# S3ModelDataSourceProperty

Specifies the S3 location of ML model data to deploy.

- Source: Generated code
- Type last changed: v2.154.0 · 22 Aug 2024

## Example

```ts
// The code below shows an example of how to instantiate this type.
// The values are placeholders you should change.
import { aws_sagemaker as sagemaker } from 'aws-cdk-lib';
const s3ModelDataSourceProperty: sagemaker.CfnModelPackage.S3ModelDataSourceProperty = {
  compressionType: 'compressionType',
  s3DataType: 's3DataType',
  s3Uri: 's3Uri',

  // the properties below are optional
  modelAccessConfig: {
    acceptEula: false,
  },
};
```

## Properties

- `compressionType`: string (required) — Specifies how the ML model data is prepared.

  If you choose `Gzip` and choose `S3Object` as the value of `S3DataType` , `S3Uri` identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.

  If you choose `None` and chooose `S3Object` as the value of `S3DataType` , `S3Uri` identifies an object that represents an uncompressed ML model to deploy.

  If you choose None and choose `S3Prefix` as the value of `S3DataType` , `S3Uri` identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.

  If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:

  - If you choose `S3Object` as the value of `S3DataType` , then SageMaker will split the key of the S3 object referenced by `S3Uri` by slash (/), and use the last part as the filename of the file holding the content of the S3 object.
  - If you choose `S3Prefix` as the value of `S3DataType` , then for each S3 object under the key name pefix referenced by `S3Uri` , SageMaker will trim its key by the prefix, and use the remainder as the path (relative to `/opt/ml/model` ) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.
  - Do not use any of the following as file names or directory names:

  - An empty or blank string
  - A string which contains null bytes
  - A string longer than 255 bytes
  - A single dot ( `.` )
  - A double dot ( `..` )
  - Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects `s3://mybucket/model/weights` and `s3://mybucket/model/weights/part1` and you specify `s3://mybucket/model/` as the value of `S3Uri` and `S3Prefix` as the value of `S3DataType` , then it will result in name clash between `/opt/ml/model/weights` (a regular file) and `/opt/ml/model/weights/` (a directory).
  - Do not organize the model artifacts in [S3 console using folders](https://docs.aws.amazon.com//AmazonS3/latest/userguide/using-folders.html) . When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.

- `modelAccessConfig`: [IResolvable](/cdk/api/v2/docs/aws-cdk-lib.IResolvable.html) | [CfnModelPackage.ModelAccessConfigProperty](/cdk/api/v2/docs/aws-cdk-lib.aws_sagemaker.CfnModelPackage.ModelAccessConfigProperty.html) — Specifies the access configuration file for the ML model.

  You can explicitly accept the model end-user license agreement (EULA) within the `ModelAccessConfig` . You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

- `s3DataType`: string (required) — Specifies the type of ML model data to deploy.

  If you choose `S3Prefix` , `S3Uri` identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by `S3Uri` always ends with a forward slash (/).

  If you choose `S3Object` , `S3Uri` identifies an object that is the ML model data to deploy.

- `s3Uri`: string (required) — Specifies the S3 path of ML model data to deploy.