# Creation of Algorithm and Model Package Resources


After your training and/or inference code is packaged in Docker containers, create algorithm and model package resources that you can use in your Amazon SageMaker AI account and, optionally, publish on AWS Marketplace.

**Topics**
+ [

# Create an Algorithm Resource
](sagemaker-mkt-create-algo.md)
+ [

# Create a Model Package Resource
](sagemaker-mkt-create-model-package.md)

# Create an Algorithm Resource


You can create an algorithm resource to use with training jobs in Amazon SageMaker AI, and you can publish it on AWS Marketplace. The following sections explain how to do that using the AWS Management Console and the SageMaker API.

To create an algorithm resource, you specify the following information:
+ The Docker containers that contains the training and, optionally, inference code.
+ The configuration of the input data that your algorithm expects for training.
+ The hyperparameters that your algorithm supports.
+ Metrics that your algorithm sends to Amazon CloudWatch during training jobs.
+ The instance types that your algorithm supports for training and inference, and whether it supports distributed training across multiple instances.
+ Validation profiles, which are training jobs that SageMaker AI uses to test your algorithm's training code and batch transform jobs that SageMaker AI runs to test your algorithm's inference code.

  To ensure that buyers and sellers can be confident that products work in SageMaker AI, we require that you validate your algorithms before listing them on AWS Marketplace. You can list products in the AWS Marketplace only if validation succeeds. To validate your algorithms, SageMaker AI uses your validation profile and sample data to run the following validations tasks:

  1. Create a training job in your account to verify that your training image works with SageMaker AI.

  1. If you included inference code in your algorithm, create a model in your account using the algorithm's inference image and the model artifacts produced by the training job.

  1. If you included inference code in your algorithm, create a transform job in your account using the model to verify that your inference image works with SageMaker AI.

  When you list your product on AWS Marketplace, the inputs and outputs of this validation process persist as part of your product and are made available to your buyers. This helps buyers understand and evaluate the product before they buy it. For example, buyers can inspect the input data that you used, the outputs generated, and the logs and metrics emitted by your code. The more comprehensive your validation specification, the easier it is for customers to evaluate your product.
**Note**  
In your validation profile, provide only data that you want to expose publicly.

  Validation can take up to a few hours. To see the status of the jobs in your account, in the SageMaker AI console, see the **Training jobs** and **Transform jobs** pages. If validation fails, you can access the scan and validation reports from the SageMaker AI console. If any issues are found, you will have to create the algorithm again.
**Note**  
To publish your algorithm on AWS Marketplace, at least one validation profile is required.

You can create an algorithm by using either the SageMaker AI console or the SageMaker AI API.

**Topics**
+ [

## Create an Algorithm Resource (Console)
](#sagemaker-mkt-create-algo-console)
+ [

## Create an Algorithm Resource (API)
](#sagemaker-mkt-create-algo-api)

## Create an Algorithm Resource (Console)


**To create an algorithm resource (console)**

1. Open the SageMaker AI console at [https://console.aws.amazon.com/sagemaker/](https://console.aws.amazon.com/sagemaker/).

1. From the left menu, choose **Training**.

1. From the dropdown menu, choose **Algorithms**, then choose **Create algorithm**.

1. On the **Training specifications** page, provide the following information:

   1. For **Algorithm name**, type a name for your algorithm. The algorithm name must be unique in your account and in the AWS region. The name must have 1 to 64 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

   1. Type a description for your algorithm. This description appears in the SageMaker AI console and in the AWS Marketplace.

   1. For **Training image, type the path in Amazon ECR where your training container is stored.**

   1. For **Support distributed training**, Choose **Yes** if your algorithm supports training on multiple instances. Otherwise, choose **No**.

   1. For **Support instance types for training**, choose the instance types that your algorithm supports.

   1. For **Channel specification**, specify up to 8 channels of input data for your algorithm. For example, you might specify 3 input channels named `train`, `validation`, and `test`. For each channel, specify the following information:

      1. For **Channel name**, type a name for the channel. The name must have 1 to 64 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

      1. To require the channel for your algorithm, choose **Channel required**.

      1. Type a description for the channel.

      1. For **Supported input modes**, choose **Pipe mode** if your algorithm supports streaming the input data, and **File mode** if your algorithm supports downloading the input data as a file. You can choose both.

      1. For **Supported content types**, type the MIME type that your algorithm expects for input data.

      1. For **Supported compression type**, choose **Gzip** if your algorithm supports Gzip compression. Otherwise, choose **None**.

      1. Choose **Add channel** to add another data input channel, or choose **Next** if you are done adding channels.

1. On the **Tuning specifications** page, provide the following information:

   1. For **Hyperparameter specification**, specify the hyperparameters that your algorithm supports by editing the JSON object. For each hyperparameter that your algorithm supports, construct a JSON block similar to the following:

      ```
      {
      "DefaultValue": "5",
      "Description": "The first hyperparameter",
      "IsRequired": true,
      "IsTunable": false,
      "Name": "intRange",
      "Range": {
      "IntegerParameterRangeSpecification": {
      "MaxValue": "10",
      "MinValue": "1"
      },
      "Type": "Integer"
      }
      ```

      In the JSON, supply the following:

      1. For `DefaultValue`, specify a default value for the hyperparameter, if there is one.

      1. For `Description`, specify a description for the hyperparameter.

      1. For `IsRequired`, specify whether the hyperparameter is required.

      1. For `IsTunable`, specify `true` if this hyperparameter can be tuned when a user runs a hyperparameter tuning job that uses this algorithm. For information, see [Automatic model tuning with SageMaker AI](automatic-model-tuning.md).

      1. For `Name`, specify a name for the hyperparameter.

      1. For `Range`, specify one of the following:
         + `IntegerParameterRangeSpecification` - the values of the hyperparameter are integers. Specify minimum and maximum values for the hyperparameter.
         + 
         + `ContinuousParameterRangeSpecification` - the values of the hyperparameter are floating-point values. Specify minimum and maximum values for the hyperparameter.
         + `CategoricalParameterRangeSpecification` - the values of the hyperparameter are categorical values. Specify a list of all of the possible values.

      1. For `Type`, specify `Integer`, `Continuous`, or `Categorical`. The value must correspond to the type of `Range` that you specified.

   1. For **Metric definitions**, specify any training metrics that you want your algorithm to emit. SageMaker AI uses the regular expression that you specify to find the metrics by parsing the logs from your training container during training. Users can view these metrics when they run training jobs with your algorithm, and they can monitor and plot the metrics in Amazon CloudWatch. For information, see [Amazon CloudWatch Metrics for Monitoring and Analyzing Training Jobs](training-metrics.md). For each metric, provide the following information:

      1. For **Metric name**, type a name for the metric.

      1. For `Regex`, type the regular expression that SageMaker AI uses to parse training logs so that it can find the metric value.

      1. For **Objective metric support** choose **Yes** if this metric can be used as the objective metric for a hyperparameter tuning job. For information, see [Automatic model tuning with SageMaker AI](automatic-model-tuning.md).

      1. Choose **Add metric** to add another metric, or choose **Next** if you are done adding metrics.

1. On the **Inference specifications** page, provide the following information if your algorithm supports inference:

   1. For **Location of inference image**, type the path in Amazon ECR where your inference container is stored.

   1. For **Container DNS host name**, type the name of a DNS host for your image.

   1. For **Supported instance types for real-time inference**, choose the instance types that your algorithm supports for models deployed as hosted endpoints in SageMaker AI. For information, see [Deploy models for inference](deploy-model.md).

   1. For **Supported instance types for batch transform jobs**, choose the instance types that your algorithm supports for batch transform jobs. For information, see [Batch transform for inference with Amazon SageMaker AI](batch-transform.md).

   1. For **Supported content types**, type the type of input data that your algorithm expects for inference requests.

   1. For **Supported response MIME types**, type the MIME types that your algorithm supports for inference responses.

   1. Choose **Next**.

1. On the **Validation specifications** page, provide the following information:

   1. For **Publish this algorithm on AWS Marketplace**, choose **Yes** to publish the algorithm on AWS Marketplace.

   1. For **Validate this resource**, choose **Yes** if you want SageMaker AI to run training jobs and/or batch transform jobs that you specify to test the training and/or inference code of your algorithm.
**Note**  
To publish your algorithm on AWS Marketplace, your algorithm must be validated.

   1. For **IAM role**, choose an IAM role that has the required permissions to run training jobs and batch transform jobs in SageMaker AI, or choose **Create a new role** to allow SageMaker AI to create a role that has the `AmazonSageMakerFullAccess` managed policy attached. For information, see [How to use SageMaker AI execution roles](sagemaker-roles.md).

   1. For **Validation profile**, specify the following:
      + A name for the validation profile.
      + A **Training job definition**. This is a JSON block that describes a training job. This is in the same format as the [https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TrainingJobDefinition.html](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TrainingJobDefinition.html) input parameter of the [https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAlgorithm.html](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAlgorithm.html) API.
      + A **Transform job definition**. This is a JSON block that describes a batch transform job. This is in the same format as the [https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TransformJobDefinition.html](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TransformJobDefinition.html) input parameter of the [https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAlgorithm.html](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAlgorithm.html) API.

   1. Choose **Create algorithm**.

## Create an Algorithm Resource (API)


To create an algorithm resource by using the SageMaker API, call the [https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAlgorithm.html](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAlgorithm.html) API. 

# Create a Model Package Resource


To create a model package resource that you can use to create deployable models in Amazon SageMaker AI and publish on AWS Marketplace specify the following information:
+ The Docker container that contains the inference code, or the algorithm resource that was used to train the model.
+ The location of the model artifacts. Model artifacts can either be packaged in the same Docker container as the inference code or stored in Amazon S3.
+ The instance types that your model package supports for both real-time inference and batch transform jobs.
+ Validation profiles, which are batch transform jobs that SageMaker AI runs to test your model package's inference code.

  Before listing model packages on AWS Marketplace, you must validate them. This ensures that buyers and sellers can be confident that products work in Amazon SageMaker AI. You can list products on AWS Marketplace only if validation succeeds. 

  The validation procedure uses your validation profile and sample data to run the following validations tasks:

  1. Create a model in your account using the model package's inference image and the optional model artifacts that are stored in Amazon S3.
**Note**  
A model package is specific to the region in which you create it. The S3 bucket where the model artifacts are stored must be in the same region where your created the model package.

  1. Create a transform job in your account using the model to verify that your inference image works with SageMaker AI.

  1. Create a validation profile.
**Note**  
In your validation profile, provide only data that you want to expose publicly.

  Validation can take up to a few hours. To see the status of the jobs in your account, in the SageMaker AI console, see the **Transform jobs** pages. If validation fails, you can access the scan and validation reports from the SageMaker AI console. After fixing issues, recreate the algorithm. When the status of the algorithm is `COMPLETED`, find it in the SageMaker AI console and start the listing process
**Note**  
To publish your model package on AWS Marketplace, at least one validation profile is required.

You can create an model package either by using the SageMaker AI console or by using the SageMaker API.

**Topics**
+ [

## Create a Model Package Resource (Console)
](#sagemaker-mkt-create-model-pkg-console)
+ [

## Create a Model Package Resource (API)
](#sagemaker-mkt-create-model-pkg-api)

## Create a Model Package Resource (Console)


**To create a model package in the SageMaker AI console:**

1. Open the SageMaker AI console at [https://console.aws.amazon.com/sagemaker/](https://console.aws.amazon.com/sagemaker/).

1. From the left menu, choose **Inference**.

1. Choose **Marketplace model packages**, then choose **Create marketplace model package**.

1. On the **Inference specifications** page, provide the following information:

   1. For **Model package name**, type a name for your model package. The model package name must be unique in your account and in the AWS region. The name must have 1 to 64 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

   1. Type a description for your model package. This description appears in the SageMaker AI console and in the AWS Marketplace.

   1. For **Inference specification options**, choose **Provide the location of the inference image and model artifacts** to create a model package by using an inference container and model artifacts. Choose **Provide the algorithm used for training and its model artifacts** to create a model package from an algorithm resource that you created or subscribe to from AWS Marketplace.

   1. If you chose **Provide the location of the inference image and model artifacts** for **Inference specification options**, provide the following information for **Container definition** and **Supported resources**:

      1. For **Location of inference image**, type the path to the image that contains your inference code. The image must be stored as a Docker container in Amazon ECR.

      1. For **Location of model data artifacts**, type the location in S3 where your model artifacts are stored.

      1. For **Container DNS host name **, type the name of the DNS host to use for your container.

      1. For **Supported instance types for real-time inference**, choose the instance types that your model package supports for real-time inference from SageMaker AI hosted endpoints.

      1. For **Supported instance types for batch transform jobs**, choose the instance types that your model package supports for batch transform jobs.

      1. **Supported content types**, type the content types that your model package expects for inference requests.

      1. For **Supported response MIME types**, type the MIME types that your model package uses to provide inferences.

   1. If you chose **Provide the algorithm used for training and its model artifacts** for **Inference specification options**, provide the following information:

      1. For **Algorithm ARN**, type the Amazon Resource Name (ARN) of the algorithm resource to use to create the model package.

      1. For **Location of model data artifacts**, type the location in S3 where your model artifacts are stored.

   1. Choose **Next**.

1. On the **Validation and scanning** page, provide the following information:

   1. For **Publish this model package on AWS Marketplace**, choose **Yes** to publish the model package on AWS Marketplace.

   1. For **Validate this resource**, choose **Yes** if you want SageMaker AI to run batch transform jobs that you specify to test the inference code of your model package.
**Note**  
To publish your model package on AWS Marketplace, your model package must be validated.

   1. For **IAM role**, choose an IAM role that has the required permissions to run batch transform jobs in SageMaker AI, or choose **Create a new role** to allow SageMaker AI to create a role that has the `AmazonSageMakerFullAccess` managed policy attached. For information, see [How to use SageMaker AI execution roles](sagemaker-roles.md).

   1. For **Validation profile**, specify the following:
      + A name for the validation profile.
      + A **Transform job definition**. This is a JSON block that describes a batch transform job. This is in the same format as the [https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TransformJobDefinition.html](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TransformJobDefinition.html) input parameter of the [https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAlgorithm.html](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAlgorithm.html) API.

1. Choose **Create marketplace model package**.

## Create a Model Package Resource (API)


To create a model package by using the SageMaker API, call the [https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateModelPackage.html](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateModelPackage.html) API.