Supercharge Your LLM Training with Airbnb's ML Platform

Table of Contents

1. Introduction
2. ML Infrastructure at Airbnb
   1. Comparison of Cloud-Native and Real-air ML Development
   2. Integration of Ray into the ML Infrastructure
   3. Benchmark Results of Offline Training
3. Journey of Machine Learning Adoption at Airbnb
4. Kubernetes Application Runtime for ML
   1. Management and Scaling of ML Applications
   2. Operational Support Issues
   3. Usability and Developer Efficiency Challenges
   4. GPU Memory Wall Challenge
5. Real Integration and API Design
   1. Setup of Real-Based Maps on Public Cloud Provider
   2. Easy User Access and Job Submission
   3. Workflow API for ML Workflow
   4. Cost Efficiency of Real Clusters
   5. Hardware Acceleration and Network Setups
6. Achieving Training Efficiency with Real
   1. Deep Speed and PyTorch Support for Large Models
   2. Elasticity of Real Clusters with AWS Integration
   3. GPU Utilization and Model Parallelism
7. Future Plans and Innovations
   1. Investigation of 3D Model Parallelism
   2. Integration of Reservoir for Cost Optimization
   3. Consolidation of Training Engines

ML Infrastructure at Airbnb

Machine learning (ML) plays a crucial role in the end-to-end user workflow on the Airbnb platform. This section provides an overview of the ML infrastructure at Airbnb, including a comparison of cloud-native and real-air ML development, the integration of Ray into the ML infrastructure, and the benchmark results of offline training.

Comparison of Cloud-Native and Real-air ML Development

Airbnb's ML infrastructure supports both cloud-native solutions built on Kubernetes and real-air ML development. This section highlights the benefits of each approach and how they are used at Airbnb.

Integration of Ray into the ML Infrastructure

Airbnb has integrated Ray, a distributed computing framework, into its ML infrastructure. This section explores the learnings from integrating Ray, including the integration of user-facing APIs and system efficiency.

Benchmark Results of Offline Training

To evaluate the effectiveness of the ML infrastructure at Airbnb, benchmark tests were conducted for offline training. This section reviews the benchmark results and provides insights into the Current standing and progress of the ML infrastructure.

Journey of Machine Learning Adoption at Airbnb

Before delving into the technical details of the ML infrastructure, it is essential to understand Airbnb's journey of adopting machine learning in general. ML plays a crucial role in various aspects of the Airbnb platform, from search ranking models to smart pricing and customer support. This section takes a closer look at the role of machine learning in the end-to-end user workflow on the platform.

Kubernetes Application Runtime for ML

Airbnb's ML applications run on top of Kubernetes, a powerful and versatile platform. However, there are several operational support, usability, and efficiency challenges faced when using Kubernetes for ML development. This section explores these challenges and provides insights into how Airbnb overcomes them.

Management and Scaling of ML Applications

Running ML applications on Kubernetes requires efficient management and scaling. This section discusses the existing ML infrastructure at Airbnb and how it supports a wide range of use cases, including offline TensorFlow distributed training and online model serving.

Operational Support Issues

Kubernetes poses several operational support issues when troubleshooting and debugging tests that experience memory or cloud-related issues. This section delves into the challenges faced and how Airbnb tackles them with the help of Ray.

Usability and Developer Efficiency Challenges

Developing ML applications with Kubernetes can present usability and developer efficiency challenges. This section highlights the issues faced when developing ML applications locally and how remote execution with Ray helps overcome these challenges.

GPU Memory Wall Challenge

The GPU memory wall challenge arises when the size of ML models exceeds the available GPU memory. This section explores this challenge in Detail and discusses innovative solutions introduced by the ML community to overcome it.

Real Integration and API Design

This section focuses on the integration of Ray into the ML infrastructure at Airbnb and the design of user APIs for easy access and job submission. It covers the setup of real-based maps on public cloud providers, the simplicity of user-facing API development, and the workflow API for ML workflow management. Additionally, it explores the cost efficiency of real clusters and the utilization of hardware acceleration and network setups.

Achieving Training Efficiency with Real

Achieving training efficiency is a crucial aspect of ML development. This section discusses how Airbnb uses Ray to achieve efficient training by enabling features like deep speed stage 3 and model parallelism. It also explores the elasticity of real clusters and the utilization of GPU resources.

Future Plans and Innovations

As the ML landscape continues to evolve, Airbnb has exciting future plans and innovations in mind. This section provides a glimpse into the upcoming projects, including the investigation of 3D model parallelism, the integration of Reservoir for cost optimization, and the consolidation of training engines.

Highlights

• Airbnb's ML infrastructure supports both cloud-native and real-air ML development.
• Ray has been integrated into Airbnb's ML infrastructure, enhancing user-facing APIs and system efficiency.
• Benchmark results for offline training showcase the progress and current standing of Airbnb's ML infrastructure.
• Machine learning plays a crucial role in the end-to-end user workflow on the Airbnb platform.
• The integration of Kubernetes into the ML infrastructure presents various operational support and efficiency challenges.
• Ray helps overcome operational support issues and improves usability and developer efficiency.
• The GPU memory wall challenge is addressed with innovative solutions like deep speed and model parallelism.
• Real integration and API design simplify user access and job submission in the ML workflow.
• Cost efficiency is achieved through the utilization of ephemeral clusters and hardware acceleration.
• Training efficiency is enhanced by leveraging deep speed stage 3 and model parallelism.
• Future plans include 3D model parallelism, Reservoir integration, and the consolidation of training engines.

FAQ

1. What is the role of machine learning in the Airbnb platform?

   • Machine learning plays a crucial role in various aspects of the Airbnb platform, including search ranking models, smart pricing, and customer support.

2. How does Airbnb integrate Ray into its ML infrastructure?

   • Airbnb integrates Ray, a distributed computing framework, into its ML infrastructure to enhance user-facing APIs and improve system efficiency.

3. What are the benchmark results of Airbnb's offline training?

   • The benchmark results demonstrate the progress and current standing of Airbnb's ML infrastructure, showcasing the effectiveness of the offline training process.

4. How does Airbnb overcome operational support issues when using Kubernetes for ML development?

   • Airbnb tackles operational support issues by leveraging the capabilities of Ray, which helps troubleshoot and debug tests experiencing memory or cloud-related issues.

5. How does Airbnb achieve training efficiency?

   • Airbnb achieves training efficiency by utilizing features like deep speed stage 3 and model parallelism, which optimize the utilization of GPU resources and enhance the training process.

6. What are Airbnb's future plans and innovations in the ML space?

   • Airbnb has plans to explore 3D model parallelism, integrate Reservoir for cost optimization, and consolidate training engines to further enhance its ML infrastructure.