From f7ebc47f3bf97689f1bb2f88c242158f1c8cb37d Mon Sep 17 00:00:00 2001 From: Dan Choi Date: Tue, 21 Aug 2018 17:33:52 -0700 Subject: [PATCH] Bump version to 1.9.1 --- CHANGELOG.rst | 4 ++-- README.rst | 6 +++--- setup.py | 2 +- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/CHANGELOG.rst b/CHANGELOG.rst index 792ccdcf4b..231e5016c3 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -2,8 +2,8 @@ CHANGELOG ========= -1.9.1dev -======== +1.9.1 +===== * bug-fix: Estimators: Fix serialization of single records * bug-fix: deprecate enable_cloudwatch_metrics from Framework Estimators. diff --git a/README.rst b/README.rst index c9e037e24f..b30a62ce81 100644 --- a/README.rst +++ b/README.rst @@ -54,7 +54,7 @@ You can install from source by cloning this repository and running a pip install git clone https://github.com/aws/sagemaker-python-sdk.git python setup.py sdist - pip install dist/sagemaker-1.9.0.tar.gz + pip install dist/sagemaker-1.9.1.tar.gz Supported Operating Systems ~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -326,7 +326,7 @@ SageMaker Automatic Model Tuning All of the estimators can be used with SageMaker Automatic Model Tuning, which performs hyperparameter tuning jobs. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm with different values of hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by a metric that you choose. -If you're not using an Amazon SageMaker built-in algorithm, then the metric is defined by a regular expression (regex) you provide. +If you're not using an Amazon SageMaker built-in algorithm, then the metric is defined by a regular expression (regex) you provide. The hyperparameter tuning job parses the training job's logs to find metrics that match the regex you defined. For more information about SageMaker Automatic Model Tuning, see `AWS documentation `__. @@ -377,7 +377,7 @@ In addition, the ``fit()`` call uses a list of ``RecordSet`` objects instead of # Start hyperparameter tuning job my_tuner.fit([train_records, test_records]) -To help attach a previously-started hyperparameter tuning job to a ``HyperparameterTuner`` instance, +To help attach a previously-started hyperparameter tuning job to a ``HyperparameterTuner`` instance, ``fit()`` adds the module path of the class used to create the tuner to the list of static hyperparameters by default. If the algorithm you are using cannot handle unknown hyperparameters (for example, an Amazon SageMaker built-in algorithm that does not have a custom estimator in the Python SDK), diff --git a/setup.py b/setup.py index 21f7db8098..092b02038e 100644 --- a/setup.py +++ b/setup.py @@ -23,7 +23,7 @@ def read(fname): setup(name="sagemaker", - version="1.9.0", + version="1.9.1", description="Open source library for training and deploying models on Amazon SageMaker.", packages=find_packages('src'), package_dir={'': 'src'},