+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>
