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Gorgonia is a library that helps facilitate machine learning in Go. Write and evaluate mathematical equations involving multidimensional arrays easily. If this sounds like Theano or TensorFlow, it's because the idea is quite similar. Specifically, the library is pretty low-level, like Theano, but has higher goals like Tensorflow.

Gorgonia:

Can perform automatic differentiation
Can perform symbolic differentiation
Can perform gradient descent optimizations
Can perform numerical stabilization
Provides a number of convenience functions to help create neural networks
Is fairly quick (comparable to Theano and TensorFlow speed)
Supports CUDA/GPGPU computation (OpenCL not yet supported, send a pull request)
Will support distributed computing

Goals

The primary goal for Gorgonia is to be a highly performant machine learning/graph computation-based library that can scale across multiple machines. It should bring the appeal of Go (simple compilation and deployment process) to the ML world. It's a long way from there currently, however, the baby steps are already there.

The secondary goal for Gorgonia is to provide a platform for the exploration of non-standard deep-learning and neural network-related things. This includes things like neo-hebbian learning, corner-cutting algorithms, evolutionary algorithms, and the like.

Why Use Gorgonia?

The main reason to use Gorgonia is developer comfort. If you're using a Go stack extensively, now you have access to the ability to create production-ready machine learning systems in an environment that you are already familiar with and comfortable with.

ML/AI at large is usually split into two stages: the experimental stage where one builds various models, tests, and retests; and the deployed state where a model after being tested and played with, is deployed. This necessitates different roles like data scientist and data engineer.

Typically the two phases have different tools: Python (PyTorch, etc) is commonly used for the experimental stage, and then the model is rewritten in some more performant language like C++ (using dlib, mlpack etc). Of course, nowadays the gap is closing and people frequently share the tools between them. Tensorflow is one such tool that bridges the gap.

Gorgonia aims to do the same but for the Go environment. Gorgonia is currently fairly performant - its speeds are comparable to PyTorch's and Tensorflow's CPU implementations. GPU implementations are a bit finicky to compare due to the heavy CGO tax, but rest assured that this is an area of active improvement.

Getting started

Installation

The package is go-gettable: go get -u gorgonia.org/gorgonia.

Gorgonia is compatible with Go modules.

Documentation

Up-to-date documentation, references, and tutorials are present on the official Gorgonia website at https://gorgonia.org.

Keeping Updated

Gorgonia's project has a Slack channel on gopherslack, as well as a Twitter account. Official updates and announcements will be posted to those two sites.

Usage

Gorgonia works by creating a computation graph and then executing it. Think of it as a programming language, but is limited to mathematical functions, and has no branching capability (no if/then or loops). In fact, this is the dominant paradigm that the user should be used to thinking about. The computation graph is an AST.

Microsoft's CNTK, with its BrainScript, is perhaps the best at exemplifying the idea that building a computation graph and running the computation graphs are different things and that the user should be in different modes of thought when going about them.

Whilst Gorgonia's implementation doesn't enforce the separation of thought as far as CNTK's BrainScript does, the syntax does help a little bit.

Here's an example - say you want to define a math expression z = x + y. Here's how you'd do it:

package gorgonia_test

import (
	"fmt"
	"log"

	. "gorgonia.org/gorgonia"
)

// Basic example of representing mathematical equations as graphs.
//
// In this example, we want to represent the following equation
//		z = x + y
func Example_basic() {
	g := NewGraph()

	var x, y, z *Node
	var err error

	// define the expression
	x = NewScalar(g, Float64, WithName("x"))
	y = NewScalar(g, Float64, WithName("y"))
	if z, err = Add(x, y); err != nil {
		log.Fatal(err)
	}

	// create a VM to run the program on
	machine := NewTapeMachine(g)
	defer machine.Close()

	// set initial values then run
	Let(x, 2.0)
	Let(y, 2.5)
	if err = machine.RunAll(); err != nil {
		log.Fatal(err)
	}

	fmt.Printf("%v", z.Value())
	// Output: 4.5
}

You might note that it's a little more verbose than other packages of similar nature. For example, instead of compiling to a callable function, Gorgonia specifically compiles into a *program which requires a *TapeMachine to run. It also requires manual a Let(...) call.

The author would like to contend that this is a Good Thing - to shift one's thinking to machine-based thinking. It helps a lot in figuring out where things might go wrong.

Additionally, there is no support for branching - that is to say, there are no conditionals (if/else) or loops. The aim is not to build a Turing-complete computer.

More examples are present in the example subfolder of the project, and step-by-step tutorials are present on the main website

Using CUDA

Gorgonia comes with CUDA support out of the box. Please see the reference documentation about how cuda works on the Gorgonia.org website, or jump to the tutorial.

About Gorgonia's development process

Versioning

We use semver 2.0.0 for our versioning. Before 1.0, Gorgonia's APIs are expected to change quite a bit. API is defined by the exported functions, variables, and methods. For the developers' sanity, there are minor differences to SemVer that we will apply before version 1.0. They are enumerated below:

The MINOR number will be incremented every time there is a deleterious break in API. This means any deletion or any change in function signature or interface methods will lead to a change in the MINOR number.
Additive changes will NOT change the MINOR version number before version 1.0. This means that if new functionality were added that does not break the way you use Gorgonia, there would not be an increment in the MINOR version. There will be an increment in the PATCH version.

API Stability

Gorgonia's API is as of right now, not considered stable. It will be stable from version 1.0 forward.

Go Version Support

Gorgonia supports 2 versions below the Master branch of Go. This means Gorgonia will support the current released version of Go, and up to 4 previous versions - providing something doesn't break. Where possible a shim will be provided (for things like new sort APIs or math/bits which came out in Go 1.9).

The current version of Go is 1.13.1. The earliest version Gorgonia supports is Go 1.11.x but Gonum supports only 1.12+. Therefore, the minimum Go version to run the master branch is Go > 1.12.

Hardware and OS supported

Gorgonia runs on :

linux/AMD64
linux/ARM7
linux/ARM64
win32/AMD64
darwin/AMD64
freeBSD/AMD64

If you have tested Gorgonia on other platforms, please update this list.

Hardware acceleration

Gorgonia uses some pure assembler instructions to accelerate some mathematical operations. Unfortunately, only amd64 is supported.

Contributing

Obviously, since you are most probably reading this on Github, Github will form the major part of the workflow for contributing to this package.

Contributors and Significant Contributors

All contributions are welcome. However, there is a new class of contributors, called Significant Contributors.

A Significant Contributor has shown a deep understanding of how the library works and/or its environs. Here are examples of what constitutes a Significant Contribution:

Wrote significant amounts of documentation on why/the mechanics of particular functions/methods and how the different parts affect one another
Wrote code and tests around the more intricately connected parts of Gorgonia
Wrote code and tests, and had at least 5 pull requests accepted
Provided expert analysis on parts of the package (for example, you may be a floating point operations expert who optimized one function)
Answered at least 10 support questions.

The significant Contributors list will be updated once a month (if anyone even uses Gorgonia that is).

How To Get Support

The best way of support right now is to open a [ticket on Github](https://github

Gorgonia

Install / Use

README