Introduction As the ROCm libraries has been updated, thoroughput of both inference and training has been improved. In this post we would like to show some comparison between the AMD GPGPU environment on ROCm2.6 and NVIDIA RTX2080ti on Cuda 10 for Machine learning inference tasks. Hardwares and softwares Here are the Hardwares and softwares we have tested these benchmarks. Hardwares and softwares of ROCm environment is as follows: OS:Ubuntu16.04 GPU:AMD RadeonVII or AMD RX Vega 64 ROCm:2.

Introduction Because there was a better ObjectDetection paper than M2Det, I checked the operation on Radeon GPU. M2Det is also Chinese, and CenterNet is a model called CenterNet written by Chinese people. According to the paper, it will be the most accurate and lightest model, YoloV3 <M2Det <CenterNet. CenterNet: Keypoint Triplets for Object Detection https://arxiv.org/abs/1904.08189 PyTorch Implementation https://github.com/xingyizhou/CenterNet/blob/master/readme/INSTALL.md Keras Implementation https://github.com/see--/keras-centernet Installation Check Clone it and put in the required package.

Introduction It is often asked by natural language processors on AMD Radeon GPU whether BERT works, so I will introduce the story when I verified it. Results Confirming Installation Official Documentation: https://github.com/google-research/bert It seems that it should be possible to move from TensorFlow 1.11 to 1.12.0. As ROC m2.3 update has come, I will set up on an experimental machine and run it on Radeon VII. Originally, on the TensorFlow 1.

Introduction Currently, PyTorch / Caffe2 base is at the cutting edge, but some of the sources on github are still based on old Caffe versions. We will look at how to set up hipCaffe (ROCm-Caffe) based on Ubuntu 16.04 + ROCm Write Installation Requirements -Ubuntu 16.04 -Complete set of basic packages required for build -ROCm driver -MIOpen library (CUDA simulation layer) -OpenCV2 or OpenCV3 -hipCaffe Here is a script that compiles with one command line.

Introduction PyTorch supports ROC m2.1! This is an installation guide for running on AMD Radeon GPUs. Installation AMDGPUdriver 2.1 supports PyTorch1.x.x It was announced that PyTorch was officially supported. https://rocm.github.io/dl.html Deep Learning on ROCm TensorFlow: TensorFlow for ROCm – latest supported version 1.13 MIOpen: Open-source deep learning library for AMD GPUs – latest supported version 1.7.1 PyTorch: PyTorch for ROCm – latest supported version 1.0 Installation Issues（2019/03/01) The official page only describes Docker-based installation methods, and there is no documentation for installing from scratch.

Introduction Summary of how TensorFlow works with AMDGPU Radeon Installation Dependencies sudo apt update sudo apt -y install software-properties-common curl wget # for add-apt-repository Install Python 3.5.2 Python3.6/Python3.7 can be unstable with AMD GPUs so we will be using Python 3.5.2 For Ubuntu 18 since the default version is Python3.6 executing the following script will configure 3.5.2 PYTHON35=false if [[ `python3 --version` == *"3.5"* ]] ; then echo 'python3.

Introduction SourceStyle Transfer This time, I am going to run the “Style transfer” which is popular in the field of image generation and image style transfer, using Tensorflow 1.3 on ROCm with AMD Radeon Vega56. System requirements AMD(TF1.3): Ubuntu 16.04.4 x64 TensorFlow 1.3 Python 3.5 Driver: ROCm 1.7.137 I used the following source code of Fast StyleTransfer when performing. https://github.com/lengstrom/fast-style-transfer.git Thank you,Logan Engstrom. Setup TensorFlow on Radeon GPU HIP-TensorFLow 1.

Introduction SourceYoloV2(Object Detection)FCN(Semantic Segmentation) The field of semantic segmentation has many popular networks, including U-Net (2015), FCN (2015), PSPNet (2017), and others. In this study, we used an AMD Radeon GPU to run these networks. We used ROCm-TensorFlow 1.3 and ROCm 1.7.137 as our operating framework. *We re-used the source code from the following repository. hellochick https://github.com/hellochick/semantic-segmentation-tensorflow Setup TensorFlow 1.3 on an AMD Radeon GPU HIP-TensorFLow 1.0.1 was recently updated to TensorFlow 1.