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Gomlx

GoMLX is an open-source framework of Go libraries and tools for machine learning and generic mathematics. It supports building, training, fine-tuning, modifying and combining models, and provides pure-Go, XLA and ONNX Runtime backends for executing computations.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Gomlx — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use GoMLX when you want to develop or run machine-learning workflows in Go, or experiment with model components while keeping the code and execution backend configurable.

Who it is for

  • Go developers building machine-learning or neural-network applications.
  • Practitioners who want to train or fine-tune models in Go.
  • Researchers and learners experimenting with model components and training ideas in Go.

Documented uses

  • Build and train machine-learning models in Go.
  • Fine-tune, modify or combine models, including supported models read from Hugging Face.
  • Run computations with a pure-Go, XLA or ONNX Runtime backend.
  • Explore model training in a Jupyter notebook with the prebuilt Docker environment.
  • Package model inference in a Go application or Docker image using the model code and trained checkpoint.
Before you choose

Requirements & limits

The project is open source under the Apache-2.0 license. Its pure-Go backend requires no separate installation; the XLA backend can automatically install required plugins, with manual installation documented for custom setups.

  • The pure-Go backend is portable and is documented to run in WebAssembly; it requires no separate backend installation.
  • XLA uses OpenXLA and can install PJRT plugins automatically; custom or production Docker setups may call for manual installation.
  • The ONNX Runtime backend supports running computations and saving models as ONNX files.
  • The prebuilt JupyterLab Docker image is about 5 GB and includes NVIDIA CUDA runtime for optional GPU support.
  • For inference, the documented workflow uses the Go model code together with the trained checkpoint and training hyperparameters.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Start a GoMLX notebook with the prebuilt Docker image

Open the included GoMLX tutorial in JupyterLab using the project's Docker image.

Before you start

  • Docker
  • A directory to mount as Jupyter's work directory
  • For optional GPU support, an NVIDIA GPU and Docker GPU access

Steps

  1. Pull the janpfeifer/gomlx_jupyterlab:latest image.

  2. Run the image with port 8888 published and your current directory mounted at /home/jupyter/work; add --gpus all for GPU support.

  3. Open the tokenized URL beginning with 127.0.0.1:8888 printed in the terminal.

  4. Open the included tutorial under Projects/gomlx/examples/tutorial.

Expected outcome

A JupyterLab environment with the GoMLX tutorial available to interact with.

Guide limitations

  • The image download is approximately 5 GB.
  • The GPU option is documented for NVIDIA CUDA; GPU use requires the --gpus all flag.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
GomlxBuild and train machine-learning models in Go.Linux, macOS, WindowsThe project is open source under the Apache-2.0 license. Its pure-Go backend requires no separate installation; the XLA backend can automatically install required plugins, with manual installation documented for custom setups.
HyperposeCustomize pose-estimation architectures and datasets.Docker, Linux, macOS, Windows
Multi Task NlpDefine multiple tasks and their data and model settings in a YAML file.Linux, macOS, WindowsThe project is an open-source library under the Apache-2.0 license, distributed through its public GitHub repository.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
12 October 2026
Original website title
gomlx/gomlx · Official homepage title · retained in its source language
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