[SYSTEMDS-3586] Fix variable release on errors in federated workers This patch improves the robustness and error handling of federated workers for batches of federated requests. So far, if a federated request for EXEC_INST causes an exception the respective instruction did not release the inputs and thus causing incomprehensible exceptions on subsequent requests of the same batch. Even more severely, the state of variables is corrupted which can be problematic because the federated workers are stateful servers. We now do a dedicated release, but only on exceptions during instruction execution to minimize overhead.
Overview: SystemDS is an open source ML system for the end-to-end data science lifecycle from data integration, cleaning, and feature engineering, over efficient, local and distributed ML model training, to deployment and serving. To this end, we aim to provide a stack of declarative languages with R-like syntax for (1) the different tasks of the data-science lifecycle, and (2) users with different expertise. These high-level scripts are compiled into hybrid execution plans of local, in-memory CPU and GPU operations, as well as distributed operations on Apache Spark. In contrast to existing systems - that either provide homogeneous tensors or 2D Datasets - and in order to serve the entire data science lifecycle, the underlying data model are DataTensors, i.e., tensors (multi-dimensional arrays) whose first dimension may have a heterogeneous and nested schema.
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Status and Build: SystemDS is renamed from SystemML which is an Apache Top Level Project. To build from source visit SystemDS Install from source
