Interactive Machine Learning with a GPU-Accelerated Toolkit Biye Jiang, Huasha Zhao, John Canny Computer Science Division University of California, Berkeley {bjiang, hzhao, jfc}@cs.berkeley.edu Berkeley Institute of Design Dashboard Model overview (Topic matrix, image cluster centers) Main loss and other evaluation metrics Sliders to change model hyper-parameters Dashboard Other Visualized Metric Cluster centers Pairwise distance (MINST dataset) between clusters Silhouette graph References [1] Huasha Zhao, Biye Jiang and John Canny SAME but Different: Fast and High-Quality Gibbs Parameter Estimation Arxiv 1409.5402 (2014) [2] John Canny and Huasha Zhao. Big data analytics with small footprint: Squaring the cloud. In ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2013 [3] John Canny and Huasha Zhao BIDMach: Large-scale Learning with Zero Memory Allocation, BIGLearn Workshop, Neural Information Processing Conference (NIPS) 2013 Motivation ML algorithms optimize mathematical criteria People have informal notions of a “good” model Real-world ML applications often involve trade-offs between multiple criteria (business logic): Revenue Advertiser satisfaction User metrics These goals should be addressed during training, not after. Interactive ML allows users to understand the effects of these trade-offs on model quality and structure. Tuning parameter: sizeWeight Metric: Histogram of cluster size Increase sizeWeight Interactive Interface Minibatch learning supports continuous data streaming, model updates happen many times per second. Parameters and visualizations are updated in real time. Algorithms converge after a few updates: Less than 10s to get stable result on MINST8M (20GB) Encoding constraints as mixins functions Users first figure out secondary optimization goal Sparseness, Consistency, Independence Then use its gradient to update the model The mixin weight  is a hyper-parameter, the mixin value is a metric. Users can interactively adjust the controls and see the effects on corresponding metrics Visualization in Browser Web Server BIDMach Grab data from GPU 10 times/s Changed Hyper-Parameter Model overview, Evaluation metrics Using D3.js Each hyper-parameter corresponds to a visualization of the metric Data structure User defined logging. Only used data will be logged. Efficient internal matrix format in BIDMach Using JSON to communicate between server and browser Can easily support deep neural network Temperature/Learning rate control Control the window size for moving average update Control the variance of the Gibbs sampler User defined annealing learning schedule