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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.
Promote uses the application NGINX as a load balancer. In Promote, NGINX is configured to require TLS (Transport Layer Security) or SSL (Secure Sockets Layer) certificates. This article goes through the step by step process of using your own TLS/SSL certificate during installation or updating your TLS/SSL certificates after installation.
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:
PRODUCT: Alteryx Promote
LAST UPDATE: 05/23/2018
Want to get started with Promote, but don't know Python?
This article describes:
What to install for Python (at least one way) and how to install the Promote library (there are many different ways to do this – this is only one)
Where to find some useful documentation (GitHub, Community)
How to test and deploy a Python model into Promote and test it on the web console
How to deploy a model from Alteryx and score it from Alteryx as well as in the web console
Download Anaconda 3.6 (https://www.anaconda.com/download/ ) – this includes Python and other programs you’ll need to get started. I found this the easiest way to proceed.
Once installed, run the Anaconda Prompt (I use this instead of command line).
Update PIP: python -m pip install -U pip
Install Promote package: python -m pip install promote
Obtain the sample models from Github.
Code-Friendly models - R and Python examples
For this document, we will use the Python models
The link will take you to the examples page of the promote-python library. Click on the parent to go up a level.
This page has a ton of useful information that I recommend you read. For getting started, download the library to your computer.
I recommend creating a repository folder on your computer and storing the files there.
Log into the Promote web app
Go to your Account page and obtain your user name and API Key – store these in a file or somewhere easy to access as you’ll need the information.
Now – let’s publish an example from Python into Promote.
Open main.py from the \repositories\promote-python-master\examples\hello-world folder using your favorite text editor. I like Sublime Text ( https://www.sublimetext.com/ ) but you can use whatever you’d like. Windows 10 has a built-in editor or some people prefer Notepad++.
Edit the USERNAME, API_KEY, and PROMOTE_URL information with your information. I would recommend copying this section of the code so it’s easy to re-use.
Before deploying, test the model. EXTREMELY IMPORTANT! I’ll show two ways to test the model: Command Prompt and Jupyter
First, comment out the line with the deploy method.
Navigate to the folder that contains the hello-world main.py file you just edited from the Anaconda Command Prompt.
Type python main.py to test the code.
Jupyter – this is great for testing and learning Python in my opinion. NOTE: you can’t deploy Jupyter notebooks to Promote. https://github.com/alteryx/promote-python/tree/master/examples/svc-classifier
Launch the Jupyter Notebook
Navigate to the hello-world folder
Click on main.py
Copy the code
Go back to the directory page and create a new Jupyter notebook
Paste the code into the notebook
Comment out the deploy method and run the module.
To deploy the model:
Now, edit the main.py file and uncomment the deploy method.
Looking at the instructions from Github (https://github.com/alteryx/promote-python/tree/master/examples/hello-world ), we need to install the requirements.txt file for this model. Type: pip install -r requirements.txt
Now deploy the model by typing python main.py
You should now see your model in Promote
Click on the model and test it from the web console. Model tests require a line-delimited JSON file (.ldjson for short) – see https://github.com/json-ld/json-ld.org
Now, try publishing the IrisClassifier model yourself. To test it, use the following code:
In Alteryx, you can easily deploy a model to Promote
When the workflow is run, the model will be promoted.
You can test the model using the Promote web console.
You can now score sample data using either the Alteryx model, or the model running on Promote.