| May,2024 |
|---|
| 2 May 11:00 am 12:00 pmIntroduction to neural network programming, lecture 4 | DAT112 - Apr 2024 |
| 7 May 11:00 am 12:00 pmIntroduction to neural network programming, lecture 5 | DAT112 - Apr 2024 |
| 8 May 1:00 pm 2:30 pmIn about 90 minutes, learn how to use the SciNet systems Niagara and Mist, from securely logging in to running computations on the supercomputer. Experienced users may still pick up some valuable pointers.Format: Virtual Virtual | HPC105 - May 2024 |
| 9 May 11:00 am 12:00 pmIntroduction to neural network programming, lecture 6 | DAT112 - Apr 2024 |
| 13 May 1:00 pm 4:00 pmPrinciples and uses of relational databases with practical examples using python and sqlite on the Niagara supercomputer.Prerequisites: Some Linux command line experience. Python experience is strongly advised. Format: Virtual Virtual | SCMP231 - May 2024 |
| 14 May 11:00 am 12:00 pmIntroduction to neural network programming, lecture 7 | DAT112 - Apr 2024 |
| 16 May 11:00 am 12:00 pmIntroduction to neural network programming, lecture 8 | DAT112 - Apr 2024 |
| 21 May 11:00 am 12:00 pmIntroduction to neural network programming, lecture 9 | DAT112 - Apr 2024 |
| 23 May 11:00 am 12:00 pmIntroduction to neural network programming, lecture 10 | DAT112 - Apr 2024 |
| 27 May 1:00 pm 4:00 pmThis workshop explores various concise and useful constructs for working with bash shell. The goal is to improve your shell skills. Attending this class requires some basic GNU/Linux command line experience.Format: Virtual | SCMP281 - May 2024 |
| 28 May 11:00 am 12:00 pmIntroduction to neural network programming, lecture 11 | DAT112 - Apr 2024 |
| 30 May 11:00 am 12:00 pmIntroduction to neural network programming, lecture 12 | DAT112 - Apr 2024 |
| June,2024 |
|---|
| 3 Jun 9:00 am 12:00 pmThis course provides you with essential knowledge and skills to effectively prepare data for analysis. Starting with an overview of the Data Analytics pipeline and processes, the course explores various statistical and visualization techniques used in Exploratory and Descriptive Analytics to understand historical data. You will then delve into the art of Data Preparation, gaining expertise in data cleaning, handling missing values, detecting, and handling outliers, as well as transforming and engineering features. By the end of the course, you will be equipped with the necessary tools to ensure data quality and integrity, enabling you to make informed decisions and derive valuable insights from their data. Level: Introductory Length: 3 Hours Format: Lecture + Hands-on Prerequisites: Basic Python (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
| 3 Jun 9:00 am 12:00 pmRunning programs on the supercomputers is done via the BASH shell. This course is two three hour live demos on using bash. No prior familiarity with bash is assumed. In addition to the basics of getting around, globbing, regular expressions, redirection, pipes, and scripting will be covered. A series of exercises are required to be done in order to complete the course. Level: Introductory Length: Two 3-Hour Sessions Format: Lecture + Hands-on Prerequisites: None (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
| 12 Jun 1:30 pm 4:30 pmBe aware. Stay secure. Join us to learn more about the tools you can use to prevent the theft of your data and possibly of your identity. Other topics of discussion will include common hacking attempts, how to recognize them, and how to avoid having your data compromised, stolen, or destroyed. We will also talk about data encryption and provide tips for when travelling with electronic devices. Level: Introductory Length: 3 hours Format: Lecture Prerequisites: None (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
| 13 Jun 9:00 am 12:00 pmNOTE: This course is divided into four (4) parts over three (3) days. Part I and Part II Description: Introduction of neural network programming concepts, theory, and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate concepts. (The Keras neural network framework will be used for neural network programming but no experience with Keras will be expected.) Part III Description: This part will continue the development of neural network programming approaches from Parts I and II. This part will focus on generative methods used to create images: variational auto-encoders, generative adversarial networks, and diffusion networks. Part IV Description: This part will continue the development of neural network programming approaches from Parts I through III. This part will focus on methods used to generate sequences: LSTM networks, sequence-to-sequence networks, and transformers. Level: Intermediate Length: Four 3-Hour Sessions (3 Days) Format: Lecture + Hands-on Prerequisites: Experience with Python (version 3.10) is assumed. Each part assumes what was covered in the previous parts of this course. Parts III and IV assume experience with neural network programming, per the first two neural network programming sessions in this course. (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
| 13 Jun 9:00 am 12:00 pmApptainer is a secure container technology designed to be used on for high performance compute clusters. This workshop will focus on how to use Apptainer as well as how to make use of tools such as Conda and Spack within Apptainer. By the end of these sessions, one: will have learnt about Apptainer and how it is installed and used on our computer clusters, how to build an Apptainer container image, how to install tools such as Conda/Spack from inside an Apptainer container shell, and, how to use Apptainer containers within job submission scripts. Level: Introductory Length: Two 3-Hour Sessions Format: Lecture + Hands-on Prerequisites: Experience using Alliance compute clusters, e.g., using the BASH shell and submitting jobs. (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
| 13 Jun 1:30 pm 4:30 pmApptainer is a secure container technology designed to be used on for high performance compute clusters. This workshop will focus on how to use Apptainer as well as how to make use of tools such as Conda and Spack within Apptainer. By the end of these sessions, one: will have learnt about Apptainer and how it is installed and used on our computer clusters, how to build an Apptainer container image, how to install tools such as Conda/Spack from inside an Apptainer container shell, and, how to use Apptainer containers within job submission scripts. Level: Introductory Length: Two 3-Hour Sessions Format: Lecture + Hands-on Prerequisites: Experience using Alliance compute clusters, e.g., using the BASH shell and submitting jobs. (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
| 13 Jun 1:30 pm 4:30 pmoneAPI is a unified application programming interface intended to be used across different compute accelerator architectures, including CPUs, GPUs and AI accelerators. It's aim is to unify the programming model as well as simplifying cross-architecture development. It also provides libraries for: deep neural network (DNN) learning applications, collective communications for machine learning and deep learning projects, and, data analytics making big data analysis faster using optimized algorithms. By the end of this workshop one will have: learned about oneAPI libraries and the inference toolkit, components, and capabilities for developing and deploying computer vision and deep learning solutions, explored techniques for optimizing pre-trained deep learning models and learn how to work with models from different frameworks like Tensorflow, PyTorch and Caffe, understood how to perform inference on different hardware such as CPU and GPU, and, considered practical computer vision applications and use cases. Level: Intermediate Length: 3 Hours Format: Lecture + Hands-on Prerequisites: Attendees having hands-on experience with Python and some experience with Tensorflow or PyTorch will get the most out of this workshop. (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
| 14 Jun 9:00 am 12:00 pmNOTE: This course is divided into four (4) parts over three (3) days. Part I and Part II Description: Introduction of neural network programming concepts, theory, and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate concepts. (The Keras neural network framework will be used for neural network programming but no experience with Keras will be expected.) Part III Description: This part will continue the development of neural network programming approaches from Parts I and II. This part will focus on generative methods used to create images: variational auto-encoders, generative adversarial networks, and diffusion networks. Part IV Description: This part will continue the development of neural network programming approaches from Parts I through III. This part will focus on methods used to generate sequences: LSTM networks, sequence-to-sequence networks, and transformers. Level: Intermediate Length: Four 3-Hour Sessions (3 Days) Format: Lecture + Hands-on Prerequisites: Experience with Python (version 3.10) is assumed. Each part assumes what was covered in the previous parts of this course. Parts III and IV assume experience with neural network programming, per the first two neural network programming sessions in this course. (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
| 14 Jun 9:00 am 12:00 pmMachine learning is a data analytics technique that teaches computers to do what comes naturally to humans and animals: learn from experience. Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model. In this hands-on introductory workshop, you will learn how to apply Machine Learning, and get familiar with the basics of Deep Learning. MATLAB provides an environment to apply advanced techniques without requiring extensive coding nor experience in machine learning. Learn the fundamentals of machine learning (supervised learning, feature extraction, and hyperparameter tuning) Explore pre-processing and powerful visualization techniques Build and evaluate machine learning models for classification and regression of various data formats (signals, images, text) Perform hyperparameter tuning and feature selection to optimize model performance Discuss interoperability with other platforms Learn how to deploy Machine Learning models Level: Intermediate Length: 3 Hours Format: Lecture + Hands-on Prerequisites: Working knowledge of MATLABMachine learning is a data analytics technique that teaches computers to do what comes naturally to humans and animals: learn from experience. Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model. In this hands-on introductory workshop, you will learn how to apply Machine Learning, and get familiar with the basics of Deep Learning. MATLAB provides an environment to apply advanced techniques without requiring extensive coding nor experience in machine learning. Learn the fundamentals of machine learning (supervised learning, feature extraction, and hyperparameter tuning) Explore pre-processing and powerful visualization techniques Build and evaluate machine learning models for classification and regression of various data formats (signals, images, text) Perform hyperparameter tuning and feature selection to optimize model performance Discuss interoperability with other platforms Learn how to deploy Machine Learning models Level: Intermediate Length: 3 Hours Format: Lecture + Hands-on Prerequisites: Working knowledge of MATLAB (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
| 14 Jun 1:30 pm 4:30 pmThis course is intended to help learners with a basic understanding of fluid dynamics and CFD bridge the knowledge gap towards the effective utilization of CFD on modern HPC architectures. This course will take an end-user approach to CFD tools on HPC systems (no coding) and, despite some prerequisites, will be given at an introductory/intermediate level (we will not cover advanced topics such as GPU or dynamic load-balancing). At the end of the course, the learner will be able to: Develop a systematic approach to estimate the HPC cost of a CFD problem. Explain the impact of modelling assumptions on HPC cost. Optimize the parameters and simulations for effective HPC usage. The course will use an entirely open source suite of CFD toolsets to mesh (Gmsh), simulate (OpenFoam/SU2), and visualize (Visit/Paraview). It should be noted that this is not a CFD course; therefore, undergraduate-level knowledge of CFD and numerical methods is expected, as well as a basic understanding of the Compute Ontario HPC system. The focus is on the effective use of CFD tools in modern HPC systems. Level: Intermediate, Length: Three 1-Hour Sessions (3 Days), Format: Lecture + Hands-on, Prerequisites: Undergraduate-level knowledge of fluid dynamics (ideally with some knowledge of turbulence), CFD, and numerical methods. (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
| 14 Jun 1:30 pm 4:30 pmNOTE: This course is divided into four (4) parts over three (3) days. Part I and Part II Description: Introduction of neural network programming concepts, theory, and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate concepts. (The Keras neural network framework will be used for neural network programming but no experience with Keras will be expected.) Part III Description: This part will continue the development of neural network programming approaches from Parts I and II. This part will focus on generative methods used to create images: variational auto-encoders, generative adversarial networks, and diffusion networks. Part IV Description: This part will continue the development of neural network programming approaches from Parts I through III. This part will focus on methods used to generate sequences: LSTM networks, sequence-to-sequence networks, and transformers. Level: Intermediate Length: Four 3-Hour Sessions (3 Days) Format: Lecture + Hands-on Prerequisites: Experience with Python (version 3.10) is assumed. Each part assumes what was covered in the previous parts of this course. Parts III and IV assume experience with neural network programming, per the first two neural network programming sessions in this course. (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
| 17 Jun 9:00 amThis is an introductory course covering programming and computing on GPUs - graphics processing units - which are an increasingly common presence in massively parallel computing architectures. The basics of GPU programming will be covered, and students will work through a number of hands on examples. The structuring of data and computations that makes full use of the GPU will be discussed in detail. Students should be able to leave the course with the knowledge necessary to begin developing their own GPU applications. Level: Introductory Length: Six 3-Hour Sessions (3 Days) Format: Lecture + Hands-on Prerequisites: Alliance Account Basic C and/or C++ experience (part of the 2024 Compute Ontario Summer School) Virtual | |
