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Alteryx Promote Knowledge Base

Definitive answers from Promote experts.
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.
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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! 
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This article provides an overview of the administrative options in Promote (excluding user management, which can be found here).   Basic   Models   Within the Admin Dashboard, an Admin user can view a list of all the models deployed to any environment by any user by clicking on the Models tab.      System Overview   Within the Admin dashboard, an Admin user can monitor system health metrics for each node in the Promote cluster by clicking on the System Overview tab.     Advanced   Within the Admin dashboard, an admin user can adjust several settings that affect the performance and behavior of the system by clicking on the Advanced tab.    Base Image   An Admin user can change the base image used to deploy both R and Python models. An admin user may do this if they create a new image that has custom R or Python libraries available on it, or if they'd like to use a different version of R or Python.       Disk Bundle Limit   An admin user can change the disk bundle limit to protect the system against running out of disk space. The disk bundle limit limits the number of versions of a model that are stored on disk.            Prediction Logging   Promote can store logs for every prediction request made for up to 14 days, with a maximum of 50GB. You can toggle this logging on and off for Development/Staging and Production in this section.         We hope this gives you a good foundation for administering your Promote instance. Good luck, we're all counting on you.   
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An overview of user management in Promote. 
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How To: Restore a Promote PostgreSQL Database   This article outlines the process of restoring the Promote PostgreSQL database from a backup. For instructions on creating a backup, please see How To: Create a Promote PostgreSQL Database Backup.   Use these steps only if there is no data in your database.    Only run these commands if: Your PostgreSQL database is corrupted. Your database is in a new state.   Prerequisites   Alteryx Promote  ≥  2018.2.1   Procedure   The PostgreSQL database must be restored from the Master node, and not just the Leader node (note: these can be the same node). To return a list of node IDs, run the following command: docker node ls --format='{{.ID}}' To check if a node is the Master node, run the following command on each node ID from the list returned by the above command, replacing {node_id} with the node ID. docker node inspect {node_id} --format='{{.Spec.Labels.master}}' Once you have determined the Master node (where "yes" is the returned response value from the above command), run the following commands from that node to start the PostgreSQL restoration process. Ensure the backed up PostgreSQL database is on your host machine.  Provide the Database Password with the following command: cat /var/promote/credentials/db.txt Copy the PostgreSQL database from the master node to the promote-db  container on the same node, where /location/path is the location of the backed-up database: docker cp /location/path $(docker ps | grep promote-db | awk '{print$1}'):/var/backups/postgres  On the host machine run the following bash command : docker exec -it $(docker ps | grep promote-db | awk '{print$1}') bash Restore the database within the promote-db container: pg_restore -c -U ${POSTGRES_USER} -d ${POSTGRES_DB} -v "/var/promote/postgres/{database name}" You should now be able to log into the UI and see your predictive models rebuild and go online. If not, please open a support ticket in the Case Portal.
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How To: Backup the Promote PostgreSQL database   Promote uses a PostgreSQL database. This article explains how to create a backup of a Promote instance's database. Backing up your Promote database can save you time and headaches in the event of a system failure.   Prerequisites   Alteryx Promote  ≥ 2018.1   Procedure   The PostgreSQL database must be backed up from the Master node, and not just the Leader node (note: these can be the same node). To return a list of node IDs, run the following command: docker node ls --format='{{.ID}}' To check if a node is the Master node, run the following command on each node ID from the list returned by the above command, replacing {node_id} with the node ID. docker node inspect {node_id} --format='{{.Spec.Labels.master}}' Once you have determined the Master node (where "yes" is the returned response value from the above command), run the following commands from that node to start the PostgreSQL backup process. On the host machine  run  the following bash command : docker exec -it $(docker ps | grep promote-db | awk '{print$1}') bash Run the backup script: sh /scripts/backups.sh Change to the directory of the database backup: cd /var/backups/postgres Copy the backup file to the host machine (the node that is hosting the promote-db docker container) from the database container (promote_app). This command needs to be run outside of the database container, changing /location/path to the database path on the host machine: docker cp $(docker ps | grep promote-db | awk '{print$1}'):/var/backups/postgres/{backup db name} /location/path A backup of your database should now be saved in your specified directory. You deserve a coffee!
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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.
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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")  
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