Batch Transform Jobs for Machine Learning on SageMaker

Question

Pulumi · Accepted Answer

Batch Transform is a feature of Amazon SageMaker that you can use to perform inference on datasets by using machine learning (ML) models. Batch Transform is especially useful when you need to do inference on an entire dataset and store the inferences, e.g., for use in a batch process in a data pipeline or for further analysis.

Here's how you can create a Batch Transform job using Pulumi:

1. **Define your model**: Before you can run a Batch Transform job, you need to have an ML model trained and available in SageMaker. This can be done by creating a `Model` resource in Pulumi.

2. **Prepare your data source**: Your input data should be placed in an Amazon S3 bucket in a format that your model can process.

3. **Create a transform job**: Using the Pulumi `aws.sagemaker.TransformJob` resource, you can start a Batch Transform job by specifying the model, data source, and the output location for the inferences.

4. **Check job status**: You can monitor and manage the Batch Transform job using AWS SDK or the AWS Console.

Let’s go through a step-by-step Pulumi program which sets up a Batch Transform job:

```python
import pulumi
import pulumi_aws as aws

# Define your existing trained SageMaker model name
model_name = "my-existing-sagemaker-model"

# Assume that the Input data is already placed in an S3 bucket
input_data_s3_location = "s3://my-bucket/input-data/"

# Define the S3 location where you want the output results to be stored
output_data_s3_location = "s3://my-bucket/output-data/"

# Create a SageMaker Transform Job
transform_job = aws.sagemaker.TransformJob("my-transform-job",
    model_name=model_name,
    transform_input=aws.sagemaker.TransformJobTransformInputArgs(
        data_source=aws.sagemaker.TransformJobTransformInputDataSourceArgs(
            s3_data_source=aws.sagemaker.TransformJobTransformInputDataSourceS3DataSourceArgs(
                s3_data_type="S3Prefix",
                s3_uri=input_data_s3_location,
            )
        ),
        content_type="text/csv", # example content type, change based on the input format required for your model
    ),
    transform_output=aws.sagemaker.TransformJobTransformOutputArgs(
        s3_output_path=output_data_s3_location,
    ),
    transform_resources=aws.sagemaker.TransformJobTransformResourcesArgs(
        instance_count=1, # specify how many instances you need to run your job
        instance_type="ml.m5.xlarge", # choose the instance type
    ),
)

# Export the name of the batch transform job
pulumi.export("transform_job_name", transform_job.name)

# (Optional) Export the S3 URI of the output location. The apply method can be used to format the output path.
output_data_s3_uri = pulumi.Output.concat(output_data_s3_location, '/', transform_job.name, '/output')
pulumi.export("output_data_s3_uri", output_data_s3_uri)

```

This program specifies the necessary components you need to run a Batch Transform job:

- The `model_name` is the name of your pre-trained SageMaker model which you want to use for predictions.
- The `transform_input` contains the information about your input data such as data source which points to the S3 location where your input data is stored. It also includes the `content_type` which is the data format expected by the model.
- The `transform_output` defines where you want your inference results to be outputted, and again, it refers to an S3 location.
- The `transform_resources` specifies the compute resources that SageMaker should use for the transform job, including the instance count and instance type.

Once this program is deployed with Pulumi, it's going to create a Batch Transform job in SageMaker, and you can monitor the job status either in the AWS Console or via AWS SDKs. You can also retrieve inference results directly from the `output_data_s3_location`.

Remember to replace placeholders like `my-existing-sagemaker-model`, `s3://my-bucket/input-data/`, and `s3://my-bucket/output-data/` with the actual values that correspond to your model and data.