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Deploying the function (local runner)

Now you can deploy the function and make it available via the LMRTFY API. This is simply done by running

$ lmrtfy deploy <> --local

The --local flag means that the script will run locally on your computer and waits for jobs from the outside. The LMRTFY API only allows job submissions that fit your deployed annotation profile.

In the future you will be able to deploy directly to the cloud. Then, you do not have to host the runner yourself.


Don't change the script after you have deployed it. The current advice would be to copy and rename the script before deployment. In later versions, this will be taken care of by the lmrtfy tool.


Let's assume that you want to deploy the example script provided in examples/free_fall/ This is simple done by running

$ cd examples/free_fall
$ python # (1)!
$ lmrtfy deploy --local
  1. This step is necessary to create the annotation profile, but testing your script locally is likely part of your development process. Therefore, often this step is not needed.

Running lmrtfy deploy ... --local does two things:

  1. Register the function with name free_fall_lmrtfy in your LMRTFY catalog. If the function already exists it will be replaced if the input and output signatures have not changed. Otherwise, the profile will be rejected. You can force the replacement with the --force-replacement flag.
  2. Start a runner on your local system that takes the submitted jobs and executes them. When you stop the runner you won't receive any results from calling the function.

When a job is submitted the types of the job's input parameters are checked by the LMRTFY API. Furthermore, they are also checked for their bounds and their units. This way, only jobs that can be run successfully with your script will get executed by it.

Deploying to the cloud

LMRTFY is currently not able to deploy your scripts directly to the cloud; however, you can do this manually by using lmrtfy inside of a docker container. The docker container can be run on your laptop, one of your own servers or in the cloud.

The current workaround would be to use lmrtfy on a server or inside a docker container which can be hosted in the cloud.

Inside the docker container you run the same command as if you were to run locally.

Easy cloud deployment will be added in the future.