Tag: API

TabPy: how do I know what models are available?

Pre…

TabPy allows deploying functions that can be called from Tableau side (more technical details are at Using Deployed Functions documentation page). With that feature, you may create a repository of models (this is what I call deployed functions in this post) with many entries with time. And at some moment the question of getting the list of all available models arises.

There are at least 3 ways to find out what models are available for a specific instance of TabPy. Let’s look at them.

Option 0. Index Page (updated Feb 02, 2020)

With TabPy v0.9.0 release you can just open your TabPy instance host:port in a browser and deployed models together with some other data will be displayed on the page.

Option 1. TabPy Logs

TabPy outputs some logging data in the console and the same log entries are preserved in a log file. The lines you are looking for have “Load endpoint: ” with a model name following it (endpoint here is just another name for a deployed function). In the screenshot below you can see models PCA, Sentiment Analysis, ttest, add, and anova being loaded on TabPy startup and available for being used in Tableau calculations.

Option 2. TabPy State File

On TabPy startup, it informs in the log output about state file location (example in the screenshot below). State file is used by Tornado web-server which TabPy is built around.

The log entry shows the location for the state file. In the file itself, there is [Query Objects Service Versions] section which lists all the deployed models. As you can see the same models you can find in TabPy logs are listed in the section.

Option 3. Use the API, Luke

In the Invoking TabPy API with Postman post, I explained how to use Postman to call TabPy API. And using the API is the best way for this scenario as well – implementation may (and will) change in the future (what is logged, how and where, how functions are deployed and preserved, where the state is preserved and so on) but the API hides all those details.

As documented for /endpoints method it returns a list of all deployed models. And Postman file in TabPy repo has the method in the collection. Simply use that method (specify your TabPy address) and you’ll see something like this:

Returned JSON lists all the deployed models and their properties.

Invoking TabPy API with Postman

TabPy server provides REST API which can be used outside of Tableau (e.g. for debugging purposes), more detail about it can be found here:

At the moment TabPy only supports API v1, but new versions with added or different functionality are possible in the future.

Additionally, read Using Python in Tableau Calculations for how parameters and data are passed to TabPy.

You can call the API from the command line using curl or similar tool, e.g.:

c:\Users\TabPyUser>curl -X POST http://my-tabpy-server:9004/evaluate -d "{\"data\":{\"_arg1\":[1, 2, 3],\"_arg2\":[3, -1, 5]},\"script\":\"return [x + y for x, y in zip(_arg1, _arg2)]\"}"
[4, 1, 8]

But as you can see it is a lot of typing, hard to read and impossible to see if anything is wrong in the address, headers, request body and so on.

Postman is the tool created just for the purpose of invoking REST API. And TabPy has Postman file in the repository with all the supported methods.

Install Postman, download https://github.com/tableau/TabPy/blob/master/misc/TabPy.postman_collection.json file in it and you have UI ready for making TabPy calls:

After loading the file open TabPy collection and click on a method you want to exercise. Replace {{endpoint}} variable with your TabPy server name and port or define the variable to reuse it and click Send button.

Note some methods are GET and some are POST – for POST methods you can specify request body as shown on the screenshot above.

If your TabPy is configured for secure connection simply use https:// instead of http:// in the URL.

In case TabPy is configured with authentication (read how to configure TabPy authentication at https://github.com/tableau/TabPy/blob/master/docs/server-config.md#authentication) use Auth tab for request as shown at screenshot below.

Note TabPy only supports Basic Auth method at the moment as specified on TabPy Authentication page.