Now, we have seen how we can call a deployed function from our code, but how do we deploy something ourselves?
This requires two main steps:
- You need to annotate your script to let LMRTFY know about the input variables and the results of your script.
- The actual deployment of your script.
Annotate your script¶
The annotation of your script tells the lmrtfy tool which python variables are considered inputs and
outputs, which is done via the
This step is important, because lmrtfy traces the calls to
result to create a profile
for the code. This profile includes the inputs and outputs as well as the additional meta information
unit, and possibly more in the future).
Let's assume that you have create a script to calculate the velocity of an object after a certain time:
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Now, if you want to recalculate for a different time, you would edit the script and run it again. While this might work for a small script like this, this becomes tedious if you have different input variables and want others to use your script easily, too.
Let's change the script in such a way that lmrtfy can create a profile which can be used to deploy the function and make it available to other users:
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resultare the imports that are needed to annotate your code to make it work with LMRTFY.
variableannotates any inputs of your script.
resultannotates a result of script. A script can have multiple results.
If you run
python free_fall_lmrtfy.py you get the exact same result as before. During the run,
lmrtfy created the profile for
free_fall_lmrtfy.py which will be needed to deploy the function.
Create the annotation profile¶
It is required to run your script at least once with the regular python interpreter to create the annotation profile which will be used to generate the API.
$ python <script.py>
The profile is currently saved under
~/.lmrtfy/profiles. The profile is necessary for the deployment,
and is human-readable; however, there should be no need to check the created profile unless for