🤗 Hugging Cast S2E1 - LLMs on AWS Trainium and Inferentia!

HuggingCast - AI News and Demos
22 Feb 202445:06

TLDRThe Hugging Cast returns for its second season, focusing on building AI with open models and source. The show will feature more demos and practical examples for application in various computing environments. The first episode highlights the collaboration with AWS, showcasing how to use Optimum Neuron for training and inference on AWS' custom silicon, Trainium and Inferentia. The discussion includes the benefits of using these AWS instances for AI workloads, cost savings, and the ease of deploying Hugging Face models. The episode also covers the use of Text Generation Inference (TGI) on Inferentia 2 and explores the potential of distributed training techniques like data, tensor, and pipeline parallelism to scale large language models (LLMs) effectively.

Takeaways

  • 🎉 The new season of Hugging Cast focuses on building AI with open models and open source, aiming for more demos and practical examples.
  • 🚀 This season will feature less news and more interactive live demos, with a goal of providing applicable use cases for companies.
  • 📅 The show will continue to be live and interactive, taking questions from the live chat after about 30 minutes of demos.
  • 🌐 Hugging Face aims to build an open platform, working with various cloud and hardware platforms to simplify the use of their models and libraries.
  • 🤖 The first episode highlights a collaboration with AWS, showcasing how to use optimal AI models with AWS's compute options.
  • 🧠 The episode features a demo on deploying large language models on AWS Inferentia 2 using Text Generation Inference (TGI).
  • 🛠️ Optimum Neuron is introduced as a library that bridges the gap between Hugging Face models and the software/hardware stack of Tranium and Inferentia.
  • 📈 AWS Tranium and Inferentia are custom AI accelerators designed specifically for AI workloads, offering significant cost savings for large training jobs or production inference workloads.
  • 🔧 The documentation on huggingface.co provides comprehensive guides on using Optimum Neuron with AWS Tranium and Inferentia, including numerous examples and notebooks.
  • 📊 The episode discusses various instance sizes for Inferentia 2, with larger instances supporting multiple cores for running very large language models (LLMs).
  • 🔗 The show also addresses questions from the chat, such as transferring trained models between different hardware and the support for different machine learning tasks.

Q & A

  • What is the main focus of the new season of Hugging Cast?

    -The main focus of the new season of Hugging Cast is to provide more demos and practical examples that viewers can apply to their use cases in their companies, while continuing to promote live and interactive sessions.

  • How often can viewers expect new episodes of Hugging Cast to be aired?

    -Viewers can expect a new episode of Hugging Cast to be aired about every month.

  • What is the purpose of the Optimum Neuron library mentioned in the transcript?

    -The purpose of the Optimum Neuron library is to act as a bridge between Hugging Face models and the software and hardware stack of Tranium and Inferentia, making it easy for users to leverage the acceleration and hardware features of these AWS custom silicon instances.

  • What are the benefits of using Inferentia 2 for deploying Hugging Face models?

    -The benefits of using Inferentia 2 for deploying Hugging Face models include significant cost savings, especially for large training jobs or production inference workloads, and faster performance compared to other instances.

  • How can users get started with using Optimum Neuron for training and inference on AWS Tranium instances?

    -Users can get started with using Optimum Neuron by referring to the documentation available on Hugging Face's documentation website, which provides guides and examples on how to set up and use the library with AWS Tranium and Inferentia instances.

  • What is the difference between data parallelism and tensor parallelism as mentioned in the transcript?

    -Data parallelism involves sharding the input batch across multiple devices, while tensor parallelism involves sharding the matrix multiplications of the model across multiple devices. Tensor parallelism is more advanced and can save memory, but it requires more communication between devices.

  • What is the role of the AWS Tranium instance in training large language models?

    -The AWS Tranium instance provides multiple neuron cores, which can be used to distribute the training of large language models using parallelism techniques like data parallelism, tensor parallelism, and pipeline parallelism. This allows for training larger models that may not fit in the memory of a single device.

  • How does the streaming feature in Text Generation Inference (TGI) improve the user experience?

    -The streaming feature in TGI allows for immediate responses as the model generates tokens, rather than waiting for the entire response. This provides a more interactive and efficient experience, especially for applications that require real-time feedback.

  • What are some of the large language models that can be deployed using the AWS Inferentia 2 instances?

    -Some of the large language models that can be deployed using AWS Inferentia 2 instances include Hugging Face's Sapphire 7B, LAMa 7B, and models from other major tech companies like Google and Microsoft.

  • What is the significance of the partnership between Hugging Face and AWS in the context of the transcript?

    -The partnership between Hugging Face and AWS allows for the optimization of Hugging Face models for use on AWS's custom silicon instances like Tranium and Inferentia. It also facilitates the creation of resources like AMIs and Docker containers that are pre-configured for using Hugging Face models, making it easier for users to deploy and run these models.

Outlines

00:00

🎉 Welcome to the Second Season of Hugging Cast

The script begins with a warm welcome to the second season of Hugging Cast, an interactive live show focused on building AI with open models and open source. The host expresses excitement to be back and acknowledges the returning audience members. The new season aims to provide a mix of previous elements with a fresh approach, including fewer news segments and more practical demos. The goal is for viewers to gain applicable knowledge for their own AI projects. The show will continue to be live, with a new episode released monthly and a segment for audience questions. The first episode highlights a special collaboration with AWS, showcasing the best computational options available on their platform.

05:03

🚀 Understanding AWS Custom Silicon and Optimum Neuron

This paragraph delves into the specifics of using Hugging Face on AWS, emphasizing the use of custom silicon, specifically the AWS Trainium and Inferentia instances designed for AI workloads. It explains the collaboration between Hugging Face and AWS engineers to streamline model usage on these instances. The Optimum Neuron library is introduced as a bridge between Hugging Face models and the hardware stack, simplifying the process for users. The paragraph also discusses the cost-effectiveness of using these custom accelerators and provides resources for further learning, including documentation and examples of deploying various models on AWS.

10:06

📚 Preparing for the Demos: Context and Resources

The host provides context for the upcoming demos, explaining the capabilities of Inferentia 2 and its rapid processing speeds. The cost savings from using these accelerators are highlighted, with examples of significant reductions in compute costs. The paragraph also mentions the availability of comprehensive documentation on the Hugging Face website, detailing the use of Optimum Neuron and AWS instances. An audience question about transferring trained models between different hardware is addressed, confirming the flexibility of model deployment. The paragraph concludes with information about deep learning containers and AMIs provided by Hugging Face for streamlined setup and deployment.

15:07

🤖 Deploying Large Language Models with Text Generation Inference (TGI) on Inferentia 2

The host introduces the first demo, led by Phillip, on deploying large language models using TGI on Inferentia 2. The Optimum Neuron documentation is praised for its tutorials and comprehensive guides, including one for sentence Transformers on AWS Inferentia. The ease of deploying models to endpoints for application integration is discussed, along with the benefits of streaming capabilities provided by TGI. The paragraph outlines the different instance sizes available for Inferentia and their pricing, highlighting the affordability compared to other options. The demo will showcase deploying the Sapphire 7B model on Inferentia, with a focus on the compilation process and the use of the Optimum CLI for exporting model parameters.

20:08

🧠 Training Large Language Models (LLMs) on Trinium Instances

Mikel takes over to discuss the training of LLMs on Trinium instances. He emphasizes the importance of understanding memory requirements for model training, including model weights, gradients, optimizer state, and activations. The memory availability on Tranium instances is outlined, explaining the necessity of distributed training for larger models. Mikel introduces various parallelism methods integrated into Optimum Neuron to enable training on multiple devices, including data parallelism, tensor parallelism, and pipeline parallelism. A simple code snippet is provided to demonstrate the ease of use for these methods. The paragraph concludes with information on accessing Optimum Neuron and the support for upcoming models, as well as a discussion on pipeline parallelism's role in memory fitting and potential speedups.

25:10

🌐 Closing Remarks and Future Directions

The episode concludes with a summary of the key points discussed, including the deployment and training of large language models on AWS's custom silicon instances. The host expresses gratitude to the guests and the audience for their participation. The versatility of AWS and Hugging Face's collaboration is highlighted, with a tease for the next episode where different computing environments will be explored. The host encourages audience interaction and questions, wrapping up with a reminder of the show's monthly schedule and an invitation to join the next episode.

Mindmap

Keywords

💡Hugging Face

Hugging Face is an open-source company focused on building AI with open models and tools. In the context of the video, they are hosting a live show called 'Hugging Cast' to discuss and demonstrate the practical applications of AI, particularly focusing on how to build AI using their open models and libraries on various compute stacks.

💡AWS

Amazon Web Services (AWS) is a cloud computing platform provided by Amazon that offers a wide range of services including compute, storage, and databases. In the video, AWS is highlighted as a key partner for Hugging Face, showcasing the integration of Hugging Face's models with AWS's custom silicon instances like Trainium and Inferentia for efficient AI workloads.

💡Inferentia

AWS Inferentia is a custom AI accelerator designed by Amazon specifically for machine learning inference workloads. It is part of the AWS cloud services and is used to speed up the processing of AI models. In the video, the speakers demonstrate how to use Inferentia 2 instances for deploying large language models with Text Generation Inference (TGI).

💡Trainium

AWS Trainium is a custom-designed inference chip by Amazon specifically for training machine learning models. It is part of AWS's suite of AI services and is used to enhance the training process by providing high performance for deep learning tasks. In the video, the focus is on using Trainium instances to train large language models effectively by leveraging technologies like tensor parallelism and pipeline parallelism.

💡Optimum Neuron

Optimum Neuron is a library developed by Hugging Face that serves as a bridge between their AI models and the software and hardware stack of AWS's Trainium and Inferentia. It is designed to enable users to easily deploy and run Hugging Face models on AWS's custom silicon instances, taking advantage of the hardware acceleration features.

💡Tensor Parallelism

Tensor Parallelism is a technique used in distributed training of deep learning models where the weight matrices involved in the model's layers are split across multiple devices or cores. This method allows for the training of larger models that wouldn't fit into the memory of a single device by spreading the model's computations across several devices.

💡Pipeline Parallelism

Pipeline Parallelism is a strategy in deep learning where the layers of a neural network model are divided across multiple devices or cores, with each device handling a subset of layers. This allows for the model to be trained more efficiently by overlapping the computation and communication processes, improving the overall training throughput.

💡Text Generation Inference (TGI)

Text Generation Inference (TGI) is a purpose-built solution created by Hugging Face to simplify the deployment and running of large language models (LLMs) for text generation tasks. TGI provides an interface for efficient and cost-effective use of these models on various compute environments, including AWS's Inferentia.

💡Cost Savings

In the context of the video, cost savings refer to the financial benefits achieved by using AWS's custom silicon instances like Inferentia and Trainium for AI workloads. These instances offer high performance at a lower cost compared to traditional GPU instances, making them an attractive option for deploying and training large language models.

💡Distributed Training

Distributed training is a method of training machine learning models where the computation is divided across multiple devices or nodes. This approach enables the training of larger models that wouldn't fit into the memory of a single device by spreading the model's parameters, gradients, and computations across the distributed system.

Highlights

Introduction of the second season of Hugging Cast, a live show about building AI with open models and open source.

Goal of the new season is to have more demos and practical examples for application in companies.

Focus on building AI with Hugging Face's partners' tools, starting with AWS as the first partner.

AWS collaboration to showcase the use of the best open-source models with the best compute options available on AWS.

Introduction of Mikel, who works on Optimum Neuron, a library for training and inference on AWS Trainum and Inferia instances.

Explanation of AWS custom silicon, specifically designed for AI workloads like deep learning.

Hugging Face's work with AWS engineers to facilitate the use of models on Trainum and Inferia.

Optimum Neuron as a compiler and runtime SDK to bridge models with the software and hardware stack.

Inferentia 2's high speed and cost-effectiveness for large training jobs and production inference workloads.

Documentation on Hugging Face's website for using Optimum Neuron with AWS Trainum and Inferia.

Ability to transfer a model trained on Trainum to an H100 machine and vice versa.

Introduction of Phillip, an AWS Hero, known for his expertise and contributions in AWS tutorials.

Explanation of the four different instance sizes available for Inferentia 2.

Text Generation Inference (TGI) on AWS Inferentia, providing the same interface and features as GPU.

Demonstration of streaming with TGI, allowing for immediate response as the model generates tokens.

Blog post and Jupiter notebook available for guidance on deploying models with TGI on Inferentia 2.

Sapphire 7B model deployment on Inferentia, showcasing the use of Hugging Face's model repository.

Explanation of the compilation process for models on Inferentia, optimizing computation for the chip.

AWS and Hugging Face working on a cache for popular public models to skip the compilation process.

Demonstration of deploying large language models on Inferentia 2 using the TGI container.

Shift from inference to training large language models (LLMs) on AWS Tranium instances.

Discussion on the memory requirements for training models and the design of Tranium instances.

Integration of parallelism methods in Optimum Neuron to enable training of larger models.

Overview of data parallelism, tensor parallelism, and pipeline parallelism in Optimum Neuron.

Ease of use with Optimum Neuron for parallelism methods without needing detailed knowledge.

Access to Optimum Neuron through documentation and AWS Tranium instance setup.

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