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 I’m going to continue my description of the CIFAR10 benchmark, from where I left off. Related articles Mar 7, 2018 Benchmarks on MATRIX MULTIPLICATION | A comparison between AMD Vega and NVIDIA GeForce series Mar 20, 2018 Benchmarks on MATRIX MULTIPLICATION | TitanV TensorCore (FP16=>FP32) CIFAR10 Average examples pre second Introduction I took the CIFAR10 dataset, which is widely used throughout the world in competitions and benchmarks, and used the public release of TensorFlow in order to measure its training speed.

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.

PLAIDML, which is rumored to be faster than HIP-TENSORFLOW Introduction Hello! HIP-TensorFlow is a library implemented by performing an CUDA simulation of TensorFlow, but since its execution speed is still under development or based on the old TensorFlow, there is a speed difference when compared against the latest NVIDIA + TensorFlow in the DeepLearning. Also, since it works at the same speed for RX 580 as for superior GPUs like Vega 56 and Vega 64, it is still an immature library in that it cannot demonstrate the potential of the Vega series.