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2022 Spring Series


Overview: This tutorial will introduce students to HAL system, how to interact with it through Open OnDemand interface, including Jupiter notebook, and through command line interface via SSH. The tutorial will cover the Anaconda environment, batch job submission, data transfer, as well as an overview to the basic machine learning workflow.

Introduction to Machine Learning and Neural Networks

Overview: This tutorial will introduce how machine learning can be accomplished with neural networks and will go over various examples from simple dense networks to convolutional network architectures using TensorFlow framework on HAL system.

Introduction to TensorFlow – Asad Khan

Overview: This tutorial will introduce basics of TensorFlow necessary to build a neural network, train it and evaluate the accuracy of the model

  • Training Slides
  • Instructor: Asad Khan
  • Date: February 16, 2022

Introduction to PyTorch

Overview: This tutorial will introduce basics of PyTorch framework necessary to build a neural network, train it and evaluate the accuracy of the model.

Training Slides:
Instructor: Joshua Yao-Yu Lin
Date: February 22, 2022

MACHINE LEARNING First Deep Neural Network


Overview: Everyone talks about Deep Learning and using Neural Networks. The algorithms have been found in certain cases to produce better estimates than humans! They are great for data prediction and understanding more about your data. This workshop is an introduction to Deep Learning using Tensorflow and Keras. A Jupyter notebook is used for hands-on student exercises along with a set of lecture slides.

Training Github Notebook
Instructor: Kristopher Keipert, NVIDIA
Date: March 2, 2022

Convolution Neural Network Models


Overview: Convolutional Neural Networks (CNNs) are used primarily for anything involving images – image classification, to image segmentation, to putting bounding boxes around an object. This interactive workshop includes a Jupyter notebook using TensorFlow and Keras, along with a set of lecture slides. In addition, this Lecture introduces the concept of Inference. This is processing data through a trained model without any back propagation.

Instructor: Jeff Layton, NVIDIA
Date: March 9, 2022

Machine Learning with H2O on HAL

Overview: This tutorial will cover basic machine learning techniques, such as Linear Regression, Decision Tree, Support Vector Machine, Naive Bayes, K-Nearest Neighbors, K-Means, and Random Forest, using H2O machine learning platform on HAL system.

Instructor: Dawei Mu
Date: March 23, 2022

Data Loaders

Overview: This tutorial will demonstrate how to use data loaders provided with PyTorch and TensorFlow and how to develop application-specific data loaders.

Training items:
Instructor: William Eustis
Date: March 30, 2022

Graph Neural Networks (GNNs)

Overview: This tutorial will introduce basic concepts in Graph Neural Networks (GNNs) and will show how to use PyTorch Geometric Library to start implementing GNNs.

Instructors: Minyang Tian & Michael Volk
Date: April 6, 2022

Inference Optimization with NVIDIA TensorRT

Overview: In many applications of deep learning models, we would benefit from reduced latency (time taken for inference). This tutorial will introduce NVIDIA TensorRT, an SDK for high-performance deep learning inference. We will go through all the steps necessary to convert a trained deep learning model to an inference-optimized model on HAL.

Instructors: Nikil Ravi & Pranshu Chaturvedi
Date: April 13, 2022