# PyTorch
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## Activating PyTorch
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When a stable Conda package of a framework is released, it's tested and pre-installed on the DLAMI. If you want to run the latest, untested nightly build, you can [Install PyTorch's Nightly Build (experimental)](#tutorial-pytorch-install) manually. 

To activate the currently installed framework, follow these instructions on your Deep Learning AMI with Conda.

For PyTorch on Python 3 with CUDA and MKL-DNN, run this command:

```
$ source activate pytorch_p310
```

Start the iPython terminal.

```
(pytorch_p310)$ ipython
```

Run a quick PyTorch program.

```
import torch
x = torch.rand(5, 3)
print(x)
print(x.size())
y = torch.rand(5, 3)
print(torch.add(x, y))
```

You should see the initial random array printed, then its size, and then the addition of another random array.

## Install PyTorch's Nightly Build (experimental)
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**How to install PyTorch from a nightly build**

You can install the latest PyTorch build into either or both of the PyTorch Conda environments on your Deep Learning AMI with Conda.

1. 
   + (Option for Python 3) - Activate the Python 3 PyTorch environment:

     ```
     $ source activate pytorch_p310
     ```

1. The remaining steps assume you are using the `pytorch_p310` environment. Remove the currently installed PyTorch:

   ```
   (pytorch_p310)$ pip uninstall torch
   ```

1. 
   + (Option for GPU instances) - Install the latest nightly build of PyTorch with CUDA.0:

     ```
     (pytorch_p310)$ pip install torch_nightly -f https://download.pytorch.org/whl/nightly/cu100/torch_nightly.html
     ```
   + (Option for CPU instances) - Install the latest nightly build of PyTorch for instances with no GPUs:

     ```
     (pytorch_p310)$ pip install torch_nightly -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
     ```

1. To verify you have successfully installed latest nightly build, start the IPython terminal and check the version of PyTorch.

   ```
   (pytorch_p310)$ ipython
   ```

   ```
   import torch
   print (torch.__version__)
   ```

   The output should print something similar to `1.0.0.dev20180922`

1. To verify that the PyTorch nightly build works well with the MNIST example, you can run a test script from PyTorch's examples repository:

   ```
   (pytorch_p310)$ cd ~
   (pytorch_p310)$ git clone https://github.com/pytorch/examples.git pytorch_examples
   (pytorch_p310)$ cd pytorch_examples/mnist
   (pytorch_p310)$ python main.py || exit 1
   ```

## More Tutorials
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For further tutorials and examples refer to the framework's official docs, [PyTorch documentation](http://pytorch.org/docs/master/), and the [PyTorch](http://pytorch.org) website.