Prerequisites for using Amazon SageMaker Inference Recommender

Create an IAM role for Amazon SageMaker.

Create an IAM role for Amazon SageMaker that has the AmazonSageMakerFullAccess IAM managed policy attached.

Set up your environment.

Import dependencies and create variables for your AWS Region, your SageMaker IAM role (from Step 1), and the SageMaker client.

!pip install --upgrade pip awscli botocore boto3  --quiet
from sagemaker import get_execution_role, Session, image_uris
import boto3

region = boto3.Session().region_name
role = get_execution_role()
sagemaker_client = boto3.client("sagemaker", region_name=region)
sagemaker_session = Session()

(Optional) Review existing models benchmarked by Inference Recommender.

Inference Recommender benchmarks models from popular model zoos. Inference Recommender supports your model even if it is not already benchmarked.

Use ListModelMetaData to get a response object that lists the domain, framework, task, and model name of machine learning models found in common model zoos.

You use the domain, framework, framework version, task, and model name in later steps to both select an inference Docker image and register your model with SageMaker Model Registry. The following demonstrates how to list model metadata with SDK for Python (Boto3):

list_model_metadata_response=sagemaker_client.list_model_metadata()

The output includes model summaries (ModelMetadataSummaries) and response metadata (ResponseMetadata) similar to the following example:

{
    'ModelMetadataSummaries': [{
            'Domain': 'NATURAL_LANGUAGE_PROCESSING',
            'Framework': 'PYTORCH:1.6.0',
             'Model': 'bert-base-cased',
             'Task': 'FILL_MASK'
             },
            {
             'Domain': 'NATURAL_LANGUAGE_PROCESSING',
             'Framework': 'PYTORCH:1.6.0',
             'Model': 'bert-base-uncased',
             'Task': 'FILL_MASK'
             },
            {
            'Domain': 'COMPUTER_VISION',
             'Framework': 'MXNET:1.8.0',
             'Model': 'resnet18v2-gluon',
             'Task': 'IMAGE_CLASSIFICATION'
             },
             {
             'Domain': 'COMPUTER_VISION',
             'Framework': 'PYTORCH:1.6.0',
             'Model': 'resnet152',
             'Task': 'IMAGE_CLASSIFICATION'
             }],
    'ResponseMetadata': {
                            'HTTPHeaders': {
                            'content-length': '2345',
                            'content-type': 'application/x-amz-json-1.1',
                            'date': 'Tue, 19 Oct 2021 20:52:03 GMT',
                            'x-amzn-requestid': 'xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx'
                          },
    'HTTPStatusCode': 200,
    'RequestId': 'xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx',
    'RetryAttempts': 0
    }
}

For this demo, we use a PyTorch (v1.7.1) ResNet-18 model to perform image classification. The following Python code sample stores the framework, framework version, domain, and task into variables for later use:

# ML framework details
framework = 'pytorch'
framework_version = '1.7.1'

# ML model details
ml_domain = 'COMPUTER_VISION'
ml_task = 'IMAGE_CLASSIFICATION'

Upload your machine learning model to Amazon S3.

Use this PyTorch (v1.7.1) ResNet-18 model if you do not have a pre-trained machine learning model:

# Optional: Download a sample PyTorch model
import torch
from torchvision import models, transforms, datasets

# Create an example input for tracing
image = torch.zeros([1, 3, 256, 256], dtype=torch.float32)

# Load a pretrained resnet18 model from TorchHub
model = models.resnet18(pretrained=True)

# Tell the model we are using it for evaluation (not training). Note this is required for Inferentia compilation.
model.eval()
model_trace = torch.jit.trace(model, image)

# Save your traced model
model_trace.save('model.pth')

Download a sample inference script inference.py. Create a code directory and move the inference script to the code directory.

# Download the inference script
!wget https://aws-ml-blog-artifacts.s3.us-east-2.amazonaws.com/inference.py

# move it into a code/ directory
!mkdir code
!mv inference.py code/

Amazon SageMaker requires pre-trained machine learning models to be packaged as a compressed TAR file (*.tar.gz). Compress your model and inference script to satisfy this requirement:

!tar -czf test.tar.gz model.pth code/inference.py

When your endpoint is provisioned, the files in the archive are extracted to /opt/ml/model/ on the endpoint.

After you compress your model and model artifacts as a .tar.gz file, upload them to your Amazon S3 bucket. The following example demonstrates how to upload your model to Amazon S3 using the AWS CLI:

!aws s3 cp test.tar.gz s3://{your-bucket}/models/

Select a prebuilt Docker inference image or create your own Inference Docker Image.

SageMaker provides containers for its built-in algorithms and prebuilt Docker images for some of the most common machine learning frameworks, such as Apache MXNet, TensorFlow, PyTorch, and Chainer. For a full list of the available SageMaker images, see Available Deep Learning Containers Images.

If none of the existing SageMaker containers meet your needs and you don't have an existing container of your own, create a new Docker image. See Containers with custom inference code for information about how to create your Docker image.

The following demonstrates how to retrieve a PyTorch version 1.7.1 inference image using the SageMaker Python SDK:

from sagemaker import image_uris

## Uncomment and replace with your own values if you did not define  
## these variables a previous step.
#framework = 'pytorch'
#framework_version = '1.7.1'

# Note: you can use any CPU-based instance here, 
# this is just to set the arch as CPU for the Docker image
instance_type = 'ml.m5.2xlarge' 

image_uri = image_uris.retrieve(framework, 
                                region, 
                                version=framework_version, 
                                py_version='py3', 
                                instance_type=instance_type, 
                                image_scope='inference')

For a list of available SageMaker Instances, see Amazon SageMaker Pricing.

Create a sample payload archive.

Create an archive that contains individual files that the load testing tool can send to your SageMaker endpoints. Your inference code must be able to read the file formats from the sample payload.

The following downloads a .jpg image that this example uses in a later step for the ResNet-18 model.

!wget https://cdn.pixabay.com/photo/2020/12/18/05/56/flowers-5841251_1280.jpg

Compress the sample payload as a tarball:

!tar -cvzf payload.tar.gz flowers-5841251_1280.jpg

Upload the sample payload to Amazon S3 and note the Amazon S3 URI:

!aws s3 cp payload.tar.gz s3://{bucket}/models/

You need the Amazon S3 URI in a later step, so store it in a variable:

bucket_prefix='models'
bucket = '<your-bucket-name>' # Provide the name of your S3 bucket
payload_s3_key = f"{bucket_prefix}/payload.tar.gz"
sample_payload_url= f"s3://{bucket}/{payload_s3_key}"

Prepare your model input for the recommendations job

For the last prerequisite, you have two options to prepare your model input. You can either register your model with SageMaker Model Registry, which you can use to catalog models for production, or you can create a SageMaker model and specify it in the ContainerConfig field when creating a recommendations job. The first option is best if you want to take advantage of the features that Model Registry provides, such as managing model versions and automating model deployment. The second option is ideal if you want to get started quickly. For the first option, go to step 7. For the second option, skip step 7 and go to step 8.

Option 1: Register your model in the model registry

With SageMaker Model Registry, you can catalog models for production, manage model versions, associate metadata (such as training metrics) with a model, manage the approval status of a model, deploy models to production, and automate model deployment with CI/CD.

When you use SageMaker Model Registry to track and manage your models, they are represented as a versioned model package within model package groups. Unversioned model packages are not part of a model group. Model package groups hold multiple versions or iterations of a model. Though it is not required to create them for every model in the registry, they help organize various models that all have the same purpose and provide automatic versioning.

To use Amazon SageMaker Inference Recommender, you must have a versioned model package. You can create a versioned model package programmatically with the AWS SDK for Python (Boto3) or with Amazon SageMaker Studio Classic. To create a versioned model package programmatically, first create a model package group with the CreateModelPackageGroup API. Next, create a model package using the CreateModelPackage API. Calling this method makes a versioned model package.

See Create a Model Group and Register a Model Version for detailed instructions about how to programmatically and interactively create a model package group and how to create a versioned model package, respectively, with the AWS SDK for Python (Boto3) and Amazon SageMaker Studio Classic.

The following code sample demonstrates how to create a versioned model package using the AWS SDK for Python (Boto3).

1. Create a model package group

   Create a model package group with the CreateModelPackageGroup API. Provide a name to the model package group for the ModelPackageGroupName and optionally provide a description of the model package in the ModelPackageGroupDescription field.

   model_package_group_name = '<INSERT>'
   model_package_group_description = '<INSERT>' 
   
   model_package_group_input_dict = {
    "ModelPackageGroupName" : model_package_group_name,
    "ModelPackageGroupDescription" : model_package_group_description,
   }
   
   model_package_group_response = sagemaker_client.create_model_package_group(**model_package_group_input_dict)

   See the Amazon SageMaker API Reference Guide for a full list of optional and required arguments you can pass to CreateModelPackageGroup.

   Create a model package by specifying a Docker image that runs your inference code and the Amazon S3 location of your model artifacts and provide values for InferenceSpecification. InferenceSpecification should contain information about inference jobs that can be run with models based on this model package, including the following:

   • The Amazon ECR paths of images that run your inference code.

   • (Optional) The instance types that the model package supports for transform jobs and real-time endpoints used for inference.

   • The input and output content formats that the model package supports for inference.

   In addition, you must specify the following parameters when you create a model package:

   • Domain: The machine learning domain of your model package and its components. Common machine learning domains include computer vision and natural language processing.

   • Task: The machine learning task your model package accomplishes. Common machine learning tasks include object detection and image classification. Specify "OTHER" if none of the tasks listed in the API Reference Guide satisfy your use case. See the Task API field descriptions for a list of supported machine learning tasks.

   • SamplePayloadUrl: The Amazon Simple Storage Service (Amazon S3) path where the sample payload are stored. This path must point to a single GZIP compressed TAR archive (.tar.gz suffix).

   • Framework: The machine learning framework of the model package container image.

   • FrameworkVersion: The framework version of the model package container image.

   If you provide an allow list of instance types to use to generate inferences in real-time for the SupportedRealtimeInferenceInstanceTypes, Inference Recommender limits the search space for instance types during a Default job. Use this parameter if you have budget constraints or know there's a specific set of instance types that can support your model and container image.

   In a previous step, we downloaded a pre-trained ResNet18 model and stored it in an Amazon S3 bucket in a directory called models. We retrieved a PyTorch (v1.7.1) Deep Learning Container inference image and stored the URI in a variable called image_uri. Use those variables in the following code sample to define a dictionary used as input to the CreateModelPackage API.

   # Provide the Amazon S3 URI of your compressed tarfile
   # so that Model Registry knows where to find your model artifacts
   bucket_prefix='models'
   bucket = '<your-bucket-name>' # Provide the name of your S3 bucket
   model_s3_key = f"{bucket_prefix}/test.tar.gz"
   model_url= f"s3://{bucket}/{model_s3_key}"
   
   # Similar open source model to the packaged model
   # The name of the ML model as standardized by common model zoos
   nearest_model_name = 'resnet18'
   
   # The supported MIME types for input and output data. In this example, 
   # we are using images as input.
   input_content_type='image/jpeg'
   
   
   # Optional - provide a description of your model.
   model_package_description = '<INSERT>'
   
   ## Uncomment if you did not store the domain and task in an earlier
   ## step 
   #ml_domain = 'COMPUTER_VISION'
   #ml_task = 'IMAGE_CLASSIFICATION'
   
   ## Uncomment if you did not store the framework and framework version
   ## in a previous step.
   #framework = 'PYTORCH'
   #framework_version = '1.7.1'
   
   # Optional: Used for optimizing your model using SageMaker Neo
   # PyTorch uses NCHW format for images
   data_input_configuration = "[[1,3,256,256]]"
   
   # Create a dictionary to use as input for creating a model pacakge group
   model_package_input_dict = {
           "ModelPackageGroupName" : model_package_group_name,
           "ModelPackageDescription" : model_package_description,
           "Domain": ml_domain,
           "Task": ml_task,
           "SamplePayloadUrl": sample_payload_url,
           "InferenceSpecification": {
                   "Containers": [
                       {
                           "Image": image_uri,
                           "ModelDataUrl": model_url,
                           "Framework": framework.upper(), 
                           "FrameworkVersion": framework_version,
                           "NearestModelName": nearest_model_name,
                           "ModelInput": {"DataInputConfig": data_input_configuration}
                       }
                       ],
                   "SupportedContentTypes": [input_content_type]
           }
       }

2. Create a model package

   Use the CreateModelPackage API to create a model package. Pass the input dictionary defined in the previous step:

   model_package_response = sagemaker_client.create_model_package(**model_package_input_dict)

   You need the model package ARN to use Amazon SageMaker Inference Recommender. Note the ARN of the model package or store it in a variable:

   model_package_arn = model_package_response["ModelPackageArn"]
   
   print('ModelPackage Version ARN : {}'.format(model_package_arn))

Option 2: Create a model and configure the ContainerConfig field

Use this option if you want to start an inference recommendations job and don't need to register your model in the Model Registry. In the following steps, you create a model in SageMaker and configure the ContainerConfig field as input for the recommendations job.

1. Create a model

   Create a model with the CreateModel API. For an example that calls this method when deploying a model to SageMaker Hosting, see Create a Model (AWS SDK for Python (Boto3)).

   In a previous step, we downloaded a pre-trained ResNet18 model and stored it in an Amazon S3 bucket in a directory called models. We retrieved a PyTorch (v1.7.1) Deep Learning Container inference image and stored the URI in a variable called image_uri. We use those variables in the following code example where we define a dictionary used as input to the CreateModel API.

   
   model_name = '<name_of_the_model>'
   # Role to give SageMaker permission to access AWS services.
   sagemaker_role= "arn:aws:iam::<region>:<account>:role/*"
   
   # Provide the Amazon S3 URI of your compressed tarfile
   # so that Model Registry knows where to find your model artifacts
   bucket_prefix='models'
   bucket = '<your-bucket-name>' # Provide the name of your S3 bucket
   model_s3_key = f"{bucket_prefix}/test.tar.gz"
   model_url= f"s3://{bucket}/{model_s3_key}"
   
   #Create model
   create_model_response = sagemaker_client.create_model(
       ModelName = model_name,
       ExecutionRoleArn = sagemaker_role, 
       PrimaryContainer = {
           'Image': image_uri,
           'ModelDataUrl': model_url,
       })
   

2. Configure the ContainerConfig field

   Next, you must configure the ContainerConfig field with the model you just created and specify the following parameters in it:

   • Domain: The machine learning domain of the model and its components, such as computer vision or natural language processing.

   • Task: The machine learning task that the model accomplishes, such as image classification or object detection.

   • PayloadConfig: The configuration for the payload for a recommendation job. For more information about the subfields, see RecommendationJobPayloadConfig.

   • Framework: The machine learning framework of the container image, such as PyTorch.

   • FrameworkVersion: The framework version of the container image.

   • (Optional) SupportedInstanceTypes: A list of the instance types that are used to generate inferences in real-time.

   If you use the SupportedInstanceTypes parameter, Inference Recommender limits the search space for instance types during a Default job. Use this parameter if you have budget constraints or know there's a specific set of instance types that can support your model and container image.

   In the following code example, we use the previously defined parameters, along with NearestModelName, to define a dictionary used as input to the CreateInferenceRecommendationsJob API.

   ## Uncomment if you did not store the domain and task in a previous step
   #ml_domain = 'COMPUTER_VISION'
   #ml_task = 'IMAGE_CLASSIFICATION'
   
   ## Uncomment if you did not store the framework and framework version in a previous step
   #framework = 'PYTORCH'
   #framework_version = '1.7.1'
   
   # The name of the ML model as standardized by common model zoos
   nearest_model_name = 'resnet18'
   
   # The supported MIME types for input and output data. In this example, 
   # we are using images as input
   input_content_type='image/jpeg'
   
   # Optional: Used for optimizing your model using SageMaker Neo
   # PyTorch uses NCHW format for images
   data_input_configuration = "[[1,3,256,256]]"
   
   # Create a dictionary to use as input for creating an inference recommendation job
   container_config = {
           "Domain": ml_domain,
           "Framework": framework.upper(), 
           "FrameworkVersion": framework_version,
           "NearestModelName": nearest_model_name,
           "PayloadConfig": { 
               "SamplePayloadUrl": sample_payload_url,
               "SupportedContentTypes": [ input_content_type ]
            },
           "DataInputConfig": data_input_configuration
           "Task": ml_task,
           }