Sagemaker Endpoint Should Have Data Capture Enabled

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1. Prerequisites

1. You must have:
   • An existing SageMaker endpoint.
   • An S3 bucket where captured data will be stored (or permission to create one).
   • IAM permissions to update the endpoint configuration and write to the S3 bucket.

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2. Identify the Endpoint Configuration

1. Sign in to the AWS Management Console.
2. Go to Amazon SageMaker.
3. In the left navigation pane, select Inference → Endpoints.
4. Click your endpoint name.
5. On the endpoint details page, note the Endpoint configuration name (you’ll need it if you want to clone/modify it).

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3. Create a New Endpoint Configuration with Data Capture Enabled

1. In the SageMaker left navigation pane, go to Inference → Endpoint configurations.
2. Click Create endpoint configuration.
3. Under Endpoint configuration name, enter a new name (e.g., my-endpoint-config-with-datacapture).
4. Under Production variants, replicate the settings from your existing configuration:
   • Model name
   • Instance type
   • Initial instance count
   • Any other variant settings needed (weight, variant name, etc.).
5. Scroll down to Data capture (or Enable data capture section):
   • Check Enable data capture.
   • S3 location: choose or browse to an S3 bucket/prefix, e.g., s3://my-bucket/sagemaker/datacapture/.
   • Sampling percentage: enter a value (e.g., 100 to capture all requests/responses, or a lower percentage).
   • Capture options: select what to capture:
     • Requests
     • Responses
   • Encryption key (optional): specify a KMS key if you want encryption using a customer-managed key.
   • Leave other options as defaults unless you have specific compliance/logging needs.
6. Review all settings and click Create endpoint configuration.

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4. Update the Endpoint to Use the New Configuration

1. Go back to Endpoints.
2. Click your endpoint.
3. At the top right, click Update (or Modify).
4. In the Endpoint configuration section, select the new endpoint configuration you just created (e.g., my-endpoint-config-with-datacapture).
5. Click Update endpoint.

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5. Verify Data Capture

1. Send some inference requests to the endpoint (via your application, SDK, or SageMaker notebook).
2. In the S3 console, navigate to the bucket/prefix you configured for data capture.
3. Confirm that JSON lines files are appearing under paths like:
   • s3://my-bucket/sagemaker/datacapture/EndpointName/AllTraffic/2024/...

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6. (Optional) Clean Up Old Endpoint Configuration

1. In Endpoint configurations, find the old configuration that no longer has data capture.
2. Confirm that no other endpoints are using it.
3. Select it and choose Delete if safe to do so.

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1. Prerequisites

• An S3 bucket for data capture (example: s3://my-sagemaker-data-capture/)
• The IAM role used by the endpoint must have s3:PutObject to that bucket.

{
  "Effect": "Allow",
  "Action": ["s3:PutObject"],
  "Resource": "arn:aws:s3:::my-sagemaker-data-capture/*"
}

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2. Check current endpoint and its config

aws sagemaker describe-endpoint \
  --endpoint-name my-endpoint

aws sagemaker describe-endpoint-config \
  --endpoint-config-name my-endpoint-config

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3. Create a new endpoint configuration with DataCaptureConfig

[
  {
    "VariantName": "AllTraffic",
    "ModelName": "my-model-name",
    "InitialInstanceCount": 1,
    "InstanceType": "ml.m5.xlarge"
  }
]

aws sagemaker create-endpoint-config \
  --endpoint-config-name my-endpoint-config-with-dc \
  --production-variants file://production-variants.json \
  --data-capture-config '{
    "EnableCapture": true,
    "InitialSamplingPercentage": 100,
    "DestinationS3Uri": "s3://my-sagemaker-data-capture/",
    "CaptureOptions": [
      {"CaptureMode": "Input"},
      {"CaptureMode": "Output"}
    ],
    "CaptureContentTypeHeader": {
      "JsonContentTypes": ["application/json"],
      "CsvContentTypes": ["text/csv"]
    }
  }'

• InitialSamplingPercentage: e.g., 10 for 10% of traffic
• CaptureOptions: Input, Output, or both
• DestinationS3Uri: your bucket/prefix

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4. Update the endpoint to use the new config

aws sagemaker update-endpoint \
  --endpoint-name my-endpoint \
  --endpoint-config-name my-endpoint-config-with-dc

aws sagemaker describe-endpoint \
  --endpoint-name my-endpoint \
  --query 'EndpointStatus'

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5. Verify data capture

aws s3 ls s3://my-sagemaker-data-capture/ --recursive

1. Create (or update) an endpoint configuration with DataCaptureConfig.
2. Point it to an S3 bucket/prefix.
3. Update the endpoint to use that new endpoint configuration.

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0. Prerequisites

pip install boto3

• An existing SageMaker endpoint name (e.g. my-endpoint)
• An S3 bucket/prefix to store captured data (e.g. s3://my-bucket/sagemaker/data-capture/)
• IAM role used by SageMaker with permissions to write to that S3 location

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1. Get current endpoint configuration

import boto3

sm = boto3.client("sagemaker", region_name="us-east-1")

endpoint_name = "my-endpoint"

# Get the current endpoint info
endpoint_desc = sm.describe_endpoint(EndpointName=endpoint_name)
current_config_name = endpoint_desc["EndpointConfigName"]

# Get current endpoint config to reuse settings (production variants, etc.)
current_config = sm.describe_endpoint_config(
    EndpointConfigName=current_config_name
)

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2. Build a new EndpointConfig with DataCaptureConfig

import time

s3_capture_location = "s3://my-bucket/sagemaker/data-capture/"
# Optional: new config name
new_config_name = f"{endpoint_name}-config-with-capture-{int(time.time())}"

# Reuse production variants from the existing config
production_variants = current_config["ProductionVariants"]

# Optional: reuse tags, kms, etc. if needed
kms_key_id = current_config.get("KmsKeyId")

data_capture_config = {
    "EnableCapture": True,
    "InitialSamplingPercentage": 100,  # capture 100% of traffic (adjust as needed)
    "DestinationS3Uri": s3_capture_location,
    "CaptureOptions": [
        {"CaptureMode": "Input"},
        {"CaptureMode": "Output"}
    ],
    # Optional: specify content types/encodings; here capture everything
    "CaptureContentTypeHeader": {
        "CsvContentTypes": ["text/csv"],
        "JsonContentTypes": ["application/json"]
    }
}

create_config_args = {
    "EndpointConfigName": new_config_name,
    "ProductionVariants": production_variants,
    "DataCaptureConfig": data_capture_config
}
if kms_key_id:
    create_config_args["KmsKeyId"] = kms_key_id

sm.create_endpoint_config(**create_config_args)

print(f"Created new endpoint config: {new_config_name}")

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3. Update the Endpoint to use the new config

sm.update_endpoint(
    EndpointName=endpoint_name,
    EndpointConfigName=new_config_name
)

# Optionally wait until update is complete
waiter = sm.get_waiter("endpoint_in_service")
waiter.wait(EndpointName=endpoint_name)
print("Endpoint updated and InService.")

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4. Verify data capture is enabled

updated_desc = sm.describe_endpoint(EndpointName=endpoint_name)
updated_config_name = updated_desc["EndpointConfigName"]
updated_config = sm.describe_endpoint_config(
    EndpointConfigName=updated_config_name
)

print(updated_config.get("DataCaptureConfig"))