kube_install | R Documentation |
Install packages and create binaries using a BiocParallelParam for a specific bioconductor docker image. The kube_install function can be scaled to a large cluster to reduce times even further (in theory). Please note that this command will charge your google billing account, beware of the charges.
kube_install(lib_path, bin_path, logs_path, deps, BPPARAM = NULL)
lib_path |
character() path where R package libraries are stored. |
bin_path |
character() path where R package binaries are stored. |
logs_path |
character() path where R package binary build logs are stored. |
deps |
package dependecy graph as computed by '.pkg_dependencies()'. |
BPPARAM |
A 'BiocParallelParam' object specifying how each level of the dependency graph will be parallelized. Use 'SerialParam()' for debugging; 'RedisParam()' for use in kubernetes. |
## Not run: ## First method: ## Run with a pre-existing bucket with some packages. ## This will update only the new packages binary_repo <- "anvil-rstudio-bioconductor/0.99/3.11/" deps <- pkg_dependencies(binary_repo = binary_repo) kube_install( lib_path = "/host/library", bin_path = "/host/binaries", deps = deps ) ## Second method: ## Create a new google CRAN style bucket and populate with binaries. gcloud_create_cran_bucket("gs://my-new-binary-bucket", "1.0", "3.11", secret = "/home/mysecret.json", public = TRUE) deps_new <- pkg_dependencies(binary_repo = "my-new-binary-bucket/1.0/3.11") kube_install( workers = 6L, lib_path = "/host/library", bin_path = "/host/binaries", deps = deps_new ) ## End(Not run)
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