Run PyTorch locally with a remote GPU backend
I integrated a remote GPU execution backend into PyTorch through the same system that custom hardware accelerators get integrated into PyTorch. You can create a remote machine and obtain its CUDA device whenever you want to create or move tensors onto the remote GPU. machine = mycelya_torch.RemoteMachine("modal", "A100") cuda_device = machine.device("cuda") x = torch.randn(1000, 1000, device=cuda_device) y = torch.randn(1000, 1000).to(cuda_device) I made it reasonably performant by having most operations dispatch asynchronously whenever possible. For cases where slow performance is…
Run PyTorch locally with a remote GPU backend launched on October 12, 2025 with 5 votes and more than 75% of that year's launches. The site was still online in September 2026, 1 year on.
What it does
Contribute to alyxya/mycelya-torch development by creating an account on GitHub.
Run your PyTorch code anywhere and power it with cloud GPUs. Mycelya integrates a remote GPU backend into PyTorch, allowing tensor operations to execute on cloud infrastructure with minimal code changes. import torch import mycelya_torch # Create a remote machine with cloud GPU machine = mycelya_torch . RemoteMachine ( "modal" , "A100" ) cuda_device = machine . device ( "cuda" ) # Your existing PyTorch code just works x = torch . randn ( 1000 , 1000 , device = cuda_device ) y = torch . randn ( 1000 , 1000 ). to ( cuda_device ) # Move tensor to remote GPU result = x @ y # Computed on remote A100! # Transfer result back to local machine result_local = result . cpu () print ( f"Result: {…from github.com
In the maker’s words, at launch
I integrated a remote GPU execution backend into PyTorch through the same system that custom hardware accelerators get integrated into PyTorch. You can create a remote machine and obtain its CUDA device whenever you want to create or move tensors onto the remote GPU. machine = mycelya_torch.RemoteMachine("modal"…
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