Ml2p

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* Added `ml2p dataset delete` which deletes an entire dataset.
* Added `ml2p dataset ls` which lists the contents of a dataset.
* Added `ml2p dataset up` which uploads a local file to a dataset.
* Added `ml2p dataset dn` which downloads a file from a dataset.
* Added `ml2p dataset rm` which deletes a file from a dataset.

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* Correctly handle folder keys when downloading datasets from S3. Previously folder
keys created files, now they created folders.

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* Added support for local environments. These allow ML2P models to be trained and used
to make predictions locally, as though they were being loaded in SageMaker.
* Added support for downloading datasets and models from S3 into local environments.

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* Fix support for recording predictions in S3 (in first release of this feature, the code
attempted to pass a boolean value as an environment variable, which failed as expected).

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* Add support for recording predictions in S3.

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* Improve batch prediction support to allow models to separately implement batch
prediction (e.g. a model might want to implement batch prediction separately to
improve performance).
* Tweak training job version format to only include major and minor versions numbers.
Patch version numbers are now reserved for models and intended for use in the case
where the code used to make predictions changes but the underlying model is the same.
* Model creation now defaults to using the training job with the same version as the model
but with the patch number removed.
* Endpoint creation now defaults to using the model with the same version as the endpoint.
* When creating training jobs or models, specifying the model type is now required if
the ml2p configuration file contains more than one model. If there is exactly one model
type listed, that is the default. If there are no model types, the docker file
must specify the model on the command line.
* Metadata returned by predictions now includes the ML2P version number.
* Version bumped to 0.1.0 now that versioning support is complete(-ish).