Added reference documentation.
Added a tutorial.
Added tests for the ML2P command line utilities.
Added support for attaching training and deployment instances to VPCs.
Open sourced ML2P under the ISCL.
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.
Correctly handle folder keys when downloading datasets from S3. Previously folder keys created files, now they created folders.
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.
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).
Add support for recording predictions in S3.
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).
Add support for client and server error exception handling.
Deprecate passing a channel name to dataset_folder and add a new data_channel_folder method to allow data in other channels to be accessed.
Add dataset create and list commands to ml2p CLI.
Add –version to ml2p and ml2p-docker CLIs.
Allow model and endpoint version numbers to be multiple digits.
Added validation of naming convention
Added Sphinx requirements to build file.
Cleaned up support for passing ML2P environment data into training jobs and model deployments. Environment settings such as the S3 URL and the project name are now passed into training jobs via hyperparameters and into model deployments via model environment variables.
Added support for training and serving multiple models using the same docker image by optionally passing the model to use into training jobs and endpoint deployments.
Added support for rich hyperparameters. This sidesteps SageMaker API’s limited hyperparameter support (it only supports string values) by encoding any JSON-compatible Python dictionary to a flattened formed and then decoding it when it is read by the training job.
Added skeleton for Sphinx documentation.
Removed old pre-0.0.1 example files.
Disabled direct internet access from notebooks by default.
Added tests for cli_utils.
Fixed bug in setting of ML2P_S3_URL on model creation.
Added new ml2p notebook command group for creating, inspecting, and deleting SageMaker Notebook instances.
Added new ml2p repo command group for inspecting code repository SageMaker resources.
Added new ml2p-docker command added that assists with training and deploying models in SageMaker.
Initial hackathon release.