finish!

nmfs-opensci · Jun 1, 2024 · 35f66e3 · 35f66e3
1 parent feb51ad
commit 35f66e3
Show file tree

Hide file tree

Showing 2 changed files with 35 additions and 28 deletions.
diff --git a/README.Rmd b/README.Rmd
@@ -6,7 +6,7 @@ output: github_document
 
 ### Beta release June 1, 2024.
 
-These are a collection of container images that provide standardized environments for Python and R with Jupyter Lab, RStudio and VS Code IDEs. The images are built off the [Rocker](https://rocker-project.org/images/devcontainer/images.html), [Pangeo](https://github.com/pangeo-data/pangeo-docker-images) and [Jupyter](https://jupyter-docker-stacks.readthedocs.io/en/latest/) base images. This repo holds the stable Docker stack for specific pipelines used in Fisheries. The images are designed to work out-of-box and identically in Jupyter Hubs, Codespaces, Binder, etc.Read the Design section below on what the NMFS Open Sci Docker Stack does.  For use, see [Instructions](#instructions) and [Link to files](#files).
+These are a collection of container images that provide standardized environments for Python and R with Jupyter Lab, RStudio and VS Code IDEs. The images are built off the [Rocker](https://rocker-project.org/images/devcontainer/images.html), [Pangeo](https://github.com/pangeo-data/pangeo-docker-images) and [Jupyter](https://jupyter-docker-stacks.readthedocs.io/en/latest/) base images. This repo holds the stable Docker stack for specific pipelines used in Fisheries. The images are designed to work out-of-box and identically in Jupyter Hubs, Codespaces, Binder, etc.Read the Design section below on what the NMFS Open Sci Docker Stack does.  For use, see [Instructions](#instructions) and [Link to files](#files). This Docker Stack was the joint of a number of people. See [Acknowledgements](#thanks).
 
 ## Stable set of images
 
@@ -53,17 +53,16 @@ for(i in imgs) table_line(i)
 The images are designed to be deployable "out of the box" from JupyterHubs, Codespaces, GitPod, Colab, Binder, and on your computer via Docker or Podman with no modification. See instructions below. Each will spin up Jupyter Lab with Jupyter Lab (and Notebook), RStudio and VS Code with the specific development environment.
 
 -   Python environment follows Pangeo images with micromamba installed as the solver and base and notebook environments. The Jupyter modules are installed in notebook conda environment and images will launch with the notebook environment activated, again following Pangeo design structure. Images that use Pangeo as base will have user jovyan and user home directory home/jovyan.
--   Images with R ONLY follow Rocker's environment design with the exception that the user home directory is home/jovyan so it plays nice with JupyterHub deployments. The user is rstudio however.
--   When an image contains both R and Python, the base image is rocker and micromamba is installed along with the Pangeo environment structure. RStudio will use the Python environment in the notebook conda environment when Python is used from within RStudio.
+-   When an image contains both R and Python, the base image is a rocker image and adheres to the rocker norms for R and RStudio environment design. For the Python side of these images, micromamba is installed and the Pangeo conda environment structure is applied as in the Python only images. RStudio will use the Python environment in the conda notebook environment when Python is used from within RStudio. The user is `rstudio` but the home directory is `home/jovyan` so images play nice with standard JupyterHub deployments with persistent memory.
 -   These images are not terribly light-weight (they are large). Use the original Jupyter, Pangeo or Rocker images if you are looking for lightweight data science images.
 
 ## Why use a container?
 
 The main reason is that geospatial, bioinformatics, and TMB/INLA environments can be hard to get working right. Using a Docker image means you use a stable environment. Watch this video from Yuvi Panda (Jupyter Project) [video](https://www.youtube.com/watch?v=qgLPpULvBbQ) and read about the Rocker Project in the R Project Journal [article](https://journal.r-project.org/archive/2017/RJ-2017-065/RJ-2017-065.pdf) by Carl Boettiger and Dirk Eddelbuettel.
 
-### Acknowledgements
+### <a name="thanks">Acknowledgements
 
-The core stack is credited to the work of Luis Lopez (NASA) who developed the NASA Openscapes Python image used in countless workshops on cloud-computing with NASA Earth Data. Subsequently the NASA Openscapes mentor cloud-infrastructure Slack group and weekly co-work sessions plugged away at the problem of helping users 'fledge' off the Openscapes JupyterHub, which involved creating images that were more versitile. Carl Boettiger (UC Berkeley & Rocker Project) and Eli Holmes (NOAA Fisheries) took on different aspects of this. The GitHub Action tooling is curtesy of Carl. Yuvi Panda (Jupyter, 2i2c) was also very helpful in desiging the 'scaffolding' in the images that helps them be robust and versitile. The Codespaces and devcontainer code is based on Michael Akridge's [Open Science Codespaces](https://github.com/MichaelAkridge-NOAA/Open-Science-Codespaces) work. Individual images have different core developers: Tim Haverland (arcgis), Sunny Hospital (coastwatch), Luke Thompson (aomlomics).
+The motivation of the Docker Stack was the success of the NASA Openscapes "corn" image developed by Luis Lopez (NASA) and used in countless workshops on cloud-computing with NASA Earth Data. Subsequently the NASA Openscapes mentor cloud-infrastructure Slack group met during weekly co-work sessions and plugged away at the problem of helping users 'fledge' off the Openscapes JupyterHub, which involved creating images that could be used outside of JupyterHubs, and updating the original "py-rocket" R image created by Luis. Carl Boettiger (UC Berkeley & Rocker Project) and Eli Holmes (NOAA Fisheries) took on different aspects of this work. The GitHub Action tooling is curtesy of Carl. "py-rocket-base" is derived from Carl's "version 2.conda" of py-rocket. Eli further developed py-rocket into the form in this repo to bring it closer to the "corn" and Pangeo designs. Yuvi Panda (Jupyter, 2i2c) was instrumental in helping sort through so many mystery bugs. The Codespaces and devcontainer code is based on Michael Akridge's [Open Science Codespaces](https://github.com/MichaelAkridge-NOAA/Open-Science-Codespaces) work. Individual images have different core developers: Tim Haverland (arcgis), Sunny Hospital (coastwatch), Luke Thompson (aomlomics-jh), Eli Holmes (the various py-rocket versions). 
 
 
 ## License information

diff --git a/README.md b/README.md
@@ -15,7 +15,9 @@ images. This repo holds the stable Docker stack for specific pipelines
 used in Fisheries. The images are designed to work out-of-box and
 identically in Jupyter Hubs, Codespaces, Binder, etc.Read the Design
 section below on what the NMFS Open Sci Docker Stack does. For use, see
-[Instructions](#instructions) and [Link to files](#files).
+[Instructions](#instructions) and [Link to files](#files). This Docker
+Stack was the joint of a number of people. See
+[Acknowledgements](#thanks).
 
 ## Stable set of images
 
@@ -54,13 +56,14 @@ with the specific development environment.
   the notebook environment activated, again following Pangeo design
   structure. Images that use Pangeo as base will have user jovyan and
   user home directory home/jovyan.
-- Images with R ONLY follow Rocker’s environment design with the
-  exception that the user home directory is home/jovyan so it plays nice
-  with JupyterHub deployments. The user is rstudio however.
-- When an image contains both R and Python, the base image is rocker and
-  micromamba is installed along with the Pangeo environment structure.
-  RStudio will use the Python environment in the notebook conda
-  environment when Python is used from within RStudio.
+- When an image contains both R and Python, the base image is a rocker
+  image and adheres to the rocker norms for R and RStudio environment
+  design. For the Python side of these images, micromamba is installed
+  and the Pangeo conda environment structure is applied as in the Python
+  only images. RStudio will use the Python environment in the conda
+  notebook environment when Python is used from within RStudio. The user
+  is `rstudio` but the home directory is `home/jovyan` so images play
+  nice with standard JupyterHub deployments with persistent memory.
 - These images are not terribly light-weight (they are large). Use the
   original Jupyter, Pangeo or Rocker images if you are looking for
   lightweight data science images.
@@ -75,23 +78,28 @@ and read about the Rocker Project in the R Project Journal
 [article](https://journal.r-project.org/archive/2017/RJ-2017-065/RJ-2017-065.pdf)
 by Carl Boettiger and Dirk Eddelbuettel.
 
-### Acknowledgements
-
-The core stack is credited to the work of Luis Lopez (NASA) who
-developed the NASA Openscapes Python image used in countless workshops
-on cloud-computing with NASA Earth Data. Subsequently the NASA
-Openscapes mentor cloud-infrastructure Slack group and weekly co-work
-sessions plugged away at the problem of helping users ‘fledge’ off the
-Openscapes JupyterHub, which involved creating images that were more
-versitile. Carl Boettiger (UC Berkeley & Rocker Project) and Eli Holmes
-(NOAA Fisheries) took on different aspects of this. The GitHub Action
-tooling is curtesy of Carl. Yuvi Panda (Jupyter, 2i2c) was also very
-helpful in desiging the ‘scaffolding’ in the images that helps them be
-robust and versitile. The Codespaces and devcontainer code is based on
-Michael Akridge’s [Open Science
+### <a name="thanks">Acknowledgements
+
+The motivation of the Docker Stack was the success of the NASA
+Openscapes “corn” image developed by Luis Lopez (NASA) and used in
+countless workshops on cloud-computing with NASA Earth Data.
+Subsequently the NASA Openscapes mentor cloud-infrastructure Slack group
+met during weekly co-work sessions and plugged away at the problem of
+helping users ‘fledge’ off the Openscapes JupyterHub, which involved
+creating images that could be used outside of JupyterHubs, and updating
+the original “py-rocket” R image created by Luis. Carl Boettiger (UC
+Berkeley & Rocker Project) and Eli Holmes (NOAA Fisheries) took on
+different aspects of this work. The GitHub Action tooling is curtesy of
+Carl. “py-rocket-base” is derived from Carl’s “version 2.conda” of
+py-rocket. Eli further developed py-rocket into the form in this repo to
+bring it closer to the “corn” and Pangeo designs. Yuvi Panda (Jupyter,
+2i2c) was instrumental in helping sort through so many mystery bugs. The
+Codespaces and devcontainer code is based on Michael Akridge’s [Open
+Science
 Codespaces](https://github.com/MichaelAkridge-NOAA/Open-Science-Codespaces)
 work. Individual images have different core developers: Tim Haverland
-(arcgis), Sunny Hospital (coastwatch), Luke Thompson (aomlomics).
+(arcgis), Sunny Hospital (coastwatch), Luke Thompson (aomlomics-jh), Eli
+Holmes (the various py-rocket versions).
 
 ## License information