Using the command-line interface

Open your command line shell to run commands:

$ om
Usage: om <command> [<action>] [<args>...] [options]
    om (models|datasets|scripts|jobs) [<args>...] [--replace] [--csv...] [options]
    om runtime [<args>...] [--async] [--result] [--param] [options]
    om cloud [<args>...] [options]
    om shell [<args>...] [options]
    om help [<command>]

Similar in structure to the Python API the command-line interface provides access to the

  • storages - access datasets, models, scripts, jobs

  • runtime - interact with the omega|ml runtime

  • cloud - managed service configuration and access

  • shell - the Python shell with omegaml initialized

See the respective section in this guide to learn more about the various commands.

Getting help

The cli provides built-in help

$ om help
Usage: om <command> [<action>] [<args>...] [options]
       om (models|datasets|scripts|jobs) [<args>...] [--replace] [--csv...] [options]
       om runtime [<args>...] [--async] [--result] [--param] [options]
       om cloud [<args>...] [options]
       om shell [<args>...] [options]
       om help [<command>]

Usage of datasets
  om datasets list [<pattern>] [--raw] [-E|--regexp] [options]
  om datasets put <path> <name> [--replace] [--csv=<param=value>]... [options]
  om datasets get <name> <path> [--csv <param>=<value>]... [options]
  om datasets drop <name> [--force] [options]
  om datasets metadata <name> [options]

Usage of models
  om models list [<pattern>] [--raw] [-E|--regexp] [options]
  om models put <module.callable> <name>
  om models drop <name>
  om models metadata <name>

Usage of runtime
  om runtime model <name> <model-action> [<X>] [<Y>] [--result=<output-name>] [--param=<kw=value>]... [--async] [options]
  om runtime script <name> [<script-action>] [<kw=value>...] [--async] [options]
  om runtime job <name> [<job-action>] [<args...>] [--async] [options]
  om runtime result <taskid> [options]
  om runtime ping [options]
  om runtime env <action> [<package>] [--file <requirements.txt>] [--every] [options]
  om runtime log [-f] [options]
  om runtime status [workers|labels|stats] [options]
  om runtime restart app <name> [options]
  om runtime [control|inspect|celery] [<celery-command>...] [--worker=<worker>] [--queue=<queue>] [--celery-help] [--flags <celery-flags>...] [options]

Usage of scripts
    om scripts list [<pattern>] [--raw] [--hidden] [-E|--regexp] [options]
    om scripts put <path> <name> [options]
    om scripts get <name>
    om scripts drop <name> [options]
    om scripts metadata <name>

Usage of jobs
  om jobs list [<pattern>] [--raw] [options]
  om jobs put <path> <name> [options]
  om jobs get <name> <path> [options]
  om jobs drop <name>
  om jobs metadata <name> [options]
  om jobs schedule <name> [show|delete|<interval>] [options]
  om jobs status <name>

Usage of cloud
  om cloud login [<userid>] [<apikey>] [options]
  om cloud config [show] [options]
  om cloud (add|update|remove) <kind> [--specs <specs>] [options]
  om cloud status [runtime|pods|nodes|storage] [options]
  om cloud log <pod> [--since <time>] [options]
  om cloud metrics [<metric_name>] [--since <time>] [--start <start>] [--end <end>] [--step <step>] [--plot] [options]

Usage of shell
    om shell [<command>] [options]

  -h, --help         Show this screen
  --version          Show version.
  --loglevel=LEVEL   INFO,ERROR,DEBUG [default: INFO]
  --copyright        Show copyright
  --config=CONFIG    configuration file
  --bucket=BUCKET    the bucket to use
  --local-runtime    use local runtime
  -q, --noinput      don't ask for user input, assume yes
  -E                 treat patterns as regular expressions

Options for datasets
  --raw   return metadata

Options for runtime
  --async           don't wait for results, will print taskid
  -f                tail log
  --require=VALUE   worker label
  --flags=VALUE     celery flags, list as "--flag VALUE"
  --worker=VALUE    celery worker
  --queue=VALUE     celery queue
  --celery-help     show celery help
  --file=VALUE      path/to/requirements.txt
  --local           if specified the task will run locally. Use this for testing
  --every           if specified runs task on all workers

Options for cloud
  --userid=USERID   the userid at (see account profile)
  --apikey=APIKEY   the apikey at (see account profile)
  --apiurl=URL      the cloud URL [default:]
  --count=NUMBER    how many instances to set up [default: 1]
  --node-type=TYPE  the type of node [default: small]
  --specs=SPECS     the service specifications as "key=value[,...]"
  --since=TIME      recent log time, defaults to 5m (5 minutes)
  --start=DATETIME  start datetime of range query
  --end=DATETIME    end datetime of range query
  --step=UNIT       step in seconds or duration unit (s=seconds, m=minutes)
  --plot            if specified use plotext library to plot (preliminary)

Options for scripts
    --hidden   list hidden entries

Options for jobs
  --cron <spec>       the cron spec, use
  --weekday <wday>    a day number 0-6 (0=Sunday)
  --monthday <mday>   a day of the month 1-31
  --month <month>     a month number 1-12
  --at <hh:mm>        the time (same as --hour hh --minute mm)
  --hour <hour>       the hour 0-23
  --minute <minute>   the minute 0-59
  --next <n>          show next n triggers according to interval

Working with datasets
     For csv files, put and get accept the --csv option multiple times.
     The <param>=<value> pairs will be used as kwargs to pd.read_csv (on put)
     and df.to_csv methods (on get)

Working with models
    Work with models

Working with runtime
  model, job and script commands

  <model-action> can be any valid model action like fit, predict, score,
  transform, decision_function etc.

  <script-action> defaults to run
  <job-action> defaults to run

    om runtime model <name> fit <X> <Y>
    om runtime model <name> predict <X>
    om runtime job <name>
    om runtime script <name>
    om runtime script <name> run myparam="value"

  running asynchronously

  model, job, script commands accept the --async paramter. This will submit
  the a task and return the task id. To wait for and get the result run use
  the result command

        om runtime model <name> fit <X> <Y> --async
        => <task id>
        om runtime result <task id>
        => result of the task

  restart app

  This will restart the app on omegaml apphub. Requires a login to omegaml cloud.


  Prints workers, labels, list of active tasks per worker, count of tasks

    om runtime status             # defaults to workers
    om runtime status workers
    om runtime status labels
    om runtime status stats

  celery commands

  This is the same as calling celery -A omegaml.celeryapp <commands>. Command
  commands include:

  inspect active         show currently running tasks
  inspect active_queues  show active queues for each worker
  inspect stats          show stats of each worker, including pool size (processes)
  inspect ping           confirm that worker is connected

  control pool_shrink N  shrink worker pool by N, specify 99 to remove all
  control pool_grow N    grow worker poool by N
  control shutdown       stop and restart the worker

        om runtime celery inspect active
        om runtime celery control pool_grow N

  env commands

  This talks to an omegaml worker's pip environment

  a) install a specific package

     env install <package>    install the specified package, use name==version pip syntax for specific versions
     env uninstall <package>  uninstall the specified package

     <package> is in pip install syntax, e.g.

     env install "six==1.0.0"
     env install "git+"

  b) use a requirements file

     env install --file requirements.txt
     env uninstall --file requirements.txt

  c) list currently installed packages

     env freeze
     env list

  d) install on all or a specific worker

     env install --require gpu package
     env install --every package

     By default the installation runs on the default worker only. If there are multiple nodes where you
     want to install the package(s) worker nodes, be sure to specify --every

        om runtime env install pandas
        om runtime env uninstall pandas
        om runtime env install --file requirements.txt
        om runtime env install --file gpu-requirements.txt --require gpu
        om runtime env install --file requirements.txt --every

Working with cloud
  om cloud is available for the omega|ml managed service at

  Logging in

  $ om cloud login <userid> <apikey>

  Showing the configuration

  $ om cloud config

  Building a cluster

  Set up a cluster

  $ om cloud add nodepool --specs "node-type=<node-type>,role=worker,size=1"
  $ om cloud add runtime --specs "role=worker,label=worker,size=1"

  Switch nodes on and off

  $ om cloud update worker --specs "node-name=<name>,scale=0" # off
  $ om cloud update worker --specs "node-name=<name>,scale=1" # on

  Using Metrics

  The following metrics are available

  * node-cpu-usage      node cpu usage in percent
  * node-memory-usage   node memory usage in percent
  * node-disk-uage      node disk usage in percent
  * pod-cpu-usage       pod cpu usage in percent
  * pod-memory-usage    pod memory usage in bytes

  Get the specific metrics as follows, e.g.

  $ om cloud metrics node-cpu-usage
  $ om cloud metrics pod-cpu-usage --since 30m
  $ om cloud metrics pod-memory-usage --start 20dec2020T0100 --end20dec2020T0800

Working with scripts
    Work with scripts

Working with jobs
    Specify the schedule either as

    * a natural language-like text, with any time components separated
      by comma

      om jobs schedule myjob "every 5 minutes, on fridays, in april"
      om jobs schedule myjob "at 6:00, on fridays"
      om jobs schedule myjob "at 6:00/10:00, on fridays"
      om jobs schedule myjob "every 2nd hour, every 15 minutes, weekdays"

Working with shell
    Without a command will start an IPython shell with omega|ml ready to use

    $ om shell
    => { ... }

    By passing a command, run arbitrary Python code

    $ om shell ""