This course was first offered virtually on September 1 & 2, 2021.
- Slides are available in this repository: https://github.com/amanzi/ats-short-course/tree/main/slides
- Video recordings of the course are available here: https://www.youtube.com/playlist?list=PLisa2eqmVBFZ1mpoYqNmtFUy7yR9RaFks
To follow along with the demonstrations, participants will perform simulations and visualize results within Jupyter notebooks running under JupyterLab within a Docker container. The container contains:
- A Linux-based operating system, including common command line tools.
- A build of Amanzi-ATS, with corresponding commonly-used environment variables, e.g.
$ATS_SRC_DIRdefined. - All needed Third-Party Libraries and utilities (
h5dump,ncdump,meshconvert, etc) - A python3 build with all needed libraries for common ATS-based tasks.
- Watershed Workflow and TINerator, two common ATS meshing workflow tools.
The short course demo files will reside on the participants' computers and any changes will be available after exiting the Docker container.
git clone https://github.com/amanzi/ats-short-course; cd ats-short-course- Download the short course Docker image and run the container
docker pull metsi/ats-short-course:latest
docker run -it -v $(pwd):/home/ats_sc_user/short-course:delegated -w /home/ats_sc_user/short-course -p 8899:8899 metsi/ats-short-course:latest- Open the Jupyter lab instance
If that didn't work or didn't make sense, see the expanded version of these points below.
This has been tested on Linux, Mac OSX, and, to a lesser extent, Windows systems. The expectation is that this short course should work on any of these systems, but it is our first short course, so please be patient. If you find bugs in the docker container or this material, please feel free to ask for help on the ATS user's group or by submitting an Issue here.
As a first step you need to have the following tools installed on your system
- Docker: Download
- Windows: If you are not using an Administrator account, you will have to add your user account to the docker-users group as outlined here. Note also that WSL 2 is required for the current version of Docker Desktop, thus during installation you may be prompted to update and/or set WSL 2 as your default WSL and follow steps 4 and 5.
- VisIt: Download
- (or ParaView: Download if you prefer it and already know how to use it)
- git (so you can clone this repo):
- Mac OSX: git is included in the command line tools, installed via
xcode-select --install, or in XCode itself. - Linux: git is included as a standard package under most package managers, e.g.
sudo apt-get install git - Windows: See Git Downloads. Note that the GitHub Desktop is also an option for Windows users and provides a GUI.
- Mac OSX: git is included in the command line tools, installed via
The jupyter notebooks, as well as the corresponding input files and data, are provided in this git repository. To get started you need to clone this repository:
#
# Select a local directory (here we assume starting at the top-level of your home directory)
#
cd ~/
git clone https://github.com/amanzi/ats-short-courseAfter cloning, change to the repository directory:
cd ats-short-courseThe Docker container for this short-course includes installations of Watershed Workflow, TINerator, amanzi, and ats. As a result it is fairly large and best to separate the initial download and testing before we run JupyterLab. To get started lets download the container:
docker pull metsi/ats-short-course:latestIf this downloads successful, let's check the version of ATS:
docker run -it --rm metsi/ats-short-course:latest ats --version
> ATS version 1.2.0_c043aec4If this worked - great! Move on to Run JupyterLab under Docker. But if you ran into trouble with the download and/or docker storage on your system check some of the troubleshooting tips.
The goal is for you to enable JupyterLab to interact with the ats-short-course repository files on your local system. This is accomplished through the -v or --volume option which shares your present working directory ($pwd) with the Docker container. So make sure your $pwd is the top-level of the ats-short-course repository and cut-and-paste one of the following commands:
docker run -it -v $(pwd):/home/ats_sc_user/short-course:delegated -w /home/ats_sc_user/short-course -p 8899:8899 metsi/ats-short-course:latestOR if you prefer the verbose version
docker run \
--interactive \
--tty \
--volume $(pwd):/home/ats_sc_user/short-course:delegated \
--publish 8899:8899 \
--workdir /home/ats_sc_user/short-course \
metsi/ats-short-course:latestIf you are using Windows 10's Command Prompt or PowerShell, where the variable $(pwd) is not be recognized, it may be easier to simply type the location of ats-short-course explicitly, for example if C:\Users\USERNAME\ats-short-course is the top-level of the ats-short-course repository, then:
docker run -it -v C:\Users\USERNAME\ats-short-course:/home/ats_sc_user/short-course:delegated -w /home/ats_sc_user/short-course -p 8899:8899 metsi/ats-short-course:latestIf you are getting a Docker error that the working directory is not valid and you don't recognize the directory Docker returns, it is possible you are using (or installed as part of packages like Git for Windows) a command line interface that changes the paths you are passing to Docker. If so, please try double slashes in your Docker command:
docker run -it -v C:\\Users\\USERNAME\\ats-short-course://home//ats_sc_user//short-course:delegated -w //home//ats_sc_user//short-course -p 8899:8899 metsi/ats-short-course:latestLinux is a little tricky and requires an additional update to the Docker image. So first, pull this update,
docker pull metsi/ats-short-course:latest-linuxThen the command to run the jupyter needs one additional option to specify your user id (on the linux side) and the ats_sc_user group (on the container side):
docker run --user $(id -u):999 -it -v $(pwd):/home/ats_sc_user/short-course:delegated -w /home/ats_sc_user/short-course -p 8899:8899 metsi/ats-short-course:latest-linuxIndependent of the OS you're using, the docker run command described above will output several status messages to the screen, one of which is about the Jupyter server that it started. For example, you should see something like
[I 2021-08-17 21:59:38.111 ServerApp] Jupyter Server 1.10.2 is running at:
# This address is unique to each system, so don't copy this one in your case
[I 2021-08-17 21:59:38.111 ServerApp] http://58557662c177:8899/lab
# This address is generic and will work on any system where this port on local host has not been allocated to another process
[I 2021-08-17 21:59:38.111 ServerApp] or http://127.0.0.1:8899/lab
# To kill this server
[I 2021-08-17 21:59:38.111 ServerApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).Once this is running you can open this URL: (http://127.0.0.1:8899/lab) in your browser and you're ready to go - you should see JupyterLab and the files from this repository.
Note that most users see the message:
No web browser found: could not locate runnable browser.
This message is safe to ignore -- by manually copying and pasting the above address into your browser, you should see the Jupyter Lab instance.
In addition to the notes for Participants, developers may need additional background information.
If you are interested in building the Docker image, follow these steps:
#
# Assuming your PWD is the top-level of the repository
#
cd docker/
make all
# Or...
docker build -t metsi/ats-short-course:wwtin-latest -f Dockerfile-Ubuntu-WW+TIN ./
docker build -t metsi/ats-short-course:TPLs-latest -f Dockerfile-Workshop-TPLs ./
docker build -t metsi/ats-short-course:Amanzi-latest -f Dockerfile-Workshop-Amanzi ./
docker build -t metsi/ats-short-course:latest -f Dockerfile-Workshop-ATS ./
If you attended the course, please provide us feedback (a short, ~5 minute survey) at: https://forms.gle/HEdaU7xCts9RpWd1A