GitHub - ymcrcat/rgpu: Keep Python on your laptop. Run PyTorch operations and hold tensors on a remote GPU, including from a Mac with no CUDA installation.

rGPU runs GPU work on a remote NVIDIA machine while the application stays on the client. It currently offers two paths: Path Use it for Interface PyTorch device PyTorch programs that can opt into an rgpu device torch operations over TCP CUDA shim Existing Linux CUDA programs, including stock CUDA PyTorch libcuda, CUDA Runtime, cuBLAS, cuBLASLt, and cuDNN shims The PyTorch device is the simpler integration. The CUDA shim covers existing binaries but has a larger compatibility surface. Documentation The Fumadocs site in website/ is the product documentation: Quickstart Training nanoGPT example CUDA shim Operations Configuration reference Performance Troubleshooting Engineering records and experiments are indexed in docs/README.md. Quick start: PyTorch device Install rGPU with pip install rgpu, or pip install -e ./python from this checkout, then follow the quickstart to deploy the server. Save this as smoke.py in your workload directory: import torch import rgpu x = torch.ones(4, device="rgpu") print((x * 2).sum().item()) # 8.0 Run it in the environment where rGPU is installed, using your server's SSH destination and options: rgpu-run --host user@gpu-host --ssh-port 2222 -i ~/.ssh/gpu_key \ python smoke.py The program selects the device; rgpu-run opens the tunnel and configures the connection. The expected output is 8.0. For existing Linux CUDA programs, follow the CUDA shim guide, starting with ./scripts/build_client.sh. Neither protocol authenticates or encrypts connections. Keep rgpu-opserver on its default localhost bind and use SSH. The CUDA server listens on all IPv4 interfaces: restrict port 9713 with host/cloud firewall rules before starting it, even when using an SSH tunnel. See deployment.

Generated C++ is committed; its policy and regeneration steps are in codegen/README.md. Repository map Path Purpose client/ CUDA client shims and transport server/ CUDA server and dispatch common/ Shared protocol and generated API metadata python/ PyTorch device and launcher tests/ C++, Python, CUDA, and hardware checks codegen/ CUDA header parser and source generators website/ Fumadocs product documentation docs/ Design records, measurements, and experiment reports jax/ Experimental JAX work; not a supported product path scripts/ Build, deployment, cloud, and test helpers skills/ Installable agent guidance for using rGPU Historical implementation notes and experimental results are indexed in docs/README.md. License Licensed under the Apache License 2.0.