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Remove HTML tags from README.md (#9296)

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Summary:
This change makes README.md compatible with both Github and VSTS markdown engines. Images can be reduced if necessary
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9296

Differential Revision: D8874931

Pulled By: soumith

fbshipit-source-id: 0c530c1e00b06fc891301644c92c33007060bf27
This commit is contained in:
Thiago Crepaldi 2018-07-17 07:14:08 -07:00 committed by Facebook Github Bot
parent 4ff636a3fd
commit 11fc16dc98
1 changed files with 11 additions and 29 deletions
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@ -1,4 +1,4 @@
<p align="center"><img width="40%" src="docs/source/_static/img/pytorch-logo-dark.png" /></p>
![PyTorch Logo](https://github.com/pytorch/pytorch/blob/master/docs/source/_static/img/pytorch-logo-dark.png)
--------------------------------------------------------------------------------
@ -34,32 +34,14 @@ See also the [ci.pytorch.org HUD](https://ezyang.github.io/pytorch-ci-hud/build/
At a granular level, PyTorch is a library that consists of the following components:
<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>
| Component | Description |
| ---- | --- |
| **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.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 |
Usually one uses PyTorch either as:
@ -72,7 +54,7 @@ Elaborating further:
If you use NumPy, then you have used Tensors (a.k.a ndarray).
<p align=center><img width="30%" src="docs/source/_static/img/tensor_illustration.png" /></p>
![Tensor illustration](https://github.com/pytorch/pytorch/blob/master/docs/source/_static/img/tensor_illustration.png)
PyTorch provides Tensors that can live either on the CPU or the GPU, and accelerate
compute by a huge amount.
@ -99,7 +81,7 @@ from several research papers on this topic, as well as current and past work suc
While this technique is not unique to PyTorch, it's one of the fastest implementations of it to date.
You get the best of speed and flexibility for your crazy research.
<p align=center><img width="80%" src="docs/source/_static/img/dynamic_graph.gif" /></p>
![Dynamic graph](https://github.com/pytorch/pytorch/blob/master/docs/source/_static/img/dynamic_graph.gif)
### Python First