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docker pull pytorch/pytorch:nightly-devel-cuda10.0-cudnn7, in which you can install Apex using the Quick Start commands. official Pytorch -devel Dockerfiles, e.g.

To use the latest Amp API, you may need to pip uninstall apex then reinstall Apex using the Quick Start commands below.

NVIDIA Pytorch containers from NGC, which come with Apex preinstalled.

It's often convenient to use Apex in Docker containers. We also test against the latest master branch, obtainable from. We recommend the latest stable release, obtainable from The CUDA and C++ extensions require pytorch 1.0 or newer. Also note that we recommend calling the load_state_dict methods after amp.initialize. Note that we recommend restoring the model using the same opt_level. initialize( model, optimizer, opt_level = opt_level) save( checkpoint, 'amp_checkpoint.pt')Ĭheckpoint = torch. scale_loss( loss, optimizer) as scaled_loss:

# Initialization opt_level = 'O1' model, optimizer = amp. In order to get bitwise accuracy, we recommend the following workflow: To properly save and load your amp training, we introduce the amp.state_dict(), which contains all loss_scalers and their corresponding unskipped steps,Īs well as amp.load_state_dict() to restore these attributes. Synchronous BN has been observed to improve converged accuracy in some of our research models. Global batch size across all processes (which, technically, is the correct Synchronous BN has been used in cases where only a smallĪllreduced stats increase the effective batch size for the BN layer to the It allreduces stats across processes during multiprocess (DistributedDataParallel) training. Synchronized Batch NormalizationĪ extends torch.nn.modules.batchnorm._BatchNorm to Optimized for NVIDIA's NCCL communication library. It enables convenient multiprocess distributed training, Torch.nn.parallel.DistributedDataParallel. Distributed TrainingĪ is a module wrapper, similar to Moving to the new Amp API (for users of the deprecated "Amp" and "FP16_Optimizer" APIs) 2. (The flag cast_batchnorm has been renamed to keep_batchnorm_fp32). Users can easily experiment with different pure and mixed precision training modes by supplying

Amp: Automatic Mixed PrecisionĪpex.amp is a tool to enable mixed precision training by changing only 3 lines of your script. Full API Documentation: GTC 2019 and Pytorch DevCon 2019 Slides Contents 1. The intention of Apex is to make up-to-date utilities available to Some of the code here will be included in upstream Pytorch eventually. Mixed precision and distributed training in Pytorch. This repository holds NVIDIA-maintained utilities to streamline