1. Hosting AI Model Serving Artifacts on AWS S3


    Hosting AI Model Serving Artifacts on AWS S3 involves creating an S3 bucket and then uploading your model artifacts to that bucket. The Amazon S3 service provides a highly scalable object storage system, which is ideal for storing and retrieving large files like machine learning models.

    In the context of AI models, these artifacts typically include trained model files, configuration, and possibly accompanying data that helps the model to make predictions. Once you've uploaded the model artifacts to S3, they can be used by machine learning services such as Amazon SageMaker to deploy your model.

    The following Pulumi program demonstrates how to create an S3 bucket using the aws.s3.Bucket resource and then use an aws.s3.BucketObject to upload a local model file to that bucket.

    Before running this program, ensure that you have the Pulumi CLI installed and AWS provider configured with the necessary access credentials. You will also need to replace the local_path_to_model_file placeholder with the actual path to your model file on your local machine.

    import pulumi import pulumi_aws as aws # Create an S3 bucket where the model artifacts will be stored. model_bucket = aws.s3.Bucket("modelBucket", acl="private", # Access control list - 'private' means no public access to this bucket. tags={ "Name": "AIModelArtifacts", "Purpose": "Serving AI Model Artifacts", } ) # Upload a model file to the created S3 bucket. Replace 'local_path_to_model_file' with your actual model file path. model_artifact = aws.s3.BucketObject("modelArtifact", bucket=model_bucket.id, # Reference to the created bucket. key="model.tar.gz", # The name that should be used for the stored object. source=pulumi.FileAsset("local_path_to_model_file") # FileAsset is used to point to the local model file. ) # Exporting the bucket name and the URL of the model artifact. pulumi.export("bucket_name", model_bucket.id) pulumi.export("model_artifact_url", model_artifact.bucket.apply(lambda bucket_name: f"https://{bucket_name}.s3.amazonaws.com/{model_artifact.key}"))

    The aws.s3.Bucket resource is used to create a new S3 bucket. The acl argument specifies the access control level, and tags can be used to add metadata to your S3 bucket for identification and management.

    The aws.s3.BucketObject resource uploads a local file to the specified S3 bucket. The bucket parameter is the ID of the bucket you're uploading to, the key is the desired object key on S3, and the source argument uses the pulumi.FileAsset type to identify the local file path.

    The program also exports two outputs: the name of the created S3 bucket and the URL of the uploaded model artifact, which you may use to access the model within other AWS services or applications.

    Remember to install the required Pulumi AWS package in your environment by running:

    pip install pulumi_aws

    After writing this code to a file, say __main__.py, you can run pulumi up from the command line in the same directory as the file to provision the infrastructure. Make sure that you've already logged into AWS CLI with the correct permissions and have initialized a Pulumi stack (you can create one by running pulumi stack init).