From c924202a8c7fcae6257f29ae65ccec3e26488bf9 Mon Sep 17 00:00:00 2001 From: Mohammad Wasil Date: Thu, 24 Aug 2023 11:35:29 +0200 Subject: [PATCH] replace badge and add monitoring --- README.md | 26 +++++++++++++++++++++----- figures/nvtop.png | Bin 0 -> 59457 bytes 2 files changed, 21 insertions(+), 5 deletions(-) create mode 100644 figures/nvtop.png diff --git a/README.md b/README.md index 0dd9afe..edb759a 100644 --- a/README.md +++ b/README.md @@ -1,21 +1,26 @@ -[![Build Status](https://github.com/a2s-institute/docker-stacks/workflows/CI/badge.svg)](https://github.com/a2s-institute/docker-stacks/actions?workflow=CI) +[![Release cuda11.3.1-ubuntu20.04](https://github.com/a2s-institute/docker-stacks/actions/workflows/cuda11.3.1-ubuntu20.04.yml/badge.svg)](https://github.com/a2s-institute/docker-stacks/actions?workflow=cuda11.3.1-ubuntu20.04) +[![Release cuda11.8.0-ubuntu22.04](https://github.com/a2s-institute/docker-stacks/actions/workflows/cuda11.8.0-ubuntu22.04.yml/badge.svg)](https://github.com/a2s-institute/docker-stacks/actions?workflow=cuda11.8.0-ubuntu22.04) # a2s-institute docker images -## GPU Notebook +Our stacks provide GPU-enabled Jupyter Notebook in Docker containers, which can also be run on Kubernetes. The image is based on [released cuda version](https://hub.docker.com/r/nvidia/cuda/tags?page=1&name=12.) on docker hub and the Jupyter stacks are based on [jupyter/docker-stacks](https://github.com/jupyter/docker-stacks/). All images are published on our [github registry](https://github.com/orgs/a2s-institute/packages). -This image contains several packages for deep learning projects with NVidia GPU support. +The stacks contain several machine learning packages such as TensorFlow, PyTorch, scikit-learn, and other machine learning tools. + +## Building and running gpu-notebook in a local Docker container + +The base image contains several packages for deep learning projects with NVidia GPU support. * Build notebook image with gpu support ``` - bash build_and_publish.sh --registry ghcr.io --publish "" + bash build_and_publish.sh --registry ghcr.io --publish "" --cuda-version cuda11.8.0-ubuntu22.04 ``` You can build this image using different cuda versions available [here](https://hub.docker.com/r/nvidia/cuda/tags). * Run the image locally ``` - docker run --gpus all --name gpu-notebook -it --rm -d -p 8880:8888 ghcr.io/b-it-bots/docker/gpu-notebook:cuda11.3.1-ubuntu20.04 + docker run --gpus all --name gpu-notebook -it --rm -d -p 8880:8888 ghcr.io/b-it-bots/docker/gpu-notebook:cuda11.8.0-ubuntu22.04 ``` * Login to the container @@ -25,3 +30,14 @@ This image contains several packages for deep learning projects with NVidia GPU # check nvidia nvidia-smi ``` + +## Available images + +* `cuda11.3.1-ubuntu20.04` (python=3.10, pytorch=1.12.1) +* `cuda11.8.9-ubuntu22.04` (python=3.11, pytorch=2.0.0) + +## Monitoring + +You can monitor the GPU usage using nvtop + +![nvtop gpu monitoring](figures/nvtop.png) diff --git a/figures/nvtop.png b/figures/nvtop.png new file mode 100644 index 0000000000000000000000000000000000000000..35ccd0f3bd6d397ef1d08e587eb531a74fc91011 GIT binary patch literal 59457 zcmbTeWmFtp(=H4lkl+axoFotk?(QLIaCdiicYxd((Tms&>hBRTHTwFNujpjD~=KfGI8YSs4N0c?<#qQs7Gz_!&+1 zLv8r4=PsY5RbRrtd|#SIz>kSs#Wh`39L!xkjGWC7EbJZZ%otrvoXyPaT`V13!ALzq z@Ilmn28lVF8M#_H*uPb^vNJSpnljpHpVD<2CRA1CWuHWoH^p6Bkj2ncTx zq(6(QdS;)j`{=5=J>Hxz^0E(Lyu{GQxxhJ2O)#rXL7$^8%`)!z&|IYIud!lX)V-PL 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