🤗 Hugging Cast S2E2 - Accelerating AI with NVIDIA!

HuggingCast - AI News and Demos
21 Mar 202444:18

TLDRThe Hugging Cast episode focuses on building AI with open models and sources, highlighting the new season's emphasis on practical demos over news. The show introduces a collaboration with Nvidia, showcasing Train on Djx Cloud for easy, code-free training with H100s directly on the Hugging Face Hub. Optimum Nvidia is also presented as an open-source toolkit for AI acceleration, offering significant improvements in latency and throughput. The episode features live demos, discussions on GPU training, and the benefits of using Optimum Nvidia with the latest Nvidia GPUs.

Takeaways

  • 🚀 The show focuses on building AI with open models and open source, aiming to provide practical examples for application in various companies.
  • 🎥 This season will have more demos and less news compared to the previous one, with collaborations featuring in each episode.
  • 🤖 The goal is to make the latest GPU technology accessible to everyone, eliminating the divide between 'GPU rich' and 'GPU poor'.
  • 🌐 Hugging Face collaborates with major cloud and hardware platforms like AWS, Google Cloud, Azure, Cloudflare, Nvidia, Intel, AMD, Dell, and IBM.
  • 🔥 A new service called 'Train on Djx Cloud' was announced, allowing users to train models with H100s directly on the Hugging Face Hub without any code.
  • 📈 The collaboration with Nvidia aims to accelerate AI workloads, offering faster training and inference times using Hugging Face open models and open source.
  • 🛠 Optimum Nvidia is an open source toolkit for accelerating AI workloads, providing benefits with just a single line of code change in the user's existing projects.
  • 🎯 Optimum Nvidia leverages TensorRT and provides specialized compile artifacts for the underlying hardware, offering significant performance improvements.
  • 💡 The show demonstrated how to use 'Train on Djx Cloud' and Optimum Nvidia for fine-tuning models and highlighted their ease of use and cost-effectiveness.
  • 📊 The benefits of using Optimum Nvidia include reduced first token latency and increased max throughput, especially when used with the latest Nvidia hardware.
  • 🔗 Hugging Face's collaboration with Nvidia also explores integration with inference endpoints and the potential for leveraging advanced features like float 8 engines.

Q & A

  • What is the main focus of the show discussed in the transcript?

    -The main focus of the show is about building AI with open models and open source, and demonstrating practical examples that can be applied to use cases in a company.

  • What does the term 'GPU rich' and 'GPU poor' refer to in the context of the show?

    -The terms 'GPU rich' and 'GPU poor' refer to the accessibility and affordability of high-performance GPUs for AI development. The goal of the show is to provide 'GPU poor' with on-demand access to the latest GPUs, eliminating the need for physical possession of such resources.

  • What is the significance of the 'train on djx cloud' service mentioned in the transcript?

    -The 'train on djx cloud' service is a new service that allows users to train models directly on the Hugging Face Hub using H100s GPUs without any code or server setup, making AI development more accessible and straightforward.

  • How does the collaboration with Nvidia aim to benefit AI workloads?

    -The collaboration with Nvidia aims to accelerate AI workloads by providing access to the latest GPU acceleration capabilities, enabling faster training and inference times for Hugging Face open models and open source projects.

  • What is Optimum Nvidia and how does it enhance AI performance?

    -Optimum Nvidia is an open-source toolkit for accelerating AI workloads. It leverages the best of Nvidia's open-source technology with TensorRT and provides significant improvements in first token latency and max throughputs when used with Nvidia's latest hardware.

  • What is Auto Train and how does it evolve over time?

    -Auto Train, initially called Auto NLP, is a project that started as a closed-source project for training Transformer models for natural language processing. It evolved to include various tasks like image classification and dream boot Laura, and eventually became open-source under the name Auto Train Advanced.

  • How does the 'train on dgx cloud' feature work in the context of fine-tuning models?

    -The 'train on dgx cloud' feature allows users to fine-tune models using GPU cards by selecting the type of test, uploading a dataset, and adjusting training parameters through a user-friendly interface. It enables training to happen without the need for extensive coding or cloud setup knowledge.

  • What are the benefits of using Hugging Face's inference endpoints with private link?

    -Using private link with Hugging Face's inference endpoints ensures that data never goes onto the internet, staying within the same data center region. This enhances security and reduces latency by allowing direct communication between the user's VPN and the endpoint.

  • Is it possible to use Optimum Nvidia with Hugging Face Spaces and Gradio SDK?

    -Yes, it is possible to use Optimum Nvidia with Hugging Face Spaces and Gradio SDK. Although specific Docker images may be required, there are no major blockers to integrating Optimum Nvidia into these environments.

  • How is the cost calculated for using 'train on djx cloud' service?

    -The cost for using the 'train on djx cloud' service is calculated based on the type and size of the GPU used, as well as the duration of the training job. Users are billed by the hour and are only charged for the resources they actually use.

Outlines

00:00

🚀 Introduction to the Show and Collaboration with Nvidia

The paragraph introduces the show's focus on building AI with open models and open source, emphasizing practical examples and demos. It highlights the collaboration with Nvidia to accelerate AI workloads and announces a new service called 'train on djx cloud' for easy access to GPU training. The goal is to make the latest GPU acceleration capabilities accessible to everyone, regardless of their computational resources.

05:02

🌟 Features of Train on Djx Cloud and Optimum Nvidia

This paragraph delves into the features of 'train on djx cloud', explaining its ease of use for fine-tuning models without coding or server setup. It also introduces Optimum Nvidia, a toolkit for accelerating AI workloads with optimized inference pipelines. The benefits of using these services, such as faster training and inference, are emphasized, along with the cost-effectiveness and accessibility for users.

10:05

🛠️ Auto Train Framework and Its Evolution

The speaker discusses the history and evolution of Auto Train, from its beginnings as a closed-source project to its current open-source status as 'Auto Train Advanced'. The framework's user-friendly interface and its support for various tasks like image classification and dream boot are highlighted. The paragraph also touches on the ease of creating a new project and selecting hardware for training.

15:06

📊 Training Process and Metrics with Auto Train

This section provides a walkthrough of the training process using Auto Train, from uploading datasets to selecting parameters and starting the training. It emphasizes the real-time visibility of training metrics and logs, and the ability to host the resulting model privately. The paragraph also discusses the simplicity and speed of the training process, with a focus on the user experience.

20:07

🧠 Exploring Training Metrics and Model Artifacts

The paragraph focuses on the training metrics and artifacts generated by Auto Train. It explains how users can view the last checkpoint, training board logs, and other artifacts. The usefulness of these logs for tracking training convergence and adjusting parameters is highlighted. The paragraph also addresses questions about data set size limitations and access to training data.

25:08

🔄 Optimum Nvidia Demo and Its Capabilities

This section presents a live demo of Optimum Nvidia, showcasing its ease of use and the benefits of leveraging Nvidia's TensorRT for inference. The demo illustrates the process of converting a model to use Optimum Nvidia, including the use of float8 for quantization. The paragraph emphasizes the performance gains and the potential for integrating Optimum Nvidia with other services like Hugging Face's inference endpoints.

30:09

📈 Optimum Nvidia's Advantages and Future Plans

The paragraph discusses the advantages of Optimum Nvidia, particularly for users targeting specific Nvidia GPUs that support float8 data types. It also addresses how Optimum Nvidia fits into the broader strategy of Hugging Face as an open platform, making it easy for users to leverage various compute environments. The paragraph concludes with a discussion of future plans, including integration with asynchronous runtimes and inference endpoints.

35:11

🎥 Closing Remarks and Upcoming Episodes

The paragraph wraps up the show with a reminder that the content will be available for on-demand viewing and on YouTube. It also invites viewers to return for future episodes, which will continue to showcase building AI with open source and open models in collaboration with various partners. The hosts thank the guests for their contributions and sign off, encouraging viewers to engage with the content and return for more informative episodes.

Mindmap

Keywords

💡AI with open models

The term 'AI with open models' refers to the development and utilization of artificial intelligence systems that are built using open-source models. These models are publicly accessible and modifiable, allowing a collaborative approach to AI development. In the context of the video, it emphasizes the show's focus on demonstrating how to build AI applications using such open models, highlighting the importance of community involvement and shared knowledge in advancing AI technology.

💡Live show

A 'live show' refers to a real-time broadcast or demonstration that viewers can watch as it unfolds, often with the ability to interact with the hosts. In the video, the term is used to describe the format of the show, emphasizing its interactive nature and the immediacy of the content being shared. This approach is designed to engage the audience directly and provide a dynamic learning experience.

💡Hugging Face

Hugging Face is an open-source community and platform focused on natural language processing (NLP) models. It provides a wide range of tools and resources for developers to build, train, and deploy AI models, particularly in the field of NLP. In the video, Hugging Face is highlighted as a key partner in showcasing how to build AI applications using open models, underlining its role in promoting open-source collaboration in AI development.

💡NVIDIA

NVIDIA is a renowned company known for its graphics processing units (GPUs) and AI technologies. In the context of the video, NVIDIA is a partner that collaborates with Hugging Face to provide hardware and software solutions for accelerating AI model training and inference. The collaboration focuses on leveraging NVIDIA's advanced GPU technology to enhance the capabilities of open-source AI models.

💡DGX Cloud

DGX Cloud refers to a platform that provides access to NVIDIA's high-performance DGX systems through a cloud-based service. This allows users to train AI models using powerful GPUs without the need for physical access to the hardware. In the video, DGX Cloud is presented as a service that enables users to train with H100 GPUs directly from the Hugging Face Hub, emphasizing the ease of access to cutting-edge AI training capabilities.

💡Fine-tuning

In the context of machine learning and AI, 'fine-tuning' is the process of adjusting a pre-trained model to a specific task or dataset. This often involves retraining the model with a new set of data to improve its performance for a particular application. The video discusses the ability to fine-tune models using the services provided by the collaboration between Hugging Face and NVIDIA, showcasing the practical application of this technique in AI development.

💡Transformers

Transformers are a class of deep learning models that have become the foundation for many state-of-the-art natural language processing systems. They are designed to process sequential data and have self-attention mechanisms that allow the model to weigh the importance of different parts of the input data. In the video, Transformers are central to the discussion of AI models and their optimization, highlighting their significance in contemporary AI development.

💡TensorRT

TensorRT is an NVIDIA software library designed for high-performance deep learning inference. It optimizes and accelerates AI applications by reducing the time required to process data and increasing the efficiency of computations on NVIDIA GPUs. In the video, TensorRT is mentioned as a technology that Optimum NVIDIA leverages to provide accelerated inference pipelines for AI models.

💡Float16 and Float8

Float16 and Float8 are data types used in deep learning and AI to represent numbers with reduced precision. Float16 is a 16-bit floating-point format, while Float8 is an 8-bit variant. These formats are employed to reduce the memory footprint and increase the speed of computations on GPUs. In the context of the video, Float8 is specifically mentioned as a feature that enables faster inference on NVIDIA GPUs with the help of Optimum NVIDIA.

💡Inference

In the field of AI and machine learning, 'inference' refers to the process of using a trained model to make predictions or decisions based on new input data. It is the application of the learned patterns or knowledge from the training phase to unseen data. The video emphasizes the importance of optimizing inference for AI models, particularly when using NVIDIA's hardware, to achieve faster and more efficient predictions.

💡Auto Train

Auto Train is a tool or framework mentioned in the video that simplifies the process of training AI models. It appears to abstract away much of the complexity involved in model training, allowing users to focus on the essential parameters and tasks. The tool seems to support various AI tasks and model types, aiming to make the training process more accessible and straightforward for users.

Highlights

The introduction of a new service called 'train on djx cloud' that allows training with H100s directly without any code, server setup, or cloud account.

The goal of the collaboration with Nvidia is to accelerate AI workloads and provide faster training and inference with Hugging Face open models and open source.

The unveiling of the 'train on djx cloud' service at GTC, providing on-demand access to the latest GPUs and acceleration capabilities.

The Enterprise Hub organization feature for security and advanced compute features in the Hugging Face platform.

The ability to fine-tune LLMs directly on the Hugging Face Hub using H100s on demand or L4s.

The demonstration of the Auto Train framework UI, showcasing its ease of use for various tasks like fine-tuning, image classification, and dream boot.

The open-sourcing of Auto Train, now available as Auto Train Advanced, to simplify the training process for users.

The showcasing of the training process on Nvidia DGX Cloud, highlighting the simplicity and speed of training models using the platform.

The introduction of Optimum Nvidia, an open source toolkit for accelerating AI workloads with Tensor RT LLM and supporting the latest hardware from Nvidia.

The demonstration of the significant improvements in time to first token and max throughputs by leveraging Optimum Nvidia.

The explanation of how Optimum Nvidia makes it easy for users to switch from vanilla Transformers pipeline to the accelerated pipeline with minimal code changes.

The potential of Optimum Nvidia to support various popular open models for fine-tuning and other tasks, such as image generation and dream booth.

The cost-effectiveness of training models on Nvidia DGX Cloud, as demonstrated by the fine-tuning of a 7B model for less than half a dollar.

The discussion on the maximum size of data sets that can be used with 'train on dx cloud' and the lack of hard limitations.

The assurance of data privacy and security, as the training data set is processed and stored in the user's Hugging Face account as a private repository.

The potential integration of Optimum Nvidia with Hugging Face Spaces and Gradio SDK, expanding the usability and accessibility of the toolkit.

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