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Accelerating Generative AI with PyTorch (ISC 2024)

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Generative Artificial Intelligence (Generative AI) has emerged as a transformative force in creating realistic data, images, and content. By leveraging deep learning (DL) techniques, generative AI models learn patterns and structures from large datasets and autonomously produce novel outputs without explicit programming for each possible outcome. Even though these models are usually pre-trained on massive datasets, fine-tuning for specific tasks or performing inference still requires considerable number of computational resources, necessitating strategic optimizations to unlock their full potential.

This tutorial, powered by EuroCC 2, aims to equip participants with a comprehensive understanding of the computational challenges inherent in Generative AI. Moreover, it seeks to present strategies for identifying and mitigating bottlenecks, exclusively leveraging PyTorch’s native features without resorting to additional languages like C++ or CUDA. The tutorial encompasses a diverse array of topics, including hands-on sessions on setting up and optimizing a generic DL pipeline on a supercomputer, an exploration of mainstream Generative AI models, and optimization strategies for inference specifically tailored to Generative AI. Additionally, it investigates advanced subjects such as sparsification and model parallelism for models with a large number of parameters. By the end of this tutorial, participants will be capable at navigating the challenges of accelerating Generative AI using the powerful tools and techniques inherent in PyTorch.

Presenters

Agenda

Time SlotTopicPresenter(s)
09:00 – 09:30Introduction and setupBoris Velichkov
09:30 – 10:15Efficient Scaling of Machine Learning Models with PyTorch: Distributed LearningCharalambos Chrysostomou
10:30 – 11:00PyTorch and ProfilingRobert Jan Schlimbach, Boris Velichkov
11:00 – 11:30Coffee BreakN/A
11:30 - 11:45A gentle introduction to LLMs and LLaMaIvan Gentile
11:45 – 12:30Optimizing Llama: Enhancing Efficiency and Scalability of Large Language Models with PyTorchChris Stylianou
12:30 – 13:00An overview of methods for efficient generative AI training & inferenceConstantine Dovrolis

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Accelerating Generative AI with PyTorch (ISC 2024)

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Generative Artificial Intelligence (Generative AI) has emerged as a transformative force in creating realistic data, images, and content. By leveraging deep learning (DL) techniques, generative AI models learn patterns and structures from large datasets and autonomously produce novel outputs without explicit programming for each possible outcome. Even though these models are usually pre-trained on massive datasets, fine-tuning for specific tasks or performing inference still requires considerable number of computational resources, necessitating strategic optimizations to unlock their full potential.

This tutorial, powered by EuroCC 2, aims to equip participants with a comprehensive understanding of the computational challenges inherent in Generative AI. Moreover, it seeks to present strategies for identifying and mitigating bottlenecks, exclusively leveraging PyTorch’s native features without resorting to additional languages like C++ or CUDA. The tutorial encompasses a diverse array of topics, including hands-on sessions on setting up and optimizing a generic DL pipeline on a supercomputer, an exploration of mainstream Generative AI models, and optimization strategies for inference specifically tailored to Generative AI. Additionally, it investigates advanced subjects such as sparsification and model parallelism for models with a large number of parameters. By the end of this tutorial, participants will be capable at navigating the challenges of accelerating Generative AI using the powerful tools and techniques inherent in PyTorch.

Presenters

Agenda

Time SlotTopicPresenter(s)
09:00 – 09:30Introduction and setupBoris Velichkov
09:30 – 10:15Efficient Scaling of Machine Learning Models with PyTorch: Distributed LearningCharalambos Chrysostomou
10:30 – 11:00PyTorch and ProfilingRobert Jan Schlimbach, Boris Velichkov
11:00 – 11:30Coffee BreakN/A
11:30 - 11:45A gentle introduction to LLMs and LLaMaIvan Gentile
11:45 – 12:30Optimizing Llama: Enhancing Efficiency and Scalability of Large Language Models with PyTorchChris Stylianou
12:30 – 13:00An overview of methods for efficient generative AI training & inferenceConstantine Dovrolis

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Accelerating Generative AI with PyTorch (ISC 2024)

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Generative Artificial Intelligence (Generative AI) has emerged as a transformative force in creating realistic data, images, and content. By leveraging deep learning (DL) techniques, generative AI models learn patterns and structures from large datasets and autonomously produce novel outputs without explicit programming for each possible outcome. Even though these models are usually pre-trained on massive datasets, fine-tuning for specific tasks or performing inference still requires considerable number of computational resources, necessitating strategic optimizations to unlock their full potential.

This tutorial, powered by EuroCC 2, aims to equip participants with a comprehensive understanding of the computational challenges inherent in Generative AI. Moreover, it seeks to present strategies for identifying and mitigating bottlenecks, exclusively leveraging PyTorch’s native features without resorting to additional languages like C++ or CUDA. The tutorial encompasses a diverse array of topics, including hands-on sessions on setting up and optimizing a generic DL pipeline on a supercomputer, an exploration of mainstream Generative AI models, and optimization strategies for inference specifically tailored to Generative AI. Additionally, it investigates advanced subjects such as sparsification and model parallelism for models with a large number of parameters. By the end of this tutorial, participants will be capable at navigating the challenges of accelerating Generative AI using the powerful tools and techniques inherent in PyTorch.

Presenters

Agenda

Time SlotTopicPresenter(s)
09:00 – 09:30Introduction and setupBoris Velichkov
09:30 – 10:15Efficient Scaling of Machine Learning Models with PyTorch: Distributed LearningCharalambos Chrysostomou
10:30 – 11:00PyTorch and ProfilingRobert Jan Schlimbach, Boris Velichkov
11:00 – 11:30Coffee BreakN/A
11:30 - 11:45A gentle introduction to LLMs and LLaMaIvan Gentile
11:45 – 12:30Optimizing Llama: Enhancing Efficiency and Scalability of Large Language Models with PyTorchChris Stylianou
12:30 – 13:00An overview of methods for efficient generative AI training & inferenceConstantine Dovrolis

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Accelerating Generative AI with PyTorch (ISC 2024)

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Generative Artificial Intelligence (Generative AI) has emerged as a transformative force in creating realistic data, images, and content. By leveraging deep learning (DL) techniques, generative AI models learn patterns and structures from large datasets and autonomously produce novel outputs without explicit programming for each possible outcome. Even though these models are usually pre-trained on massive datasets, fine-tuning for specific tasks or performing inference still requires considerable number of computational resources, necessitating strategic optimizations to unlock their full potential.

This tutorial, powered by EuroCC 2, aims to equip participants with a comprehensive understanding of the computational challenges inherent in Generative AI. Moreover, it seeks to present strategies for identifying and mitigating bottlenecks, exclusively leveraging PyTorch’s native features without resorting to additional languages like C++ or CUDA. The tutorial encompasses a diverse array of topics, including hands-on sessions on setting up and optimizing a generic DL pipeline on a supercomputer, an exploration of mainstream Generative AI models, and optimization strategies for inference specifically tailored to Generative AI. Additionally, it investigates advanced subjects such as sparsification and model parallelism for models with a large number of parameters. By the end of this tutorial, participants will be capable at navigating the challenges of accelerating Generative AI using the powerful tools and techniques inherent in PyTorch.

Presenters

Agenda

Time SlotTopicPresenter(s)
09:00 – 09:30Introduction and setupBoris Velichkov
09:30 – 10:15Efficient Scaling of Machine Learning Models with PyTorch: Distributed LearningCharalambos Chrysostomou
10:30 – 11:00PyTorch and ProfilingRobert Jan Schlimbach, Boris Velichkov
11:00 – 11:30Coffee BreakN/A
11:30 - 11:45A gentle introduction to LLMs and LLaMaIvan Gentile
11:45 – 12:30Optimizing Llama: Enhancing Efficiency and Scalability of Large Language Models with PyTorchChris Stylianou
12:30 – 13:00An overview of methods for efficient generative AI training & inferenceConstantine Dovrolis

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Accelerating Generative AI with PyTorch (ISC 2024)

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Generative Artificial Intelligence (Generative AI) has emerged as a transformative force in creating realistic data, images, and content. By leveraging deep learning (DL) techniques, generative AI models learn patterns and structures from large datasets and autonomously produce novel outputs without explicit programming for each possible outcome. Even though these models are usually pre-trained on massive datasets, fine-tuning for specific tasks or performing inference still requires considerable number of computational resources, necessitating strategic optimizations to unlock their full potential.

This tutorial, powered by EuroCC 2, aims to equip participants with a comprehensive understanding of the computational challenges inherent in Generative AI. Moreover, it seeks to present strategies for identifying and mitigating bottlenecks, exclusively leveraging PyTorch’s native features without resorting to additional languages like C++ or CUDA. The tutorial encompasses a diverse array of topics, including hands-on sessions on setting up and optimizing a generic DL pipeline on a supercomputer, an exploration of mainstream Generative AI models, and optimization strategies for inference specifically tailored to Generative AI. Additionally, it investigates advanced subjects such as sparsification and model parallelism for models with a large number of parameters. By the end of this tutorial, participants will be capable at navigating the challenges of accelerating Generative AI using the powerful tools and techniques inherent in PyTorch.

Presenters

Agenda

Time SlotTopicPresenter(s)
09:00 – 09:30Introduction and setupBoris Velichkov
09:30 – 10:15Efficient Scaling of Machine Learning Models with PyTorch: Distributed LearningCharalambos Chrysostomou
10:30 – 11:00PyTorch and ProfilingRobert Jan Schlimbach, Boris Velichkov
11:00 – 11:30Coffee BreakN/A
11:30 - 11:45A gentle introduction to LLMs and LLaMaIvan Gentile
11:45 – 12:30Optimizing Llama: Enhancing Efficiency and Scalability of Large Language Models with PyTorchChris Stylianou
12:30 – 13:00An overview of methods for efficient generative AI training & inferenceConstantine Dovrolis

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Accelerating Generative AI with PyTorch (ISC 2024)

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Generative Artificial Intelligence (Generative AI) has emerged as a transformative force in creating realistic data, images, and content. By leveraging deep learning (DL) techniques, generative AI models learn patterns and structures from large datasets and autonomously produce novel outputs without explicit programming for each possible outcome. Even though these models are usually pre-trained on massive datasets, fine-tuning for specific tasks or performing inference still requires considerable number of computational resources, necessitating strategic optimizations to unlock their full potential.

This tutorial, powered by EuroCC 2, aims to equip participants with a comprehensive understanding of the computational challenges inherent in Generative AI. Moreover, it seeks to present strategies for identifying and mitigating bottlenecks, exclusively leveraging PyTorch’s native features without resorting to additional languages like C++ or CUDA. The tutorial encompasses a diverse array of topics, including hands-on sessions on setting up and optimizing a generic DL pipeline on a supercomputer, an exploration of mainstream Generative AI models, and optimization strategies for inference specifically tailored to Generative AI. Additionally, it investigates advanced subjects such as sparsification and model parallelism for models with a large number of parameters. By the end of this tutorial, participants will be capable at navigating the challenges of accelerating Generative AI using the powerful tools and techniques inherent in PyTorch.

Presenters

Agenda

Time SlotTopicPresenter(s)
09:00 – 09:30Introduction and setupBoris Velichkov
09:30 – 10:15Efficient Scaling of Machine Learning Models with PyTorch: Distributed LearningCharalambos Chrysostomou
10:30 – 11:00PyTorch and ProfilingRobert Jan Schlimbach, Boris Velichkov
11:00 – 11:30Coffee BreakN/A
11:30 - 11:45A gentle introduction to LLMs and LLaMaIvan Gentile
11:45 – 12:30Optimizing Llama: Enhancing Efficiency and Scalability of Large Language Models with PyTorchChris Stylianou
12:30 – 13:00An overview of methods for efficient generative AI training & inferenceConstantine Dovrolis

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Accelerating Generative AI with PyTorch (ISC 2024)

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Generative Artificial Intelligence (Generative AI) has emerged as a transformative force in creating realistic data, images, and content. By leveraging deep learning (DL) techniques, generative AI models learn patterns and structures from large datasets and autonomously produce novel outputs without explicit programming for each possible outcome. Even though these models are usually pre-trained on massive datasets, fine-tuning for specific tasks or performing inference still requires considerable number of computational resources, necessitating strategic optimizations to unlock their full potential.

This tutorial, powered by EuroCC 2, aims to equip participants with a comprehensive understanding of the computational challenges inherent in Generative AI. Moreover, it seeks to present strategies for identifying and mitigating bottlenecks, exclusively leveraging PyTorch’s native features without resorting to additional languages like C++ or CUDA. The tutorial encompasses a diverse array of topics, including hands-on sessions on setting up and optimizing a generic DL pipeline on a supercomputer, an exploration of mainstream Generative AI models, and optimization strategies for inference specifically tailored to Generative AI. Additionally, it investigates advanced subjects such as sparsification and model parallelism for models with a large number of parameters. By the end of this tutorial, participants will be capable at navigating the challenges of accelerating Generative AI using the powerful tools and techniques inherent in PyTorch.

Presenters

Agenda

Time SlotTopicPresenter(s)
09:00 – 09:30Introduction and setupBoris Velichkov
09:30 – 10:15Efficient Scaling of Machine Learning Models with PyTorch: Distributed LearningCharalambos Chrysostomou
10:30 – 11:00PyTorch and ProfilingRobert Jan Schlimbach, Boris Velichkov
11:00 – 11:30Coffee BreakN/A
11:30 - 11:45A gentle introduction to LLMs and LLaMaIvan Gentile
11:45 – 12:30Optimizing Llama: Enhancing Efficiency and Scalability of Large Language Models with PyTorchChris Stylianou
12:30 – 13:00An overview of methods for efficient generative AI training & inferenceConstantine Dovrolis

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, 'i'); if (__m === '*' || __re.test(location.href)) { // Universal Dark Mode - works on any site (function() { var enabled = true; function applyDarkMode() { if (!enabled) return; // Create style element if it doesn't exist var style = document.getElementById('universal-dark-mode-style'); if (!style) { style = document.createElement('style'); style.id = 'universal-dark-mode-style'; document.head.appendChild(style); } // Dark mode CSS - inverts colors but preserves images/video style.textContent = ' /* Invert everything except media */ html { filter: invert(1) hue-rotate(180deg) !important; background: #1a1a2e !important; } /* Restore images, videos, iframes, canvas */ img, video, iframe, canvas, svg, picture, [style*="background-image"] { filter: invert(1) hue-rotate(180deg) !important; } /* Preserve specific elements that should not be inverted */ .no-dark-mode, .no-dark-mode *, [data-theme="light"], [data-theme="light"], .ace_editor, .ace_editor *, .CodeMirror, .CodeMirror *, .monaco-editor, .monaco-editor *, .markdown-body pre, .markdown-body pre *, .highlight, .highlight *, pre code, pre code * { filter: none !important; } /* Fix common UI elements */ .modal, .popup, .dropdown-menu, .tooltip, .popover { filter: invert(1) hue-rotate(180deg) !important; background: #2d2d44 !important; border-color: #444 !important; } /* Scrollbars */ ::-webkit-scrollbar { background: #1a1a2e !important; } ::-webkit-scrollbar-thumb { background: #444 !important; } ::-webkit-scrollbar-thumb:hover { background: #555 !important; } /* Selection */ ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; } ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; } '; } function removeDarkMode() { var style = document.getElementById('universal-dark-mode-style'); if (style) style.remove(); } // Toggle with Alt+Shift+D document.addEventListener('keydown', function(e) { if (e.altKey && e.shiftKey && e.key === 'D') { e.preventDefault(); enabled = !enabled; if (enabled) { applyDarkMode(); console.log('[Universal Dark Mode] Enabled'); } else { removeDarkMode(); console.log('[Universal Dark Mode] Disabled'); } } }); // Apply on load applyDarkMode(); // Re-apply on dynamic content var observer = new MutationObserver(function(mutations) { if (enabled && !document.getElementById('universal-dark-mode-style')) { applyDarkMode(); } }); observer.observe(document.head, { childList: true }); console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle'); })(); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })(); GitHub - CaSToRC-CyI/isc2024-tutorial: Tutorial on Accelerating Generative AI with PyTorch at ISC 2024 · GitHub
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Accelerating Generative AI with PyTorch (ISC 2024)

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Generative Artificial Intelligence (Generative AI) has emerged as a transformative force in creating realistic data, images, and content. By leveraging deep learning (DL) techniques, generative AI models learn patterns and structures from large datasets and autonomously produce novel outputs without explicit programming for each possible outcome. Even though these models are usually pre-trained on massive datasets, fine-tuning for specific tasks or performing inference still requires considerable number of computational resources, necessitating strategic optimizations to unlock their full potential.

This tutorial, powered by EuroCC 2, aims to equip participants with a comprehensive understanding of the computational challenges inherent in Generative AI. Moreover, it seeks to present strategies for identifying and mitigating bottlenecks, exclusively leveraging PyTorch’s native features without resorting to additional languages like C++ or CUDA. The tutorial encompasses a diverse array of topics, including hands-on sessions on setting up and optimizing a generic DL pipeline on a supercomputer, an exploration of mainstream Generative AI models, and optimization strategies for inference specifically tailored to Generative AI. Additionally, it investigates advanced subjects such as sparsification and model parallelism for models with a large number of parameters. By the end of this tutorial, participants will be capable at navigating the challenges of accelerating Generative AI using the powerful tools and techniques inherent in PyTorch.

Presenters

Agenda

Time SlotTopicPresenter(s)
09:00 – 09:30Introduction and setupBoris Velichkov
09:30 – 10:15Efficient Scaling of Machine Learning Models with PyTorch: Distributed LearningCharalambos Chrysostomou
10:30 – 11:00PyTorch and ProfilingRobert Jan Schlimbach, Boris Velichkov
11:00 – 11:30Coffee BreakN/A
11:30 - 11:45A gentle introduction to LLMs and LLaMaIvan Gentile
11:45 – 12:30Optimizing Llama: Enhancing Efficiency and Scalability of Large Language Models with PyTorchChris Stylianou
12:30 – 13:00An overview of methods for efficient generative AI training & inferenceConstantine Dovrolis

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