Hi Kyle, I'm actively working on it now. It's pretty close to completion, I'm just trying to figure out bottlenecks and optimize as much as possible. As Phase 1, I implemented multi model training on Gradient Descent. Instead of performing Vector-Vector operations on rows (examples) and weights, I've batched them into matrices so that we can use Level 3 BLAS to speed things up. I've also added support for Sparse Matrices (https://github.com/apache/spark/pull/2294) as making use of sparsity will allow you to train more models at once.
Best, Burak ----- Original Message ----- From: "Kyle Ellrott" <kellr...@soe.ucsc.edu> To: dev@spark.apache.org Sent: Tuesday, September 16, 2014 3:21:53 PM Subject: [mllib] State of Multi-Model training I'm curious about the state of development Multi-Model learning in MLlib (training sets of models during the same training session, rather then one at a time). The JIRA lists it as in progress targeting Spark 1.2.0 ( https://issues.apache.org/jira/browse/SPARK-1486 ). But there hasn't been any notes on it in over a month. I submitted a pull request for a possible method to do this work a little over two months ago (https://github.com/apache/spark/pull/1292), but haven't yet received any feedback on the patch yet. Is anybody else working on multi-model training? Kyle --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org
