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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. 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
Often, when deploying a model up to Promote, the model requires certain dependencies to run. These dependencies can be certain functions, files, etc. If your model requires them, you’ll need to create a promote.sh, which contains commands to import these dependencies. This will be one of the factors needed to ensure your model will be set up for success on Promote, because sometimes a model needs a little help.
If we go to https://github.com/alteryx/promote-python we can go into the article-summarizer example, which contains one of these promote.sh files. You’ll notice that if you open the file, you’ll see this command:
python -c "import nltk; nltk.download('punkt')"
This is required because the newspaper package in the model (main.py) requires an NLP dataset. Now, when we deploy the model, the promote.sh file will run at the same time, which will ensure the dependencies live inside the model environment (docker model image). We can now properly test the model in Promote!
If we're looking at an R example (there is one on the Promote GitHub), you will have the same folder structure, except the promote.sh file will look something like this:
curl https://packages.microsoft.com/keys/microsoft.asc | apt-key add -
curl https://packages.microsoft.com/config/ubuntu/16.04/prod.list > /etc/apt/sources.list.d/mssql-release.list
ACCEPT_EULA=Y apt-get -y install msodbcsql17
apt-get -y install unixodbc-dev
apt-get -y install r-cran-rodbc
apt-get -y install libiodbc2-dev
In this case, our model requires an ODBC driver, therefore our model container will also need it in order to run on Promote. Just as in the above Python example, when we deploy this model, the promote.sh file will run and the proper driver will be installed, enabling us to work and test this model on Promote!
Once you get these all set, you'll be good to venture on and make your model the best it can be!
This article outlines the step-by-step procedure for enabling a set of backup nodes as a production cluster. This article does not describe how to capture the backup clones. Typically, these backup clones should come from a snapshot. The specific process for capturing these clones will depend on your deployment's infastructure.
Although the Admin page indicates that there are models deployed to the Promote instance, the models are not appearing in the Promote UI home page.
Alteryx Promote ≥ 2018.2.1
There was an issue with your web browser cache, causing an incomplete view of the Promote UI home page to load.
Clear your browser's cache for the Promote UI site address.
Follow the steps provided for your browser here.
If following the steps to clear the cache for your browser does not resolve the issue, please open a support ticket using the Case Portal.
Models deployed to Promote can be queried through a couple of different ways, one of them being a standard REST API post request. Querying a model consists of sending in the predictor variables to the model, allowing the model to process the data and make predictions. After the prediction is made from the model, the return is the score based on the predictor variables entered.
One of the most important features of Promote is its ability to return near-real-time predictions from deployed models. Here is a list of frequently asked questions relating to Promote prediction requests.
Promote is data science model hosting and management software that allows its users to seamlessly deploy their data science models as highly available microservices that return near-real-time predictions by leveraging REST APIs. In this article, we provide an overview of Promote’s technical requirements and architecture.
Some Promote customers have run into an issue where the status of the predictive model will flicker between online and offline continuously on the Promote UI page. This article discusses the cause of the issue, as well as how to resolve it.
Welcome to part 3 of the Supporting Promote series. In this series, we will tackle some common issues and questions, and provide best practices for troubleshooting. This article will step through the process of restoring the Promote web app.
Welcome to part 4 of the Supporting Promote series. In this series, we will tackle some common issues and questions, and provide best practices for troubleshooting. This article will demonstrate backing up and restoring your Promote PostgreSQL database.
Welcome to part 2 of the Supporting Promote series. In this series, we will tackle some common issues and questions, and provide best practices for troubleshooting. In this article, we will be investigating one common "Promote Service Down" scenario - when the promote_logspout and promote_logstash services are down. You can follow these same steps to start troubleshooting other downed services.