NVIDIA提出PLASTER框架：深度学习性能评估七大挑战
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更新于2024-09-08
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深度学习性能评估是现代人工智能领域中的关键课题，尤其是在英伟达(NVIDIA)于2018年GTC大会提出的PLASTER框架下。该框架旨在应对深度学习(DL)广泛应用所面临的双重挑战：一是如何部署复杂且快速发展的深度学习解决方案，二是如何充分利用这些技术所带来的机会并解决随之而来的问题。
首先，让我们理解什么是PLASTER。它是由TIRIAS Research赞助，由NVIDIA的David A. Teich和Paul R. Teich共同开发的框架，其全称为"Performance Language for Artificial Intelligence, Software, and Training Evaluation and Research"（人工智能、软件和训练性能评估与研究语言）。PLASTER框架涵盖了七个核心挑战，这七个挑战旨在帮助业界更好地理解和优化深度学习的性能表现。
1. **模型效率**：PLASTER强调了模型的效率，包括硬件加速和优化，如GPU的专用计算能力，以及如何减少模型的计算复杂性和内存需求，以提高整体系统的执行速度。
2. **可扩展性**：随着数据集的增长和模型的复杂度增加，如何保持系统的可扩展性，处理大规模数据和多任务同时运行的能力，是PLASTER关注的重要方面。
3. **硬件选择与优化**：选择合适的硬件平台对深度学习性能至关重要。PLASTER考虑了不同架构之间的比较，以及如何针对特定任务调整硬件配置以达到最佳性能。
4. **训练时间与成本**：深度学习模型的训练过程往往耗时且昂贵，PLASTER框架探讨如何通过改进算法和工具来缩短训练周期，降低成本。
5. **模型精度与稳定性**：在追求高性能的同时，保持模型的准确性和稳定性是不可忽视的。PLASTER提供了一套评估指标和方法，以确保模型在各种条件下的性能一致性。
6. **隐私与安全**：随着深度学习在敏感领域的应用增加，数据隐私和模型安全成为关注点。PLASTER关注如何在保障性能的同时，保护数据和个人隐私，以及对抗恶意攻击。
7. **生态系统整合**：深度学习涉及众多开源库和工具，PLASTER鼓励社区合作，推动标准化和互操作性，以便开发者可以更轻松地构建、测试和部署深度学习系统。
PLASTER框架是一个全面的工具包，它不仅提供了对深度学习性能评估的框架，还为业界开发者和研究人员提供了一套指导原则，帮助他们面对和解决深度学习部署过程中遇到的实际问题。通过深入理解和利用这个框架，业界能够更加高效地推动AI的发展，加速创新，同时也确保了技术的安全性和可持续性。
PLASTER:
A Framework for Deep Learning Performance
May 2018 Copyright © 2018 TIRIAS Research. All Rights Reserved Page 2
Programmability
Machine learning is experiencing explosive growth not only in the size and complexity of the
models but also the burgeoning diversity of neural network architectures. It is difficult even for
experts to understand the model choices and then choose the appropriate model to solve their AI
business problems.
After a deep learning model is coded and trained, it is then optimized for a specific runtime
inference environment. NVIDIA addresses training and inference challenges with two key tools.
For coding, AI-based service developers use CUDA, a parallel computing platform and
programming model for general computing on GPUs. For inference, AI-based service developers
use TensorRT, NVIDIA’s programmable inference accelerator.
CUDA helps data scientists by simplifying the steps needed to implement an algorithm on the
NVIDIA platform. The TensorRT Programmable Inference Accelerator tool takes a trained
neural network and optimizes it for runtime deployment. It tests different levels of floating point
and integer precision, so that developers and operations can balance system-required accuracy
and performance to provide an optimized solution.
Developers can use TensorRT directly from within the TensorFlow framework to optimize
models for AI-based service delivery. TensorRT can import Open Neural Network Exchange
(ONNX) models from a variety of frameworks, including Caffe2, MXNet, and PyTorch. While
deep learning is still coding at a technical level, this will help the data scientist better leverage
valuable time.
Measuring Programmability
Programmability affects developer productivity and therefore time-to-market. TensorRT
accelerates AI inference on multiple popular frameworks, including Caffe2, Kaldi, MXNet,
PyTorch, and TensorFlow. In addition, TensorRT can ingest CNNs, RNNs and MLP networks,
and offers a Custom Layer API for novel, unique, or proprietary layers, so developers can
implement their own CUDA kernel functions. TensorRT also supports the Python scripting
language, allowing developers to integrate a TensorRT-based inference engine into a Python
development environment.
Programmability in Action
Baker Hughes (BHGE) is a leading oil field services company. It helps oil and gas companies in
all aspects of exploration, extraction, processing, and delivery. At each step of the process, AI
can help oil and gas companies better understand the massive volumes of data their operations
create. Each type of business need can lean on a different type of deep learning model. That
means programmers must efficiently be able to implement, test, and instantiate multiple models.
BHGE uses CUDA and TensorRT to create the deep learning models that help its customers
identify and locate oil and gas resources. BHGE also uses NVIDIA hardware, including DGX-1
servers for model training; DGX Stations at the deskside or on remote offshore platforms for
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