PyTorch 2.x & Lightning 2.0 Advanced Masterclass | Generative AI, Edge, Distributed & Production
This advanced PyTorch course is designed for AI professionals, researchers, and engineers aiming to leverage PyTorch 2.x and Lightning 2.0 for state-of-the-art AI applications. The course covers the latest innovations in generative AI, large language models (LLMs), diffusion models, and multi-modal AI workflows.
Participants will learn how to efficiently train and fine-tune LLMs, orchestrate distributed training with DDP and FSDP, and optimize inference on CPUs, GPUs, and Edge devices. The course also explores PyTorch Edge, ExecuTorch for on-device AI, TorchRL for reinforcement learning, and tools for bias reduction in AI models.
Additionally, learners will gain hands-on knowledge of PyTorch optimization techniques, including TorchCompile, TorchInductor, CUDA Graphs, and profiling with TorchBench. Lightning talks and keynotes from PyTorch conferences demonstrate real-world applications from leading companies like Meta, Intel, IBM, Hugging Face, and Microsoft.
By the end of this course, learners will be able to implement cutting-edge AI models, deploy them efficiently across cloud and Edge platforms, optimize training and inference pipelines, and contribute to high-performance, production-ready AI systems.
تاريخ التحديثمنذ ساعتين
اللغةالإنجليزية
عدد الدروس68 درس
إجمالي الوقت17:19:44 ساعة
المستوىمبتدئ
محتوى الكورس
جميع الدروس
09:02
Lightning Talk: TorchFix - a Linter for PyTorch-Using Code with Autofix Support - Sergii Dymchenko
08:29
Lightning Talk: Enhancements Made to MPS Backend in PyTorch for Applications Running... - Kulin Seth
08:51
Lightning Talk: Harnessing NVIDIA Tensor Cores: An Exploration of CUTLASS & OpenAI..- Matthew Nicely
12:58
Lightning Talk: Accelerated Inference in PyTorch 2.X with Torch...- George Stefanakis & Dheeraj Peri
13:56
Lightning Talk: Large-Scale Distributed Training with Dynamo and... - Yeounoh Chung & Jiewen Tan
10:18
Lightning Talk: Streamlining Model Export with the New ONNX Exporter - Maanav Dalal & Aaron Bockover
15:51
Lightning Talk: Efficient Inference at the Edge: Performance You Need at the Lowest... - Felix Baum
10:24
Lightning Talk: Accelerating LLM Training on Cerebras Wafer-Scale... - Mark; Natalia; Behzad & Emad
20:21
PyTorch Edge: Developer Journey for Deploying AI Models Onto Edge Devices - Mengwei Liu & Angela Yi
24:42
PyTorch Edge: Vendor Integration Journey for Compilers and Backends - Kimish Patel, & Chen Lai
13:34
Lightning Talk: The Fastest Path to Production: PyTorch Inference in Python - Mark Saroufim, Meta
12:33
Lightning Talk: Exploring PiPPY, Tensor Parallel and Torchserve for Large... - Hamid Shojanazeri
08:18
Lightning Talk: Standardizing CPU Benchmarking with TorchBench for PyTorch... - Xu Zhao & Mingfei Ma
09:30
Lightning Talk: Profiling and Memory Debugging Tools for Distributed ML Workloads on GPUs- Aaron Shi
14:56
Lightning Talk: PT2 Export - A Sound Full Graph Capture Mechanism for PyTorch - Avik Chaudhuri, Meta
22:35
Llama V2 in Azure AI for Finetuning, Evaluation and Deployment from the Model Catalog - Swati Gharse
21:01
Cost Effectively Deploy Thousands of Fine Tuned Gen AI Models Like... - Saurabh Trikande, Li Ning
13:19
Lightning Talk: Adding Backends for TorchInductor: Case Study with Intel GPU - Eikan Wang, Intel
11:46
Keynote: How PyTorch Became the Foundation of the AI Revolution - Joe Spisak, Product Director, Meta
15:16
Lightning Talk: AOTInductor: Ahead-of-Time Compilation for PT2 Exported Models - Bin Bao, Meta
10:59
Lightning Talk: Lessons from Using Pytorch 2.0 Compile in IBM's Watsonx.AI Inference - Antoni Martin
04:57
Keynote: The Promise of PyTorch as a General-Purpose Array-Oriented Computational..- Travis Oliphant
05:56
Keynote: Intel and PyTorch: Enabling AI Everywhere with Ubiquitous Hardware and Open... - Fan Zhao
06:14
Keynote: PyTorch Lightning: Powering the GenAI Revolution from Research to the... - William Falcon
26:00
Accelerating Explorations in Vision and Multimodal AI Using Pytorch...- Nicolas, Philip, Evan & Peng
22:27
Training a LLaMA in your Backyard: Fine-tuning Very Large... - Sourab Mangrulkar & Younes Belkada
20:59
Lessons Learned in WatsonX Training: Scaling Cloud-Native...- Davis Wertheimer & Supriyo Chakraborty
15:31
Lightning Talk: TorchFix - a Linter for PyTorch-Using Code with Autofix Support - Sergii Dymchenko
10:53
Lightning Talk: Enhancements Made to MPS Backend in PyTorch for Applications Running... - Kulin Seth
12:06
Lightning Talk: Harnessing NVIDIA Tensor Cores: An Exploration of CUTLASS & OpenAI..- Matthew Nicely
17:38
Lightning Talk: Accelerated Inference in PyTorch 2.X with Torch...- George Stefanakis & Dheeraj Peri
10:24
Lightning Talk: Large-Scale Distributed Training with Dynamo and... - Yeounoh Chung & Jiewen Tan
24:46
Lightning Talk: Streamlining Model Export with the New ONNX Exporter - Maanav Dalal & Aaron Bockover
12:22
Lightning Talk: Efficient Inference at the Edge: Performance You Need at the Lowest... - Felix Baum
09:04
Lightning Talk: Accelerating LLM Training on Cerebras Wafer-Scale... - Mark; Natalia; Behzad & Emad
09:47
PyTorch Edge: Developer Journey for Deploying AI Models Onto Edge Devices - Mengwei Liu & Angela Yi
12:08
PyTorch Edge: Vendor Integration Journey for Compilers and Backends - Kimish Patel, & Chen Lai
محتوى الكورس
جميع الدروس
09:02
Lightning Talk: TorchFix - a Linter for PyTorch-Using Code with Autofix Support - Sergii Dymchenko
08:29
Lightning Talk: Enhancements Made to MPS Backend in PyTorch for Applications Running... - Kulin Seth
08:51
Lightning Talk: Harnessing NVIDIA Tensor Cores: An Exploration of CUTLASS & OpenAI..- Matthew Nicely
12:58
Lightning Talk: Accelerated Inference in PyTorch 2.X with Torch...- George Stefanakis & Dheeraj Peri
13:56
Lightning Talk: Large-Scale Distributed Training with Dynamo and... - Yeounoh Chung & Jiewen Tan
10:18
Lightning Talk: Streamlining Model Export with the New ONNX Exporter - Maanav Dalal & Aaron Bockover
15:51
Lightning Talk: Efficient Inference at the Edge: Performance You Need at the Lowest... - Felix Baum
10:24
Lightning Talk: Accelerating LLM Training on Cerebras Wafer-Scale... - Mark; Natalia; Behzad & Emad
20:21
PyTorch Edge: Developer Journey for Deploying AI Models Onto Edge Devices - Mengwei Liu & Angela Yi
24:42
PyTorch Edge: Vendor Integration Journey for Compilers and Backends - Kimish Patel, & Chen Lai
13:34
Lightning Talk: The Fastest Path to Production: PyTorch Inference in Python - Mark Saroufim, Meta
12:33
Lightning Talk: Exploring PiPPY, Tensor Parallel and Torchserve for Large... - Hamid Shojanazeri
08:18
Lightning Talk: Standardizing CPU Benchmarking with TorchBench for PyTorch... - Xu Zhao & Mingfei Ma
09:30
Lightning Talk: Profiling and Memory Debugging Tools for Distributed ML Workloads on GPUs- Aaron Shi
14:56
Lightning Talk: PT2 Export - A Sound Full Graph Capture Mechanism for PyTorch - Avik Chaudhuri, Meta
22:35
Llama V2 in Azure AI for Finetuning, Evaluation and Deployment from the Model Catalog - Swati Gharse
21:01
Cost Effectively Deploy Thousands of Fine Tuned Gen AI Models Like... - Saurabh Trikande, Li Ning
13:19
Lightning Talk: Adding Backends for TorchInductor: Case Study with Intel GPU - Eikan Wang, Intel
11:46
Keynote: How PyTorch Became the Foundation of the AI Revolution - Joe Spisak, Product Director, Meta
15:16
Lightning Talk: AOTInductor: Ahead-of-Time Compilation for PT2 Exported Models - Bin Bao, Meta
10:59
Lightning Talk: Lessons from Using Pytorch 2.0 Compile in IBM's Watsonx.AI Inference - Antoni Martin
04:57
Keynote: The Promise of PyTorch as a General-Purpose Array-Oriented Computational..- Travis Oliphant
05:56
Keynote: Intel and PyTorch: Enabling AI Everywhere with Ubiquitous Hardware and Open... - Fan Zhao
06:14
Keynote: PyTorch Lightning: Powering the GenAI Revolution from Research to the... - William Falcon
26:00
Accelerating Explorations in Vision and Multimodal AI Using Pytorch...- Nicolas, Philip, Evan & Peng
22:27
Training a LLaMA in your Backyard: Fine-tuning Very Large... - Sourab Mangrulkar & Younes Belkada
20:59
Lessons Learned in WatsonX Training: Scaling Cloud-Native...- Davis Wertheimer & Supriyo Chakraborty
15:31
Lightning Talk: TorchFix - a Linter for PyTorch-Using Code with Autofix Support - Sergii Dymchenko
10:53
Lightning Talk: Enhancements Made to MPS Backend in PyTorch for Applications Running... - Kulin Seth
12:06
Lightning Talk: Harnessing NVIDIA Tensor Cores: An Exploration of CUTLASS & OpenAI..- Matthew Nicely
17:38
Lightning Talk: Accelerated Inference in PyTorch 2.X with Torch...- George Stefanakis & Dheeraj Peri
10:24
Lightning Talk: Large-Scale Distributed Training with Dynamo and... - Yeounoh Chung & Jiewen Tan
24:46
Lightning Talk: Streamlining Model Export with the New ONNX Exporter - Maanav Dalal & Aaron Bockover
12:22
Lightning Talk: Efficient Inference at the Edge: Performance You Need at the Lowest... - Felix Baum
09:04
Lightning Talk: Accelerating LLM Training on Cerebras Wafer-Scale... - Mark; Natalia; Behzad & Emad
09:47
PyTorch Edge: Developer Journey for Deploying AI Models Onto Edge Devices - Mengwei Liu & Angela Yi
12:08
PyTorch Edge: Vendor Integration Journey for Compilers and Backends - Kimish Patel, & Chen Lai
