正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

From torch.profiler to Hardware Cycles: A Practical Profiling Playbook AWS Trainium: designed from the ground up for next generation AI training and inference is now a standard PyTorch device. When you need to optimize, the tools are built in. Esha Lakhotia and Pinak Panigrahi of Amazon Web Services will... show more

PyTorch

516,101 subscribers

14,523 次观看 • 2 天前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

3 条评论

Chandrakumar R Pillai 的头像

Chandrakumar R Pillai1 天前

Connecting traces to hardware cycles can make optimization less of a black box. Showing the original bottleneck, suggested change, and measured effect on the same workload would make the AI-assisted path easier to validate.

Anmol 的头像

Anmol2 天前

first-class profiling is the real moat for alternative silicon

Patch 的头像

Patch2 天前

I'd like to see how the AI-assisted analysis gets from a hardware trace to a suggested fix.

相关视频

What You Cannot Profile, You Cannot Optimize: Learning to Read PyTorch Traces In his talk at PyTorch Conference North America 2026, Suvaditya Mukherjee from Hugging Face will share practical guidance on how to start using the PyTorch Profiler on your models to extract maximum performance efficiency. Whether you are aiming to identify performance bottlenecks or optimize model execution, this talk will help you get the most out of your hardware. Join the open source AI community in San Jose, October 20-21: #PyTorchCon Suvaditya Mukherjee

PyTorch

16,264 次观看 • 8 天前

Interested in optimizing Mixture of Experts LLM inference on ARM CPUs? In his talk at PyTorch Conference North America, Maajid Khan from Fujitsu Research India will explore efficient MoE LLM inference using vLLM and OpenVINO, sharing practical strategies for running these models effectively on ARM architectures. Register and join us in San Jose, October 20-21: #PyTorchCon

Interested in optimizing Mixture of Experts LLM inference on ARM CPUs? In his talk at PyTorch Conference North America, Maajid Khan from Fujitsu Research India will explore efficient MoE LLM inference using vLLM and OpenVINO, sharing practical strategies for running these models effectively on ARM architectures. Register and join us in San Jose, October 20-21: #PyTorchCon

PyTorch

16,448 次观看 • 13 天前

PyTorch Conference is where the open source Al community gathers to talk about what we are all building together. Priya Nagpurkar, VP of AI Native Systems at IBM Research will give a keynote at #PyTorchCon North America on IBM & Red Hat's contributions to the PyTorch ecosystem, including exciting developments on infrastructure for out-of-tree accelerators, enablement of IBM's Spyre Accelerator, and work with Ilm-d for distributed inference. Come to the talk, meet the community, and hear what's next in open source Al. Join us in San Jose, October 20-21:

PyTorch Conference is where the open source Al community gathers to talk about what we are all building together. Priya Nagpurkar, VP of AI Native Systems at IBM Research will give a keynote at #PyTorchCon North America on IBM & Red Hat's contributions to the PyTorch ecosystem, including exciting developments on infrastructure for out-of-tree accelerators, enablement of IBM's Spyre Accelerator, and work with Ilm-d for distributed inference. Come to the talk, meet the community, and hear what's next in open source Al. Join us in San Jose, October 20-21:

PyTorch

16,019 次观看 • 3 天前

Build a neural network from scratch, train it, and optimize it for real-world AI infrastructure during our PyTorch Associate Training the day before PyTorch Conference North America on October 19th. Your instructor, Faradawn Yang of NVIDIA AI explains how this training will give you hands on experience with the latest AI accelerators & prepare you for the PyTorch Certified Associate (PTCA) certification exam. #PyTorchCon

Build a neural network from scratch, train it, and optimize it for real-world AI infrastructure during our PyTorch Associate Training the day before PyTorch Conference North America on October 19th. Your instructor, Faradawn Yang of NVIDIA AI explains how this training will give you hands on experience with the latest AI accelerators & prepare you for the PyTorch Certified Associate (PTCA) certification exam. #PyTorchCon

PyTorch

17,476 次观看 • 10 天前

"It's really great to feel the energy and see all the collaboration that's happening, lots of conversations. We have people here who are just starting to learn about PyTorch, and we have people who are maintainers. It's super nice to see that energy and see how together we're going to be able to solve some of these very difficult problems for humanity." Ankit Patel, NVIDIA, at PyTorch Conference Europe 2026 Register today to join the open source AI community at PyTorch Conference North America in San Jose, October 20–21: #PyTorchCon

"It's really great to feel the energy and see all the collaboration that's happening, lots of conversations. We have people here who are just starting to learn about PyTorch, and we have people who are maintainers. It's super nice to see that energy and see how together we're going to be able to solve some of these very difficult problems for humanity." Ankit Patel, NVIDIA, at PyTorch Conference Europe 2026 Register today to join the open source AI community at PyTorch Conference North America in San Jose, October 20–21: #PyTorchCon

PyTorch

17,000 次观看 • 25 天前

ERRC: Entropy-Reinvested Residual Correction for Tensor-Parallel LLM Communication PyTorch Foundation Ambassador Abdulsalam Bande will present a poster on ERRC (Entropy-Reinvested Residual Correction for Tensor-Parallel LLM Communication) at PyTorch Conference North America 2026. ERRC focuses on optimizing GPU data exchange during LLM inference. By compressing inter-GPU communication and using the saved bandwidth to mitigate quantization errors, ERRC makes multi-GPU inference significantly more efficient. Register and join us to San Jose, October 20-21: #PyTorchCon Abdulsalam Bande

ERRC: Entropy-Reinvested Residual Correction for Tensor-Parallel LLM Communication PyTorch Foundation Ambassador Abdulsalam Bande will present a poster on ERRC (Entropy-Reinvested Residual Correction for Tensor-Parallel LLM Communication) at PyTorch Conference North America 2026. ERRC focuses on optimizing GPU data exchange during LLM inference. By compressing inter-GPU communication and using the saved bandwidth to mitigate quantization errors, ERRC makes multi-GPU inference significantly more efficient. Register and join us to San Jose, October 20-21: #PyTorchCon Abdulsalam Bande

PyTorch

16,886 次观看 • 5 天前

The open source AI community thrives on collaboration, choice, and open contribution. As Joseph Groenenboom from Red Hat highlights, empowering developers to freely contribute and build options is essential for growing the entire AI ecosystem. PyTorch Conference North America 2026 is where that community comes together in person. Join us to collaborate with open source pioneers, expand your deployment capabilities, and directly contribute to shaping the future of machine learning. Register for PyTorch Conference North America today: #PyTorchCon

The open source AI community thrives on collaboration, choice, and open contribution. As Joseph Groenenboom from Red Hat highlights, empowering developers to freely contribute and build options is essential for growing the entire AI ecosystem. PyTorch Conference North America 2026 is where that community comes together in person. Join us to collaborate with open source pioneers, expand your deployment capabilities, and directly contribute to shaping the future of machine learning. Register for PyTorch Conference North America today: #PyTorchCon

PyTorch

17,740 次观看 • 26 天前

New chips are shipping faster than ever before, but the ecosystem is being held back by having to rewrite and then re-debug the same code over & over again. Chris Lattner CEO and Co-founder at Modular and EVP of Advanced AI Software & Platforms at Qualcomm, will deliver a keynote at PyTorch Conference North America about an open software platform for heterogeneous compute powered by Mojo and MAX. PyTorch has always been the place where the best models come together, and now there's a way to get those models onto all kinds of hardware. If you're interested in Al and compute, join us at the PyTorch Conference in San Jose, CA. Register now: #PyTorchCon

New chips are shipping faster than ever before, but the ecosystem is being held back by having to rewrite and then re-debug the same code over & over again. Chris Lattner CEO and Co-founder at Modular and EVP of Advanced AI Software & Platforms at Qualcomm, will deliver a keynote at PyTorch Conference North America about an open software platform for heterogeneous compute powered by Mojo and MAX. PyTorch has always been the place where the best models come together, and now there's a way to get those models onto all kinds of hardware. If you're interested in Al and compute, join us at the PyTorch Conference in San Jose, CA. Register now: #PyTorchCon

PyTorch

20,090 次观看 • 23 天前

Liang Zhang (Huawei) delivers “Ascend & PyTorch: Pioneer New AI Open Ecosystem” at PyTorch Conference China 2026. Drawing on eight years of open source practice, Liang Zhang looks at how Ascend’s collaboration with the PyTorch community is evolving from hardware adaptation to joint research on technical standards. The keynote also covers next-generation AI hardware-software codesign, interoperability between Chinese and global AI technology stacks, and building an open and sustainable AI industrial ecosystem. Here's Liang Zhang, introduced by Mark Collier 柯理怀: #PyTorchCon

Liang Zhang (Huawei) delivers “Ascend & PyTorch: Pioneer New AI Open Ecosystem” at PyTorch Conference China 2026. Drawing on eight years of open source practice, Liang Zhang looks at how Ascend’s collaboration with the PyTorch community is evolving from hardware adaptation to joint research on technical standards. The keynote also covers next-generation AI hardware-software codesign, interoperability between Chinese and global AI technology stacks, and building an open and sustainable AI industrial ecosystem. Here's Liang Zhang, introduced by Mark Collier 柯理怀: #PyTorchCon

PyTorch

16,367 次观看 • 1 个月前

At PyTorch Conference North America 2026, hear directly from and connect with the people working on PyTorch, vLLM, DeepSpeed, ray, Helion, and Safetensors, alongside experts working across the AI stack. Keynote speaker Simon Mo (vLLM, Inferact) says vLLM’s goal is to become “the easiest to use and most efficient inference engine.” At #PyTorchCon NA 2025, he spoke about the difficulty and cost of using large language models and continuing to push inference efficiency forward. PyTorch conferences are the open source AI community’s town square, where what’s next gets decided. Register by September 4 to save on your conference pass:

At PyTorch Conference North America 2026, hear directly from and connect with the people working on PyTorch, vLLM, DeepSpeed, ray, Helion, and Safetensors, alongside experts working across the AI stack. Keynote speaker Simon Mo (vLLM, Inferact) says vLLM’s goal is to become “the easiest to use and most efficient inference engine.” At #PyTorchCon NA 2025, he spoke about the difficulty and cost of using large language models and continuing to push inference efficiency forward. PyTorch conferences are the open source AI community’s town square, where what’s next gets decided. Register by September 4 to save on your conference pass:

PyTorch

22,334 次观看 • 1 个月前

At PyTorch Conference North America 2026, hear directly from and connect with the people working on PyTorch, vLLM, DeepSpeed, ray, Helion, and Safetensors, alongside experts working across the AI stack. At #PyTorchCon NA 2025, Richard Liaw (ray, Anyscale) spoke about the Ray team’s focus on building something “useful and critical to the AI infrastructure space” and about “how important Ray can be for the stack.” PyTorch conferences are the open source AI community’s town square, where what’s next gets decided. Register by September 4 (tomorrow) to save on your conference pass: #PyTorchCon

At PyTorch Conference North America 2026, hear directly from and connect with the people working on PyTorch, vLLM, DeepSpeed, ray, Helion, and Safetensors, alongside experts working across the AI stack. At #PyTorchCon NA 2025, Richard Liaw (ray, Anyscale) spoke about the Ray team’s focus on building something “useful and critical to the AI infrastructure space” and about “how important Ray can be for the stack.” PyTorch conferences are the open source AI community’s town square, where what’s next gets decided. Register by September 4 (tomorrow) to save on your conference pass: #PyTorchCon

PyTorch

16,342 次观看 • 1 个月前

At #PyTorchCon North America 2026, Liz Li and Shekhar of AMD will discuss their work enabling scalable MXFP8 pretraining on MI355X GPUs. Liz and Shekhar will cover optimization of key MXFP8 operators in TorchAO and how those kernels were integrated with TorchTitan to build an end-to-end upstream PyTorch training stack for large language models. Their session, “Scaling MXFP8 Pretraining on 1K+ AMD Instinct MI355X: TorchAO Kernels and TorchTitan Training,” will also cover Triton and FlyDSL implementations and lessons from numerical validation, convergence, and performance tuning. Join us in San Jose on October 20-21. Register today:

At #PyTorchCon North America 2026, Liz Li and Shekhar of AMD will discuss their work enabling scalable MXFP8 pretraining on MI355X GPUs. Liz and Shekhar will cover optimization of key MXFP8 operators in TorchAO and how those kernels were integrated with TorchTitan to build an end-to-end upstream PyTorch training stack for large language models. Their session, “Scaling MXFP8 Pretraining on 1K+ AMD Instinct MI355X: TorchAO Kernels and TorchTitan Training,” will also cover Triton and FlyDSL implementations and lessons from numerical validation, convergence, and performance tuning. Join us in San Jose on October 20-21. Register today:

PyTorch

16,137 次观看 • 14 天前

Think DeepSpeed is only good for ZeRO and its usability, not training throughput? At #PyTorchCon North America 2026, Masahiro Tanaka will cover tensor, sequence, and expert parallelism in DeepSpeed and walk through benchmarks. His session, “DeepSpeed Is Not Just ZeRO: Tensor, Sequence, and Expert Parallelism with ZeRO-Level Usability,” will cover AutoTP, AutoSP, and AutoEP, how these capabilities can be combined with ZeRO, and where each parallelism strategy fits. Register for PyTorch Conference North America 2026:

Think DeepSpeed is only good for ZeRO and its usability, not training throughput? At #PyTorchCon North America 2026, Masahiro Tanaka will cover tensor, sequence, and expert parallelism in DeepSpeed and walk through benchmarks. His session, “DeepSpeed Is Not Just ZeRO: Tensor, Sequence, and Expert Parallelism with ZeRO-Level Usability,” will cover AutoTP, AutoSP, and AutoEP, how these capabilities can be combined with ZeRO, and where each parallelism strategy fits. Register for PyTorch Conference North America 2026:

PyTorch

15,756 次观看 • 2 天前

TOMORROW 🎉 Join us for the virtual premiere of the PyTorch Documentary! We'll be live on YouTube at 10am PT on June 25 along with some of our key PyTorch collaborators from AMD, AWS, Google, Meta and Microsoft 💜 🔗

TOMORROW 🎉 Join us for the virtual premiere of the PyTorch Documentary! We'll be live on YouTube at 10am PT on June 25 along with some of our key PyTorch collaborators from AMD, AWS, Google, Meta and Microsoft 💜 🔗

PyTorch

48,761 次观看 • 2 年前

PyTorch 2.13 brings FlexAttention to Apple Silicon, cuts peak memory by up to 4× for large-vocabulary models with nn.LinearCrossEntropyLoss, and updates distributed training, compilation, profiling, and on-device inference. Our live Q&A examined CUDA version support, CuTeDSL in TorchInductor, Python 3.15, ExecuTorch, torch.compile, and plans for PyTorch 2.14. Andrey Talman (Meta), albanD (Meta), and Piotr Bialecki (NVIDIA) joined moderator Chris Gottbrath (Gottbrath Technologies). This clip explains how FlexAttention compiles kernels specialized for the requested masking pattern, allowing them to skip unnecessary computation. It also covers speedups of up to approximately 12× over SDPA in tested sparse configurations and a deterministic backward path on CUDA that removes one source of nondeterminism in gradient computation. 🔗 Watch the full PyTorch 2.13 Release Live Q&A:

PyTorch 2.13 brings FlexAttention to Apple Silicon, cuts peak memory by up to 4× for large-vocabulary models with nn.LinearCrossEntropyLoss, and updates distributed training, compilation, profiling, and on-device inference. Our live Q&A examined CUDA version support, CuTeDSL in TorchInductor, Python 3.15, ExecuTorch, torch.compile, and plans for PyTorch 2.14. Andrey Talman (Meta), albanD (Meta), and Piotr Bialecki (NVIDIA) joined moderator Chris Gottbrath (Gottbrath Technologies). This clip explains how FlexAttention compiles kernels specialized for the requested masking pattern, allowing them to skip unnecessary computation. It also covers speedups of up to approximately 12× over SDPA in tested sparse configurations and a deterministic backward path on CUDA that removes one source of nondeterminism in gradient computation. 🔗 Watch the full PyTorch 2.13 Release Live Q&A:

PyTorch

18,591 次观看 • 2 个月前

Chips are the foundation of every AI experience. That's why understanding the hardware behind the software has never mattered more. When you ask a chatbot a question or generate an image, there's a physical chip somewhere doing the heavy lifting. For most of computing history, that was a CPU—the "brain" of a computer—great for general tasks needed to run software and operating systems. AI is more complex: for workloads called training and inference, AI needs to perform trillions of calculations in parallel. That's where AI accelerators come in. Purpose-built accelerators can deliver significantly better performance and efficiency than general-purpose chips. Amazon Web Services Trainium chips are an example—purpose-built for AI training and inference. New chips for a new era. ⬇️

Chips are the foundation of every AI experience. That's why understanding the hardware behind the software has never mattered more. When you ask a chatbot a question or generate an image, there's a physical chip somewhere doing the heavy lifting. For most of computing history, that was a CPU—the "brain" of a computer—great for general tasks needed to run software and operating systems. AI is more complex: for workloads called training and inference, AI needs to perform trillions of calculations in parallel. That's where AI accelerators come in. Purpose-built accelerators can deliver significantly better performance and efficiency than general-purpose chips. Amazon Web Services Trainium chips are an example—purpose-built for AI training and inference. New chips for a new era. ⬇️

Amazon

27,151 次观看 • 4 个月前

Want to hear more about new vLLM and SGLang engines built on a new PyTorch-native TPU backend, TorchTPU? Find Qi Zhou of Google, Colin Taylor and Angela Yi of Meta at PyTorch Conference North America in San Jose where they will discuss how they focused on making the existing SGLang & vLLM infrastructure work natively on TPU, preserving the schedulers, batching systems, OpenAI-compatible APIs, and torch.compile workflows already familiar to GPU users. Because the serving engines remain upstream, new models and features can run on TPU without requiring separate TPU-specific implementations. Learn more about how it works across the stacks & what they learned running this in production, including compile time, device placement under tracing, KV cache layout, and cross-chip collectives. Register for #PyTorchCon NA now:

Want to hear more about new vLLM and SGLang engines built on a new PyTorch-native TPU backend, TorchTPU? Find Qi Zhou of Google, Colin Taylor and Angela Yi of Meta at PyTorch Conference North America in San Jose where they will discuss how they focused on making the existing SGLang & vLLM infrastructure work natively on TPU, preserving the schedulers, batching systems, OpenAI-compatible APIs, and torch.compile workflows already familiar to GPU users. Because the serving engines remain upstream, new models and features can run on TPU without requiring separate TPU-specific implementations. Learn more about how it works across the stacks & what they learned running this in production, including compile time, device placement under tracing, KV cache layout, and cross-chip collectives. Register for #PyTorchCon NA now:

PyTorch

16,759 次观看 • 7 天前

IBM’s Christian Jacobi (IBM Fellow and CTO, IBM Systems Development) on why the PyTorch community matters for AI today, the role of open source, and what he’s hearing from clients deploying AI in core IT and business processes. 🎥

IBM’s Christian Jacobi (IBM Fellow and CTO, IBM Systems Development) on why the PyTorch community matters for AI today, the role of open source, and what he’s hearing from clients deploying AI in core IT and business processes. 🎥

PyTorch

14,342 次观看 • 9 个月前