Use GPU for training & common Tensor&Hub modules
Data and models need to be passed into CUDA for calculation.
In terms of code:
the difference from the traditional one is that we need to specify a device, this device is set to cuda: 0
needs to place the model in the device we set (in CUDA) .to(device)
needs to place the data in In the device I set (in CUDA) .to(device)
What are the common forms of Tensor?
| form | Remark | Code & Comments |
|---|---|---|
| Scalar | value | dim is 0, a scalar |
| Vector | vector | dim is 1, which usually refers to features in deep learning, such as word vectors, features of a certain dimension, etc. |
| matrix | matrix | dim is 2, and one row is a vector. It can be considered that each row is each person, and each column is the same feature. Matrix can do multiplication and inner product |
| n-dimensal tensor | High-dimensional tensor | Oh yeah... |
Powerful Hub Module
Hub is to make it easier for developers to call existing codes. It is a model zoo
torch.hub.load()
learning generally does not need this