incubator-systemml-website git commit: [MINOR] Add security page under Apache

Repository: incubator-systemml-website Updated Branches: refs/heads/master 0c170c572 -> b2f52ca65 [MINOR] Add security page under Apache Closes #42. Project: http://git-wip-us.apache.org/repos/asf/incubator-systemml-website/repo Commit: http://git-wip-us.apache.org/repos/asf/incubator-syste

[1/2] incubator-systemml git commit: [SYSTEMML-1411] Add CPU and GPU bias multiply operation

Repository: incubator-systemml Updated Branches: refs/heads/master 6e7e8873a -> d127dfa2d http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/d127dfa2/src/main/java/org/apache/sysml/runtime/matrix/data/LibMatrixDNN.java

[2/2] incubator-systemml git commit: [SYSTEMML-1411] Add CPU and GPU bias multiply operation

[SYSTEMML-1411] Add CPU and GPU bias multiply operation This operation is similar to bias_add, except that it performs element-wise multiplication rather than addition. It avoids unnecessary multiplication of ones and reshape. This pattern is common in deep learning layers such as batch normalizat

incubator-systemml git commit: [SYSTEMML-1403] Fixed the bug in GPU relu_maxpooling and also cleaned up LibMatrixCUDA code.

Repository: incubator-systemml Updated Branches: refs/heads/master c663e31ce -> 6e7e8873a [SYSTEMML-1403] Fixed the bug in GPU relu_maxpooling and also cleaned up LibMatrixCUDA code. Closes #432. Project: http://git-wip-us.apache.org/repos/asf/incubator-systemml/repo Commit: http://git-wip

[2/4] incubator-systemml git commit: [SYSTEMML-1372] Extended code generator (celltmpl w/ matmult as root)

[SYSTEMML-1372] Extended code generator (celltmpl w/ matmult as root) In order to handle the case where previous rewrites modified sum(X^2) - which is fusable to any cellwise operations that created X - to t(X)%*%X, we now also support dot products as roots of cellwise templates (cellwise multiply

[3/4] incubator-systemml git commit: [SYSTEMML-1314] Robust memory handling for RDD parallelization

[SYSTEMML-1314] Robust memory handling for RDD parallelization So far we only checked on RDD parallelization if the pinned matrix block as well as the partitioned matrix fit together into the driver memory budget; otherwise the matrix is exported (w/ a multi-threaded write in binary block format

[4/4] incubator-systemml git commit: [MINOR] Robust hdfs file delete w/ retries (fix occasional test issues)

[MINOR] Robust hdfs file delete w/ retries (fix occasional test issues) Project: http://git-wip-us.apache.org/repos/asf/incubator-systemml/repo Commit: http://git-wip-us.apache.org/repos/asf/incubator-systemml/commit/c663e31c Tree: http://git-wip-us.apache.org/repos/asf/incubator-systemml/tree/c6

[1/4] incubator-systemml git commit: [SYSTEMML-1364] Hardened code generator for sparse-unsafe outer products

Repository: incubator-systemml Updated Branches: refs/heads/master 81090134d -> c663e31ce [SYSTEMML-1364] Hardened code generator for sparse-unsafe outer products Project: http://git-wip-us.apache.org/repos/asf/incubator-systemml/repo Commit: http://git-wip-us.apache.org/repos/asf/incubator-s

incubator-systemml git commit: [SYSTEMML-1370] Convert numpy to matrixblock by passing multiple blocks

Repository: incubator-systemml Updated Branches: refs/heads/master f32bd8ebd -> 81090134d [SYSTEMML-1370] Convert numpy to matrixblock by passing multiple blocks Here is the code to test this functionality: from systemml import MLContext, dml, convertToMatrixBlock import pandas as pd nr = 469