Creating Execution Environments

Problem

You want to create a custom environment 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.

Note

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.

Warning

Creating an environment via the v1/environments endpoint requires the administrator role.

Python
R

from posit import connect

CONTAINER_REGISTRY = "myorg/myrepo"

client = connect.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)

CONTAINER_REGISTRY = "myorg/myrepo"

client = connect()

json_payload <- toJSON(list(
  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)

client$POST("/v1/environments", json_payload)