Creating Execution Environments
Problem
You want to create a custom enviornment for your content.
Solution
First, we define our image which can be used to develop our content on Workbench, and then later is also used to execute our content on Connect. We are using the r-session-complete image as the base and we are installing additional Python and R packages that are required by our content.
FROM ghcr.io/rstudio/r-session-complete:jammy-2023.06.1--cd1a0c5
ARG GIT_SHA="4e4be3f59f0fbcf3ccecc724a00b0da7a4ad6f07"
ARG CRAN_MIRROR="https://p3m.dev/cran/__linux__/jammy/latest"
ARG PYPI_MIRROR="https://p3m.dev/pypi/latest/simple"
# Install the Python packages
# This commands installs the Python packages defined in the requirements.txt
# which pins the package versions and provides an immutable set of Python dependencies.
RUN pip install --upgrade pip && \
curl -sSfL https://raw.githubusercontent.com/sol-eng/python-examples/${GIT_SHA}/reticulated-image-classifier/requirements.txt \
-o /tmp/requirements.txt && \
pip install --default-timeout=1000 --index-url=${PYPI_MIRROR} -r /tmp/requirements.txt && \
rm /tmp/requirements.txt
# Install the R packages
ENV RENV_PATHS_LIBRARY renv/library
RUN R -e $"install.packages('renv', repos = c(CRAN = '${CRAN_MIRROR}'))" && \
curl -sSfL https://raw.githubusercontent.com/sol-eng/python-examples/${GIT_SHA}/reticulated-image-classifier/renv.lock \
-o /tmp/renv.lock && \
R -e $"renv::restore(lockfile='/tmp/renv.lock', repos = c(CRAN = '${CRAN_MIRROR}'))" && \
rm /tmp/renv.lock
Then, build and push the image to your organizations container registry
# use a container registry that you have push access to
CONTAINER_REGISTRY="myorg/myrepo"
docker build . -t ${CONTAINER_REGISTRY}/image-classifier:jammy
docker push ${CONTAINER_REGISTRY}/image-classifier:jammy
Next, we use the v1/environments
endpoint to create an execution environment.
The value for matching in the environment created is exact. This indicates that the environment should only be used if it is explicitly requested by a piece of content. Connect never chooses this environment during automatic selection.
Creating an environment via the v1/environments
endpoint requires the administrator role.
from posit import connect
= "myorg/myrepo"
CONTAINER_REGISTRY
= connect.Client()
client
client.post("v1/environments",
={
json"title": "Custom Image Classifier",
"description": "My custom image classifier",
"cluster_name": "Kubernetes",
"name": f"{CONTAINER_REGISTRY}/image-classifier:jammy",
"matching": "exact",
"r": {
"installations": [
"version": "4.2.3", "path": "/opt/R/4.2.3/bin/R"}
{
]
},"python": {
"installations": [
"version": "3.9.14", "path": "/opt/python/3.9.14/bin/python"}
{
]
},
}, )
library(connectapi)
= "myorg/myrepo"
CONTAINER_REGISTRY
= connect()
client
<- toJSON(list(
json_payload title = "Custom Image Classifier",
description = "My custom image classifier",
cluster_name = "Kubernetes",
name = paste0(CONTAINER_REGISTRY, "/image-classifier:jammy"),
matching = "exact",
r = list(
installations = list(
list(version = "4.2.3", path = "/opt/R/4.2.3/bin/R")
)
),python = list(
installations = list(
list(version = "3.9.14", path = "/opt/python/3.9.14/bin/python")
)
)auto_unbox = TRUE, pretty = TRUE)
),
$POST("/v1/environments", json_payload) client