This sets up a pipeline for periodic re-training of your Personalize model. Once deployed, the sample will automatically trigger a re-import of your data and re-train the Personalize model (using the same parameters that you initially configured). The cadence of the re-training can be configured in the template (defaults to once per 7 days).
The pipeline deployed via the templates provided here does not setting up include the model in Amazon Personalize, but relies on an already predeployed model. In the following, we'll list the required steps to be able to deploy this template and its prerequisites.
These prerequisites are required before setting this pipeline up:
- You need to have a set up of Amazon Personalize, for later steps you will need ARNs of
- A role that is able to import the datasets
- Amazon S3 paths of the datasets
- ARNs of solution and/or campaigns to be updated via this pipeline
- The AWS CLI installed
- Make installed: If you don't have make, you might work around this by executing the commands in the Makefile using a script
- An Amazon S3 bucket for deploying these scripts as part of the Makefile
Once you have done the steps listed in the Prerequisites, you need to:
- Fill in the S3 bucket you created for housing the deployment files, as well as the AWS Region to the first lines of the Makefile.
- Update the required parameters (e.g., ARNs from the datasets, retraining rate,...) in the parameters.cfg. You'll find a list of all parameters and their description below.
- (Optional): You might want to adjust the (currently fixed)
--stack-namefrom the Makefile in case you want to deploy multiple pipelines. (See Advanced use)
Now you can run make from your command line to deploy the pipeline.
The parameters are
| Parameter Name | Description | Required |
|---|---|---|
| ImportRoleArn | ARN of the role that is used to re-import the dataset from S3 (requires read privileges to the S3 bucket containing the dataset). | Yes |
| SolutionArn | ARN of the Solution that should be used to create a new version for the re-imported dataset. | Yes |
| UserDatasetArn | ARN of the user dataset (in Personalize) that should be re-imported. If left empty, the dataset will not be re-imported. | No (*) |
| S3UserDatasourcePath | S3 Path to the csv file, the user dataset should be re-imported from. If left empty, the dataset will not be re-imported. | No (*) |
| ItemDatasetArn | ARN of the item dataset (in Personalize) that should be re-imported. If left empty, the dataset will not be re-imported. | No (*) |
| S3ItemDatasourcePath | S3 Path to the csv file, the item dataset should be re-imported from. If left empty, the dataset will not be re-imported. | No (*) |
| UserInteractionDatasetArn | ARN of the user item interaction dataset (in Personalize) that should be re-imported. If left empty, the dataset will not be re-imported. | No (*) |
| S3UserInteractionDatasourcePath | S3 Path to the csv file, the user item interaction dataset should be re-imported from. If left empty, the dataset will not be re-imported. | No (*) |
| CampaignArn | The ARN of the campaign that should be updated (with the retrained solution version). No campaign is updated if this is left empty. | No |
| RetrainingRate | Rate at which the Personalize should be retrained, defaults to 7 days if not set. | No |
(*) One of the datasources needs to be updated, otherwise, creating a new solution version does not make sense. For the datasource that should be updated, the dataset ARN as well as the S3 path are required.
The parameter --stack-name in Makefile determines the name of the stack. You can modify it by setting the environment variable STACK_NAME before running make, like so
$ STACK_NAME=personalize-retraining-2 makeParameters by default are loaded form parameters.cfg. You can override this behaviour by setting the PARAMS_FILE environment variable.
The combination of the above two options, allows you to practically run multiple instances of retraining automation in parallel. Let's say you have a dataset for your production enviroment and another one for your staging:
$ STACK_NAME=personalize-retraining-staging PARAMS_FILE=parameters.staging.cfg make # For staging
$ STACK_NAME=personalize-retraining-prod PARAMS_FILE=parameters.prod.cfg make # For prod
High level architecture overview of Personalize retraining setup
This library is licensed under the MIT-0 License. See the LICENSE file.