Thanks Deron. Let's move forward with this. Several of us are interested in initiating research in this area, so I'll reach out.
........................... Jeremy Anderson Github: https://github.com/objectadjective Twitter: https://twitter.com/ObjectAdjective LinkedIn: http://www.linkedin.com/in/objectadjective On 1 November 2016 at 21:05, Madison Myers <madisonjmy...@gmail.com> wrote: > +1 to all. Really believe that visualization is a problem area that needs > to be improved. Let me know if I can help as well. > > On Mon, Oct 31, 2016 at 1:05 PM, Deron Eriksson <deroneriks...@gmail.com> > wrote: > > > Hi Jeremy, > > > > I think moving forward with visualization and design is a great idea, > > especially since I feel there is currently momentum after the great > design > > refactoring of the project website. Mike and Jeremy, please let me know > if > > there's any way in which I can help. > > > > Deron > > > > > > On Fri, Oct 28, 2016 at 8:03 PM, Jeremy Anderson < > > jer...@objectadjective.com > > > wrote: > > > > > > > > > > Visualization is a good topic to bring up for the project. I would > like > > > to > > > > add another possible option of using TensorBoard directly. I have not > > > > looked into the file format used for TensorBoard, but it may be > > possible > > > to > > > > simple adopt that format, and simply write our stats to that type of > > > file. > > > > That would allow us to reuse that project without having to write our > > > own. > > > > > > > > > Mike, I think this is a great place to start. I'd love to collaborate > > from > > > a design perspective, with anyone that wants to technical side. > > > > > > ........................... > > > > > > Jeremy Anderson > > > Github: https://github.com/objectadjective > > > Twitter: https://twitter.com/ObjectAdjective > > > LinkedIN: http://www.linkedin.com/in/objectadjective > > > > > > On 29 October 2016 at 02:46, <dusenberr...@gmail.com> wrote: > > > > > > > Visualization is a good topic to bring up for the project. I would > like > > > to > > > > add another possible option of using TensorBoard directly. I have not > > > > looked into the file format used for TensorBoard, but it may be > > possible > > > to > > > > simple adopt that format, and simply write our stats to that type of > > > file. > > > > That would allow us to reuse that project without having to write our > > > own. > > > > > > > > -- > > > > > > > > Mike Dusenberry > > > > GitHub: github.com/dusenberrymw > > > > LinkedIn: linkedin.com/in/mikedusenberry > > > > > > > > Sent from my iPhone. > > > > > > > > > > > > > On Oct 28, 2016, at 8:13 AM, Niketan Pansare <npan...@us.ibm.com> > > > wrote: > > > > > > > > > > Hi Matthias, > > > > > > > > > > Thanks for your feedback. > > > > > > > > > > There is a tradeoff between keeping a feature in-house until it is > > > > stable, v/s continually getting community feedback as the work is > > getting > > > > done via PR and discussions. I am for the latter as it encourages > > > community > > > > feedback as well as participation. > > > > > > > > > > I agree that our goal should be to complete the features you > > mentioned > > > > asap and yes, we are working hard towards making the GPU backend, the > > > deep > > > > learning built-in functions and the algorithm wrappers (ones that are > > > > already added) to be 'non-experimental' in the 1.0 release :) ... > Also, > > > > like you hinted, it is important to explicitly mark the experimental > > > > features in the documentation to avoid the 'bad impression'. The > Python > > > DSL > > > > will remain experimental until there is more interest from the > > > community. I > > > > am fine with deleting the debugger since it is rarely used, if at > all. > > > > > > > > > > Keeping inline with the Apache guidelines, this discussion is to > > allow > > > > community to decide on whether SystemML community should consider > > adding > > > > new visualization functionality (since this feature is user facing). > If > > > > there is no interest, we can either postpone or discard this > discussion > > > :) > > > > > > > > > > Thanks, > > > > > > > > > > Niketan. > > > > > > > > > >> On Oct 28, 2016, at 1:24 AM, Matthias Boehm < > mboe...@googlemail.com > > > > > > > wrote: > > > > >> > > > > >> Thanks for putting this together Niketan. However, could we please > > > > >> postpone this discussion after our 1.0 release? Right now, I'm > > > concerned > > > > >> to see that we're adding many experimental features without really > > > > >> getting them done. This includes for example, the GPU backend, the > > new > > > > >> MLContext API, the Python DSL, the deep learning builtin > functions, > > > the > > > > >> Scala algorithm wrappers, the old Spark debugger interface, and > > > > >> compressed linear algebra. I think we should finish these features > > > first > > > > >> before moving on. If we're not careful about that, it would > quickly > > > > >> create a very bad impression for new users. > > > > >> > > > > >> Regards, > > > > >> Matthias > > > > >> > > > > >>> On 10/28/2016 1:20 AM, Niketan Pansare wrote: > > > > >>> > > > > >>> > > > > >>> Hi all, > > > > >>> > > > > >>> To give every context, I am working on a new deep learning API > for > > > > SystemML > > > > >>> that is backed by the NN library ( > > > > >>> https://github.com/apache/incubator-systemml/tree/ > > > > master/scripts/staging/SystemML-NN/nn > > > > >>> ). This API allows the users to express their model using Caffe > > > > >>> specification and perform fit/predict similar to scikit-learn > > APIs. I > > > > have > > > > >>> created a sample notebook explaining the usage of the API: > > > > >>> https://github.com/niketanpansare/incubator-systemml/blob/ > > > > 1b655ebeec6cdffd66b282eadc4810ecfd39e4f2/samples/jupyter- > > > > notebooks/Barista-API-Demo.ipynb > > > > >>> . This API also allows the user to load and store pre-trained > > models. > > > > See > > > > >>> https://github.com/niketanpansare/model_zoo/tree/ > > > > master/caffe/vision/vgg/ilsvrc12 > > > > >>> > > > > >>> As part of this API, I added a mini-tensorboard like > functionality > > > (see > > > > >>> step 6 and 7) using matplotlib. If there is enough interest, we > can > > > > extend > > > > >>> and standardize the visualization functionality across all over > > > > algorithms. > > > > >>> Here are some initial discussion points: > > > > >>> 1. Primary visualization mechanism (Jupyter or a standalone app > or > > > > both => > > > > >>> former is useful for cloud offering such as DSX and latter > provides > > > the > > > > >>> design team more creative control) > > > > >>> 2. What to plot for each algorithm (data scientists and > algorithms > > > > >>> developers will help us here). > > > > >>> 3. Standardize UI (if we decide to go with Jupyter, we need to > > extend > > > > the > > > > >>> code in _visualize method: > > > > >>> https://github.com/niketanpansare/incubator-systemml/blob/ > > > > 1b655ebeec6cdffd66b282eadc4810ecfd39e4f2/src/main/python/ > > > > systemml/mllearn/estimators.py#L621 > > > > >>> ) > > > > >>> 4. Primary APIs to target (python, scala, command-line or all) > > > > >>> > > > > >>> Thanks, > > > > >>> > > > > >>> Niketan Pansare > > > > >>> IBM Almaden Research Center > > > > >>> E-mail: npansar At us.ibm.com > > > > >>> http://researcher.watson.ibm.com/researcher/view.php? > > > person=us-npansar > > > > >>> > > > > >> > > > > > > > > > > > > > > > > > > -- > *Madison J. Myers* > *UC Berkeley, Master of Information & Data Science '17* > > *King's College London, MA Political Science '14* > *New York University, BA Political Science '12* > > - > LinkedIn <http://linkedin.com/in/madisonjmyers> >
