Is .data still useful in pytorch?

I'm new to pytorch. I read much pytorch code which heavily uses tensor's .data member. But I search .data in the official document and Google, finding little. I guess .data contains the data in the tensor, but I don't know when we need it and when not?

.data was an attribute of Variable (object representing Tensor with history tracking e.g. for automatic update), not Tensor. Actually, .data was giving access to the Variable's underlying Tensor.

However, since PyTorch version 0.4.0, Variable and Tensor have been merged (into an updated Tensor structure), so .data disappeared along the previous Variable object (well Variable is still there for backward-compatibility, but is deprecated).

Paragraph from Release Notes for version 0.4.0 (I recommend reading the whole section about Variable/Tensor updates):

What about .data?

.data was the primary way to get the underlying Tensor from a Variable. After this merge, calling y = x.data still has similar semantics. So y will be a Tensor that shares the same data with x, is unrelated with the computation history of x, and has requires_grad=False.

However, .data can be unsafe in some cases. Any changes on x.data wouldn't be tracked by autograd, and the computed gradients would be incorrect if x is needed in a backward pass. A safer alternative is to use x.detach(), which also returns a Tensor that shares data with requires_grad=False, but will have its in-place changes reported by autograd if x is needed in backward.

Aside from @benjaminplanche 's answer, I'd use it to manually change values of parameters.

For instance, I have the following model:

model = nn.Sequential(nn.Linear(10, 1))

and for some reason, I'd like to manually update the values of its parameters. Then, I can do:

for param in model.parameters():
    param.data = 10 * param.data  # multiply the parameter values by 10.

Note that if we remove .data behind param, the parameter values won't be updated.

Such use can be found in BYOL (Bootstrap your own latent) and this Github webpage for BYOL pytorch implementation.