Hopsworks 0.10 brings the latest features, improvements and bug fixes. It is the biggest release done so far, made up of 191 JIRAs including many new features. Also, this version marks the last of the 0.x series, as Hopsworks is gearing up towards its 1.x series starting with 1.0 end of Q3 2019.
With HOPSWORKS-954, Hopsworks becomes the first Big Data & AI platform to support AMD’s ROCm open source Deep Learning framework. Users of a Hopsworks cluster that is equipped with AMD GPUs, can now run their Deep Learning TensorFlow/PyTorch etc. applications on AMD GPUs from within the familiar environment of a Hopsworks project. You can find more information in the 4th HopsML meetup talk AMD/ROCm for Hopsworks and in the Spark Summit talk, ROCm and Distributed Deep Learning on Spark and TensorFlow.
Expanding support for ML model serving, HOPSWORKS-751 brings support for scikit-learn serving, by running Flask servers as local processes or in Kubernetes scaling up and down dynamically based on load. Users can manage their scikit-learn serving from within the Model Serving service of their Hopsworks projects.
HOPSWORKS-1089 introduces Maggy, a framework for efficient asynchronous optimization of expensive black-box functions on top of Apache Spark. More information is available at maggy.readthedocs.io pages, on Maggy’s GitHub repository and in the Maggy talk at the 4th HopsML meetup in Stockholm.
HOPSWORKS-852 integrates Petastorm in Hopsworks and its Feature Store. Petastorm, is an open source data access library that enables single machine or distributed training and evaluation of deep learning models directly from datasets in Apache Parquet format. You can get started with these notebooks in your Hopsworks instance!
Further highlights include:
You can get started with Hopsworks by visiting our getting started guide.
Users looking to migrate from a previous Hopsworks version, need to read this guide first which contains all the steps and configurations required for a smooth upgrade process.
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