Alteryx Promote Knowledge Base

PRODUCT: Alteryx Promote VERSION: 2018.1 LAST UPDATE: 05/23/2018 ATTACHMENT: none  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) Python3   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: install.packages("promote")  

PRODUCT: Alteryx Promote VERSION: 2018.1 LAST UPDATE: 05/23/2018 ATTACHMENT: 2018.1   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 Command Prompt 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.