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blog.tensorflow.org
TensorFlow aims to make it easy for you to build and deploy ML models across many different devices. Yet, what it means to “build and deploy ML models” is not static and continues to change with increased investment in the ML ecosystem.
, while application developers are enhancing their products with these new techniques across has sparked a proliferation of new hardware aimed at specific ML use cases. Traditional chip makers, startups, and software companies alike (including , but the current TensorFlow stack is optimized for graph execution, and incurs non-trivial overhead when dispatching a single op. A high-performance low-level runtime is a key to enable the trends of today and empower the innovations of tomorrow.
Enter TFRT, a new TensorFlow RunTime. It aims to provide a unified, extensible infrastructure layer with best-in-class performance across a wide variety of domain specific hardware. It provides efficient use of multithreaded host CPUs, supports fully asynchronous programming models, and focuses on low-level efficiency.
TFRT will benefit a broad range of users, including:
- Researchers looking for faster iteration time and better error reporting when developing complex new models in eager mode.
- Application developers looking for improved performance when training and serving models in production.
- Hardware makers looking to integrate edge and datacenter devices into TensorFlow in a modular way.
What is TFRT?
TFRT is a new runtime that will replace the existing TensorFlow runtime. It is responsible for efficient execution of kernels – low-level device-specific primitives – on targeted hardware. It plays a critical part in both eager and graph execution, which is illustrated by this simplified diagram of the TensorFlow training stack:. For example:
Initial ResultsEarly performance results from the inference and serving use case are encouraging. As part of a benchmarking study for and measured the latency of sending requests to the model and getting prediction results back. We picked a common available to the community. We are limiting contributions to begin with, but encourage participation in the form of requirements and design discussions. To learn more, please check out our , where we provided a detailed overview of TFRT’s core components, low-level abstractions, and general design principles. And finally, if you want to keep up with all things TFRT, please join our new mailing list. Thanks! Vollständiger Original-Bericht Ausführliche Details, Code-Beispiele & Hersteller-Stellungnahme auf blog.tensorflow.org. Wie bewertest du diesen Beitrag? 1 Klick Feedback Teilen mit Netzwerk & Team: Hat Ihnen dieser Tipp / Anleitung geholfen? Community-Analysen & Experten-Meinungen 0Verfasse deine eigene Analyse, teile Workarounds oder diskutiere diesen Vorfall im Blog. Noch keine Community-Analyse verfasst. Markiere einen Textabschnitt oder klicke oben auf „ Eigene Analyse verfassen“! Community Pulse: Relevanz-Einschätzung 1 Klick Experten-Votum 🔴 Akute Relevanz 0% 🟡 In Evaluierung 0% 🟢 Keine Auswirkung 0% Spannende Innovation 0% Verwandte Story-Cluster & Quellen (Vektor-KI) Tipp: Mit Pfeiltasten [ ← ] und [ → ] blättern 
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