🤗 Hugging Cast S2E2 - Accelerating AI with NVIDIA!
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 service, 'Train on DJX Cloud,' allowing users to train models with H100 GPUs directly on the Hugging Face Hub without coding. The collaboration with Nvidia is also discussed, showcasing how Optimum Nvidia accelerates AI workloads with Tensor RT LLM and仅需一行代码更改. The episode is interactive, with a Q&A session addressing various aspects of the discussed technologies.
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 feature more demos and practical applications, with collaborations with companies like Nvidia to accelerate AI workloads.
- 🌐 Hugging Face aims to build an open platform that simplifies the use of their models and libraries across different compute stacks.
- 💡 A new service called 'Train on Djx Cloud' was announced, allowing users to train models directly on the Hugging Face Hub without any code or server setup.
- 📈 The collaboration with Nvidia introduces Optimum Nvidia, a toolkit for accelerating AI workloads with just a single line of code change.
- 🏎️ Optimum Nvidia leverages TensorRT LLM for optimized inference on Nvidia GPUs, offering significant improvements in latency and throughput.
- 📊 The benefits of Optimum Nvidia include faster training and inference, making it accessible for those without access to high-end GPUs.
- 🛠️ Auto Train, now open source, simplifies the training process for various tasks, including natural language processing and image classification.
- 🔧 Auto Train Advanced provides an easy-to-use interface for fine-tuning models with basic or full parameter sets.
- 💻 The demo showcased how to use Train on Djx Cloud for fine-tuning a model with just a few clicks and how the training progress can be monitored in real-time.
- 📈 The cost-effectiveness of using Train on Djx Cloud was highlighted, with an example of fine-tuning a model for less than half a dollar.
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 new service was unveiled during the show?
-A new service called 'train on djx cloud' was unveiled, which allows training with H100s directly without any code, server setup, or cloud account creation, directly on the Hugging Face Hub.
What is the goal of the collaboration between Hugging Face and Nvidia?
-The goal of the collaboration is to help accelerate AI workloads when working with Hugging Face open models and open source, providing faster training and inference.
How does the 'train on djx cloud' service work?
-The 'train on djx cloud' service works by allowing users to fine-tune models directly on the Hugging Face Hub using H100s available on demand or L4s, without having to add any code.
What are some of the features of 'train on djx cloud'?
-The features include advanced fine-tuning options, reinforcement learning from human feedback, image generation, and the ability to use popular open models like Latu, Mistral, Mixol, Gemma, and more.
What is the cost associated with using 'train on djx cloud'?
-The cost is based on the usage duration, computed by the hour, with great prices in collaboration with Nvidia, and users only pay for what they actually use.
What is Optimum Nvidia and how does it benefit AI workloads?
-Optimum Nvidia is an open-source toolkit for acceleration of AI workloads, providing the best of Nvidia open source with TensorRT LLM and just one line change within the code for accelerated inference.
What are the main metrics measured to evaluate the benefits of using Optimum Nvidia?
-The main metrics measured are the time to First token (first token latency) and max throughputs, with significant improvements observed when using the latest hardware from Nvidia.
How does Auto Train work within the Hugging Face Spaces infrastructure?
-Auto Train creates a project within the Hugging Face Spaces infrastructure, where the model artifacts and training metrics are stored, allowing users to easily manage and monitor their training processes.
What is the process for training a model using 'train on djx cloud'?
-The process involves selecting a model, choosing GPU card options, selecting the type of test, uploading a dataset, and setting training parameters before starting the training process.
What happens under the hood after clicking the train button on 'train on djx cloud'?
-After clicking the train button, Auto Train creates a project, the training metrics are stored in real-time, and the progress of downloading data and training steps is displayed through live logs from the GPU machines.
Outlines
🎥 Introduction to the Show and Collaboration with Nvidia
The paragraph introduces the show, which is about building AI with open models and open source. It highlights the focus on practical demos and collaboration with partners like Nvidia. The goal is to provide viewers with applicable examples for their use cases. The show aims to be interactive, taking questions from the live chat. The collaboration with Nvidia is emphasized, discussing the new service 'train on djx cloud' which allows training with Nvidia H100s directly on the Hugging Face Hub without any code. The aim is to make the latest GPU acceleration accessible to everyone.
🚀 Overview of 'Train on Djx Cloud' and Optimum Nvidia
This paragraph delves into the details of 'Train on Djx Cloud', explaining its features and how it works. It allows fine-tuning of models using H100s on demand or L4s without coding. The benefits of the Enterprise Hub organization for security and compute features are discussed. The paragraph also introduces Optimum Nvidia, a toolkit for accelerating AI workloads, and its advantages when used with the latest Nvidia hardware. The improvements in first token latency and max throughput are highlighted.
🛠️ Auto Train History, Features, and Framework UI
The speaker shares the history and evolution of Auto Train, from a closed-source project to an open-source library. The various tasks Auto Train can perform, such as image classification and dream boot, are mentioned. The user interface of Auto Train is demonstrated, showing how to create a project, select tasks, and adjust parameters. The integration with Hugging Face Spaces and the process of training on 'Dgx Cloud' are also explained.
📊 Training Metrics, Model Cards, and Uploading Data Sets
The paragraph discusses the training metrics and model cards generated by Auto Train, showing how they can be viewed and used. The process of uploading a data set and the options available for training parameters are detailed. The speaker also talks about the cost-effectiveness of using 'Train on Dgx Cloud' and the potential for training large data sets. Questions from the audience about data set sizes, access to training data, and adapter models are addressed.
🌟 Optimum Nvidia Demo and Benefits
A live demonstration of Optimum Nvidia is provided, showcasing how it simplifies the use of Tensor RT for fast inference on Nvidia GPUs. The ease of switching from vanilla Transformers to Optimum Nvidia is emphasized. The demo includes downloading a model from The Hub, using the float8 engine for quantization, and running inferences. The potential for integrating Optimum Nvidia with other Hugging Face products and the advantages it offers over TGI (Text Generation Inference) are also discussed.
📌 Final Q&A and Discussion on Optimum Nvidia and Hugging Face Inference Endpoints
The final paragraph covers a Q&A session, addressing questions about training with live data, private link connections, and the use of Optimum Nvidia with Gradio SDK in Hugging Face Spaces. The speakers clarify that live data training is not supported, private link ensures data stays within the same data center, and payloads are not persisted or cached. They also confirm that Optimum Nvidia can be used with Gradio SDK for building applications. The paragraph concludes with a reminder that the show will be available for viewing on demand and on YouTube.
Mindmap
Keywords
💡Open Models
💡AI Workloads
💡Hugging Face Hub
💡GPUs
💡Cloud Platforms
💡Nvidia
💡Auto Train
💡TensorRT
💡Quantization
💡Inference
💡Enterprise Hub Organization
Highlights
The show focuses on building AI with open models and open source.
The new season presents practical examples of building AI with up models in collaboration with partners.
The goal is to make AI accessible to companies by providing practical examples they can apply to their use cases.
The platform aims to be interactive and live, with a focus on demos rather than news.
A new service called 'train on djx cloud' is introduced, allowing users to train models directly on the Hugging Face Hub without any code.
The collaboration with Nvidia is aimed at accelerating AI workloads with Hugging Face open models and open source.
The show discusses the challenges faced by the GPU poor and how the new service helps them access the latest GPUs on demand.
The 'train on djx cloud' service is designed to make it easy for users to fine-tune models using H100s on demand or L4s.
The service allows users to train models with just a few clicks, making AI more accessible and affordable.
The show introduces Auto Train, a tool for training models on Hugging Face's cloud platform.
Auto Train simplifies the process of training models by providing basic and full parameter options for users.
The collaboration with Nvidia has resulted in Optimum Nvidia, a toolkit for accelerating AI workloads.
Optimum Nvidia leverages the best of Nvidia's open source with TensorRT and provides significant improvements in latency and throughput.
The show demonstrates how to use Optimum Nvidia with the latest hardware for faster inference.
Users can expect more demos, new Hugging Faces sharing their work, and ways to build AI in various compute environments in the new season.
The show emphasizes the importance of making GPU acceleration accessible to everyone, regardless of their starting point.
The 'train on djx cloud' service is highlighted as a way to make training models more affordable and efficient.
The show concludes with a discussion on how to make the most of the new services and tools introduced in collaboration with Nvidia.