Gomlx

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GoMLX is an accelerated machine learning framework for Go, providing a comprehensive set of libraries and tools for building, training, and deploying ML models. It supports various backends, including OpenXLA for CPU, GPU, and TPU acceleration.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is gomlx?

GoMLX is an open-source, accelerated machine learning framework designed for the Go programming language, offering a complete suite of libraries and tools for ML development. It functions similarly to PyTorch, Jax, or TensorFlow but within the Go ecosystem, enabling users to train, fine-tune, modify, and combine machine learning models. Key features include a full set of differentiable operators, UI tools for plotting metrics during training, and support for multiple backends. It runs almost everywhere Go runs, including in the browser with WASM, and offers an optimized OpenXLA backend for just-in-time compilation to CPUs, GPUs (Nvidia, AMD ROCm, Intel, Macs), and Google's TPUs, supporting modern distributed execution. GoMLX emphasizes readability, transparency, and flexibility, making it suitable for both production and experimental ML ideas.

Best used for

Ideal for developers and data scientists who need to implement machine learning models directly in Go, leverage GPU/TPU acceleration, and integrate ML into Go applications. Especially valuable for those seeking a performant, flexible, and transparent ML framework that aligns with Go's philosophy for production-ready and experimental projects.

Common actions

build machine learning models

train neural networks

deploy AI models

accelerate ML computations

experiment with ML algorithms

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Capabilities

Key features

Accelerated ML framework
Differentiable operators
Multiple backends (XLA, Go, CoreML)
Automatic differentiation
ML layers library
Jupyter notebook integration
Distributed execution

Target Audience

developerdata scientist

Integrations

jupyter

Pricing & Plans

Open Source

Free

FAQs

What hardware does GoMLX support for accelerated computation?

GoMLX supports a wide range of hardware through its OpenXLA backend, including CPUs, Nvidia GPUs, and Google's TPUs. It also has a pure Go backend for maximum portability and a go-darwinml backend for Apple's CoreML, Metal, and MLX acceleration on DarwinOS devices.

Can GoMLX be used for distributed machine learning training?

Yes, GoMLX supports modern distributed execution for multi-TPU or multi-GPU setups using XLA Shardy, an evolution of the GSPMD distribution. Users only need to configure a distributed dataset, and the trainer handles the rest, making it suitable for large models and datasets.

How does GoMLX handle model inference and production deployment?

Model inference is done by using the Go code that created the model along with the trained weights and hyperparameters. This allows for straightforward deployment, such as creating a Docker image with a pre-trained model. Future plans include exporting models to ONNX or XLA's StableHLO for broader compatibility.

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