autocvd-go

A golang cli tool for setting CUDA_VISIBLE_DEVICES based on GPU utilization. This project is heavily inspired by the python version of jonasricker/autocvd.

The aim of this tool, as well as jonasricker/autocvd, is as follows:

On a system with multiple NVIDIA GPUs, autocvd eliminates the need for manually specifying the CUDA_VISIBLE_DEVICES environment variable. This comes in especially handy on systems with multiple users, like a shared GPU server. It is dependency-free and requires no code changes.

Requirements

autocvd-go uses nvidia-smi to query GPU utilization. Make sure that it is installed and callable.

Installation

Download the binary from GitHub Releases and drop it in your $PATH.

wget https://github.com/shunk031/autocvd-go/releases/latest/download/autocvd_linux_x86_64.tar.gz \
    && tar -xvzf autocvd_linux_x86_64.tar.gz autocvd \
    && rm autocvd_linux_x86_64.tar.gz

# Then, move `autocvd` binary in your $PATH

Usage

To execute a command on a free GPU as well as jonasricker/autocvd, run the following:

$ eval $(autocvd) <command>

Possible use cases when training deep learning models that use GPUs include the following:

$ eval $(autocvd) python train.py

Examples

This example is identical to jonasricker/autocvd.

# run command on two free GPUs
$ eval $(autocvd -n 2) <command>

# run command on least-used GPU (i.e., do not wait if no GPU is free)
$ eval $(autocvd -l) <command>

# exclude certain GPUs
$ eval $(autocvd -x 0 2) <command>

# if no free GPU is available immediately, wait for 60 seconds only
$ eval $(autocvd -t 60) <command>

# export environment variables into the current shell
$ . <(autocvd -e)  # alternatively: source <(autocvd -e)

LICENSE

MIT

Acknowledgment

• jonasricker/autocvd: Tool to automatically set CUDA_VISIBLE_DEVICES based on GPU utilization. Usable from command line and code. https://github.com/jonasricker/autocvd#examples