Intel® Optimization for TensorFlow*
Production Performance for AI and Machine Learning
Accelerate TensorFlow* Training and Inference on Intel® Hardware
TensorFlow* is an open source AI and machine learning platform used widely for production AI development and deployment. Often these applications require deep neural networks and extremely large datasets, which can become compute bottlenecks.
Software optimizations in open source TensorFlow accelerate training and inference on Intel hardware. You can further boost TensorFlow training and inference and take advantage of the latest Intel hardware features with Intel® Extension for TensorFlow*.
Using this framework with Intel optimizations, you can:
- Develop, train, and deploy AI models using a Python* API.
- Speed up TensorFlow-based training and inference turnaround times on Intel hardware.
- Extend TensorFlow to further accelerate performance on Intel CPU and GPU hardware.
Download as Part of the Toolkit
Intel® Optimization for TensorFlow* is available as part of the Intel® AI Analytics Toolkit, which provides accelerated machine learning and data analytics pipelines with optimized deep learning frameworks and high-performing Python libraries.
Download the Stand-Alone Version
Stand-alone versions of TensorFlow and Intel Extension for TensorFlow are available. You can install them using a package manager or build from the source.
Develop in the Cloud
Build and optimize oneAPI multiarchitecture applications using the latest optimized Intel® oneAPI and AI tools, and test your workloads across Intel® CPUs and GPUs. No hardware installations, software downloads, or configuration necessary. Free for 120 days with extensions possible.
Features
TensorFlow Machine Learning Framework
- Create, train, and deploy deep learning models using a Python API.
- Process and load data with standard datasets and common input preprocessing layers.
- Speed up model development with distributed training, debugging with Keras*, and model analysis from TensorFlow.
- Deploy TensorFlow models to production servers with TensorFlow Serving.
Intel® Optimizations
- Accelerate AI performance with Intel® oneAPI Deep Neural Network Library (oneDNN) features such as graph optimizations and memory pool allocation.
- Automatically use Intel® Deep Learning Boost instruction set features to parallelize and accelerate AI workloads.
- Reduce inference latency for models deployed using TensorFlow Serving.
- Starting with TensorFlow 2.9, take advantage of oneDNN optimizations automatically.
- Enable optimizations by setting the environment variable TF_ENABLE_ONEDNN_OPTS=1 in TensorFlow 2.5 through 2.8.
Intel Extension for TensorFlow
- Plug into TensorFlow 2.10 or later to accelerate training and inference on Intel GPU hardware with no code changes.
- Automatically mix precision using bfloat16 or float16 data types to reduce memory footprint and improve performance.
- Use TensorFloat-32 (TF32) math mode on Intel GPU hardware.
- Optimize CPU performance settings for latency or throughput using an autotuned CPU launcher.
- Perform more aggressive fusion through the oneDNN Graph API.
Benchmarks
Documentation & Code Samples
- TensorFlow Documentation
- Intel Extension for TensorFlow
- Get Started with TensorFlow in Docker* Containers:
Demonstrations
How to Accelerate TensorFlow on Intel® Hardware
Accelerate deep learning inference by applying default optimizations in TensorFlow for Intel hardware and quantizing to INT8.
Accelerate TensorFlow with oneDNN
See the latest from the collaboration efforts between Google* and Intel to accelerate TensorFlow performance. This collaboration includes support for features such as INT8 and bfloat16 vector and matrix extensions.
Improve TensorFlow Performance on AWS* Instances
Review inference benchmark results for several popular TensorFlow models (with and without oneDNN optimizations) on AWS C6i instance types powered by 3rd generation Intel® Xeon® Scalable processors.
News
Accelerate TensorFlow on Intel® Data Center GPU Flex Series
Google* and Intel coarchitected PluggableDevice, a mechanism that lets hardware vendors add device support by using plug-in packages that can be installed alongside TensorFlow. Intel Extension for TensorFlow is the newest PluggableDevice.
oneDNN AI Optimizations Enabled by Default in TensorFlow
Intel and Google team up to enable this library as the default back end CPU optimization for TensorFlow 2.9.
Meituan* Optimizes TensorFlow
China's leading e-commerce platform for lifestyle services boosted distributed scalability more than tenfold in its recommendation system scenarios.
Specifications
Processor:
- Intel® Xeon® Scalable processor
- Intel GPU
Operating systems:
- Linux*
- Windows*
Languages:
- Python
- C++
Get Help
Your success is our success. Access these support resources when you need assistance.
Stay in the Know with All Things CODE
Sign up to receive the latest trends, tutorials, tools, training, and more to
help you write better code optimized for CPUs, GPUs, FPGAs, and other
accelerators—stand-alone or in any combination.
Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.