BiocDockerManager 1.8.1
The BiocDockerManager package was designed to work analogous to
BiocManager but for docker images. Use the BiocDockerManager package
manage docker images provided by the Bioconductor project. The package
provides convenient ways to install images, update images, confirm
validity and find which Bioconductor based docker images are
available.
If you are reading this document and have not yet installed any software on your computer, visit http://bioconductor.org and follow the instructions for installing R and Bioconductor. Once you have installed R and Bioconductor, you are ready to go with this document. In the future, you might find that you need to install one or more additional packages. The best way to do this is to start an R session and evaluate commands.
if (!require("BiocManager"))
install.packages("BiocManager")
BiocManager::install("BiocDockerManager")We require the system to have the docker engine installed. It is supported on all platforms. Please take a look at https://docs.docker.com/install/.
NOTE: For certain functions, it might be the case that you need to have signed into the Docker Desktop engine on your local machine as well. Eg: https://docs.docker.com/docker-for-mac/
library(BiocDockerManager)
library(dplyr)The following code is to run the vignette even if docker is not
available
.is_docker_installed <- function() {
code <- suppressWarnings(
system("docker --version", ignore.stderr = TRUE, ignore.stdout = TRUE)
)
code == 0
}
## execute code if the date is later than a specified day
do_it = BiocDockerManager:::.is_docker_installed()The best way to describe the package and it’s contents are to showcase
some of the functions available in this package. A few functions use
the the REST API provided by https://hub.docker.com/, and a few of
them interact with the docker command line tool installed on your
local machine.
Please note that, unless docker is installed on the machine some of
the functions in this vignette will not run. The same goes for when
you are testing this package as well.
Some Linux machines require that sudo be passed in order to run
docker on the command line. But there is a post-installation step
which needs to be setup correctly so the user will not need sudo,
these steps are located at
linux-postinstall.
First we can check what Bioconductor docker images are
“available”. This returns a tibble which we can manipulate with basic
dplyr functionality.
res <- BiocDockerManager::available()
res## # A tibble: 13 × 4
## IMAGE TAGS REPOS…¹ DOWNL…²
## <chr> <chr> <chr> <dbl>
## 1 experimenthub_docker latest biocon… 549
## 2 annotationhub_docker latest biocon… 51
## 3 bioconductor_full devel, release, RELEASE_3_10… biocon… 2990
## 4 bioconductor_docker latest, devel, RELEASE_3_15,… biocon… 449557
## 5 website latest biocon… 44
## 6 orchestratingsinglecellanalysis latest, RELEASE_3_12 biocon… 1076
## 7 bioc-redis RELEASE_3_15, RELEASE_3_16, … biocon… 42266
## 8 bioc-redis-jupyter RELEASE_3_14, RELEASE_3_13 biocon… 850
## 9 galaxy-jupyter 21.10 biocon… 12
## 10 bioc-redis-manager RELEASE_3_16-azure, RELEASE_… biocon… 8
## 11 bioc-redis-worker RELEASE_3_16-azure, RELEASE_… biocon… 8
## 12 rstudio_yescds yescds biocon… 11
## 13 yes-jupyter 21.10 biocon… 17
## # … with abbreviated variable names ¹REPOSITORY, ²DOWNLOADSWe can use the result of available() to extract what we need. In the
example below, simply finding the details related to the
bioconductor_docker image is shown.
res %>%
select(IMAGE, TAGS) %>%
filter(IMAGE == "bioconductor_docker")## # A tibble: 1 × 2
## IMAGE TAGS
## <chr> <chr>
## 1 bioconductor_docker latest, devel, RELEASE_3_15, RELEASE_3_14, RELEASE_3_13, …There is a simpler way to extract the same information though, with
the help of the pattern argument.
res2 <- BiocDockerManager::available(pattern = "bioconductor_docker")
res2 %>% select(IMAGE, TAGS)## # A tibble: 1 × 2
## IMAGE TAGS
## <chr> <chr>
## 1 bioconductor_docker latest, devel, RELEASE_3_15, RELEASE_3_14, RELEASE_3_13, …We can see that for the bioconductor/bioconductor_docker image the
tags latest, devel, RELEASE_3_15, RELEASE_3_14, RELEASE_3_13, RELEASE_3_12, RELEASE_3_11, bioc2020.1, bioc2020, RELEASE_3_10 are available.
Bioconductor also has a list of images which have been recently deprecated. These images can be obtained in the following way.
BiocDockerManager::available(deprecated=TRUE) %>%
select(IMAGE)## # A tibble: 42 × 1
## IMAGE
## <chr>
## 1 devel_core
## 2 devel_base
## 3 release_base
## 4 release_core
## 5 release_flow
## 6 release_microarray
## 7 release_proteomics
## 8 devel_flow
## 9 devel_microarray
## 10 devel_sequencing
## # … with 32 more rowsThe custom help function takes users to the Bioconductor Docker help
page. This provides easy access to the help page should they need
it. The help page contains all the information needed on how to start
up the bioconductor/bioconductor_docker image, and use it.
if(do_it) {
BiocDockerManager::help()
}The install() function lets users download or pull new images on to
their machine. This function comes in handy when users are trying to
get a new bioconductor_docker image on their machine.
The function pulls the image from Dockerhub which is always kept up to date.
if (do_it) {
BiocDockerManager::install(
repository = "bioconductor/bioconductor_docker",
tag = "latest"
)
}The installed() function allows the users to check which images are
installed on their local machine. This is similar to checking with the
docker command line function docker images.
For this function to work you need to have the Docker engine running.
if (do_it)
BiocDockerManager::installed()You can also filter the list of installed images by providing the
repository argument
if (do_it)
BiocDockerManager::installed(
repository = "bioconductor/bioconductor_docker"
)Bioconductor developed and distributed images that have a required
standard. Each Dockerfile which is built into an image, has LABEL
fields which identify the maintainer, version, description, url and a
few other entities. These LABEL fields serve a similar purpose to
the DESCRIPTION file in a Bioconductor package.
The following functions allow to query the Docker image for these
LABEL identities.
Since these functions are specific to Bioconductor Docker images, they will not work when given generic docker images.
NOTE: These images need to be present on your local machine before you can query for the LABEL.
if (do_it) {
## Get version for the "latest" tag bioconductor image
BiocDockerManager::maintainer(
repository = "bioconductor/bioconductor_docker",
tag = "latest"
)
## The above functions works the same as
BiocDockerManager::maintainer(tag = "latest")
}The version number of the Docker image is very informative as it tells
us the Bioconductor version number in the x.y coordinates, and the .z
informs us the version of the Dockerfile which is used to build the
Docker image.
if(do_it) {
BiocDockerManager::version(
repository = "bioconductor/bioconductor_docker",
tag = "latest"
)
## Get image version
BiocDockerManager::version(tag = "latest")
BiocDockerManager::version(tag = "devel")
BiocDockerManager::version(tag = "RELEASE_3_10")
}It is always a good idea to check if the images you are running are the latest ones produced by Bioconductor and if they are up to date.
The valid() function takes the digest of the docker image you
have locally and the docker image hosted on Dockerhub to check if the
digest SHA’s match.
If the docker image is not updated, you might want to install() the
latest version of the Docker image.
The function returns a tibble with the images that need to be updated. The tibble contains repository name and tag.
if(do_it) {
BiocDockerManager::valid(
repository = "bioconductor/bioconductor_docker",
tag = "latest"
)
}This use_dockerfile() functionality is for people who want to
contribute Docker images to the Bioconductor ecosystem. The function
has side-effects where it creates a directory and populates it with a
Dockerfile and README.md.
The Dockerfile has fields which are required for the submission process of the Docker image.
There are best practices and standards on how to contribute new docker images to Bioconductor. The following link has more information. http://bioconductor.org/help/docker/#contribute
BiocDockerManager::use_dockerfile()We hope to provide functionality which is useful to R and Bioconductor Docker users in the form of an R package.
The typical workflow would look like the following:
First, you check the available() images. Then you install() a
required image say bioconductor/bioconductor_docker:devel.
if (do_it) {
## 1. Check available images
BiocDockerManager::available()
## 2. Install a new image
BiocDockerManager::install(
repository = "bioconductor/bioconductor_docker",
tag = "devel"
)
}Once some time has passed and if you are not sure if you image is up
to date, you have to check if the image is valid().
Then, install() an update if the validity check returns that it is
out of date. Check the version() of the latest image to make sure
you understand the consequences of updating.
if (do_it) {
## 3. Check if image is valid
BiocDockerManager::valid(
"bioconductor/bioconductor_docker",
tag = "devel"
)
## 4. Download update to image
BiocDockerManager::install(
"bioconductor/bioconductor_docker",
tag = "devel"
)
## 5. Check version
BiocDockerManager::version(
"bioconductor/bioconductor_docker",
tag = "devel"
)
}These are just general examples on how to use this package and it’s functionality.
sessionInfo()## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
##
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## BLAS: /home/biocbuild/bbs-3.15-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.15-bioc/R/lib/libRlapack.so
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## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
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## other attached packages:
## [1] dplyr_1.0.9 BiocDockerManager_1.8.1 BiocStyle_2.24.0
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## [1] bslib_0.4.0 compiler_4.2.1 pillar_1.8.1
## [4] BiocManager_1.30.18 jquerylib_0.1.4 tools_4.2.1
## [7] digest_0.6.29 jsonlite_1.8.0 evaluate_0.16
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