http://blog.csdn.net/jerr__y/article/details/53695567 Introduction: This article mainly describes how to configure the GPU version of the TensorFlow environment in Ubuntu system. Mainly include:-Cuda Installation-CUDNN Installation-TensorFlow Installation-Keras InstallationAmong them, Cuda installs this part is the most important, Cuda installs after, whether is tensorflow or other deep learning framework c
Nowadays, AI is getting more and more attention, and this is largely attributed to the rapid development of deep learning. The successful cross-border between AI and different industries has a profound impact on traditional industries.Recently, I also began to keep in touch with deep learning, before I read a lot of articles, the history of deep learning and related theoretical knowledge also have a general understanding.But as the saying goes: The end of the paper is shallow, it is known that t
. Then this version should be a driver that matches CUDA8 with each other. )
Install cudnn5.1 (HTTPS://DEVELOPER.NVIDIA.COM/CUDNN) unzip the installation package just down, copy the files under these three folders to the Cuda folder below.
After the Anaconda installation is complete, you should be able to see whether the version is 3.5 by tapping Python directly in the Windows Command window.
Create a TensorFlow virtual environment c:> Conda create-n TensorFlow python=3.5, everything in th
Win10 + python3.6 + VSCode + tensorflow-gpu + keras + cuda8 + cuDN6N environment configuration, win10cudn6n
Preface:
Before getting started, I knew almost nothing about python or tensorflow, so I took a lot of detours When configuring this environment, it took a whole week to complete the environment... However, the most annoying thing is that it is difficult to set up the environment. Because my laptop is
Today, the GPU is used to speed up computing, that feeling is soaring, close to graduation season, we are doing experiments, the server is already overwhelmed, our house server A pile of people to use, card to the explosion, training a model of a rough calculation of the iteration 100 times will take 3, 4 days of time, not worth the candle, Just next door there is an idle GPU depth learning server, decided
the profile file ( Note: If you are not using version 8.0, you need to modify the version number ):→~ Export cuda_home=/usr/local/cuda-8.0→~ Export Path=/usr/local/cuda-8.0/bin${path:+:${path}}→~ Export Ld_library_path=/usr/local/cuda-8.0/lib64${ld_library_path:+:${ld_library_path}}After modification:→~ Source/etc/profileVerify that the configuration is successful:→~ nvcc-vThe following message appears to be successful: 4. Installing the CUDNN Acceleration LibraryThis article uses the CUDA8.0,
Reprint Please specify:Look at Daniel's small freshness : http://www.cnblogs.com/luruiyuan/This article original website : http://www.cnblogs.com/luruiyuan/p/6660142.htmlThe Ubuntu version I used was 16.04, and using Gnome as the desktop (which doesn't matter) has gone through a lot of twists and turns and finally completed the installation of Keras with TensorFlow as the back end.Installation of the TENSORFLOW-GP
Keras in the use of the GPU when the feature is that the default is full of video memory. That way, if you have multiple models that need to run with a GPU, the restrictions are huge and a waste to the GPU. So when using Keras, you need to consciously set how much capacity y
RNN, or the combination of both
Seamless CPU and GPU switching
?? If you want to use Keras on your computer, you need the following tools:
Python
TensorFlow
Keras
Here we choose TensorFlow as the back-end tool for Keras. Use the following Python code to output the version numbers of Pyth
Multi-GPU development of OPENCL (by the way OpenGL multi-GPU development)
Label (Space delimited): accelerates OpenCL
Reprint description Source: http://blog.csdn.net/hust_sheng/article/details/75912004 Demand
GPU is used in some accelerated optimization projects, and somet
Keras is a python library for deep learning that contains efficient numerical libraries Theano and TensorFlow.
The purpose of this article is to learn how to load data from CSV and make it available for keras use, how to model the data of multi-class classification using neural network, and how to use Scikit-learn to evaluate
fine-tuning (iii)4, Keras series ︱ Facial Expression Classification and recognition: OpenCV Face Detection +keras emotional Classification (iv)5, Keras series of ︱ Migration learning: Using InceptionV3 for fine-tuning and forecasting, complete case (v)
. One, CIFAR10 small picture Classification example (sequential type)
To train a model, you first have to know
Original Author: Fei Hong surprised snow address Click to open the link
This paper mainly explores the problem of the heterogeneous computing of the GPU and multi-core CPUs of OpenCL, and briefly expounds what is the OpenCL heterogeneous computing, describes the characteristics of CPU and GPU, and combines them to make the foreground of heterogeneous computing. T
The Keras has many advantages, and building a model is quick and easy, but it is recommended to understand the basic principles of neural networks.
Backend suggested using TensorFlow, much faster than Theano.
From sklearn.datasets import Load_iris from sklearn.model_selection import train_test_split import Keras from Keras.model s import sequential from keras.layers import dense, dropout from keras.optim
Learning notes TF040: Multi-GPU parallelTensorFlow parallelism, model parallelism, and data parallelism. Different parallel modes are designed for different models in parallel. Different computing nodes of the model are placed on different hardware workers for resource operations. Data parallelism is more common and easy to implement large-scale parallel mode. Multiple hardware resources are used to compute
range (k)]Third, the training processCopy the full model parameters onto multiple GPUs and perform multi-GPU training on a single small batch at each iteration:Def train (Num_gpus, Batch_size, LR): train_iter, test_iter = Gb.load_data_fashion_mnist (batch_size) CTX = [ Mx.gpu (i) for I in Range (Num_gpus)] # device designator list print (' Running on: ', CTX) # Copies the model parameters to t
Caffe allows parallel computing between multiple GPU, and multi-GPU mode is "not sharing data, but sharing network". When the number of GPU on the target machine is greater than 1 o'clock, Caffe will allow multiple solver to exist and be applied to different GPU.
Vector
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Tag: Code screen--line XOR does not have Mina content valueNvidia's graphics card is overclocking-enabled, with tools such as afterburning in Windows.But there is no such thing as a ready-made tool under Linux.But Coolbits's settings are also very simple.Just modify the xorg.conf file to add coolbit and you can overclock it with nvidia-setting.Manual editing is still a hassle, in fact Nvidia provides commands to implement this edit.$sudo nvidia-xconfig -a --cool-bits=24 --allow-empty-initial-con
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