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Background

  • Background The paper discusses the rapid evolution of machine learning models and computing platforms, particularly large language models (LLMs) and diffusion models. These models have shown outstanding abilities in language tasks and generating realistic images and videos in response to textual prompts, but their deployment presents significant challenges. The models not only require substantial computational resources but also necessitate repeated executions within a single request.

  • Existing Work Existing ML frameworks typically focus on major or specific platforms, such as NVIDIA GPUs' CUDA. Current frameworks cannot adequately support and optimize emerging computing platforms, limiting the potential scenarios for ML applications.

Core Contributions

  • Introduced the TAPML framework

    • Challenge 1: Lack of support and optimization for emerging platforms Existing ML frameworks often focus solely on major or specific computing platforms like CUDA, thereby underserving emerging platforms. The TAPML framework aims to bridge this gap, adopting a top-down approach and leveraging universal runtime to support and optimize various emerging computing platforms, thus catalyzing the development of ML applications.

    • Challenge 2: Complexity and efficiency issues in model deployment As ML models grow and become more complex, the development community urgently needs a solution that allows for convenient, powerful, and broadly deployable models. TAPML has significantly improved developer productivity through automated testing and a migration-based strategy. In practice, TAPML has proven effective, having successfully deployed 82 emerging models across five new platforms, including the latest AI accelerators released by high-performance computing companies like NVIDIA, AMD, Intel, and Google.

Implementation and Deployment

The TAPML framework has not only successfully addressed challenges in testing and debugging ML frameworks but also demonstrated its effectiveness by diversifying experimental configurations, supported models, platforms, and involving developers. The paper presents experiments deploying 82 important models on five emerging and serious platforms, and the methodology has been applied over the past year, significantly accelerating the deployment process and ensuring the reliability and efficiency of emerging models.

Summary

The focus of this paper is on supporting and optimizing the deployment of machine learning models on emerging computing platforms, introducing a framework named TAPML. The framework aims to advance the widespread deployment, convenience, and power of model deployment through top-down methods and universal runtime, providing practical deployment cases as deep insights and best practices for developing ML systems on emerging platforms.