By default, TensorFlow maps nearly all of the GPU memory of all GPUs (subject to CUDA_VISIBLE_DEVICES) visible to the process. This is done to more efficiently use the relatively precious GPU memory resources on the devices by reducing memory fragmen…

keras 自适应分配显存 & 清理不用的变量释放 GPU 显存 Intro Are you running out of GPU memory when using keras or tensorflow deep learning models, but only some of the time? Are you curious about exactly how much GPU memory your tensorflow model uses during training? Are…

最近使用github上的一个开源项目训练基于CNN的翻译模型,使用THEANO_FLAGS='floatX=float32,device=gpu2,lib.cnmem=1' python run_nnet.py -w data/exp1/,运行时报错,打印"The image and the kernel must have the same type. inputs(float64), kerns(float32)"的错误,然后使用THEANO_FLAGS='floatX=float…

Sometimes CUDA program crashed during execution, before memory was flushed. As a result, device memory remained occupied. There are some solutions: 1. Try using: nvidia-smi --gpu-reset or simply: nvidia-smi -r 2. Although it should be unecessary to d…

问题描述: Tensorflow 训练时运行越来越慢,重启后又变好. 用的是Tensorflow-GPU 1.2版本,在GPU上跑,大概就是才开始训练的时候每个batch的时间很低,然后随着训练的推进,每个batch的耗时越来越长,但是当我重启后,又一切正常了? 问题查找: 一开始查到的原因是batch_size 和 batch_num的问题,通过python yield 数据生成器解决,确保内存每次处理的数据确定是batch_size大小,但是发现运行效率还是不高,所以查阅google的一些资…

Jupyter notebook 每次运行完tensorflow的程序,占着显存不释放.而又因为tensorflow是默认申请可使用的全部显存,就会使得后续程序难以运行.暂时还没有找到在jupyter notebook里面自动释放显存的方法,但是我们可以做的是通过指定config为使用的显存按需自动增长,这样可以避免大多数的问题.代码如下: gpu_no = '0' # or '1' os.environ["CUDA_VISIBLE_DEVICES"] = gpu_no # 定义Ten…

xgboost的可以参考:https://xgboost.readthedocs.io/en/latest/gpu/index.html 整体看加速5-6倍的样子. Gradient Boosting, Decision Trees and XGBoost with CUDA By Rory Mitchell | September 11, 2017  Tags: CUDA, Gradient Boosting, machine learning and AI, XGBoost   Gradie…

NVIDIA GPU Pascal架构简述 本文摘抄自英伟达Pascal架构官方白皮书:https://www.nvidia.com/en-us/data-center/resources/pascal-architecture-whitepaper/ SM 相比Maxwell架构,Pascal架构改进了16-nm FinFET的制造工艺,并提供了各种其它架构改进. Pascal further improves the already excellent power efficiency pr…

一.问题源起 从以下的异常堆栈可以看到是BLAS程序集初始化失败,可以看到是执行MatMul的时候发生的异常,基本可以断定可能数据集太大导致memory不够用了. 2021-08-10 16:38:04.917501: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_NOT_INITIALIZED 2021-08-10 16:38:04.960048…

catalog . OpenCL . Linux DMA(Direct Memory Access) . GPU rootkit PoC by Team Jellyfish . GPU keylogger . DMA Hack 1. OpenCL OpenCL(Open Computing Language)是第一个面向异构系统通用目的并行编程的开放式.免费标准,也是一个统一的编程环境,便于软件开发人员为高性能计算服务器.桌面计算系统.手持设备编写高效轻便的代码,而且广泛适用于多核心处理器(CP…

You can use Microsoft Platform SDK functions to get the heap size at a specific point. If get the heap size at point A in your code, and then again at point B, you can see how much it has grown. This might help you isolate here memory growth occurs.…

INTRODUCTION GPUs (Graphic Processing Units) have become much more popular in recent years for computationally intensive calculations.  Despite these gains, the use of this hardware has been very limited in the R programming language.  Although possi…

本小节笔记大纲: 1.Communication patterns gather,scatter,stencil,transpose 2.GPU hardware & Programming Model SMs,threads,blocks,ordering Synchronization Memory model: local, shared, global Atomic Operation 3.Efficient GPU Programming Access memory faster co…

一.传统的提高计算速度的方法 faster clocks (设置更快的时钟) more work over per clock cycle(每个时钟周期做更多的工作) more processors(更多处理器) 二.CPU & GPU CPU更加侧重执行时间,做到延时小 GPU则侧重吞吐量,能够执行大量的计算 更形象的理解就是假如我们载一群人去北京,CPU就像那种敞篷跑车一样速度贼快,但是一次只能坐两个人,而GPU就像是大巴车一样,虽然可能速度不如跑车,但是一次能载超多人. 总结起来相比于CP…

这篇文章属于典型的剥洋葱文,由表及里,逐步引入新的知识点,挖掘最本质的原因.这篇文的逻辑是先假设再证明,按照这个思路去阅读会比较轻松. Maya里的GPU Cache导入的几何体为什么不能编辑顶点?这可以算是一个高频问题了,这个问题可以转换为:GPU Cache导入的几何体为什么不能编辑Mesh(不仅不能编辑顶点,为它添加bevel一类的节点也不可以)?实际上这个问题在Maya的帮助文档中是有明确答案的. 我们来看看官方是如何解释的: GPU caches are Alembic-based f…

<1> Basic #include <stdio.h> #include <cuda_runtime.h> #include <device_launch_parameters.h> #define NUM 15 __global__ void square(float *dout,float *din) { int idx = threadIdx.x; float f = din[idx]; dout[idx] = f*f; } int main(int…

本小节笔记大纲: 1.Communication patterns gather,scatter,stencil,transpose 2.GPU hardware & Programming Model SMs,threads,blocks,ordering Synchronization Memory model: local, shared, global Atomic Operation 3.Efficient GPU Programming Access memory faster co…

It’s coming up on a year since we published our last memory review; possibly the longest hiatus this section of the site has ever seen. To be honest, the reason we’ve refrained from posting much of anything is because things haven’t changed all that…

一.传统的提高计算速度的方法 faster clocks (设置更快的时钟) more work over per clock cycle(每个时钟周期做更多的工作) more processors(更多处理器) 二.CPU & GPU CPU更加侧重执行时间,做到延时小 GPU则侧重吞吐量,能够执行大量的计算 更形象的理解就是假如我们载一群人去北京,CPU就像那种敞篷跑车一样速度贼快,但是一次只能坐两个人,而GPU就像是大巴车一样,虽然可能速度不如跑车,但是一次能载超多人. 总结起来相比于CP…

Memory kernel性能高低是不能单纯的从warp的执行上来解释的.比如之前博文涉及到的,将block的维度设置为warp大小的一半会导致load efficiency降低,这个问题无法用warp的调度或者并行性来解释.根本原因是获取global memory的方式很差劲. 众所周知,memory的操作在讲求效率的语言中占有极重的地位.low-latency和high-bandwidth是高性能的理想情况.但是购买拥有大容量,高性能的memory是不现实的,或者不经济的.因此,我们就要尽量…

The OpenCV GPU module is a set of classes and functions to utilize GPU computational capabilities. It is implemented using NVIDIA* CUDA* Runtime API and supports only NVIDIA GPUs. 1.      getCudaEnableDeviceCount:returns the number of installed CUDA-…

windows如何查看nvidia显卡(GPU)的利用率和温度 nvidia-smi 只要在文件夹C:\Program Files\NVIDIA Corporation\NVSMI里找到文件nvidia-smi.exe,把该文件拖到命令提示符窗口(win+R,再输入'CMD'进入),就可以显示关于GPU的信息,如下图所示: Windows 上不显示每个程序显存占用 N/A nvidia-smi 主要原因是这个功能在显卡显示画面时不能用 Not available in WDDM driver m…

想着开始学习tf了怎么能不用GPU,网上查了一下发现GeForce GTX确实支持GPU运算,所以就尝试部署了一下,在这里记录一下,避免大家少走弯路. 使用个人笔记本电脑thinkpadE570,内存4G,显卡GeForce GTX 950M 前期电脑已经安装win0+Ubuntu16双系统,thinkpad安装win0+Ubuntu16配置参照这里(本人为了方便) 安装顺序为: (1)安装NVIDIA Driver 安装电脑对应的显卡驱动,安装完成能够在程序中找到NVIDIA.和windows…

Linux查看显卡信息: lspci | grep -i vga 使用nvidia GPU可以: lspci | grep -i nvidia [root@gpu-server-002 ~]# lspci | grep -i nvidia 02:00.0 VGA compatible controller: NVIDIA Corporation Device 1b06 (rev a1) 02:00.1 Audio device: NVIDIA Corporation Device 10ef (r…

lspci  | grep -i vga 这样就可以显示机器上的显卡信息,比如 [root@localhost conf]# lspci | grep -i vga01:00.0 VGA compatible controller: nVidia Corporation Device 1081 (rev a1)02:00.0 VGA compatible controller: nVidia Corporation GT215 [GeForce GT 240] (rev a2)08:05.0 V…

https://kubernetes.io/docs/tasks/manage-gpus/scheduling-gpus/#deploying-nvidia-gpu-device-plugin 1. 安装 nvidia-docker(ubuntu14.04) https://github.com/NVIDIA/nvidia-docker 卸载旧版 docker volume ls -q -f driver=nvidia-docker | xargs -r -I{} -n1 docker ps -…

here were various changes to memory related DMVs, DBCC memory status, and Perfmon counters in SQL Server 2012 as part of the redesign of the Memory Manager component of SQLOS. The Memory Manager redesign resulted in being able to more accurately size…

原文地址:http://blogs.msdn.com/b/tess/archive/2006/02/15/532804.aspx I hate to give away the resolution in the title of the blog since it takes away a lot of the suspense:) but I can't figure out a better way to name the blog posts and still keep them ni…

GPU .caret, .dropup > .btn > .caret { border-top-color: #000 !important; } .label { border: 1px solid #000; } .table { border-collapse: collapse !important; } .table td, .table th { background-color: #fff !important; } .table-bordered th, .table-bor…

A data processor supports the use of multiple memory models by computer programs. At a device external to a data processor, such as a memory controller, memory transactions requests are received from the data processor. Each memory transaction reques…