Originally posted to r/tonalgym on Reddit.
Hey folks, I started a project to provide a completely custom controller for
the Tonal 1. This replaces the Android tablet and directly talks to the motor
controller using the internal USB serial connection.
This is super barebones and not well tested, so you’re on your own if it bricks
your system. I figured I should publish it and see if folks were interested in
contributing things such as workouts, programs, etc. The standard Tonal
subscription is good enough for me, so I’m not sure how much I’ll actually push
on this.
Originally published on the PyTorch Developer Log.
TL;DR - Python can drive GPUDirect RDMA at line rate without a large
transfer-engine abstraction. rdma4py provides lightweight, backend-specific
bindings for ibverbs, AWS EFA, and NVMe-oF, reaching about 400 gbps in our
benchmarks while preserving direct access to the underlying APIs.
Introduction
Recently, quite a few different “transfer engines” have been developed for fast
weight synchronization between machines using RDMA-capable transports and
libraries such as NVLink, ibverbs, EFA, NVMe-oF, and SPDK. These include projects
such as Mooncake,
NIXL, and
Uniflow.
As with any large project and abstraction, these projects make trade-offs around
specific use cases that might not be optimal for yours.
Originally published on the PyTorch Developer Log.
TL;DR – Modifying the C++ comms layer is a big barrier when researchers want to prototype new collective features. We’ve added Python bindings to torchcomms (#2080) and built two pure-Python backend prototypes — one wrapping NVIDIA’s new nccl4py bindings (#2515) and one built on SymmetricMemory + Triton (#2521) — both passing the core torchcomms integration test suite. Since they plug into torch.distributed, researchers can fork, tweak, and mix them with existing projects like TorchTitan without touching C++.