Help Info
The most important command in Skelebot is the help command. This command will provide you a high level overview of commands that can be run through Skelebot, as well as detailed information for running specific commands, and even project specific jobs.
> skelebot -h
If this command is executed from inside a folder that is not a Skelebot project, you will be met with a simple message stating that your only options from this particular directory are to scaffold a new project, or install a plugin.
usage: skelebot [-h] [-v] {scaffold,plugin} ...
Skelebot Version: 2.0.0
positional arguments:
{scaffold,plugin}
scaffold Scaffold a new or existing project with Skelebot
plugin Install a plugin for skelebot from a local zip file
optional arguments:
-h, --help show this help message and exit
-v, --version Display the version number of Skelebot
If the help command is run from inside a Skelebot project, the output looks quite different. The scaffold option is no longer available, as it is not needed, and the rest of the Standard Tasks are now present. More details for each task can be obtained by running the desired command with -h appended.
There are also more optional arguments available for these tasks, which allows you to run tasks natively (not in Docker), in specific environments, as well as to skip the build process for Docker. These optional arguments exist for all Skelebot custom jobs and therefore the argument must be placed directly after the skelebot command and before the job name (ex: skelebot -d train).
NOTE: The Artifactory tasks (push and pull) will only be present if artifacts are configuring in the skelebot.yaml file of the project.
usage: skelebot [-h] [-v] [-e ENV] [-d HOST] [-s] [-n] [-c] [-V] {loadData,train,score,push,pull,jupyter,plugin,bump,prime,exec,publish,envs} ...
Iris Example
Example Skelebot Project
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Version: 1.1.0
Environment: None
Skelebot Version: 2.0.0
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positional arguments:
{loadData,train,score,push,pull,jupyter,plugin,bump,prime,exec,publish,envs}
loadData Load the Iris Dataset and save it into the data folder for the train job to access (src/loadData.py)
train Use the data loaded in the loadData job to train the iris model (src/train.py)
score Use the model that was built in the train job to score new data against the iris model (src/score.py)
push Push an artifact to Artifactory
pull Pull an artifact from Artifactory
jupyter Spin up Jupyter in a Docker Container (port=8888, folder=.)
plugin Install a plugin for skelebot from a local zip file
bump Bump the skelebot.yaml project version
prime Generate Dockerfile and .dockerignore and build the docker image
exec Exec into the running Docker container
publish Publish your versioned Docker Image to the registry
envs Display the available environments for the project
optional arguments:
-h, --help show this help message and exit
-v, --version Display the version number of Skelebot
-e ENV, --env ENV Specify the runtime environment configurations
-d HOST, --docker-host HOST
Set the Docker Host on which the command will be executed
-s, --skip-build Skip the build process and attempt to use previous docker build
-n, --native Run natively instead of through Docker
-c, --contact Display the contact email of the Skelebot project
-V, --verbose Print all job commands to the screen just before execution
Version Parameter
The version of Skelebot is printed in the help output, but sometimes that is the only thing you want to check. If you just want to see the currently installed Skelebot version, you can use the version parameter (-v --version) to do just that.
> skelebot --version
Skelebot v.2.0.0
Contact Parameter
Each project specifies a contact email address. This contact email can be retrieved from the command line directly by using the contact parameter (-c --contact).
> skelebot --contact
me@my-email.com