# Creating a trained model in AWS Clean Rooms ML
<a name="create-trained-model"></a>

Prerequisites: 
+ An AWS account with access to AWS Clean Rooms
+ A collaboration set up in AWS Clean Rooms
+ A configured model algorithm associated with the collaboration
+ At least one configured ML input channel
+ Appropriate permissions to create and manage ML models in the collaboration

After you have associated the configured model algorithm to a collaboration, then created and configured an ML input channel, you are ready to create a trained model. A *trained model* is used by members of a collaboration to jointly analyze their data.

You can create a trained model using the following procedure. 

Alternatively, you can use incremental training to improve an existing model with new data, or distributed training to train models across multiple compute instances. 

**Topics**
+ [Using incremental training in AWS Clean Rooms ML](use-incremental-training.md)
+ [Using distributed training in AWS Clean Rooms ML](use-distributed-training.md)

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#### [ Console ]

**To create a trained model (console)**

1. Sign in to the AWS Management Console and open the AWS Clean Rooms console at [https://console.aws.amazon.com/cleanrooms](https://console.aws.amazon.com/cleanrooms/home).

1. In the left navigation pane, choose **Collaborations**.

1. On the **Collaborations** page, choose the collaboration where you want to create a trained model.

1. After the collaboration opens, choose the **ML models** tab.

1. Under **Custom ML models**, in the **Trained models** section, choose **Create trained model**.

1. On the **Create trained model** page, for **Associated model algorithm**, specify the algorithm.

1. For **Trained model details**, enter the following:

   1. For **Name**, enter a unique name for the model in the collaboration.

   1. (Optional) For **Description**, enter a description of the trained model.

   1. For **Training data input mode**, choose one of the following: 
      + Select **File** if you have a smaller dataset that can fit on the ML storage volume and you prefer traditional file system access for your training script.
      + Select **Pipe** for large datasets to stream data directly from S3, avoiding the need to download everything to disk, which can improve training speed and reduce storage requirements.
      + Select **FastFile** if you want to combine the benefits of streaming from S3 with file system access, especially for sequentially read data or when dealing with fewer files for faster startup times.

1. For **ML input channel details**, do the following: 

   1. For **ML input channel**, specify the ML input channel that provides data to the model algorithm. 

      To add another channel, choose **Add another ML input channel**. You can add up to 19 additional ML input channels. 

   1. For **Channel name**, enter the name of the ML input channel.

   1. For **Amazon S3 data distribution type**, choose one of the following:
      + Select **Fully replicated** to give each training instance with a complete copy of your dataset. This works best when your dataset is small enough to fit in memory or when each instance needs access to all data.
      + Select **Sharded by S3 key** to divide your dataset across training instances based on S3 keys. Each instance receives about 1/n of the total S3 objects, where 'n' is the number of instances. This works best for large datasets that you want to process in parallel.
**Note**  
Consider your dataset size and training requirements when selecting a distribution type. **Fully replicated** provides complete data access but requires more storage, while **Sharded by S3 key** enables distributed processing of large datasets.

1. For **Maximum training duration**, choose the maximum amount of time you want to train your model.

1. For **Hyperparameters**, specify any algorithm-specific parameters and their intended values. Hyperparameters are specific to the model being trained and are used to fine-tune model training.

1. For **Environment variables**, specify any algorithm-specific variables and their intended values. Environment variables are set in the Docker container.

1. For **Encryption**, to use a custom AWS KMS key, select the **Encrypt secret with a custom KMS key** checkbox.

1. For **EC2 Resource configuration**, specify information about the compute resources that are used for model training. 

   1. For **Instance type**, choose the type of instance you want to run. 

   1. For **Instance count**, enter the number of instances.

   1. For **Volume size in GB**, enter the ML storage volume size.

1. Choose **Create trained model**. 

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#### [ API ]

To create a trained model (API)

The member with the ability to train a model starts training by selecting the ML input channel and the model algorithm.

Run the following code with your specific parameters:

```
import boto3 
acr_ml_client= boto3.client('cleanroomsml')

acr_ml_client.create_trained_model(
    membershipIdentifier= 'membership_id',
    configuredModelAlgorithmAssociationArn = 'arn:aws:cleanrooms-ml:region:account:membership/membershipIdentifier/configured-model-algorithm-association/identifier',
    name='trained_model_name',
    resourceConfig={
        'instanceType': "ml.m5.xlarge",
        'volumeSizeInGB': 1
    },
    dataChannels=[
        {
            "mlInputChannelArn": channel_arn_1,
            "channelName": "channel_name"
        },
        {
            "mlInputChannelArn": channel_arn_2,
            "channelName": "channel_name"
        }
    ]
)
```

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**Note**  
After the trained model is created, you can't edit it. To make changes, delete the trained model and create a new one.