![]() For more savvy developers who wish to unlock the highest throughput, AMP training with FP16 remains the most performant option and yet can be enabled easily with either no code change (when using the NVIDIA NGC TensorFlow container) or just a single line of extra code. TF32 is designed to bring the processing power of NVIDIA Tensor Cores technologies to all DL workloads without any required code changes. Two modes of operations on Ampere Tensor Cores: TF32 and FP16. Figure 1 shows a comparison between various numerical formats.įigure 2. Specifically, TF32 uses the same 10-bit mantissa as FP16 to ensure accuracy while sporting the same range as FP32, thanks to using an 8-bit exponent.Ī wider representable range matching FP32 eliminates the need of a loss-scaling operation when using TF32, thus simplifying the mixed precision training workflow. TF32 is a hybrid format defined to handle the work of FP32 with greater efficiency. The third-generation Tensor Cores on Ampere support a novel math mode: TF32. On Volta and Turing, the inputs are two matrices of size 4×4 in FP16 format, while the accumulator is in FP32. ![]() They can carry out a complete matrix multiplication and accumulation operation (MMA) in a single clock cycle. NVIDIA Tensor Cores are specialized arithmetic units on NVIDIA Volta and newer generation GPUs. For more information, see the Mixed Precision Training whitepaper by NVIDIA Research. This strategy results in a significant reduction in computation, memory, and memory bandwidth requirements while most often converging to the similar final accuracy. Numerical precisions supported by NVIDIA A100ĭeep neural networks (DNNs) can often be trained with a mixed precision strategy, employing mostly FP16 but also FP32 precision when necessary. This release also includes important updates to automatic mixed precision (AMP), XLA, and TensorFlow-TensorRT integration. This release allows you to realize the speed advantage of TF32 on NVIDIA Ampere architecture GPUs with no code change for DL workloads. With this release, we provide out-of-the-box support for TF32 on NVIDIA Ampere architecture GPUs while also enhancing the support for previous-generation GPUs, such as Volta and Turing. The NVIDIA TensorFlow 1.15.2 from 20.06 release is based on upstream TensorFlow version 1.15.2. We continue to release NVIDIA TensorFlow 1.15 every month to support the significant number of NVIDIA customers who are still using TensorFlow 1.x. In this post, we focus on TensorFlow 1.15–based containers and pip wheels with support for NVIDIA GPUs, including the A100. Starting from the 20.06 release, we have added support for the new NVIDIA A100 features, new CUDA 11 and cuDNN 8 libraries in all the deep learning framework containers. On NVIDIA A100 Tensor Cores, the throughput of mathematical operations running in TF32 format is up to 10x more than FP32 running on the prior Volta-generation V100 GPU, resulting in up to 5.7x higher performance for DL workloads.Įvery month, NVIDIA releases containers for DL frameworks on NVIDIA NGC, all optimized for NVIDIA GPUs: TensorFlow 1, TensorFlow 2, PyTorch, and “NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet”. TF32 is designed to accelerate the processing of FP32 data types, commonly used in DL workloads. The NVIDIA A100, based on the NVIDIA Ampere GPU architecture, offers a suite of exciting new features: third-generation Tensor Cores, Multi-Instance GPU ( MIG) and third-generation NVLink.Īmpere Tensor Cores introduce a novel math mode dedicated for AI training: the TensorFloat-32 (TF32).
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