* Updated xgboost_customer_churn.ipynb for SageMaker SDK v2
Notebook needed updates to be compatible with the v2 of the SageMaker SDK, which is the new Default SDK version in SageMaker.
* Fixed xgboost_customer_churn.ipynb after code review
1. Remove the old import of SDK V1.
2. Added imports for TrainingInput and CSVSerializer to keep code cleaner.
3. The check_neo_region was removed since it is not supported in SDK v2, and from Sep 26, 2019, Amazon SageMaker Neo is Available in 12 additional Regions.
5. get_image_uri in Neo compilation was removed since it is not needed at all.
5. Moved the print of cutoffs, and costs to a diffferent cell since it used to throw an error on the first run.
* Add Neo folder and move Neo notebook under it
* Update README
* Update mxnet_mnist_neo.ipynb with Evidence
* add compile section in mnist notebook for mxnet and tensorflow
* clean serializer function in neo tensorflow mnist
* Add Neo Region Check for tensorflow_mnist and mxnet_mnist
* Add Neo folder and move Neo notebook under it
* Update README
* Update mxnet_mnist_neo.ipynb with Evidence
* add compile section in mnist notebook for mxnet and tensorflow
* clean serializer function in neo tensorflow mnist
* Add Neo Region Check for tensorflow_mnist and mxnet_mnist
* Add Neo folder and move Neo notebook under it
* Update README
* Update mxnet_mnist_neo.ipynb with Evidence
* add compile section in mnist notebook for mxnet and tensorflow
* clean serializer function in neo tensorflow mnist
Explicit text about columns we are dropping due to high feature correlation. This makes it easier for the reader to follow why we are dropping the features 'Day Charge', 'Eve Charge', 'Night Charge', 'Intl Charge', which was confusing if you didn't notice the feature pairs.
Related: https://github.com/awslabs/amazon-sagemaker-examples/pull/74
I have not run this notebook, but I ran the related notebook above.
Help on class s3_input in module sagemaker.session:
class s3_input(builtins.object)
| Amazon SageMaker channel configurations for S3 data sources.
|
| Attributes:
| config (dict[str, dict]): A SageMaker ``DataSource`` referencing a SageMaker ``S3DataSource``.
|
| Methods defined here:
|
| __init__(self, s3_data, distribution='FullyReplicated', compression=None, content_type=None, record_wrapping=None, s3_data_type='S3Prefix')
| Create a definition for input data used by an SageMaker training job.
|
| See AWS documentation on the ``CreateTrainingJob`` API for more details on the parameters.
|
| Args:
| s3_data (str): Defines the location of s3 data to train on.
| distribution (str): Valid values: 'FullyReplicated', 'ShardedByS3Key'
| (default: 'FullyReplicated').
| compression (str): Valid values: 'Gzip', 'Bzip2', 'Lzop' (default: None).
| content_type (str): MIME type of the input data (default: None).
| record_wrapping (str): Valid values: 'RecordIO' (default: None).
| s3_data_type (str): Value values: 'S3Prefix', 'ManifestFile'. If 'S3Prefix', ``s3_data`` defines
| a prefix of s3 objects to train on. All objects with s3 keys beginning with ``s3_data`` will
| be used to train. If 'ManifestFile', then ``s3_data`` defines a single s3 manifest file, listing
| each s3 object to train on. The Manifest file format is described in the SageMaker API documentation:
| https://aws.amazon.com/sagemaker/latest/dg/API_S3DataSource.html
|
Zhaoqi