replace badge and add monitoring

README.md

@@ -1,21 +1,26 @@
-[<!--lint ignore no-dead-urls-->![Build Status](https://github.com/a2s-institute/docker-stacks/workflows/CI/badge.svg)](https://github.com/a2s-institute/docker-stacks/actions?workflow=CI)
+[<!--lint ignore no-dead-urls-->![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)
+[<!--lint ignore no-dead-urls-->![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)