SageMaker Python SDK 1.15.2

• bug-fix: Fix FileNotFoundError for entry_point without source_dir
• doc-fix: Add missing feature 1.5.0 in change log
• doc-fix: Add README for airflow

SageMaker Python SDK 1.15.1

• enhancement: Local Mode: add explicit pull for serving
• feature: Estimators: dependencies attribute allows export of additional libraries into the container
• feature: Add APIs to export Airflow transform and deploy config
• bug-fix: Allow code_location argument to be S3 URI in training_config API
• enhancement: Local Mode: add explicit pull for serving

SageMaker Python SDK 1.15.0

• bug-fix: Changes to use correct S3 bucket and time range for dataframes in TrainingJobAnalytics.
• bug-fix: Local Mode: correctly handle the case where the model output folder doesn't exist yet
• feature: Add APIs to export Airflow training, tuning and model config
• doc-fix: Fix typos in tensorflow serving documentation
• doc-fix: Add estimator base classes to API docs
• feature: HyperparameterTuner: add support for Automatic Model Tuning's Warm Start Jobs
• feature: HyperparameterTuner: Make input channels optional
• feature: Add support for Chainer 5.0
• feature: Estimator: add support for MetricDefinitions
• feature: Estimators: add support for Amazon IP Insights algorithm

SageMaker Python SDK 1.14.2

• bug-fix: support CustomAttributes argument in local mode invoke_endpoint requests
• enhancement: add content_type parameter to sagemaker.tensorflow.serving.Predictor
• doc-fix: add TensorFlow Serving Container docs
• doc-fix: fix rendering error in README.rst
• enhancement: Local Mode: support optional input channels
• build: added pylint
• build: upgrade docker-compose to 1.23
• enhancement: Frameworks: update warning for not setting framework_version as we aren't planning a breaking change anymore
• enhancement: Session: remove hardcoded 'training' from job status error message
• bug-fix: Updated Cloudwatch namespace for metrics in TrainingJobsAnalytics

SageMaker Python SDK 1.14.1

• feature: Estimators: add support for Amazon Object2Vec algorithm

SageMaker Python SDK 1.14.0

• feature: add support for sagemaker-tensorflow-serving container
• feature: Estimator: make input channels optional

SageMaker Python SDK 1.13.0

• feature: Estimator: add input mode to training channels
• feature: Estimator: add model_uri and model_channel_name parameters
• enhancement: Local Mode: support output_path. Can be either file:// or s3://
• enhancement: Added image uris for SageMaker built-in algorithms for SIN/LHR/BOM/SFO/YUL
• feature: Estimators: add support for MXNet 1.3.0, which introduces a new training script format
• feature: Documentation: add explanation for the new training script format used with MXNet
• feature: Estimators: add distributions for customizing distributed training with the new training script format

SageMaker Python SDK 1.12.0

• feature: add support for TensorFlow 1.11.0

SageMaker Python SDK 1.11.3

• feature: Local Mode: Add support for Batch Inference
• feature: Add timestamp to secondary status in training job output
• bug-fix: Local Mode: Set correct default values for additional_volumes and additional_env_vars
• enhancement: Local Mode: support nvidia-docker2 natively
• warning: Frameworks: add warning for upcoming breaking change that makes framework_version required

SageMaker Python SDK 1.11.2

• enhancement: Enable setting VPC config when creating/deploying models
• enhancement: Local Mode: accept short lived credentials with a warning message
• bug-fix: Local Mode: pass in job name as parameter for training environment variable