-
Session 3.3 Practical Guidelines for Training Deep Learning on HPC
Session 3.3 Practical Multinode
-
Session 3.1 Machine Learning (ML) Experiment Tracking
Session 3.1 Introduction to tools for ML experiment tracking.
-
Session 3.2 Introduction to Neural Networks and Convolution Neural Networks
Session 3.2 An overview of the main concepts of neural networks and feature discovery; the basic convolution neural...
-
Session 3.5 Deep Learning Transfer Learning
Session 3.5 Overview of deep learning concepts, including layers, architectures, applications, and libraries
-
Session 3.4 Deep Learning Layers and Architectures
Session 3.4 Overview of deep learning concepts, including layers, architectures, applications, and libraries.
-
Session 4.3 Spark
Session 4.3 An introduction to performing machine learning at scale, with hands-on exercises using Spark.
-
Session 4.2 R on HPC Demo
Session 4.2 A presentation and demo of parallelizing R; also an example case study of several ML tools and R...
-
Session 4.4 LLM Overview
Session 4.4 In this session we will present an introduction to Large Language Models and the possible use cases
-
Session 3.6 Deep Learning – Special Connections
Session 3.6 The architecture of many networks use paths and connections in flexible ways; we will review gate...
-
Getting Started with Batch Job Scheduling
Topic 2.3- Batch job schedulers are used to manage and fairly distribute the shared resources of high-performance...
-
GPU Computing - Hardware architecture and software infrastructure
Topic 2.5- Brief overview of the massively parallel GPU architecture that enables large-scale deep learning...
-
Session 2.6 Software Containers for Scientific and High-Performance Computing
Topic 2.6- Singularity is an open-source container engine designed to bring operating system-level virtualization...
-
Data Management and File Systems
Topic 2.4- Managing data efficiently on a supercomputer is important from both users' and system's perspectives...
-
Parallel Concepts
Topic 2.2- Parallel Computing Concepts talk by SDSC, delivered through XSEDE
-
Accounts, Login, Environments, Running Jobs, Logging into Expanse User Portal
Session 1.2_accounts_login_environments_running_jobs_expanse_portal
-
NumPy Intro
instructions for Expanse users to run NumPy_intro notebooks in the Expanse.
-
Clustering Visualizations
instructions for Expanse users on how to run basic clustering methods, implement them in Python notebooks, and execute them on Expanse.
-
String Processing
instructions for Expanse users to run String_Processing notebooks in the Expanse. A brief introduction to regression using scikit-learn. Covers basic linear regression, multiple linear regression, combining scikit-learn with pandas and working with categorical data.
-
Python Data Analysis Library
instructions for Expanse users to run Python_Data_Analysis_Library using CPU on Expanse.
-
Data-Analysis
instructions for Expanse users to run data analyis notebooks. The notebook covers pandas, a useful Python data analysis toolkit. We will look at two pandas objects- Series and DataFrame (1D and 2D data structures).
-
Law of Cosines on a CUDA GPU (NVIDIA)
instructions for Expanse users to run CUDA notebooks on GPU nodes. Code authored by Abe Stern, NVIDIA.
-
Computing distance matrices on a CUDA GPU (NVIDIA)
instructions for Expanse users to run CUDA notebooks on GPU nodes. Code authored by Abe Stern, NVIDIA.
-
Computing Pi on a CUDA GPU (NVIDIA)
instructions for Expanse users to run CUDA notebooks on GPU nodes. Code authored by Abe Stern, NVIDIA.
-
Parallel Programming with DASK on CPU
instructions for Expanse users to run Parallel_Programming notebooks in the Expanse. Introduces the Dask module with a simple example and illustrates the Dask graph.
-
Parallel Programming with DASK on GPU
instructions for Expanse users to run Parallel_Programming notebooks in the Expanse. Introduces the Dask module with a simple example and illustrates the Dask graph.
-
Hello_World GPU
instructions for Expanse users to print 'Hello, World!' using both CPU and GPU on Expanse.
-
Decision Trees
instructions for Expanse users to run DecisionTrees notebooks in the Expanse. Introduces the scikit-learn machine learning package, using a classic decision tree example.
-
Graphs & Networks
instructions for Expanse users on how to run notebooks related to building, visualizing, and analyzing graphs and networks.
-
Hello_World CPU
instructions for Expanse users to print 'Hello, World!' using both CPU and GPU on Expanse.
-
Data Analysis with CuPy
instructions for Expanse users to run data analyis notebooks. The notebook covers pandas, a useful Python data analysis toolkit. We will look at two pandas objects- Series and DataFrame (1D and 2D data structures).
-
Matplotlib Intro
instructions for Expanse users on running Matplotlib Python Package.
-
Image Processing
instructions for Expanse users on learning about the PILLOW package, authored by Leo Gu.
-
Tensorflow Simple Training
instructions for Expanse users on how to run TensorFlow on Expanse, both on CPU and GPU.
-
Tensorflow
instructions for Expanse users on how to run TensorFlow on Expanse, both on CPU and GPU.