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articles/machine-learning/azure-machine-learning-release-notes.md

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@@ -17,6 +17,106 @@ In this article, learn about Azure Machine Learning releases. For the full SDK
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See [the list of known issues](resource-known-issues.md) to learn about known bugs and workarounds.
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## 2020-05-26
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### Azure Machine Learning SDK for Python v1.MinorVersionTBD.0
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+ **New features**
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+ [Insert new features below. Reference articles and/or doc pages]
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+ **Preview features**
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+ [Contrib features below]
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+ **Breaking changes**
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+ [Reference upcoming breaking changes and old API support drop date]
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+ **Bug fixes and improvements**
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+ **azureml-automl-core**
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+ Fixed the bug where a warning may be printed during `get_output` that asked user to downgrade client.
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+ Updated Mac to rely on cudatoolkit=9.0 as it is not available at version 10 yet.
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+ Removing restrictions on phrophet and xgboost models when trained on remote compute.
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+ Improved logging in AutoML
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+ The error handling for custom featurization in forecasting tasks was improved.
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+ Added functionality to allow users to include lagged features to generate forecasts.
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+ Updates to error message to correctly display user error.
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+ Support for cv_split_column_names to be used with training_data
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+ Update logging the exception message and traceback.
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+ **azureml-automl-runtime**
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+ Enable guardrails for forecasting missing value imputations.
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+ Improved logging in AutoML
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+ Added fine grained error handling for dataprep exceptions
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+ Removing restrictions on phrophet and xgboost models when trained on remote compute.
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+ `azureml-train-automl-runtime` and `azureml-automl-runtime` have updated dependencies for `pytorch`, `scipy`, and `cudatoolkit`. we now support `pytorch==1.4.0`, `scipy>=1.0.0,<=1.3.1`, and `cudatoolkit==10.1.243`.
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+ The error handling for custom featurization in forecasting tasks was improved.
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+ The forecasting data set frequency detection mechanism was improved.
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+ Fixed issue with Prophet model training on some data sets.
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+ The auto detection of max horizon during the forecasting was improved.
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+ Added functionality to allow users to include lagged features to generate forecasts.
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+ Adds functionality in the forecast function to enable providing forecasts beyond the trained horizon without re-training the forecasting model.
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+ Support for cv_split_column_names to be used with training_data
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+ **azureml-contrib-automl-dnn-forecasting**
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+ Improved logging in AutoML
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+ **azureml-contrib-dataset**
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+ Significantly Improved error text in case of Dataset execution failures.
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+ **azureml-contrib-mir**
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+ Added support for Windows services in ManagedInferencing
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+ Remove old MIR workflows such as attach MIR compute, SingleModelMirWebservice class - Clean out model profiling placed in contrib-mir package
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+ **azureml-contrib-pipeline-steps**
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+ Quick fix for ParallelRunStep where loading from YAML was broken
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+ ParallelRunStep is released to General Availability - azureml.contrib.pipeline.steps has a deprecation notice and is move to azureml.pipeline.steps - new features include: 1. Datasets as PipelineParameter 2. New parameter run_max_retry 3. Configurable append_row output file name
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+ **azureml-contrib-reinforcementlearning**
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+ RL Load testing tool
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+ RL estimator has smart defaults
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+ **azureml-core**
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+ Remove old MIR workflows such as attach MIR compute, SingleModelMirWebservice class - Clean out model profiling placed in contrib-mir package
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+ Fixed the information provided to the user in case of profiling failure: included request id and reworded the message to be more meaningful. Added new profiling workflow to profiling runners
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+ Significantly Improved error text in case of Dataset execution failures.
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+ Workspace private link cli support added.
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+ Added an optional parameter `invalid_lines` to `Dataset.Tabular.from_json_lines_files` that allows for specifying how to handle lines that contain invalid JSON.
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+ We will be deprecating the run based creation of compute in the next release. We recommend creating an actual Amlcompute cluster as a persistent compute target, and using the cluster name as the compute target in your run configuration. See example notebook here: aka.ms/amlcomputenb
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+ **azureml-dataprep**
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+ Made warning to upgrade pyarrow version more explicit.
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+ Significantly Improved error text in case of Dataset execution failures.
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+ Improved error handling and message returned in case of failure to execute dataflow.
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+ ParallelRunStep is released to General Availability - azureml.contrib.pipeline.steps has a deprecation notice and is move to azureml.pipeline.steps - new features include: 1. Datasets as PipelineParameter 2. New parameter run_max_retry 3. Configurable append_row output file name
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+ **azureml-interpret**
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+ Documentation updates to azureml-interpret package.
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+ fixed interpretability packages and notebooks to be compatible with latest sklearn update
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+ **azureml-opendatasets**
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+ return None when there is no data returned.
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+ Improve the performance of to_pandas_dataframe.
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+ `azureml-train-automl-runtime` and `azureml-automl-runtime` have updated dependencies for `pytorch`, `scipy`, and `cudatoolkit`. we now support `pytorch==1.4.0`, `scipy>=1.0.0,<=1.3.1`, and `cudatoolkit==10.1.243`.
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+ **azureml-pipeline-core**
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+ Quick fix for ParallelRunStep where loading from YAML was broken
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+ ParallelRunStep is released to General Availability - azureml.contrib.pipeline.steps has a deprecation notice and is move to azureml.pipeline.steps - new features include: 1. Datasets as PipelineParameter 2. New parameter run_max_retry 3. Configurable append_row output file name
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+ **azureml-pipeline-steps**
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+ Deprecated azureml.dprep.Dataflow as a valid type for input data.
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+ Quick fix for ParallelRunStep where loading from YAML was broken
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+ ParallelRunStep is released to General Availability - azureml.contrib.pipeline.steps has a deprecation notice and is move to azureml.pipeline.steps - new features include: 1. Datasets as PipelineParameter 2. New parameter run_max_retry 3. Configurable append_row output file name
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+ **azureml-telemetry**
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+ Update logging the exception message and traceback.
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+ **azureml-train-automl-client**
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+ Improved logging in AutoML
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+ Updates to error message to correctly display user error.
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+ Support for cv_split_column_names to be used with training_data
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+ Deprecated azureml.dprep.Dataflow as a valid type for input data.
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+ Updated Mac to rely on cudatoolkit=9.0 as it is not available at version 10 yet.
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+ Removing restrictions on phrophet and xgboost models when trained on remote compute.
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+ `azureml-train-automl-runtime` and `azureml-automl-runtime` have updated dependencies for `pytorch`, `scipy`, and `cudatoolkit`. we now support `pytorch==1.4.0`, `scipy>=1.0.0,<=1.3.1`, and `cudatoolkit==10.1.243`.
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+ Added functionality to allow users to include lagged features to generate forecasts.
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+ **azureml-train-automl-runtime**
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+ Improved logging in AutoML
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+ Added fine grained error handling for dataprep exceptions
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+ Removing restrictions on phrophet and xgboost models when trained on remote compute.
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+ `azureml-train-automl-runtime` and `azureml-automl-runtime` have updated dependencies for `pytorch`, `scipy`, and `cudatoolkit`. we now support `pytorch==1.4.0`, `scipy>=1.0.0,<=1.3.1`, and `cudatoolkit==10.1.243`.
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+ Updates to error message to correctly display user error.
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+ Support for cv_split_column_names to be used with training_data
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+ **azureml-train-core**
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+ Added a new set of HyperDrive specific exceptions. azureml.train.hyperdrive will now throw detailed exceptions.
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+ **azureml-widgets**
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+ AzureML Widgets is not displaying in JupyterLab
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## 2020-05-11
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### Azure Machine Learning SDK for Python v1.5.0

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