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
