openblas

caffe学习笔记（1）安装 - Ubuntu 15.04

由 天涯浪子 提交于 2019-12-19 01:08:53
官方安装手册 备注：使用系统 - Ubuntu 15.04 64位操作系统（若系统位于虚拟机上，在安装CUDA后，Ubuntu将无法进入图形界面） /**************************************************/ //准备工作：CUDA,OpenBLAS/ATLAS,Boost, protobuf，OpenCV, Python /**************************************************/ 方法一： Ubuntu系统上安装caffe官方手册 （第一次安装时竟没看到这个神器。。。） 0. 基本依赖项 $sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler $sudo apt-get install --no-install-recommends libboost-all-dev 1. CUDA（使用方法二安装） 2. BLAS 若选择使用ATLAS:$sudo apt-get install libatlas-base-dev(安装较方便) 若选择使用OpenBLAS 则参考方法二安装； 3. Python（可选）

How to use multi CPU cores to train NNs using caffe and OpenBLAS

由 独自空忆成欢 提交于 2019-12-18 13:35:06
问题 I am learning deep learning recently and my friend recommended me caffe. After install it with OpenBLAS, I followed the tutorial, MNIST task in the doc. But later I found it was super slow and only one CPU core was working. The problem is that the servers in my lab don't have GPU, so I have to use CPUs instead. I Googled this and got some page like this . I tried to export OPENBLAS_NUM_THREADS=8 and export OMP_NUM_THREADS=8 . But caffe still used one core. How can I make caffe use multi CPUs?

Set max number of threads at runtime on numpy/openblas

由 不羁岁月 提交于 2019-12-18 12:29:23
问题 I'd like to know if it's possible to change at (Python) runtime the maximum number of threads used by OpenBLAS behind numpy? I know it's possible to set it before running the interpreter through the environment variable OMP_NUM_THREADS , but I'd like to change it at runtime. Typically, when using MKL instead of OpenBLAS, it is possible: import mkl mkl.set_num_threads(n) 回答1: You can do this by calling the openblas_set_num_threads function using ctypes . I often find myself wanting to do this,

Set max number of threads at runtime on numpy/openblas

由 回眸只為那壹抹淺笑 提交于 2019-12-18 12:29:11
问题 I'd like to know if it's possible to change at (Python) runtime the maximum number of threads used by OpenBLAS behind numpy? I know it's possible to set it before running the interpreter through the environment variable OMP_NUM_THREADS , but I'd like to change it at runtime. Typically, when using MKL instead of OpenBLAS, it is possible: import mkl mkl.set_num_threads(n) 回答1: You can do this by calling the openblas_set_num_threads function using ctypes . I often find myself wanting to do this,

Multiple instances of Python running simultaneously limited to 35

由 半世苍凉 提交于 2019-12-17 19:49:21
问题 I am running a Python 3.6 script as multiple separate processes on different processors of a parallel computing cluster. Up to 35 processes run simultaneously with no problem, but the 36th (and any more) crashes with a segmentation fault on the second line which is import pandas as pd . Interestingly, the first line import os does not cause an issue. The full error message is: OpenBLAS blas_thread_init: pthread_create: Resource temporarily unavailable OpenBLAS blas_thread_init: RLIMIT_NPROC

performance of NumPy with different BLAS implementations

由 落花浮王杯 提交于 2019-12-17 19:38:09
问题 I'm running an algorithm that is implemented in Python and uses NumPy. The most computationally expensive part of the algorithm involves solving a set of linear systems (i.e. a call to numpy.linalg.solve() . I came up with this small benchmark: import numpy as np import time # Create two large random matrices a = np.random.randn(5000, 5000) b = np.random.randn(5000, 5000) t1 = time.time() # That's the expensive call: np.linalg.solve(a, b) print time.time() - t1 I've been running this on: My

Tutorial for installing numpy with OpenBLAS on Windows

由 断了今生、忘了曾经 提交于 2019-12-10 15:56:35
问题 Please, I do need a light here. I want to install numpy using a good BLAS/LAPACK lib on Windows , but absolutely no page explains the process well enough. It seems OpenBLAS is a good and fast option. The goal is to use "theano" with "keras", and "theano" requires that the libraries be "dynamic", not static. (Not sure I understand what that means, but it causes slowness and memory issues) Please treat me as a complete newbie. Give me a step by step tutorial on how to do it! Don't forget to

Floating-point number vs fixed-point number: speed on Intel I5 CPU

由 ￡可爱￡侵袭症＋ 提交于 2019-12-10 09:22:25
问题 I have a C/C++ program which involves intensive 32-bit floating-point matrix math computations such as addition, subtraction, multiplication, division, etc. Can I speed up my program by converting 32-bit floating-point numbers into 16-bit fixed-point numbers ? How much speed gain can I get ? Currently I'm working on a Intel I5 CPU. I'm using Openblas to perform the matrix calculations. How should I re-implement Openblas functions such as cblas_dgemm to perform fixed-point calculations ? I

Install openblas via apt-get `sudo apt-get install openblas-dev`

由 天涯浪子 提交于 2019-12-09 08:05:37
问题 Is it possible to install openblas via apt-get like sudo apt-get install openblas-dev ? Seems on ubuntu 14.04 it can't find it. sudo apt-get install openblas-dev Reading package lists... Done Building dependency tree Reading state information... Done E: Unable to locate package openblas-dev 回答1: apt-cache search openblas libblas-test - Basic Linear Algebra Subroutines 3, testing programs libopenblas-base - Optimized BLAS (linear algebra) library based on GotoBLAS2 libopenblas-dev - Optimized

No _dotblas.so after installing OpenBLAS and Numpy

由 别来无恙 提交于 2019-12-08 01:50:03
问题 I'm trying to speed up matrix operations using NumPy in Ubuntu 14.04 LTS (64-bit). Instead of using ATLAS (actually when I use ATLAS, there is only 1 thread which is fully running, with 7 other opened threads doing nothing, even if I specify OMP_NUM_THREADS=8 for instance. Don't know why.), I decided to give OpenBLAS a try. I've spent hours by following several tutorials to build the source code of OpenBLAS and NumPy, e.g. [1], [2], [3], [4], and [5]. However, none of them can generate
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