An Introduction to Deep Learning in Julia

ByJordan Brown

Deep learning has revolutionized the fields of machine learning and artificial intelligence in recent years, powering breakthroughs in areas like computer vision, natural language processing, and robotics. While the most popular deep learning frameworks have been built around Python, there‘s a new contender that‘s rapidly gaining popularity: Julia.

Julia is a high-level, high-performance dynamic programming language designed specifically for numerical and scientific computing. It aims to combine the ease of use and expressiveness of Python with the speed of C, making it an attractive choice for computationally intensive tasks like deep learning.

In this article, we‘ll take a deep dive into deep learning in Julia. We‘ll start with an overview of the Julia language and ecosystem, and why it‘s uniquely well-suited for machine learning. Then we‘ll get hands-on and walk through the process of implementing and training deep neural networks in Julia using the Flux package. By the end, you‘ll have a solid foundation for using Julia for your own deep learning projects and research.

Why Julia for Deep Learning?

On the surface, Julia looks a lot like other dynamic programming languages such as Python and MATLAB. It has a simple, readable syntax, an interactive REPL, and a large ecosystem of packages available through a built-in package manager.

However, Julia was designed from the ground up to maximize performance, which is critical for computationally heavy applications like deep learning. It features:

  • Just-in-time (JIT) compilation using LLVM to compile code to efficient native machine code on the fly
  • A sophisticated type system that allows optional type annotations to enable the compiler to generate optimized code
  • Ability to call C and Fortran libraries with no wrappers or glue code
  • Excellent support for parallelism, with built-in primitives for multi-threading and multi-process parallelism
  • Efficient implementation of multi-dimensional arrays that allows writing MATLAB-style vectorized code

All of this allows Julia to approach and often match the performance of low-level languages like C and Fortran, while maintaining the rapid development and iteration speed of a high-level language. For deep learning, this means you can define and train large, complex models without sacrificing training speed.

Julia also has best-in-class automatic differentiation (AD) support, which is the key enabler of gradient-based optimization algorithms used to train neural networks. The Zygote AD package provides source-to-source reverse-mode AD, and is able to differentiate Julia code that includes control flow, recursion, mutation, and other dynamic language features. This flexibility makes it easy to define novel, custom model architectures and loss functions, a huge benefit for deep learning research.

Flux: The Deep Learning Library for Julia

While you can use the built-in AD and mathematical primitives in Julia to define and train neural networks from scratch, the Flux package provides a full-featured, high-level API for deep learning that makes it simple to get started.

At the core of Flux are a few simple abstractions:

  • Layers, which are differentiable Julia functions such as dense layers, convolutional layers, recurrent layers, etc. These are building blocks that are composed to define complex models.
  • Models, which are collections of layers and parameters. Flux provides an intuitive model definition API using Julia‘s native language features.
  • Optimizers, which adjust model parameters based on the gradients computed during training. Flux has a large suite of built-in optimizers, and it‘s easy to define custom optimizers as well.

One of the key design principles of Flux is to leverage the power of Julia and avoid reinventing wheels. For example, instead of providing a limited, custom tensor or array implementation, it uses plain Julia arrays and all the functionality built around them. It doesn‘t have a built-in visualization library, instead having easy interop with existing plotting and visualization packages. This allows Flux to stay lightweight while enabling seamless integration with the rest of the Julia ecosystem.

Here‘s a quick example of what a basic image classification model looks like in Flux:

using Flux

# Define the model architecture
model = Chain(
  Conv((5,5), 3=>16, relu),
  MaxPool((2,2)),
  Conv((5,5), 16=>8, relu),
  MaxPool((2,2)),
  flatten,
  Dense(288, 10),
  softmax)

# Define the loss function  
loss(x, y) = crossentropy(model(x), y)

# Define the optimizer
optimizer = ADAM()

# Train the model
for epoch in 1:10
  for (x, y) in training_data
    grads = gradient(params(model)) do
      loss(x, y)
    end
    update!(optimizer, params(model), grads)
  end
end

This example demonstrates a number of features of Flux and Julia that make it easy to work with:

  • Models are defined by composition of layers and functions using the intuitive Chain syntax
  • The loss function is a regular Julia function that makes use of Flux‘s built-in loss functions
  • Optimizers are first-class types that are passed to the update! function along with model parameters and gradients
  • The training loop is just a simple nested for-loop using native Julia iteration and control flow

One other unique aspect of Flux is that since Julia runs so fast, there isn‘t a separate graph-building and execution step like in TensorFlow or PyTorch. When you call model(x) to make a prediction, it actually runs the model code on the input directly. This means that Flux models are themselves regular Julia code, and can include things like control flow, recursion, and arbitrary Julia code.

Getting Started with Deep Learning in Julia

Sold on the promise of Julia for deep learning and ready to get started? The first step is to download and install Julia from the official website (https://julialang.org). The latest stable release as of April 2023 is v1.8.5.

Next, let‘s install the Flux package and a few other useful packages from the Julia REPL:

import Pkg
Pkg.add("Flux")
Pkg.add("MLDatasets")
Pkg.add("Images")

Now we‘re ready to start defining and training models! Let‘s walk through an example of training an image classifier on the classic MNIST dataset of handwritten digits.

First, we‘ll load the packages we need and download the MNIST dataset:

using Flux
using MLDatasets
using Images

# Load the MNIST train and test datasets
train_x, train_y = MNIST.traindata()
test_x,  test_y  = MNIST.testdata()

# Stack the images into a 4D batch tensor
train_x = cat(Float32.(train_x)..., dims=4)
test_x = cat(Float32.(test_x)..., dims=4)

# One-hot encode the labels
train_y = Flux.onehotbatch(train_y, 0:9)
test_y = Flux.onehotbatch(test_y, 0:9)

Next, we define our model architecture:

model = Chain(
    Conv((3, 3), 1=>16, pad=(1,1), relu),
    MaxPool((2,2)),
    Conv((3, 3), 16=>32, pad=(1,1), relu), 
    MaxPool((2,2)),
    Conv((3, 3), 32=>32, pad=(1,1), relu),
    MaxPool((2,2)),
    flatten,
    Dense(288, 10),
    softmax)

We‘ll use cross entropy loss and the ADAM optimizer:

loss(x, y) = Flux.crossentropy(model(x), y) 
optimizer = ADAM()

Finally, we‘re ready to train the model:

epochs = 10
for epoch in 1:epochs
  for (x, y) in zip(eachbatch(train_x, 128), eachbatch(train_y, 128))
    grads = gradient(params(model)) do
      loss(x, y)
    end
    update!(optimizer, params(model), grads)
  end

  train_loss = mean(loss.(eachbatch(train_x, 128), eachbatch(train_y, 128)))
  test_loss = mean(loss.(eachbatch(test_x, 128), eachbatch(test_y, 128)))
  println("Epoch $epoch: Train loss = $train_loss, Test loss = $test_loss")
end

After training for 10 epochs, our model achieves around 98% test accuracy, not bad! Feel free to experiment with different architectures, hyperparameters, and optimization strategies to see how high you can push the accuracy.

This example just scratches the surface of what‘s possible with deep learning in Julia. The Flux model zoo (https://github.com/FluxML/model-zoo/) contains many more advanced model examples, including deep convolutional nets, generative adversarial networks, and differentiable programming models.

The Julia Differentiable Programming Ecosystem

Perhaps the most exciting aspect of Julia‘s deep learning ecosystem is the cutting-edge work on differentiable programming. Differentiable programming frameworks let you differentiate arbitrary code, including control flow, recursion, and mutation. This enables differentiating through extremely complex programs in a variety of domains, like:

  • Physics simulation engines
  • Raytracing renderers
  • Database query optimizers
  • Quantum computing simulators

These differentiable programs can then be plugged into deep learning pipelines for end-to-end optimization. For example, a number of recent works have demonstrated using differentiable physics engines to train robotic control policies.

Julia is well-positioned as a differentiable programming language thanks to its high performance, top-notch AD support, and existing ecosystem of technical and scientific computing packages. Here are few of the exciting differentiable programming libraries available in Julia:

  • Zygote – source-to-source reverse mode AD
  • ForwardDiff – forward mode AD for scalar and vector code
  • ReverseDiff – compile-based reverse mode AD
  • Enzyme – LLVM-based source code transformation AD
  • Diffractor – generalized AD with powerful symbolic tracing
  • Lux – differentiable deep learning framework with functional, Haskell-like API

These packages push the boundaries of what‘s possible with differentiable programming, unifying deep learning with traditional scientific computing, simulations, and modeling. As the differentiable programming paradigm continues to grow, Julia is poised to be one of the leading languages for this research.

Conclusion

To sum things up, Julia is an exciting new language for deep learning that offers a unique combination of performance, ease-of-use, and state-of-the-art AD and differentiable programming capabilities. With the Flux ecosystem, you can define and train advanced models on par with other major frameworks. And thanks to Julia‘s speed, dynamic language features, and rich technical computing packages, it‘s an ideal platform for deep learning research and pushing the boundaries of AI.

The deep learning community is taking notice – major organizations like MIT, Google Brain, and Intel are increasingly investing in Julia. We‘ve seen a growing number of deep learning papers and courses using Julia as well.

Hopefully this article has piqued your interest in using Julia for deep learning. Here are some resources for learning more:

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