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Platform Product: Promote Issues – Working with Alteryx Customer Support Engineers (CSEs) (for use by CSEs and Alteryx Customers)
To EXPEDITE the resolution of your case, please include the below information.
Promote - Requested Information
*** Suggestion: copy/paste the questions below and email the supporting documentation to firstname.lastname@example.org
1. Detailed description of the Issue
2. Alteryx Version
Promote – Requested Information (Detailed Instructions):
1. Detailed Description of the Issue – What issues are you having? Has it worked in the past? When did the issue start? Are all users affected or just some? What are the steps to reproduce your issue? What have you tried to resolve the issue? Have you searched the Alteryx Community ?
2. Screenshot of Alteryx Version– Our CSEs need to know the precise version of Alteryx so we can replicate any issues.
If problem involves Alteryx Designer (Scoring or Deploy tools), please provide version too. In Designer, on your desktop or Server, click Help >> About and provide a screenshot.
The screenshot will include whether it is Server or Designer. In addition, whether it is “Running Elevated” Admin vs. Non-Admin.
Promote Part 1
Promote Part 2
Promote Part 3
There are two tools in Alteryx Designer that connect to Promote; the Deploy tool and the Score tool. The Deploy tool allows you to send trained models from Alteryx Designer to Promote. The Score tool allows you to connect to a model that has already been deployed to Promote to create predictions from a provided data set.
The first step is to have a model object from a trained model. You can use any of the standard Predictive Tools to train a model (including the R tool), as long it is not from the revoscaler package, which is not currently supported by Promote.
In this example, let's say we are interested in training a random forest model to predict forest type (classification) based on remotely sensed spectral data. The study area covers Japan, and the predictor variables include values for visible to near-infrared wavelengths, derived from ASTER satellite imagery.
After performing some data investigation and pre-processing (this dataset is already very clean) we can create, refine, and ultimately select our model.
Once we have a model we are happy with, we can send it to Promote using the Deploy tool. You can start by adding a Deploy tool to the canvas and adding it to the O anchor of your selected model.
If you haven't already, connect your Alteryx Designer instance to Promote.
To being the process of adding a Promote connection, click the Add Connection button in the Configuration window of the Deploy tool.
After clicking the Add Connection button, a modal window will pop up on your screen. Type your Promote instance's URL in the first screen and click Next.
Now add your Username and API key.
For your API key, you may need to log in to your Promote instance and navigate to the Account page.
Once you have your username and API key correctly added to the modal window, click Connect. If all your information checks out, you will see this success message.
After clicking Finish, there will be an option in your Alteryx Promote Connection drop-down menu. You will also see a new option to Remove Connection.
To deploy a model, give it a name in the Model Name setting and run your workflow. If this is a new or updated version of a model that already exists on Promote, give it the same name as the currently deployed version, and check the Overwrite existing model option.
After running the workflow, if the model deploys successfully, you will see a message from the Deploy tool that says "your model is building, check the UI for build logs" in your results window.
To check the build logs, navigate back to the Promote UI in your web browser, click on your model, and then click on the Logs tab. You still see the messages from the model building process. If all is well, the log will end with a "model built successfully" message.
Your model now lives on Promote!
PRODUCT: Alteryx Promote
LAST UPDATE: 05/23/2018
Alteryx Promote allows model deployments in 3 forms:
Designer via a Predictive Tool (i.e. Logistic Regression Tool --> Deploy Tool)
R (either from the R code tool, or from an R program such as RStudio)
For items 2 and 3, we host public repositories of example models (below) that show how to deploy predictive models. All of these examples include READMEs that explain 1) what the model does, and 2) how to deploy it.
Example Python Models | Example R Models
For Python models, you must:
Have Python3 installed
Install the promote python CLI:
pip install promote
For R models, you must:
Have R installed
Install the promote R CLI: