Installation

🛠️ + ⚙️ Installation

For most users, no installation is needed.

For XLA, it will by default auto-install the required XLA PJRT plugins (for CPU, GPU and TPUs; Linux and Macs) in the user’s local lib directory ($HOME/.local/lib/go-xla in Linux; $HOME/Library/Application Support/go-xla in Mac; $HOME\AppData\Local\go-xla in Windows). It can be disabled by setting GOMLX_NO_AUTO_INSTALL or programmatically by calling xla.EnableAutoInstall(false)).

If you want to manually pre-install for building production dockers, a specific version, or such custom setups, see github.com/gomlx/go-xla for details, there is a self-explanatory simple installer program.

If you want to use only a pure Go backend, simply do import _ "github.com/gomlx/gomlx/backends/simplego" and there is no need to install anything.

🐳 Pre-built Docker

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 all to the docker run command bellow.

docker pull janpfeifer/gomlx_jupyterlab:latest
docker run -it --rm -p 8888:8888 -v "${PWD}":/home/jupyter/work janpfeifer/gomlx_jupyterlab:latest

It 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.

It runs on Windows as well: Docker Desktop uses WSL2 under the hood.