For PyTorch Users

If you already know PyTorch, use this map to get productive quickly.

Concept mapping

• torch.nn.Module -> grilly.nn.Module

• torch.nn.Sequential -> grilly.nn.Sequential

• torch.nn.functional -> grilly.functional

• torch.optim.Adam -> grilly.optim.Adam

• torch.device(“cuda”) -> grilly.Compute() (Vulkan backend)

Workflow mapping

PyTorch style:

out = model(x)
loss.backward()
optimizer.step()

Grilly style:

out = model(x)
grad_out = ...  # gradient of loss w.r.t. out
model.backward(grad_out)
optimizer.step()

Parameter handling

Use model.parameters() with optimizers exactly like PyTorch:

optimizer = grilly.optim.Adam(model.parameters(), lr=1e-3)

Tensor type expectations

Grilly primarily operates on NumPy arrays (np.float32). If you pass PyTorch-like tensors into module calls, many module paths convert them internally.