Code samples for custom model import

The following code samples show how to set up permissions, create a custom model import job, view the details of your import jobs and imported models, and delete imported model.

Prepare model files for import
1. If you are importing from an Amazon S3 bucket, you need to supply the model files in the Hugging Face weights format. For more information, see Import source.
2. Create an Amazon S3 bucket for your model files (the names must be unique).
3. Upload the model files into the bucket.

Create a policy to access your model files and attach it to an IAM role with a Amazon Bedrock trust relationship. Select the tab corresponding to your method of choice and follow the steps:

Console

Create Amazon S3 policy to access the Amazon S3 bucket that contains your model files
1. Navigate to the IAM console at https://console.aws.amazon.com/iam and choose Policies from the left navigation pane.
2. Select Create policy and then choose JSON to open the Policy editor.
3. Paste the following policy, replacing ${model-file-bucket} with your bucket name, and then select Next.
```
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "s3:GetObject",
                "s3:ListBucket"
            ],
            "Resource": [
                "arn:aws:s3:::${model-file-bucket}",
                "arn:aws:s3:::${model-file-bucket}/*"
            ]
          }
        ]
      }
```
4. Name the policy S3BucketPolicy and select Create policy.
Create an IAM role and attach the policy.
1. From the left navigation pane, choose Roles and then select Create role.
2. Select Custom trust policy, paste the following policy, and select Next.
```
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Principal": {
                "Service": "bedrock.amazonaws.com"
            },
            "Action": "sts:AssumeRole"
        }
    ] 
}
```
3. Search for the S3BucketPolicy policy you created, select the checkbox, and choose Next.
4. Name the role MyImportModelRole and select Create role.

CLI

Create a file called BedrockTrust.json and paste the following policy into it.


{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Principal": {
                "Service": "bedrock.amazonaws.com"
            },
            "Action": "sts:AssumeRole"
        }
    ] 
}

Create another file called S3BucketPolicy.json and paste the following policy into it, replacing ${model-file-bucket} with your bucket names.


{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "s3:GetObject",
                "s3:ListBucket"
            ],
            "Resource": [
                "arn:aws:s3:::${model-file-bucket}",
                "arn:aws:s3:::${model-file-bucket}/*"
            ]
        }
     ]
}

In a terminal, navigate to the folder containing the policies you created.
Make a CreateRole request to create an IAM role called MyImportModelRole and attach the BedrockTrust.json trust policy that you created.
```
aws iam create-role \
    --role-name MyImportModelRole \
    --assume-role-policy-document file://BedrockTrust.json
```
Make a CreatePolicy request to create the S3 data access policy with the S3BucketPolicy.json file you created. The response returns an Arn for the policy.
```
aws iam create-policy \
    --policy-name S3BucketPolicy \
    --policy-document file://S3BucketPolicy.json
```
Make an AttachRolePolicy request to attach the S3 data access policy to your role, replacing the policy-arn with the ARN in the response from the previous step:
```
aws iam attach-role-policy \
    --role-name MyImportModelRole \
    --policy-arn ${policy-arn}
```

Python

Run the following code to make a CreateRole request to create an IAM role called MyImportModel and to make a CreatePolicy request to create an S3 data access policy called S3BucketPolicy. For the S3 data access policy, replace ${model-file-bucket} with your S3 bucket names.


import boto3
import json

iam = boto3.client("iam")

iam.create_role(
    RoleName="MyImportModelRole",
    AssumeRolePolicyDocument=json.dumps({
        "Version": "2012-10-17",
        "Statement": [
            {
                "Effect": "Allow",
                "Principal": {
                    "Service": "bedrock.amazonaws.com"
                },
                "Action": "sts:AssumeRole"
            }
        ] 
    })
)

iam.create_policy(
    PolicyName="S3BucketPolicy",
    PolicyDocument=json.dumps({
        "Version": "2012-10-17",
        "Statement": [
            {
                "Effect": "Allow",
                "Action": [
                    "s3:GetObject",
                    "s3:ListBucket"
                ],
                "Resource": [
                    "arn:aws:s3:::${training-bucket}",
                    "arn:aws:s3:::${training-bucket}/*"
                ]
            }
          ]
       })
     )

An Arn is returned in the response. Run the following code snippet to make an AttachRolePolicy request, replacing ${policy-arn} with the returned Arn.
```
iam.attach_role_policy(
    RoleName="MyImportModelRole",
    PolicyArn="${policy-arn}"
)
```

Select a language to see code samples to call the custom model import API operations.

CLI

To submit a custom model import job, in a terminal run the following command in the command line, replacing ${my-import-model-role-arn} with the model role that you set up and the s3-bucket-path with the S3 bucket path of your model files.


aws bedrock create-model-import-job 
    --job-name MyImportedModelJobName
    --imported-model-name MyImportedModelName
    --role-arn ${my-import-model-role-arn}  
    --model-data-source '{"s3DataSource": {"s3Uri": s3-bucket-path }}

The response returns a jobArn. The custom import job will take some time to complete. You can use the jobArn with the following command to check the status of the import job.

The following fields are optional:

To add a VPC configuration, add the following argument to the above command to specify the security group and subnets:



   -\\-vpc-config '{securityGroupIds": ["sg-xx"], "subnetIds": ["subnet-yy", "subnet-zz"]}'

To encrypt your model with a KMS key, add the following argument to the above command, replacing the values to specify the key with which you want to encrypt your model.
```
   -\\-customModelKmsKeyId 'arn:aws:kms:region:account-id:key/key-id'
                
```
To add tags, add the following argument to the above command, replacing the keys and values with the tags you want to attach to the job and/or output model and making sure to separate key/value pairs with a space:
```
   -\\-tags key=key1,value=value1 key=key2,value=value2
                
```

The response returns a jobArn. The custom import job will take some time to complete. You can use the jobArn with the following command to check the status of the import job.


aws bedrock get-model-import-job \
    --job-identifier "jobArn"

The response looks similar to this:


{
    "jobArn": ${job-arn} ,
    "jobName": MyImportedModelJobName,
    "importedModelName": MyImportedModelName,
    "roleArn": ${my-role-arn},
    "modelDataSource": {
        "s3DataSource": {
            "s3Uri": "${S3Uri}"
        }
    },
    "status": "Complete",
    "creationTime": "2024-08-13T23:38:42.457Z",
    "lastModifiedTime": "2024-08-13T23:39:25.158Z"

When the status is Complete. the import job is complete.

To run inference on your newly imported model, you must provide the ARN of the imported model as the model-id. Get ARN of the imported model.


aws bedrock list-imported-models

The response contains the model name and the model ARN. Use the model ARN to invoke the imported model. For more information, see Submit a single prompt with InvokeModel.


{
    "modelSummaries": [
        {
            "modelArn": model-arn,
            "modelName": "MyImportedModelName",
            "modelArchitecture":model-architecture,
            "instructSupported":Y,
            "creationTime": "2024-08-13T19:20:14.058Z"
            
        }
    ]
}

To delete your imported model, in a terminal run the following command in the command line, using the model name or the model ARN of the imported model you want to delete.


aws bedrock delete-imported-model 
                --model-identifier MyImportedModelName

Python

Run the following code snippet to submit an custom model import job. Replace my-region with the region where you imported the model, ${my-import-model-role-arn} with the ARN of the MyImportModelRole that you set up and replace ${model-file-bucket} with your S3 bucket name.


import boto3
import json

REGION_NAME = my-region
bedrock = boto3.client(service_name='bedrock',
                       region_name=REGION_NAME)

JOB_NAME = MyImportedModelJobName 
ROLE_ARN = ${my-import-model-role-arn}
IMPORTED_MODEL_NAME = ImportedModelName
S3_URI = ${S3Uri}

# createModelImportJob API
create_job_response = bedrock.create_model_import_job(
    jobName=JOB_NAME,
    importedModelName=IMPORTED_MODEL_NAME,
    roleArn=ROLE_ARN,
    modelDataSource={
        "s3DataSource": {
            "s3Uri": S3_URI
        }
    },
)
job_arn = create_job_response.get("jobArn")

The following fields are optional.

To add a VPC configuration, add the following argument to the above command to specify the security group and subnets:



   vpc-config = {'securityGroupIds: ["sg-xx".], 'subnetIds': [subnet-yy, 'subnet-zz']}'

To encrypt your model with a KMS key, add the following argument to the above command, replacing the values to specify the key with which you want to encrypt your model.
```
   importedModelKmsKeyId = 'arn:aws:kms:region:account-id:key/key-id'
                
```
To add tags, add the following argument to the above command, replacing the keys and values with the tags you want to attach to the job and/or output model and making sure to separate key/value pairs with a space:
```
   jobTags key=key1,value=value1 key=key2,value=value2
                
```

The response returns a jobArn



               job_arn = create_job_response.get("jobArn")

The custom import job will take some time to complete. You can use the jobArn with the following command to check the status of the import job.


bedrock.get_model_import_job(jobIdentifier=jobArn)

When the status is Completed, the import job is complete.

To run inference on your newly imported model, you must provide the ARN of the imported model as the model-id. Get ARN of the imported model.


response_pt = bedrock.list_imported_models(
    creationTimeBefore=datetime (2015,1,1,
    creationTimeAfter= datetime (2015,1,1,
    nameContains = 'MyImportedModelName,
    maxresults = 123
    nextToken = 'none',
    sortBy = 'creationTime',
    sortOrder = 'Ascending'

The response returns the modelArn along with other details of the imported model.


{
    'nextToken': '',
    'modelSummaries': [
        {
            'modelArn': 'your-model-arn',
            'modelName': 'MyImportedModelName',
            'modelArchitecture':model-architecture,
            'instructSupported':Y,
            'creationTime': datetime(2015, 1, 1)
        },
    ]

Use the model ARN to invoke the imported model. For more information, see Submit a single prompt with InvokeModel.

To delete your imported model, use the following command using the model name or the model ARN of the imported model you want to delete.


response = client.delete_imported_model(
    modelIdentifier='MyImportedModelName'
            )

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Document Conventions

Invoke your imported model

Converse API code samples for custom model import