Creating a batch inference job - Amazon Personalize

Creating a batch inference job

Create a batch inference job to get batch item recommendations for users based on input data from Amazon S3. The input data can be a list of users or items (or both) in JSON format. You can create a batch inference job with the Amazon Personalize console, the AWS Command Line Interface (AWS CLI), or AWS SDKs.

When you create a batch inference job, you specify the Amazon S3 paths to your input and output locations. Amazon S3 is prefix based. If you provide a prefix for the input data location, Amazon Personalize uses all files matching that prefix as input data. For example, if you provide s3://<name of your S3 bucket>/folderName and your bucket also has a folder with a path of s3://<name of your S3 bucket>/folderName_test, Amazon Personalize uses all files in both folders as input data. To use only the files within a specific folder as input data, end the Amazon S3 path with a prefix delimiter, such as /: s3://<name of your S3 bucket>/folderName/ For more information about how Amazon S3 organizes objects, see Organizing, listing, and working with your objects.

For more information about the batch workflow in Amazon Personalize, including permissions requirements, recommendation scoring, and preparing and importing input data, see Getting batch item recommendations with custom resources.

Creating a batch inference job (console)

After you have completed Preparing input data for batch recommendations, you are ready to create a batch inference job. This procedure assumes that you have already created a solution and a solution version (trained model).

To create a batch inference job (console)
  1. Open the Amazon Personalize console at https://console.aws.amazon.com/personalize/home and sign in to your account.

  2. On the Dataset groups page, choose your dataset group.

  3. From the navigation pane, under Custom resources, choose Batch inference jobs.

  4. Choose Create batch inference job.

  5. Choose the batch inference job type.

    • To generate item recommendations without themes, choose Item recommendations.

    • If you use the Similar-Items recipe and want to add descriptive themes to groups of similar items, choose Themed recommendations with Content Generator. To generate themes, you must have an Items dataset with item name data and textual data. For more information, see Batch recommendations with themes from Content Generator.

  6. In Batch inference job details, in Batch inference job name, specify a name for your batch inference job.

  7. For Solution, choose the solution and then choose the Solution version ID that you want to use to generate the recommendations.

  8. For Number of results, optionally specify the number of recommendations for each line of input data. The default is 25.

  9. If your batch job generates recommendations with themes, in Themed recommendations details, choose the column containing names or titles for the items in your Items dataset. This data can help generate more relevant themes. For more information, see Batch recommendations with themes from Content Generator.

  10. In Input source, specify the Amazon S3 path to your input file.

    Use the following syntax: s3://<name of your S3 bucket>/<folder name>/<input JSON file name>.json

    Your input data must be in the correct format for the recipe your solution uses. For input data examples see Batch inference job input and output JSON examples.

  11. For Decryption key, if you use your own AWS KMS key for bucket encryption, specify the Amazon Resource Name (ARN) of your key. Amazon Personalize must have permission to use your key. For information about granting permissions, see Giving Amazon Personalize permission to use your AWS KMS key.

  12. In Output destination, specify the path to your output location. We recommend using a different location for your output data (either a folder or a different Amazon S3 bucket).

    Use the following syntax: s3://<name of your S3 bucket>/<output folder name>/

  13. For Encryption key, if you use your own AWS KMS key for encryption, specify the ARN of your key. Amazon Personalize must have permission to use your key. For information about granting permissions, see Giving Amazon Personalize permission to use your AWS KMS key.

  14. For IAM service role, choose the IAM service role you created for Amazon Personalize during set up. This role must have read and write access to your input and output Amazon S3 buckets respectively.

  15. In Filters optionally choose a filter to apply a filter to the batch recommendations. If your filter uses placeholder parameters, make sure the values for the parameters are included in your input JSON. For more information, see Providing filter values in your input JSON.

  16. For Tags, optionally add any tags. For more information about tagging Amazon Personalize resources, see Tagging Amazon Personalize resources.

  17. Choose Create batch inference job. Batch inference job creation starts and the Batch inference jobs page appears with the Batch inference job detail section displayed.

    When the batch inference job's status changes to Active, you can retrieve the job's output from the designated output Amazon S3 bucket. The output file's name will be of the format input-name.out.

Creating a batch inference job (AWS CLI)

After you have completed Preparing input data for batch recommendations, you are ready to create a batch inference job with the CreateBatchInferenceJob operation.

Creating a batch inference job

You can use the create-batch-inference-job command to create a batch inference job. Specify a job name, replace Solution version ARN with the Amazon Resource Name (ARN) of your solution version, and replace the IAM service role ARN with the ARN of the IAM service role you created for Amazon Personalize during set up. This role must have read and write access to your input and output Amazon S3 buckets respectively. Optionally provide a filter ARN to filter recommendations. If your filter uses placeholder parameters, make sure the values for the parameters are included in your input JSON. For more information, see Filtering batch recommendations and user segments (custom resources).

Replace S3 input path and S3 output path with the Amazon S3 path to your input file and output locations. We recommend using a different location for your output data (either a folder or a different Amazon S3 bucket). Use the following syntax for input and output locations: s3://<name of your S3 bucket>/<folder name>/<input JSON file name>.json and s3://<name of your S3 bucket>/<output folder name>/.

The example includes optional User-Personalization recipe specific itemExplorationConfig hyperparameters: explorationWeight and explorationItemAgeCutOff. Optionally include explorationWeight and explorationItemAgeCutOff values to configure exploration. For more information, see User-Personalization recipe.

aws personalize create-batch-inference-job \ --job-name Batch job name \ --solution-version-arn Solution version ARN \ --filter-arn Filter ARN \ --job-input s3DataSource={path=s3://S3 input path} \ --job-output s3DataDestination={path=s3://S3 output path} \ --role-arn IAM service role ARN \ --batch-inference-job-config "{\"itemExplorationConfig\":{\"explorationWeight\":\"0.3\",\"explorationItemAgeCutOff\":\"30\"}}"

Creating a batch inference job that generates themes

To generate themes for similar items, you must use the Similar-Items recipe and your Items dataset must have a textual field and a column of item name data. For more information about recommendations with themes, see Batch recommendations with themes from Content Generator.

The following code creates a batch inference job that generates recommendations with themes. Leave the batch-inference-job-mode set to THEME_GENERATION. Replace COLUMN_NAME with the name of the column that stores your item name data.

aws personalize create-batch-inference-job \ --job-name Themed batch job name \ --solution-version-arn Solution version ARN \ --filter-arn Filter ARN \ --job-input s3DataSource={path=s3://S3 input path} \ --job-output s3DataDestination={path=s3://S3 output path} \ --role-arn IAM service role ARN \ --batch-inference-job-mode THEME_GENERATION \ --theme-generation-config "{\"fieldsForThemeGeneration\": {\"itemName\":\"COLUMN_NAME\"}}"

Creating a batch inference job (AWS SDKs)

After you have completed Preparing input data for batch recommendations, you are ready to create a batch inference job with the CreateBatchInferenceJob operation.

Creating a batch inference job

You can use the following code to create a batch inference job. Specify a job name, the Amazon Resource Name (ARN) of your solution version, and the ARN of the IAM service role you created for Amazon Personalize during set up. This role must have read and write access to your input and output Amazon S3 buckets.

We recommend using a different location for your output data (either a folder or a different Amazon S3 bucket). Use the following syntax for input and output locations: s3://<name of your S3 bucket>/<folder name>/<input JSON file name>.json and s3://<name of your S3 bucket>/<output folder name>/.

For numResults, specify the number of items you want Amazon Personalize to predict for each line of input data. Optionally provide a filter ARN to filter recommendations. If your filter uses placeholder parameters, make sure the values for the parameters are included in your input JSON. For more information, see Filtering batch recommendations and user segments (custom resources).

SDK for Python (Boto3)

The example includes optional User-Personalization recipe specific itemExplorationConfig hyperparameters: explorationWeight and explorationItemAgeCutOff. Optionally include explorationWeight and explorationItemAgeCutOff values to configure exploration. For more information, see User-Personalization recipe.

import boto3 personalize_rec = boto3.client(service_name='personalize') personalize_rec.create_batch_inference_job ( solutionVersionArn = "Solution version ARN", jobName = "Batch job name", roleArn = "IAM service role ARN", filterArn = "Filter ARN", batchInferenceJobConfig = { # optional USER_PERSONALIZATION recipe hyperparameters "itemExplorationConfig": { "explorationWeight": "0.3", "explorationItemAgeCutOff": "30" } }, jobInput = {"s3DataSource": {"path": "s3://<name of your S3 bucket>/<folder name>/<input JSON file name>.json"}}, jobOutput = {"s3DataDestination": {"path": "s3://<name of your S3 bucket>/<output folder name>/"}} )
SDK for Java 2.x

The example includes optional User-Personalization recipe specific itemExplorationConfig fields: explorationWeight and explorationItemAgeCutOff. Optionally include explorationWeight and explorationItemAgeCutOff values to configure exploration. For more information, see User-Personalization recipe.

public static String createPersonalizeBatchInferenceJob(PersonalizeClient personalizeClient, String solutionVersionArn, String jobName, String filterArn, String s3InputDataSourcePath, String s3DataDestinationPath, String roleArn, String explorationWeight, String explorationItemAgeCutOff) { long waitInMilliseconds = 60 * 1000; String status; String batchInferenceJobArn; try { // Set up data input and output parameters. S3DataConfig inputSource = S3DataConfig.builder() .path(s3InputDataSourcePath) .build(); S3DataConfig outputDestination = S3DataConfig.builder() .path(s3DataDestinationPath) .build(); BatchInferenceJobInput jobInput = BatchInferenceJobInput.builder() .s3DataSource(inputSource) .build(); BatchInferenceJobOutput jobOutputLocation = BatchInferenceJobOutput.builder() .s3DataDestination(outputDestination) .build(); // Optional code to build the User-Personalization specific item exploration config. HashMap<String, String> explorationConfig = new HashMap<>(); explorationConfig.put("explorationWeight", explorationWeight); explorationConfig.put("explorationItemAgeCutOff", explorationItemAgeCutOff); BatchInferenceJobConfig jobConfig = BatchInferenceJobConfig.builder() .itemExplorationConfig(explorationConfig) .build(); // End optional User-Personalization recipe specific code. CreateBatchInferenceJobRequest createBatchInferenceJobRequest = CreateBatchInferenceJobRequest.builder() .solutionVersionArn(solutionVersionArn) .jobInput(jobInput) .jobOutput(jobOutputLocation) .jobName(jobName) .filterArn(filterArn) .roleArn(roleArn) .batchInferenceJobConfig(jobConfig) // Optional .build(); batchInferenceJobArn = personalizeClient.createBatchInferenceJob(createBatchInferenceJobRequest) .batchInferenceJobArn(); DescribeBatchInferenceJobRequest describeBatchInferenceJobRequest = DescribeBatchInferenceJobRequest.builder() .batchInferenceJobArn(batchInferenceJobArn) .build(); long maxTime = Instant.now().getEpochSecond() + 3 * 60 * 60; // wait until the batch inference job is complete. while (Instant.now().getEpochSecond() < maxTime) { BatchInferenceJob batchInferenceJob = personalizeClient .describeBatchInferenceJob(describeBatchInferenceJobRequest) .batchInferenceJob(); status = batchInferenceJob.status(); System.out.println("Batch inference job status: " + status); if (status.equals("ACTIVE") || status.equals("CREATE FAILED")) { break; } try { Thread.sleep(waitInMilliseconds); } catch (InterruptedException e) { System.out.println(e.getMessage()); } } return batchInferenceJobArn; } catch (PersonalizeException e) { System.out.println(e.awsErrorDetails().errorMessage()); } return ""; }
SDK for JavaScript v3
// Get service clients module and commands using ES6 syntax. import { CreateBatchInferenceJobCommand } from "@aws-sdk/client-personalize"; import { personalizeClient } from "./libs/personalizeClients.js"; // Or, create the client here. // const personalizeClient = new PersonalizeClient({ region: "REGION"}); // Set the batch inference job's parameters. export const createBatchInferenceJobParam = { jobName: 'JOB_NAME', jobInput: { /* required */ s3DataSource: { /* required */ path: 'INPUT_PATH', /* required */ // kmsKeyArn: 'INPUT_KMS_KEY_ARN' /* optional */' } }, jobOutput: { /* required */ s3DataDestination: { /* required */ path: 'OUTPUT_PATH', /* required */ // kmsKeyArn: 'OUTPUT_KMS_KEY_ARN' /* optional */' } }, roleArn: 'ROLE_ARN', /* required */ solutionVersionArn: 'SOLUTION_VERSION_ARN', /* required */ numResults: 20 /* optional integer*/ }; export const run = async () => { try { const response = await personalizeClient.send(new CreateBatchInferenceJobCommand(createBatchInferenceJobParam)); console.log("Success", response); return response; // For unit tests. } catch (err) { console.log("Error", err); } }; run();

Processing the batch job might take a while to complete. You can check a job's status by calling DescribeBatchInferenceJob and passing a batchRecommendationsJobArn as the input parameter. You can also list all Amazon Personalize batch inference jobs in your AWS environment by calling ListBatchInferenceJobs.

Creating a batch inference job that generates themes

To generate themes for similar items, you must use the Similar-Items recipe and your Items dataset must have a textual field and a column of item name data. For more information about recommendations with themes, see Batch recommendations with themes from Content Generator.

The following code creates a batch inference job that generates recommendations with themes. Leave the batchInferenceJobMode set to "THEME_GENERATION". Replace COLUMNN_NAME with the name of the column that stores your item name data.

import boto3 personalize_rec = boto3.client(service_name='personalize') personalize_rec.create_batch_inference_job ( solutionVersionArn = "Solution version ARN", jobName = "Batch job name", roleArn = "IAM service role ARN", filterArn = "Filter ARN", batchInferenceJobMode = "THEME_GENERATION", themeGenerationConfig = { "fieldsForThemeGeneration": { "itemName": "COLUMN_NAME" } }, jobInput = {"s3DataSource": {"path": "s3://<name of your S3 bucket>/<folder name>/<input JSON file name>.json"}}, jobOutput = {"s3DataDestination": {"path": "s3://<name of your S3 bucket>/<output folder name>/"}} )