GoMLX is a fast and easy-to-use set of Machine Learning and generic math libraries and tools. It can be seen as a PyTorch/Jax/TensorFlow for Go.
It uses just-in-time compilation to CPU and GPU (hopefully soon TPUs also) and is built on top of OpenXLA/PJRT, which itself uses LLVM to JIT-compile code. It's the same engine that powers Google's Jax and TensorFlow, and it has the same speed in many cases.
🎓 Quick Start: see our tutorial, or a guided example for Kaggle Dogs Vs Cats.
It was developed to be full-featured ML platform for Go, and to easily experiment with ML ideas -- see Long-Term Goals below.
It strives to be simple to read and reason about, leading the user to a correct and transparent mental model of what is going on (no surprises) -- aligned with Go philosophy. At the cost of more typing (more verbose) at times.
It is also incredibly flexible, and easy to extend and try non-conventional things: use it to experiment with new optimizer ideas, complex regularizers, funky multi-tasking, etc.
Documentation is kept up-to-date (if it is not well documented, it is as if the code is not there) and error messages are useful and try to make it easy to solve issues.
GoMLX is still under development, and should be considered experimental.
GoMLX has many important components of an ML framework in place, from the bottom to the top of the stack. But it is still only a slice of what a major ML library/framework should provide (like TensorFlow, Jax or PyTorch).
It includes:
- Examples:
- Adult/Census model;
- Cifar-10 demo;
- Dogs & Cats classifier demo;
- IMDB Movie Review demo;
- Diffusion model for Oxford Flowers 102 dataset (generates random flowers);
- (🚀New) LLM/GenAI: See GoMLX/Gemma, a GoMLX implementation of Google DeepMind's Gemma v2 model (blog post)
- GNN model for OGBN-MAG (experimental).
- Last, a trivial synthetic linear model, for those curious to see a barebones simple model.
- Pre-Trained models to use: InceptionV3 (image model) -- more to come.
- Docker with integrated JupyterLab and GoNB (a Go kernel for Jupyter notebooks)
- Just-In-Time (JIT) compilation using OpenXLA for CPUs and GPUs -- hopefully soon TPUs.
- Autograd: automatic differentiation -- only gradients for now, no jacobian.
- Context: automatic variable management for ML models.
- ML layers library with some of the most popular machine learning "layers": FFN layers,
activation functions, layer and batch normalization, convolutions, pooling, dropout, Multi-Head-Attention (for transformer layers), KAN (with B-Splines), PiecewiseLinear (for calibration and normalization), regularizations, FFT (reverse/differentiable), etc. - Training library, with some pretty-printing. Including plots for Jupyter notebook, using GoNB, a Go Kernel.
- Also, various debugging tools: collecting values for particular nodes for plotting, simply logging the value of nodes during training, stack-trace of the code where nodes are created.
- SGD and Adam (AdamW and Adamax) optimizers.
- Various losses and metrics.
- Q&A and discussions
- Issues
- Random brainstorming on projects: just start a Q&A and I'm happy to meet in discord somewhere or VC.
TLDR;: Two options: (1) Use the Docker;
(2) Pre-built only for Linux (works in Windows WSL): install
gopjrt (see installation instructions)
(optional: Nvidia's cuda support)
and sudo apt install install hdf5-tools.
GoMLX is mostly a normal Go library, but it depends on gopjrt, which includes C wrappers to XLA (itself C++ code base). Installing gopjrt is relatively straight forward, follow the installation instructions (notice the optional Nvidia CUDA support, if you are interested).
Releases are for Linux only for now. They do work well with WSL (Windows Subsystem for Linux) in Windows. I don't have a Mac, but XLA works for Mac/DarwinOS (on arm64), so gopjrt should compile as well and GoMLX should work (contributions are welcome 😃).
The easiest to start playing with it, it's just pulling the docker image that includes GoMLX + JupyterLab + GoNB (a Go kernel for Jupyter) and Nvidia's CUDA runtime (for optional support of GPU) pre-installed -- it is ~5Gb to download.
From a directory you want to make visible in Jupyter, do:
For GPU support add the flag
--gpus allto thedocker runcommand bellow.
docker pull janpfeifer/gomlx_jupyterlab:latest
docker run -it --rm -p 8888:8888 -v "${PWD}":/home/jupyter/work janpfeifer/gomlx_jupyterlab:latestIt will display a URL starting with 127.0.0.1:8888 in the terminal (it will include a secret token needed) that you can open in your browser.
You can open and interact with the tutorial from there, it is included in the docker under the directory Projects/gomlx/examples/tutorial.
More details on the docker here.
See the tutorial here. It covers a bit of everything.
After that look at the demos in the examples/ directory.
The library itself is well documented (pls open issues if something is missing), and the code is not too hard to read. Godoc available in pkg.go.dev.
Finally, feel free to ask questions: time allowing (when not in work) I'm always happy to help -- I created groups.google.com/g/gomlx-discuss, or use GitHub discussions page.
- Building and training models in Go -- as opposed to Python (or some other language) -- with focus on:
- Being simple to read and reason about, leading the user to a correct and transparent mental model of what is going on. Even if that means being more verbose when writing.
- Clean, separable APIs: individual APIs should be self-contained and decoupled where possible.
- Composability: Any component should be replaceable, so they can be customized and experimented. That means sometimes more coding (there is not one magic train object that does everything), but it makes it clear what is happening, and it's easy to replace parts with a third party versions or something custom.
- Up-to-date documentation: if the documentation is not there or if it's badly written, it's as if the code was not there either.
- Clear and actionable error reporting
- To be a productive research and educational platform to experiment with new ML ideas and learn.
- Support mirrored training on multiple devices and various forms of distributed training (model and/or data parallelism) in particular to support for large language models and similarly large model training.
- To be a robust and reliable platform for production. Some sub-goals:
- Support modern accelerator hardware like TPUs and GPUs.
- Save models to industry tools like TensorFlow Serving.
- Import pre-trained models from Hugging Face Hub and TensorFlow Hub where possible.
- Compile models to binary as in C-libraries and/or WebAssembly, to be linked and consumed (inference) anywhere (any language).
The project is looking forward contributions for anyone interested. Many parts are not yet set in stone, so there is plenty of space for improvements and re-designs for those interested and with good experience in Go, Machine Learning and APIs in general. See the TODO file for inspiration.
No governance guidelines have been established yet.
Copyright 2024 Jan Pfeifer
GoMLX is distributed under the terms of the Apache License Version 2.0. Unless it is explicitly stated otherwise, any contribution intentionally submitted for inclusion in this project shall be licensed under Apache License Version 2.0 without any additional terms or conditions.