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Update to latest upstream #6
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When we describe a training job the data type of the hyper parameters is lost because we use a dict[str, str]. This adds a new field to Hyperparameter so that we can convert the datatypes at runtime. instead of validating with isinstance(), we cast the hp value to the type it is meant to be. This enforces a "strongly typed" value. When we deserialize from the API string responses it becomes easier to deal with too.
Update CHANGELOG and bump the version number.
when calling fit(wait=False) it will return immediately. The training job will carry on even if the process exits. by using attach() the estimator can be retrieved by providing the training job name. _prepare_init_params_from_job_description() is now a classmethod instead of being a static method. Each class is responsible to implement their specific logic to convert a training job description into arguments that can be passed to its own __init__()
Instead of manually constructing the role ARN, use the IAM boto client to do it. This properly expands service-roles and regular roles.
* Fix description of an argument of sagemaker.session.train 'input_config' should be an array which has channel objects. * Add a link to the botocore docs * Use 'list' instead of 'array' in the description
Add support for serializing python dictionaries to json Add prediction with dictionary in tf iris integ test
Execute tf_cifar test without logs to eliminate delay to detect that job has finished.
* Added: print out billable seconds after training completes * Fixed: test_session.py to pass unit tests * Fixed: removed offending tzlocal()
* Update .gitignore to ignore pytest_cache. * Support TensorFlow-1.5.0 and MXNet-1.0.0 * Update and refactor tests. Add tests for fw_utils. * Fix typo.
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Update our fork to the current
upstreammaster.