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https://github.com/ankane/torch.rb

Deep learning for Ruby, powered by LibTorch
https://github.com/ankane/torch.rb

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Deep learning for Ruby, powered by LibTorch

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# Torch.rb



:fire: Deep learning for Ruby, powered by [LibTorch](https://pytorch.org)



Check out:



- [TorchVision](https://github.com/ankane/torchvision) for computer vision tasks

- [TorchText](https://github.com/ankane/torchtext) for text and NLP tasks

- [TorchAudio](https://github.com/ankane/torchaudio) for audio tasks

- [TorchRec](https://github.com/ankane/torchrec-ruby) for recommendation systems

- [TorchData](https://github.com/ankane/torchdata-ruby) for data loading



[![Build Status](https://github.com/ankane/torch.rb/actions/workflows/build.yml/badge.svg)](https://github.com/ankane/torch.rb/actions)



## Installation



First, [install LibTorch](#libtorch-installation). With Homebrew, it’s part of the PyTorch package:



```sh

brew install pytorch

```



Add this line to your application’s Gemfile:



```ruby

gem "torch-rb"

```



It can take 5-10 minutes to compile the extension.



## Getting Started



A good place to start is [Deep Learning with Torch.rb: A 60 Minute Blitz](tutorials/blitz/README.md).



## Tutorials



- [Transfer learning](tutorials/transfer_learning/README.md)

- [Sequence models](tutorials/nlp/sequence_models.md)

- [Word embeddings](tutorials/nlp/word_embeddings.md)



## Examples



- [Image classification with MNIST](examples/mnist) ([日本語版](https://qiita.com/kojix2/items/c19c36dc1bf73ea93409))

- [Collaborative filtering with MovieLens](examples/movielens)

- [Generative adversarial networks](examples/gan)



## API



This library follows the [PyTorch API](https://pytorch.org/docs/stable/torch.html). There are a few changes to make it more Ruby-like:



- Methods that perform in-place modifications end with `!` instead of `_` (`add!` instead of `add_`)

- Methods that return booleans use `?` instead of `is_`  (`tensor?` instead of `is_tensor`)

- Numo is used instead of NumPy (`x.numo` instead of `x.numpy()`)



You can follow PyTorch tutorials and convert the code to Ruby in many cases. Feel free to open an issue if you run into problems.



## Overview



Some examples below are from [Deep Learning with PyTorch: A 60 Minutes Blitz](https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html)



### Tensors



Create a tensor from a Ruby array



```ruby

x = Torch.tensor([[1, 2, 3], [4, 5, 6]])

```



Get the shape of a tensor



```ruby

x.shape

```



There are [many functions](#tensor-creation) to create tensors, like



```ruby

a = Torch.rand(3)

b = Torch.zeros(2, 3)

```



Each tensor has four properties



- `dtype` - the data type - `:uint8`, `:int8`, `:int16`, `:int32`, `:int64`, `:float32`, `:float64`, or `:bool`

- `layout` - `:strided` (dense) or `:sparse`

- `device` - the compute device, like CPU or GPU

- `requires_grad` - whether or not to record gradients



You can specify properties when creating a tensor



```ruby

Torch.rand(2, 3, dtype: :float64, layout: :strided, device: "cpu", requires_grad: true)

```



### Operations



Create a tensor



```ruby

x = Torch.tensor([10, 20, 30])

```



Add



```ruby

x + 5 # tensor([15, 25, 35])

```



Subtract



```ruby

x - 5 # tensor([5, 15, 25])

```



Multiply



```ruby

x * 5 # tensor([50, 100, 150])

```



Divide



```ruby

x / 5 # tensor([2, 4, 6])

```



Get the remainder



```ruby

x % 3 # tensor([1, 2, 0])

```



Raise to a power



```ruby

x**2 # tensor([100, 400, 900])

```



Perform operations with other tensors



```ruby

y = Torch.tensor([1, 2, 3])

x + y # tensor([11, 22, 33])

```



Perform operations in-place



```ruby

x.add!(5)

x # tensor([15, 25, 35])

```



You can also specify an output tensor



```ruby

result = Torch.empty(3)

Torch.add(x, y, out: result)

result # tensor([15, 25, 35])

```



### Numo



Convert a tensor to a Numo array



```ruby

a = Torch.ones(5)

a.numo

```



Convert a Numo array to a tensor



```ruby

b = Numo::NArray.cast([1, 2, 3])

Torch.from_numo(b)

```



### Autograd



Create a tensor with `requires_grad: true`



```ruby

x = Torch.ones(2, 2, requires_grad: true)

```



Perform operations



```ruby

y = x + 2

z = y * y * 3

out = z.mean

```



Backprop



```ruby

out.backward

```



Get gradients



```ruby

x.grad # tensor([[4.5, 4.5], [4.5, 4.5]])

```



Stop autograd from tracking history



```ruby

x.requires_grad # true

(x**2).requires_grad # true



Torch.no_grad do

  (x**2).requires_grad # false

end

```



### Neural Networks



Define a neural network



```ruby

class MyNet < Torch::NN::Module

  def initialize

    super()

    @conv1 = Torch::NN::Conv2d.new(1, 6, 3)

    @conv2 = Torch::NN::Conv2d.new(6, 16, 3)

    @fc1 = Torch::NN::Linear.new(16 * 6 * 6, 120)

    @fc2 = Torch::NN::Linear.new(120, 84)

    @fc3 = Torch::NN::Linear.new(84, 10)

  end



  def forward(x)

    x = Torch::NN::F.max_pool2d(Torch::NN::F.relu(@conv1.call(x)), [2, 2])

    x = Torch::NN::F.max_pool2d(Torch::NN::F.relu(@conv2.call(x)), 2)

    x = Torch.flatten(x, 1)

    x = Torch::NN::F.relu(@fc1.call(x))

    x = Torch::NN::F.relu(@fc2.call(x))

    @fc3.call(x)

  end

end

```



Create an instance of it



```ruby

net = MyNet.new

input = Torch.randn(1, 1, 32, 32)

net.call(input)

```



Get trainable parameters



```ruby

net.parameters

```



Zero the gradient buffers and backprop with random gradients



```ruby

net.zero_grad

out.backward(Torch.randn(1, 10))

```



Define a loss function



```ruby

output = net.call(input)

target = Torch.randn(10)

target = target.view(1, -1)

criterion = Torch::NN::MSELoss.new

loss = criterion.call(output, target)

```



Backprop



```ruby

net.zero_grad

p net.conv1.bias.grad

loss.backward

p net.conv1.bias.grad

```



Update the weights



```ruby

learning_rate = 0.01

net.parameters.each do |f|

  f.data.sub!(f.grad.data * learning_rate)

end

```



Use an optimizer



```ruby

optimizer = Torch::Optim::SGD.new(net.parameters, lr: 0.01)

optimizer.zero_grad

output = net.call(input)

loss = criterion.call(output, target)

loss.backward

optimizer.step

```



### Saving and Loading Models



Save a model



```ruby

Torch.save(net.state_dict, "net.pth")

```



Load a model



```ruby

net = MyNet.new

net.load_state_dict(Torch.load("net.pth"))

net.eval

```



When saving a model in Python to load in Ruby, convert parameters to tensors (due to outstanding bugs in LibTorch)



```python

state_dict = {k: v.data if isinstance(v, torch.nn.Parameter) else v for k, v in state_dict.items()}

torch.save(state_dict, "net.pth")

```



### Tensor Creation



Here’s a list of functions to create tensors (descriptions from the [C++ docs](https://pytorch.org/cppdocs/notes/tensor_creation.html)):



- `arange` returns a tensor with a sequence of integers



  ```ruby

  Torch.arange(3) # tensor([0, 1, 2])

  ```



- `empty` returns a tensor with uninitialized values



  ```ruby

  Torch.empty(3) # tensor([7.0054e-45, 0.0000e+00, 0.0000e+00])

  ```



- `eye` returns an identity matrix



  ```ruby

  Torch.eye(2) # tensor([[1, 0], [0, 1]])

  ```



- `full` returns a tensor filled with a single value



  ```ruby

  Torch.full([3], 5) # tensor([5, 5, 5])

  ```



- `linspace` returns a tensor with values linearly spaced in some interval



  ```ruby

  Torch.linspace(0, 10, 5) # tensor([0, 5, 10])

  ```



- `logspace` returns a tensor with values logarithmically spaced in some interval



  ```ruby

  Torch.logspace(0, 10, 5) # tensor([1, 1e5, 1e10])

  ```



- `ones` returns a tensor filled with all ones



  ```ruby

  Torch.ones(3) # tensor([1, 1, 1])

  ```



- `rand` returns a tensor filled with values drawn from a uniform distribution on [0, 1)



  ```ruby

  Torch.rand(3) # tensor([0.5444, 0.8799, 0.5571])

  ```



- `randint` returns a tensor with integers randomly drawn from an interval



  ```ruby

  Torch.randint(1, 10, [3]) # tensor([7, 6, 4])

  ```



- `randn` returns a tensor filled with values drawn from a unit normal distribution



  ```ruby

  Torch.randn(3) # tensor([-0.7147,  0.6614,  1.1453])

  ```



- `randperm` returns a tensor filled with a random permutation of integers in some interval



  ```ruby

  Torch.randperm(3) # tensor([2, 0, 1])

  ```



- `zeros` returns a tensor filled with all zeros



  ```ruby

  Torch.zeros(3) # tensor([0, 0, 0])

  ```



## LibTorch Installation



[Download LibTorch](https://pytorch.org/) (for Linux, use the `cxx11 ABI` version). Then run:



```sh

bundle config build.torch-rb --with-torch-dir=/path/to/libtorch

```



Here’s the list of compatible versions.



Torch.rb | LibTorch

--- | ---

0.16.x | 2.3.x

0.15.x | 2.2.x

0.14.x | 2.1.x

0.13.x | 2.0.x

0.12.x | 1.13.x



### Homebrew



You can also use Homebrew.



```sh

brew install pytorch

```



## Performance



Deep learning is significantly faster on a GPU.



### Linux



With Linux, install [CUDA](https://developer.nvidia.com/cuda-downloads) and [cuDNN](https://developer.nvidia.com/cudnn) and reinstall the gem.



Check if CUDA is available



```ruby

Torch::CUDA.available?

```



Move a neural network to a GPU



```ruby

net.cuda

```



If you don’t have a GPU that supports CUDA, we recommend using a cloud service. [Paperspace](https://www.paperspace.com/) has a great free plan. We’ve put together a [Docker image](https://github.com/ankane/ml-stack) to make it easy to get started. On Paperspace, create a notebook with a custom container. Under advanced options, set the container name to:



```text

ankane/ml-stack:torch-gpu

```



And leave the other fields in that section blank. Once the notebook is running, you can run the [MNIST example](https://github.com/ankane/ml-stack/blob/master/torch-gpu/MNIST.ipynb).



### Mac



With Apple silicon, check if Metal Performance Shaders (MPS) is available



```ruby

Torch::Backends::MPS.available?

```



Move a neural network to a GPU



```ruby

device = Torch.device("mps")

net.to(device)

```



## History



View the [changelog](https://github.com/ankane/torch.rb/blob/master/CHANGELOG.md)



## Contributing



Everyone is encouraged to help improve this project. Here are a few ways you can help:



- [Report bugs](https://github.com/ankane/torch.rb/issues)

- Fix bugs and [submit pull requests](https://github.com/ankane/torch.rb/pulls)

- Write, clarify, or fix documentation

- Suggest or add new features



To get started with development:



```sh

git clone https://github.com/ankane/torch.rb.git

cd torch.rb

bundle install

bundle exec rake compile -- --with-torch-dir=/path/to/libtorch

bundle exec rake test

```



You can use [this script](https://gist.github.com/ankane/9b2b5fcbd66d6e4ccfeb9d73e529abe7) to test on GPUs with the AWS Deep Learning Base AMI (Ubuntu 18.04).



Here are some good resources for contributors:



- [PyTorch API](https://pytorch.org/docs/stable/torch.html)

- [PyTorch C++ API](https://pytorch.org/cppdocs/)

- [Tensor Creation API](https://pytorch.org/cppdocs/notes/tensor_creation.html)

- [Using the PyTorch C++ Frontend](https://pytorch.org/tutorials/advanced/cpp_frontend.html)