PyTorch Conference 2024

Arm is excited to be at the center of the world's largest compute ecosystem at the dawn of the AI era. A key tenant of our mission is to democratize AI capabilities, empowering millions of developers to put advanced AI features into the hands of billions of users.

In this presentation, we'll explore how Arm is enabling the world’s leading open-source AI frameworks to leverage power-efficient Arm-based computing platforms and Arm architecture features, as a tool for enabling fast and secure AI workloads. The session focuses on how our strategic partnership with the Pytorch and Executorch community is enabling a seamless and transparent developer experience, to run workloads everywhere from cloud to edge. This session will highlight some of our optimized libraries, upstreamed contributions and a wealth of AI-related developer material to build the future of AI on Arm.

Description: This proposal aims to provide a comprehensive introduction to the Chinese PyTorch community, we hope to inspire more users to join and contribute, fostering a vibrant and inclusive environment for PyTorch enthusiasts in China. Chinese PyTorch Homepage Introduction to the official Chinese version of the PyTorch website, highlighting its features. Navigation tips and key sections, such as documentation, tutorials, and community events. Improve the connection of users from China with PyTorch Community. Localized Tutorials and Documentation The 2.x version not have Translated version, it hard to catch up with latest features of PyTorch if the beginner not good at English. We translated official documents and tutorials, covering everything from basic PyTorch concepts to advanced applications. Interactive tutorials No interactive tutorials(Like Google Colab) for Chinese students or beginners before, they have to setup environment before start with PyTorch, which might be hard for beginners. And now, an online notebook & tutorials are available to practice or tuning steps for beginners.

The Hugging Face Hub has over 300,000 PyTorch models. Distributing such number of models poses challenges. In this talk, Omar will share how the community has tackled these challenges, including techniques to ensure torch model security and tooling for researchers to share their models. He'll also take attendees on a journey through the evolution of torch models distributed by the community, highlighting new trends and directions. Attending this talk will give attendees practical insights into the latest developments in model distribution and ecosystem trends.

A hallmark feature of PyTorch is the natural expression of computation. This enables practitioners to implement AI models with ease. However, it prompts the question how to optimize the workload for a given hardware setup because those optimizations clutter our code and are tricky to combine. Lightning Thunder provides a Python-to-Python compiler to scale and optimize PyTorch programs that focuses on usability, understandability, and extensibility. A key tool in delivering on these goals is the composability of transformations: without changing the user code, we can stack quantization, distributing the computation across multiple GPUs, dispatching to optimized kernels, offloading, and other pluggable optimizations. Lightning Thunder flourishes in the PyTorch ecosystem: with PyTorch eager and with executors like torch.compile and nvFuser. It also dispatches to libraries like cuDNN, TransformerEngine, Apex, OpenAI Triton. The ability to apply multiple optimizations just-in-time leads to significant compounded speed-ups over unoptimized code out of the box. Luca will discuss the design of Thunder and demonstrate applications on training and inference for large language and multimodal models.

StreamingDataset makes training on large datasets from cloud storage as fast, cheap, and scalable as possible. It’s specially designed for multi-node, distributed training for large models — maximizing correctness guarantees, performance, and ease of use. Key features include elastically deterministic training, instant mid-epoch resumption, effective shuffling, high training throughput, and flexible data mixing, among other features. When training with StreamingDataset, the data shards are written to cloud storage in MDS, our file format that allows for low-latency random access to samples. By being as efficient as possible with shard downloads and shuffling, StreamingDataset minimizes egress costs while ensuring that dataloading never bottlenecks model training. StreamingDataset powers training for LLMs with over 100 billion parameters like DBRX, to advanced diffusion models, to two-tower recommendation models, and more, scaling to training jobs on thousands of GPUs with ease. Join us to learn how StreamingDataset can elevate your distributed model training experience.

PyTorch supports two kinds of datasets: Iterable datasets and indexable "map-style" datasets. Iterable datasets can be more flexible and potentially faster than their indexable cousins. They are also much harder to use correctly, and can easily lead to silently wrong results. This talk is a quick and fun intro to some of the traps that Iterable datasets lay out for you, with some tips to help you avoid them.

At roboMUA we're leading the charge in building predictive AI models for diverse skin shades with the use of Convolutional Neural Networks (CNNs), and harnessing the power of Generative Adversarial Networks (GANs) specifically for generating realistic images of black hairstyles. Our session showcases PyTorch's versatility in both predictive and generative tasks, offering a comprehensive approach to inclusive AI. For predictive AI models, we leverage PyTorch's flexible framework to develop CNNs. Through innovative techniques in feature engineering and model architecture design, we demonstrate how PyTorch enables accurate prediction across 100 skin shades. Simultaneously, we showcase the transformative potential of GANs in the realm of black hairstyles. By training GANs on a curated dataset of diverse hair textures and styles, we illustrate how PyTorch facilitates the generation of lifelike images that celebrate the beauty and diversity of black hair. Attendees will gain insights into the data preprocessing, model training, and evaluation processes and and learn how PyTorch empowers developers to build inclusive solutions.

The PyTorch team has improved training speed by an order of magnitude for teams at Meta working on Small-to-Large-Scale MultiModal Video models. In this talk we’ll share our learnings on reducing GPU starvation by overcoming data loading challenges such as dealing with large distributed datasets, worker imbalance, compute-bottlenecks due to parallel video decoding and sampling, checkpointing, and debuggability. As part of our commitment to open-source, we are releasing a new decoding library and updating existing PyTorch libraries on GitHub, and invite feedback and contributions from the community.

In this session we will cover various ways teams at google are working to help the Pytorch community achieve performance and scale from cloud to edge. We will cover how Google Cloud customers can use PyTorch and OpenXLA to get competitive performance for their ML workloads.  We’ll also cover how Google AI Edge Torch works with Pytorch to help developers integrate LLMs, vision models and more to easily create new edge applications that can run on a wide set of devices.

The PyTorch Conference Startup Showcase is giving emerging companies the chance to pitch to a panel of VCs looking to support AI/ML startups with high growth potential, and meet some of the best AI focused Engineers in the Industry. This is an exciting and unique opportunity for early-stage founders to showcase their ideas and breakthroughs, connect with leading VCs, and increase visibility in the generative AI and machine learning industry.

The winning startup will be announced at the Flare Party taking place after the Startup Showcase.

Finalists

• Remix Inc.
• Cartesia
• OpenBabylon
• Remyx AI
• A2 Labs, Inc.
• QuicSnap
• Iso AI
• CTGT
• Creao.ai