Yes, I agree with Nan Zhu. I recommend these projects: https://github.com/dmlc/ps-lite (Apache License 2) https://github.com/Microsoft/multiverso (MIT License)
Alexander, You may also be interested in the demo(graph on parameter Server) https://github.com/witgo/zen/tree/ps_graphx/graphx/src/main/scala/com/github/cloudml/zen/graphx ------------------ Original ------------------ From: "Ulanov, Alexander";<alexander.ula...@hpe.com>; Date: Fri, Nov 13, 2015 01:44 AM To: "Nan Zhu"<zhunanmcg...@gmail.com>; "Guoqiang Li"<wi...@qq.com>; Cc: "dev@spark.apache.org"<dev@spark.apache.org>; "Reynold Xin"<r...@databricks.com>; Subject: RE: A proposal for Spark 2.0 Parameter Server is a new feature and thus does not match the goal of 2.0 is ??to fix things that are broken in the current API and remove certain deprecated APIs??. At the same time I would be happy to have that feature. With regards to Machine learning, it would be great to move useful features from MLlib to ML and deprecate the former. Current structure of two separate machine learning packages seems to be somewhat confusing. With regards to GraphX, it would be great to deprecate the use of RDD in GraphX and switch to Dataframe. This will allow GraphX evolve with Tungsten. Best regards, Alexander From: Nan Zhu [mailto:zhunanmcg...@gmail.com] Sent: Thursday, November 12, 2015 7:28 AM To: wi...@qq.com Cc: dev@spark.apache.org Subject: Re: A proposal for Spark 2.0 Being specific to Parameter Server, I think the current agreement is that PS shall exist as a third-party library instead of a component of the core code base, isn??t? Best, -- Nan Zhu http://codingcat.me On Thursday, November 12, 2015 at 9:49 AM, wi...@qq.com wrote: Who has the idea of machine learning? Spark missing some features for machine learning, For example, the parameter server. ?? 2015??11??12????05:32??Matei Zaharia <matei.zaha...@gmail.com> ?????? I like the idea of popping out Tachyon to an optional component too to reduce the number of dependencies. In the future, it might even be useful to do this for Hadoop, but it requires too many API changes to be worth doing now. Regarding Scala 2.12, we should definitely support it eventually, but I don't think we need to block 2.0 on that because it can be added later too. Has anyone investigated what it would take to run on there? I imagine we don't need many code changes, just maybe some REPL stuff. Needless to say, but I'm all for the idea of making "major" releases as undisruptive as possible in the model Reynold proposed. Keeping everyone working with the same set of releases is super important. Matei On Nov 11, 2015, at 4:58 AM, Sean Owen <so...@cloudera.com> wrote: On Wed, Nov 11, 2015 at 12:10 AM, Reynold Xin <r...@databricks.com> wrote: to the Spark community. A major release should not be very different from a minor release and should not be gated based on new features. The main purpose of a major release is an opportunity to fix things that are broken in the current API and remove certain deprecated APIs (examples follow). Agree with this stance. Generally, a major release might also be a time to replace some big old API or implementation with a new one, but I don't see obvious candidates. I wouldn't mind turning attention to 2.x sooner than later, unless there's a fairly good reason to continue adding features in 1.x to a 1.7 release. The scope as of 1.6 is already pretty darned big. 1. Scala 2.11 as the default build. We should still support Scala 2.10, but it has been end-of-life. By the time 2.x rolls around, 2.12 will be the main version, 2.11 will be quite stable, and 2.10 will have been EOL for a while. I'd propose dropping 2.10. Otherwise it's supported for 2 more years. 2. Remove Hadoop 1 support. I'd go further to drop support for <2.2 for sure (2.0 and 2.1 were sort of 'alpha' and 'beta' releases) and even <2.6. I'm sure we'll think of a number of other small things -- shading a bunch of stuff? reviewing and updating dependencies in light of simpler, more recent dependencies to support from Hadoop etc? Farming out Tachyon to a module? (I felt like someone proposed this?) Pop out any Docker stuff to another repo? Continue that same effort for EC2? Farming out some of the "external" integrations to another repo (? controversial) See also anything marked version "2+" in JIRA. --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org
