Capture data from batch transform job
The steps required to turn on data capture for your batch transform job are similar
whether you use the AWS SDK for Python (Boto) or the SageMaker Python SDK. If you use the AWS SDK,
define the DataCaptureConfig dictionary, along with required fields, within the
CreateTransformJob
method to turn on data capture. If you use the SageMaker
Python SDK, import the BatchDataCaptureConfig
class and initialize an
instance from this class. Then, pass this object to the
batch_data_capture_config
parameter of your transform job instance.
To use the following code snippets, replace the italicized placeholder
text
in the example code with your own information.
How to enable data capture
Specify a data capture configuration when you launch a transform job. Whether
you use the AWS SDK for Python (Boto3) or the SageMaker Python SDK, you must provide the
DestinationS3Uri
argument, which is the directory where you want
the transform job to log the captured data. Optionally, you can also set the
following parameters:
-
KmsKeyId
: The AWS KMS key used to encrypt the captured data. -
GenerateInferenceId
: A Boolean flag that, when capturing the data, indicates if you want the transform job to append the inference ID and time to your output. This is useful for model quality monitoring, where you need to ingest the Ground Truth data. The inference ID and time help to match the captured data with your Ground Truth data.
How to view data captured
Once the transform job completes, the captured data gets logged under the
DestinationS3Uri
you provided with the data capture
configuration. There are two subdirectories under DestinationS3Uri
,
/input
and /output
. If DestinationS3Uri
is s3://my-data-capture
, then the transform job creates the the
following directories:
-
s3://my-data-capture/input
: The captured input data for the transform job. -
s3://my-data-capture/output
: The captured output data for the transform job.
To avoid data duplication, the captured data under the preceding two directories are manifests. Each manifest is a JSONL file that contains the Amazon S3 locations of the source objects. A manifest file may look like the following example:
# under "/input" directory [ {"prefix":"s3://
input_S3_uri
/"}, "dummy_0.csv", "dummy_1.csv", "dummy_2.csv", ... ] # under "/output" directory [ {"prefix":"s3://output_S3_uri
/"}, "dummy_0.csv.out", "dummy_1.csv.out", "dummy_2.csv.out", ... ]
The transform job organizes and labels these manifests with a
yyyy/mm/dd/hh
S3 prefix to indicate when they were
captured. This helps the model monitor determine the appropriate portion of data
to analyze. For example, if you start your transform job at 2022-8-26 13PM UTC,
then the captured data is labeled with a 2022/08/26/13/
prefix
string.
InferenceId Generation
When you configure DataCaptureConfig
for a transform job, you can
turn on the Boolean flag GenerateInferenceId
. This is particularly
useful when you need to run model quality and model bias monitoring jobs, for
which you need user-ingested Ground Truth data. Model monitor relies on an inference ID
to match the captured data and the Ground Truth data. For addition details about Ground Truth
ingestion, see Ingest Ground Truth labels and merge
them with predictions. When
GenerateInferenceId
is on, the transform output appends an
inference ID (a random UUID) as well as the transform job start time in UTC for
each record. You need these two values to run model quality and model bias
monitoring. When you construct the Ground Truth data, you need to provide the same
inference ID to match the output data. Currently, this feature supports
transform outputs in CSV, JSON, and JSONL formats.
If your transform output is in CSV format, the output file looks like the following example:
0, 1f1d57b1-2e6f-488c-8c30-db4e6d757861,2022-08-30T00:49:15Z 1, 22445434-0c67-45e9-bb4d-bd1bf26561e6,2022-08-30T00:49:15Z ...
The last two columns are inference ID and the transform job start time. Do not modify them. The remaining columns are your transform job outputs.
If your transform output is in JSON or JSONL format, the output file looks like the following example:
{"output": 0, "SageMakerInferenceId": "1f1d57b1-2e6f-488c-8c30-db4e6d757861", "SageMakerInferenceTime": "2022-08-30T00:49:15Z"} {"output": 1, "SageMakerInferenceId": "22445434-0c67-45e9-bb4d-bd1bf26561e6", "SageMakerInferenceTime": "2022-08-30T00:49:15Z"} ...
There are two appended fields that are reserved,
SageMakerInferenceId
and SageMakerInferenceTime
. Do
not modify these fields if you need to run model quality or model bias
monitoring — you need them for merge jobs.