https://github.com/llnl/thicket-tutorial
https://github.com/llnl/thicket-tutorial
tutorial
Last synced: 9 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/llnl/thicket-tutorial
- Owner: LLNL
- License: mit
- Created: 2023-01-22T05:34:57.000Z (about 3 years ago)
- Default Branch: develop
- Last Pushed: 2024-11-02T21:00:18.000Z (about 1 year ago)
- Last Synced: 2024-11-02T21:17:04.675Z (about 1 year ago)
- Topics: tutorial
- Language: Jupyter Notebook
- Homepage: https://thicket.readthedocs.io
- Size: 40.2 MB
- Stars: 1
- Watchers: 4
- Forks: 5
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 
Thicket Tutorial
Thicket is a python-based toolkit for Exploratory Data Analysis (EDA) of
parallel performance data that enables performance optimization and
understanding of applications' performance on supercomputers. It bridges the
performance tool gap between being able to consider only a single instance of a
simulation run (e.g., single platform, single measurement tool, or single
scale) and finding actionable insights in multi-dimensional, multi-scale,
multi-architecture, and multi-tool performance datasets.
You can find detailed documentation, along with non-interactive tutorials from this repository of Thicket in the
[ReadTheDocs](https://thicket.readthedocs.io/en/latest/).
This repository contains materials for Thicket's hands-on tutorial. You can do
all of the exercises on your own laptop using BinderHub.
You find Thicket itself in its Github repository: https://github.com/llnl/thicket
### Running the notebooks
#### Docker
We provide a Dockerfile for users to run the notebooks locally. To run locally *and interactively*, you must first build the Docker container with:
```bash
docker build -t thicket-tutorial -f Dockerfile.local .
```
Then, you must create a Docker network with:
```bash
docker network create jupyterhub
```
Finally, you can launch the tutorial. To launch the tutorial without preserving any changes, run:
```bash
docker run --rm -it --entrypoint /start.sh -v /var/run/docker.sock:/var/run/docker.sock --net jupyterhub --name jupyterhub -p 8888:8888 thicket-tutorial --NotebookApp.token=''
```
If you would rather your changes be preserved, run:
```bash
docker run --rm -it --entrypoint /start.sh -v /var/run/docker.sock:/var/run/docker.sock -v .:/home/jovyan --net jupyterhub --name jupyterhub -p 8888:8888 thicket-tutorial
```
Alternatively, if you want to run the notebooks automatically (i.e., non-interactive), you can simply run the `dev_scripts/autorun.sh` script. This script executes the same commands as above, but it uses the `run_all.sh` script as an entrypoint instead of `start.sh`.
The Docker-based code for running this tutorial locally was derived from the material from the 2023 RADIUSS tutorial for Flux, which can be found here: https://github.com/flux-framework/Tutorials/tree/master/2023-RADIUSS-AWS/JupyterNotebook
#### Podman
If you want to use podman instead of docker, you can replace "docker" with "podman" for most steps.
First initialize and start podman:
```bash
podman machine init
podman machine start
```
Then build the container:
```bash
podman build -t thicket-tutorial -f Dockerfile.local .
```
Then create a network:
```bash
podman network create jupyterhub
```
Then launch the tutorial:
```bash
podman run --rm -it --entrypoint /start.sh -v /var/run/docker.sock:/var/run/docker.sock --net jupyterhub --name jupyterhub -p 8888:8888 thicket-tutorial
```
Clean up after you are done:
```bash
podman machine stop
```
### License
This repository is distributed under the terms of the MIT license.
All contributions must be made under the MIT license. Copyrights are retained
by contributors. No copyright assignment is required to contribute to this
project.
See [LICENSE](https://github.com/llnl/thicket-tutorial/blob/develop/LICENSE).
SPDX-License-Identifier: MIT
LLNL-CODE-834749