commit | 84ef71326c6781bad4ed9b39a210ee2cd4a6d4bd | [log] [tgz] |
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author | Matthias Boehm <mboehm7@gmail.com> | Sat Apr 11 23:12:37 2020 +0200 |
committer | Matthias Boehm <mboehm7@gmail.com> | Sat Apr 11 23:42:12 2020 +0200 |
tree | f56336fc811328ba08ca88413111ae424f571be9 | |
parent | 47924e6aced3dac0768756c7dfec932d696b6a3f [diff] |
[SYSTEMDS-52] Fix libsvm reader/writer integration and correctness This patch fixes a correctness issue of the libsvm local writers, which incorrectly shifted the output indexes twice for space inputs. Furthermore, the libsvm local readers were not fully integrated in all code path yet. The distributed libsvm readers/writers still remain to be integrated.
Overview: SystemDS is a versatile 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.
Documentation: SystemDS Documentation
Status and Build: SystemDS is still in pre-alpha status. The original code base was forked from Apache SystemML 1.2 in September 2018. We will continue to support linear algebra programs over matrices, while replacing the underlying data model and compiler, as well as substantially extending the supported functionalities. Until the first release, you can build your own snapshot via Apache Maven: mvn -DskipTests clean package
.