documentation: Minor updates to Clarify API documentation #2550

xgchena · 2021-08-03T18:15:14Z

Issue #, if available:

https://tiny.amazon.com/ckryhs8s

Description of changes:

Per customer feedback, we'd like to make these updates to Clarify API documentation to avoid confusion.

Updated the docstring of run_bias method and state clearly that it computes both the pretraining bias metrics and the posttraining bias metrics.
Updated the docstring of the constructor to list the supported dataset types, and add a validation at the entrypoint to fail fast if the user provided an unsupported dataset type (by xiaoyi-cheng@). A new unit test case is added to test the validation code.
Fix broken links to bias metrics documentation.

Testing done:

Unit test passed.
Lint checks passed.

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General

[X ] I have read the CONTRIBUTING doc
I used the commit message format described in CONTRIBUTING
I have passed the region in to all S3 and STS clients that I've initialized as part of this change.
I have updated any necessary documentation, including READMEs and API docs (if appropriate)

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I have used unique_name_from_base to create resource names in integ tests (if appropriate)

By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.

sagemaker-bot · 2021-08-03T18:18:35Z

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sagemaker-bot · 2021-08-03T18:57:36Z

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sagemaker-bot · 2021-08-03T19:01:57Z

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sagemaker-bot · 2021-08-03T19:11:30Z

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Updated the docstring of run_bias method and state clearly that it computes both the pretraining bias metrics and the posttraining bias metrics. Updated the docstring of the constructor to list the supported dataset types, and add a validation at the entrypoint to fail fast if the user provided an unsupported dataset type. Fix broken links to bias metrics documentation.

sagemaker-bot · 2021-08-04T05:27:13Z

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sagemaker-bot · 2021-08-04T06:10:31Z

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xgchena · 2021-08-04T17:35:17Z

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Log shows that the failed test is test_mxnet.py and it has nothing to do with this PR. (Checked a couple of recent builds of other PRs in the queue and they failed for the same reason.)

=================================== FAILURES ===================================
___________________ test_deploy_model[1.8.0-py37-1.8.0-py37] ___________________
[gw27] linux -- Python 3.7.10 /codebuild/output/src797612603/src/github.com/aws/sagemaker-python-sdk/.tox/py37/bin/python

mxnet_training_job = 'mxnet-training-2021-08-04-05-59-04-460'
sagemaker_session = <sagemaker.session.Session object at 0x7efc8ab99110>
mxnet_inference_latest_version = '1.8.0'
mxnet_inference_latest_py_version = 'py37', cpu_instance_type = 'ml.m4.xlarge'

    def test_deploy_model(
        mxnet_training_job,
        sagemaker_session,
        mxnet_inference_latest_version,
        mxnet_inference_latest_py_version,
        cpu_instance_type,
    ):
        endpoint_name = "test-mxnet-deploy-model-{}".format(sagemaker_timestamp())
    
        with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
            desc = sagemaker_session.sagemaker_client.describe_training_job(
                TrainingJobName=mxnet_training_job
            )
            model_data = desc["ModelArtifacts"]["S3ModelArtifacts"]
            script_path = os.path.join(DATA_DIR, "mxnet_mnist", "mnist.py")
            model = MXNetModel(
                model_data,
                "SageMakerRole",
                entry_point=script_path,
                py_version=mxnet_inference_latest_py_version,
                sagemaker_session=sagemaker_session,
                framework_version=mxnet_inference_latest_version,
            )
            predictor = model.deploy(1, cpu_instance_type, endpoint_name=endpoint_name)
    
            data = numpy.zeros(shape=(1, 1, 28, 28))
            result = predictor.predict(data)
            assert result is not None
    
>       predictor.delete_model()

tests/integ/test_mxnet.py:159: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
.tox/py37/lib/python3.7/site-packages/sagemaker/predictor.py:339: in delete_model
    current_model_names = self._get_model_names()
.tox/py37/lib/python3.7/site-packages/sagemaker/predictor.py:529: in _get_model_names
    current_endpoint_config_name = self._get_endpoint_config_name()
.tox/py37/lib/python3.7/site-packages/sagemaker/predictor.py:520: in _get_endpoint_config_name
    EndpointName=self.endpoint_name
.tox/py37/lib/python3.7/site-packages/botocore/client.py:386: in _api_call
    return self._make_api_call(operation_name, kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <botocore.client.SageMaker object at 0x7efc840b3e10>
operation_name = 'DescribeEndpoint'
api_params = {'EndpointName': 'test-mxnet-deploy-model-2021-08-04-06-03-25-740'}

    def _make_api_call(self, operation_name, api_params):
        operation_model = self._service_model.operation_model(operation_name)
        service_name = self._service_model.service_name
        history_recorder.record('API_CALL', {
            'service': service_name,
            'operation': operation_name,
            'params': api_params,
        })
        if operation_model.deprecated:
            logger.debug('Warning: %s.%s() is deprecated',
                         service_name, operation_name)
        request_context = {
            'client_region': self.meta.region_name,
            'client_config': self.meta.config,
            'has_streaming_input': operation_model.has_streaming_input,
            'auth_type': operation_model.auth_type,
        }
        request_dict = self._convert_to_request_dict(
            api_params, operation_model, context=request_context)
    
        service_id = self._service_model.service_id.hyphenize()
        handler, event_response = self.meta.events.emit_until_response(
            'before-call.{service_id}.{operation_name}'.format(
                service_id=service_id,
                operation_name=operation_name),
            model=operation_model, params=request_dict,
            request_signer=self._request_signer, context=request_context)
    
        if event_response is not None:
            http, parsed_response = event_response
        else:
            http, parsed_response = self._make_request(
                operation_model, request_dict, request_context)
    
        self.meta.events.emit(
            'after-call.{service_id}.{operation_name}'.format(
                service_id=service_id,
                operation_name=operation_name),
            http_response=http, parsed=parsed_response,
            model=operation_model, context=request_context
        )
    
        if http.status_code >= 300:
            error_code = parsed_response.get("Error", {}).get("Code")
            error_class = self.exceptions.from_code(error_code)
>           raise error_class(parsed_response, operation_name)
E           botocore.exceptions.ClientError: An error occurred (ValidationException) when calling the DescribeEndpoint operation: Could not find endpoint "arn:aws:sagemaker:us-east-2:142577830533:endpoint/test-mxnet-deploy-model-2021-08-04-06-03-25-740".

.tox/py37/lib/python3.7/site-packages/botocore/client.py:705: ClientError

sagemaker-bot · 2021-08-07T18:05:05Z

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Merge branch 'master' into minor-updates-to-clarify-api-doc

74201e0

Merge branch 'master' into minor-updates-to-clarify-api-doc

1266139

ahsan-z-khan approved these changes Aug 10, 2021

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Merge branch 'master' into minor-updates-to-clarify-api-doc

772e8e4

ahsan-z-khan merged commit 1a2d36f into aws:master Aug 10, 2021

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documentation: Minor updates to Clarify API documentation #2550

documentation: Minor updates to Clarify API documentation #2550

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documentation: Minor updates to Clarify API documentation #2550

documentation: Minor updates to Clarify API documentation #2550

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