Amazon Lookout for Equipment is no longer open to new customers.
Existing customers can continue to use the service as normal.
For capabilities similar to Amazon Lookout for Equipment see our
blog post
Bulk importing resources
You can import Amazon Lookout for Equipment resources (datasets and models) from a source AWS account to a target AWS account by using the ImportDataset (datasets) or ImportModelVersion (models) operations. If you need to import multiple resources, we recommend that you use the following scripts to bulk import resources.
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Resource CSV file script — Scans the source AWS account to get a list of all datasets and their respective active model versions. It then writes the list to an editable CSV file. You run the script in the source AWS account.
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Resource configuration script — Reads the CSV file generated by the Resource CSV file script and configures the resource policy for the target AWS account. The resource policy grants the target AWS account permissions to import resources from the CSV file. You run this script in the source AWS account.
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Bulk import script — Reads the CSV file that Resource CSV file script generates and calls
ImportDataseton all datasets, and callsImportModelVersionon the respective model versions. You run the script in the target AWS account, and after first running the Resource configuration script in the source AWS account.
Topics
Running the bulk import scripts
Although you can run the scripts in any environment that supports Python and
boto3, we recommend that you run the scripts in an Amazon SageMaker AI notebook instance in Jupyter
Lab. For more information, see https://jupyter.org/
Topics
Creating the Amazon SageMaker AI notebook instances
Use the following procedure to create Amazon SageMaker AI notebook instances in the source AWS account and the target AWS account.
To create the Amazon SageMaker AI notebook instances
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In the AWS account that you want to import resources from (source AWS account), open the Amazon SageMaker AI console and Create a Notebook Instance. For more information, JupyterLab versioning. Enter a name for the new notebook and use the default configurations.
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Make sure that the IAM role that you use has following managed policy permissions:
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AmazonLookoutEquipmentFullAccess. Alternatively, grant permissions to call the following Lookout for Equipment operations:
ListModels, DescribeModelVersion, PutResourcePolicy,importModelVersion,ImportDataset.
In the target AWS account that you want to bulk import resources into, repeat steps 1 and 2.
Getting the resources from the source AWS account
Use the following procedures to get an editable CSV file of resources that you can import from a source AWS account and configure them for import into a target AWS account.
To get the resources from the source AWS account
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In the source AWS account, open Jupyter Lab in the Amazon Sagemaker notebook instance that you created in step 1 of Creating the Amazon SageMaker AI notebook instances.
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Copy each of the following scripts into separate cells within the notebook.
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Run the Resource CSV file script. The script prompts for the following:
The AWS Region in which you want to run the script.
The ID of the target AWS account to which you want to import the resources.
The script generates a CSV file (import_input_file_{current_time}.csv) that you use in the next step. If necessary you can make changes to the CSV before continuing. For more information, see Resource CSV file script
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Run the Resource configuration script. The script prompts for the following information.
The AWS region in which you want to run the script.
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Permission to update the existing policy, if the policy already exists for the source resource Amazon Resource Name (ARN).
The name and path of the csv file (import_input_file_{current_time}.csv) that you created in step 3.
For more information, see Resource configuration script.
Importing the resources to the target AWS account
Use the following procedure to import the resources to the target AWS account.
To import the resources into the target AWS account
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In the target AWS account, open Jupyter Lab in the Amazon SageMaker AI notebook instance that you created in step 3 of Creating the Amazon SageMaker AI notebook instances.
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Copy the Bulk import script into a notebook cell.
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Copy the file import_input_file_{current_time}.csv from the source AWS account to the target AWS account, in the same location where this script is located in the jupyter lab.
Run the Bulk import script. The script prompts for the following:
The AWS Region in which you want to run the script.
The name and path of the csv file (import_input_file_{current_time}.csv) that you copied in step 3.
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After the script finishes, check the import results in the CSV file ( import_result_file_{current_time}.csv) that the script creates. For more information, see Bulk import script.
