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| author | Edgar Riba <edgar.riba@gmail.com> | 2017-02-21 15:33:48 +0100 |
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| committer | Soumith Chintala <soumith@gmail.com> | 2017-02-21 12:58:04 -0500 |
| commit | 6073f9b46ccc1bc42aa0b9fbe49270d124a42cbf (patch) | |
| tree | 661c8014bd7df841ceb3e0e417d38a581a01bd95 /README.md | |
| parent | 240372a991f380283d99bf4638855b6fac92aa27 (diff) | |
| download | pytorch-6073f9b46ccc1bc42aa0b9fbe49270d124a42cbf.tar.gz pytorch-6073f9b46ccc1bc42aa0b9fbe49270d124a42cbf.tar.bz2 pytorch-6073f9b46ccc1bc42aa0b9fbe49270d124a42cbf.zip | |
update table in README.md
it removes the empty top row
Diffstat (limited to 'README.md')
| -rw-r--r-- | README.md | 35 |
1 files changed, 26 insertions, 9 deletions
@@ -30,15 +30,32 @@ We are in an early-release Beta. Expect some adventures and rough edges. At a granular level, PyTorch is a library that consists of the following components: -| \_ | \_ | -| ------------------------ | --- | -| torch | a Tensor library like NumPy, with strong GPU support | -| torch.autograd | a tape based automatic differentiation library that supports all differentiable Tensor operations in torch | -| torch.nn | a neural networks library deeply integrated with autograd designed for maximum flexibility | -| torch.optim | an optimization package to be used with torch.nn with standard optimization methods such as SGD, RMSProp, LBFGS, Adam etc. | -| torch.multiprocessing | python multiprocessing, but with magical memory sharing of torch Tensors across processes. Useful for data loading and hogwild training. | -| torch.utils | DataLoader, Trainer and other utility functions for convenience | -| torch.legacy(.nn/.optim) | legacy code that has been ported over from torch for backward compatibility reasons | +<table> +<tr> + <td><b> torch </b></td> + <td> a Tensor library like NumPy, with strong GPU support </td> +</tr> +<tr> + <td><b> torch.autograd </b></td> + <td> a tape based automatic differentiation library that supports all differentiable Tensor operations in torch </td> +</tr> +<tr> + <td><b> torch.nn </b></td> + <td> a neural networks library deeply integrated with autograd designed for maximum flexibility </td> +</tr> +<tr> + <td><b> torch.multiprocessing </b></td> + <td> python multiprocessing, but with magical memory sharing of torch Tensors across processes. Useful for data loading and hogwild training. </td> +</tr> +<tr> + <td><b> torch.utils </b></td> + <td> DataLoader, Trainer and other utility functions for convenience </td> +</tr> +<tr> + <td><b> torch.legacy(.nn/.optim) </b></td> + <td> legacy code that has been ported over from torch for backward compatibility reasons </td> +</tr> +</table> Usually one uses PyTorch either as: |