This model script is available on GitHub. It is based on regular ResNet model, substituting 3x3 convolutions inside the bottleneck block for 3x3 grouped convolutions. The ResNeXt101-32x4d is a model introduced in the Aggregated Residual Transformations for Deep Neural Networks paper. This container includes the following tensor core examples. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time. The tensor core examples provided in GitHub and NVIDIA GPU Cloud (NGC) focus on achieving the best performance and convergence from NVIDIA Volta tensor cores by using the latest deep learning example networks and model scripts for training.Įach example model trains with mixed precision Tensor Cores on Volta and Turing, therefore you can get results much faster than training without Tensor Cores. Guidance and examples demonstrating can be found here.Apex AMP examples can be found here.įor more information about AMP, see the Training With Mixed Precision Guide. Additionally, GEMMs and convolutions with FP16 inputs can run on Tensor Cores, which provide an 8X increase in computational throughput over FP32 arithmetic.Īpex AMP is included to support models that currently rely on it, but is the future-proof alternative, and offers a number of advantages over Apex AMP. FP16 operations require 2X reduced memory bandwidth (resulting in a 2X speedup for bandwidth-bound operations like most pointwise ops) and 2X reduced memory storage for intermediates (reducing the overall memory consumption of your model). Amp will choose an optimal set of operations to cast to FP16. AMP enables users to try mixed precision training by adding only 3 lines of Python to an existing FP32 (default) script. For older container versions, refer to the Frameworks Support Matrix.ġ.2.0a0 including upstream commits up through commit 9130ab38 from Jas well as a cherry-pickedĪutomatic Mixed Precision (AMP) for PyTorch is available in this container through the native implementation as well as a preinstalled release of Apex. ![]() The following table shows what versions of Ubuntu, CUDA, PyTorch, and TensorRT are supported in each of the NVIDIA containers for PyTorch. ![]() Deep learning framework containers 19.11 and later include experimental support for Singularity v3.0.The latest version of Nsight Systems 2020.4.3.7.The latest version of Nsight Compute 2020.3.0.18.The latest version of NVIDIA NCCL 2.8.4.The latest version of NVIDIA cuDNN 8.0.5.PyTorch container image version 21.02 is based on 1.8.0a0+52ea372.This PyTorch release includes the following key features and enhancements. For additional support details, see Deep Learning Frameworks Support Matrix. Specifically, for a list of GPUs that this compute capability corresponds to, see CUDA GPUs. This corresponds to GPUs in the Pascal, Volta, Turing, and NVIDIA Ampere GPU architecture families. Release 21.02 supports CUDA compute capability 6.0 and higher. For more information, see CUDA Compatibility and Upgrades and NVIDIA CUDA and Drivers Support. For a complete list of supported drivers, see the CUDA Application Compatibility topic. The CUDA driver's compatibility package only supports particular drivers. However, if you are running on Data Center GPUs (formerly Tesla), for example, T4, you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51(or later R450). Release 21.02 is based on NVIDIA CUDA 11.2.0, which requires NVIDIA Driver release 460.27.04 or later. NVIDIA NCCL 2.8.4 (optimized for NVLink™ ).NVIDIA CUDA 11.2.0 including cuBLAS 11.3.1. ![]() Ubuntu 20.04 including Python 3.8 environment.The container also includes the following: It is pre-built and installed in Conda default environment ( /opt/conda/lib/python3.8/site-packages/torch/) in the container image. This container image contains the complete source of the version of PyTorch in /opt/pytorch. The NVIDIA container image for PyTorch, release 21.02, is available on NGC.
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