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 <[email protected] > 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, <[email protected]> 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 <[email protected]> > 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 <[email protected]> > > 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 > > >>> > > >> > > > > > >
