- RAPIDS: Accelerating the Data Science Ecosystem
Bradlee Rees will present: The typical data processing workflow has a data scientist spending the vast majority of their programming time (not execution runtime) on data cleaning, transformation, and preparation. The remaining time is spent on running an analytic (and waiting) to produce results that hopefully contain a meaningful answer. To increase productivity, the data scientist uses tools within the Python ecosystem, such as Pandas and Scikit-learn. Unfortunately, most libraries suffer from poor performance. This makes the data scientist’s job hard since the amount of time they spend waiting for results interrupts their train of thought. RAPIDS is an open-source software suite for GPU-accelerated data science that allows the data scientist the freedom to execute end-to-end workflows fully on GPUs through familiarity Python APIs. To do this, RAPIDS has several libraries that follow APIs similar to popular libraries: cuDF, a Pandas like dataframe library; cuML a machine learning library that follows the Scikit-Learn API; and cuGraph, a graph analytics library matching the NetworkX API. This talk will walk through a data science problem that introduce components and features of RAPIDS, including feature engineering, data manipulation, statistical tasks, machine learning, and graph analysis. Throughout the talk, code examples and benchmarked perform gains will be presented. The talk will wrap-up with a presentation of the current RAPIDS roadmap.
- Evaluating RCNNs in Existing Code with Luminoth
James Waugh will be presenting on: One of the more challenging aspects of improving existing products with ML models is deployment and productionization. Many different frameworks support different languages and stabilities, when all that's required initially for this are results. TensorFlow in particular can be difficult to set up and supports ~1.5 languages. In this talk, I'll share my experiences in skirting this process entirely to quickly evaluate a FasterRCNN-based model during a hackathon last year, usable on any machine or language. About James Waugh: James Waugh is a Software Engineer III at Accusoft in Tampa, where he works on the company's first pushes into machine learning. A graduate of the University of South Florida, James holds a Computer Engineering degree. He also attends local technology meetups such as Barcamp Tampa regularly, and is an entrepreneur in the Hillsborough Area. Outside the office, James enjoys classic cars and practicing Japanese.