Thanks for working on this Sebastian.

I will review & test this work tomorrow.

Thank you,
Janardhan

On Sat, Sep 26, 2020 at 5:39 PM Baunsgaard, Sebastian <[email protected]>
wrote:

> Hi Janardhan,
>
>
> Thanks for pointing this out!
>
> They were not put into master because the examples does not work the same,
>
> I will try to make a reasonable extract of these documents in a PR, that
> fit the current system.
>
> If you want to take a look on that PR it would be much appreciated!
>
>
> thanks again
>
> Sebastian
>
>
> ________________________________
> From: Janardhan <[email protected]>
> Sent: Friday, September 25, 2020 10:26:00 PM
> To: [email protected]
> Subject: Re: [DISCUSS] Apache SystemDS 2.0 Release
>
> Hi Arnab,
>
> We do not seem to have algorithms documentation.
> For example an equivalent of this[1] page.
>
> Ignore this message, if it exists already.
>
> [1]  http://systemds.apache.org/docs/1.2.0/algorithms-reference.html
>
> Thank you,
> Janardhan
>
> On Fri, Sep 25, 2020 at 3:53 PM arnab phani <[email protected]> wrote:
>
> > Hi All,
> >
> > Thanks for fixing the remaining issues.
> > I will cut the first release candidates later in the afternoon (CET zone)
> > today.
> >
> > Regards,
> > Arnab..
> >
> > On Tue, Sep 22, 2020 at 2:53 PM arnab phani <[email protected]>
> wrote:
> >
> > > Hi All,
> > >
> > > Thanks for fixing the bugs.
> > > A few tasks/bugs (see below) are still being worked on and hopefully
> will
> > > be closed in a couple of days.
> > > And we should be all ready to distribute the release candidates by the
> > end
> > > of this week.
> > >
> > > - python tutorials,
> > > - failing gpu tests,
> > > - error in loading native BLAS.
> > >
> > > Regards,
> > > Arnab..
> > >
> > > On Thu, Sep 10, 2020 at 11:24 AM arnab phani <[email protected]>
> > wrote:
> > >
> > >> Thank you all for the notes.
> > >> Please find the consolidated release notes below, and please let me
> know
> > >> if anything major is missing.
> > >>
> > >> *Release notes for SystemDS 2.0.*
> > >>
> > >> SystemDS 2.0 is the first major release under the new name. This
> release
> > >> contains a major refactoring, a few major features, a large number of
> > >> improvements and fixes, and some experimental features to better
> support
> > >> the end-to-end data science lifecycle. In addition to that, this
> release
> > >> also removes several features that are not up to the mark and
> outdated.
> > >>
> > >> The major changes (compared to SystemML 1.2) include
> > >>
> > >>
> > >>    - New mechanism for DML-bodied (script-level) builtin functions,
> and
> > >>    a wealth of new built-in functions for data preprocessing including
> > data
> > >>    cleaning, augmentation and feature engineering techniques, new ML
> > >>    algorithms, and model debugging.
> > >>    - Several methods for data cleaning have been implemented including
> > >>    multiple imputations with multivariate imputation by chained
> > equations
> > >>    (MICE) and other techniques, SMOTE, an oversampling technique for
> > class
> > >>    imbalance, forward and backward NA filling, cleaning using schema
> and
> > >>    length information, support for outlier detection using standard
> > deviation
> > >>    and inter-quartile range, and functional dependency discovery.
> > >>    - A complete framework for lineage tracing and reuse including
> > >>    support for loop deduplication, full and partial reuse, compiler
> > assisted
> > >>    reuse, several new rewrites to facilitate reuse.
> > >>    - New federated runtime backend including support for federated
> > >>    matrices and frames, federated builtins (transform-encode, decode
> > etc.).
> > >>    - Refactor compression package and add functionalities including
> > >>    quantization for lossy compression, binary cell operations, left
> > matrix
> > >>    multiplication.
> > >>    - New python bindings with supports for several builtins, matrix
> > >>    operations, federated tensors, and lineage traces.
> > >>    - Cuda implementation of cumulative aggregate operators (cumsum,
> > >>    cumprod etc.)
> > >>    - New model debugging technique with slice finder.
> > >>    - New tensor data model (basic tensors of different value types,
> data
> > >>    tensors with schema) [experimental]
> > >>    -  Cloud deployment scripts for AWS and scripts to set up and start
> > >>    federated operations.
> > >>    -  Performance improvements with parallel sort, gpu cum agg, append
> > >>    cbind etc.
> > >>    -  Various compiler and runtime improvements including new and
> > >>    improved rewrites, reduced Spark context creation, new eval
> > framework, list
> > >>    operations, updated native kernel libraries to name a few.
> > >>    - New data reader/writer for json frames and support for sql as a
> > >>    data source.
> > >>    -  Miscellaneous improvements: improved documentation, better
> > >>    testing, run/release scripts, improved packaging, Docker container
> > for
> > >>    systemds, bug fixes.
> > >>    -  Removed MapReduce compiler and runtime backend, pydml parser,
> > >>    Java-UDF framework, script-level debugger.
> > >>
> > >>
> > >> Regards,
> > >> Arnab.
> > >>
> > >>
> > >> On Tue, Sep 8, 2020 at 4:10 AM Mark Dokter <[email protected]>
> > >> wrote:
> > >>
> > >>> On 01.09.20 11:36, arnab phani wrote:
> > >>> > While I will aggregate the notes from two SystemDS releases, it
> will
> > be
> > >>> > great if you can update me with a few lines summarizing the
> additions
> > >>> to
> > >>> > your features (including the external contributions), especially
> > after
> > >>> > March 24, 2020 (last SystemDS release).
> > >>>
> > >>> Hi Arnab!
> > >>>
> > >>> My contributions:
> > >>>
> > >>> - new run script
> > >>> - improve/simplify release scripts
> > >>> - various release related things (improve documentation, fix license
> > >>> headers, clean up pom.xml, etc)
> > >>> - cuda implementation of cumulative aggregate operators (cumsum,
> > >>> cumprod, etc)
> > >>> - bug fixes here and there
> > >>> - maintain native blas support in a working state (now also
> supporting
> > >>> windows)
> > >>> - kmeans builtin dml function
> > >>> - builtins for image augmentation
> > >>>
> > >>> Best,
> > >>> Mark
> > >>>
> > >>
> >
>

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