device.copy_to_numpy(buf)	D2H CopyToHostF32 → numpy float32 array
device.sgemm(a,b,c,m,n,k,...)	cuBLAS SGEMM via BlasActor
device.stats()	DeviceMsg::Stats → DeviceLoad
GpuBuffer.is_stale()	Generation token check vs. DeviceState
GpuRuntimeError (and subclasses)	Typed GpuError mapping

Every method blocks the calling thread until the underlying actor replies (the GIL is released for the duration via py.allow_threads). Async wrappers can be layered later via pyo3_async_runtimes::tokio::future_into_py.

How it works

Three pieces:

1. A shared tokio runtime. The first call to System.open(...) initializes a multi-threaded scheduler; every subsequent call reuses it. Implemented in src/runtime.rs via pyo3-async-runtimes::tokio::init.
2. The _native extension module. src/lib.rs registers System, Device, GpuBuffer, exceptions, and (feature-gated) RngGenerator / NvrtcKernel. Each Python class wraps a typed ActorRef<...> from atomr-accel and converts replies via errors::map_gpu.
3. The pure-Python facade at python/atomr_accel/__init__.py. Hides _native; documents the API; gives downstream libraries a stable import path.

See docs/python-bridge.md for the full architecture write-up.

License

Apache-2.0.