chapter 3 - It Starts with a Tensor

Dec 30, 2022

Types of operations

Storage

```python In [23]: p = torch.tensor() ...: p_t = p.t() ...: p_t, p_t.storage(), p_t.stride(), p_t.is_contiguous()

Out[23]: (tensor([[4., 5., 2.], [1., 3., 1.]]), 4.0 1.0 5.0 3.0 2.0 1.0 [torch.storage.TypedStorage(dtype=torch.float32, device=cpu) of size 6], (1, 2), False)

In [24]: p_t_c = p_t.contiguous() ...: p_t_c, p_t_c.storage(), p_t_c.stride()

Out[24]: (tensor([[4., 5., 2.], [1., 3., 1.]]), 4.0 5.0 2.0 1.0 3.0 1.0 [torch.storage.TypedStorage(dtype=torch.float32, device=cpu) of size 6], (3, 1)) ```

Moving tensors to the GPU

Numpy compatibility

serializing tensors

exercises

notebook

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