Scalable Machine Learning with High Performance and Cloud Computing

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
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What you'll learn

Deep Learning is emerging as the leading AI technique owing to the current convergence of scalable computing capability (i.e., HCP and Cloud computing), easy access to large volumes of data, and the emergence of new algorithms enabling robust training of large-scale deep neural networks. The tutorial aims at providing a complete overview for an audience that is not familiar with these topics.

Lecture 1: Introduction

🏛Jülich Supercomputing Centre - Forschungszentrum Jülich

🌐Machine learning and Deep Learning in remote sensing

🎛Deep learning and Supercomputing

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Lecture 2: Levels of Parallelism and High Performance Computing

🍴The Free Lunch is Over

⛓Hardware Levels of Parallelism

📱High Performance Computing (HPC)


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Lecture 3: Distributed Deep Learning

🏄Distributed training



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Lecture 4: Hands-on Distributed Deep Learning


Become familiar with Horovod, a data distributed training framework

Understand how to modify existing code to enable parallelism

Understand the importance of distributing data beforehand

Understand what Horovod does looking at the lines of code to be added

Create a job script to execute Python code on the GPUs

Play around with model architecture, optimizer, learning rate

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Lecture 5: Big Data Analytics using Apache Spark

💥Apache Spark Basics

🔧Developing on Spark and Clouds

☁️Machine Learning on Spark

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