gpu workstation deep learning

Introduction we have been trying to build Theano deep learning development environment and install NVIDIA CUDAToolkit in recent days. During this period, I thought about building it on Windows, but after learning about it on the Internet, I found that it is more appropriate in the Linux environment. In the process of building this development environment, there a

Several application cases of R language H2O packageAuthor's message: Inspired to understand the H2O platform of some R language implementation, online has a H2O demo file. I post some cases here, and put some small examples of their own practice.About H2O platform long what kind, can see H2O's official website, about deep learning long what kind of, you can see some tutorials, such as PARALLELR blog in the

mainstream framework, of course, not to say that Keras and CNTK are not mainstream, the article does not have any interest related things, but the keras itself has a variety of frameworks as the back end, So there is no point in contrast to its back-end frame, Keras is undoubtedly the slowest. and CNTK because the author of Windows is not feeling so also not within the range of evaluation (CNTK is also a good framework, of course, also cross-platform, interested parties can go to trample on the

Deep learning to practice, an indispensable path is to the intelligent terminal, embedded equipment and other directions. But the terminal device does not have the powerful performance of GPU server, how to make the end device application deep learning? Fortunately, Googl

Python to do deep learning caffe design CombatEssay background: In a lot of times, many of the early friends will ask me: I am from other languages transferred to the development of the program, there are some basic information to learn from us, your frame feel too big, I hope to have a gradual tutorial or video to learn just fine. For learning difficulties do no

convolution in Caffe? Let me enlightened. Focus on understanding Im2col and Col2im. At this point you know the forward propagation of convolution, but also almost can understand how to achieve the latter. I suggest you die. Caffe the calculation process of the convolution layer, make clear every step, after the painful process you will have a new experience of the reverse communication. After that, you should have the ability to add your own layers. Add a complete tutorial for adding a new la

Configuring Solver Parameters Training: such as Caffe Train-solver Solver.prototxt-gpu 0 Training in Python:Document examples:https://github.com/bvlc/caffe/pull/1733Core code: $CAFFE/python/caffe/_caffe.cppDefine BLOB, Layer, Net, Solver class $CAFFE/python/caffe/pycaffe.pyNET classes for enhanced functionality Debug: Set debug in Make.config: = 1 Set the debug_info:true in Solver.prototxt Python/matla

install-y Python-pip Recommendation:The installation process is best a command one command implementation, there was a mistake to facilitate timely discovery.Installation process has failed to install the situation, do not worry, usually because of network reasons, re-execute the command, generally try a few times will be good ~3. cuda8.0DownloadOfficial website Download: https://developer.nvidia.com/cuda-downloadsDirect download: cuda-repo-ubuntu1604-8-0-local_8.0.44-1_amd64.debInstallatio

Before I have been using Theano, the previous five deeplearning related articles are also learning Theano some notes, at that time already feel Theano use up a little trouble, sometimes want to achieve a new structure, it will take a lot of time to programming, so think about the code modularity, Easy to reuse, but because it's too busy to do it. Recently discovered a framework called Keras, which coincides with my ideas, is particularly simple to use

most popular causes of deep CNN's growing popularity: more powerful GPU; More data (e.g. imagenet); Relu the proposed, accelerate the convergence while maintaining good quality. CNN was previously used for natural image denoising and removing noisy patterns (dirt/rain), which was used for the first time in SR.This is the importance of telling good stories, nothing more than

Please do not reprint without permission, original zhxfl,http://www.cnblogs.com/zhxfl/p/5287644.htmlDirectory:First, IntroductionSecond, the Environment configurationThird, run the demoIv. Hardware Configuration RecommendationsV. OtherFirst, IntroductionDeep learning multi-machine multi-card cluster has become the mainstream, relative to Caffe and mxnet two more active open source, purine appears more worthy of the students in the university reading ,

[emailprotected]:/# Lsbin Dev Home lib64 mnt proc run SRV tmp var Boot etc Lib media opt root sbin sys USR[EMAILNbsp;protected]:/# Note: there exist a error in the Chinese guide provided by Badu. (http://www.paddlepaddle.org/doc_cn/build_and_install/install/docker_install.html)$ docker run-it Paddledev/paddlepaddle:latest-cpuShould is replaced by$ docker run-it Paddledev/paddle:cpu-latestYou can also choose other paddlepaddle images, Baidu provide six Docker images Paddledev/paddle:cpu

The deep learning framework Caffe is compiled and installed in Ubuntu. The deep learning framework Caffe features expressive, fast, and modular. The following describes how to compile and install Caffe on Ubuntu.1. Prerequisites: CUDA is used for computing in GPU mode.

Directory Deep learning environment Construction: tensorflow1.4.0+ubuntu16.04+python3.5+cuda8.0+cudnn6.0 Reference Hardware Description: Software Preparation: 1. Installing Ubuntu16.04 2. Install the video driver 3. Installing Cuda8.0 4. Installing Cudnn6.0 5. Tsinghua Source Installation Anaconda 6. Installing TensorFlow 7. Verify your Insta

toinclude_dirs: = $ (python_include)/usr/local/include/usr/include/hdf5/serial/ Modify makefile File 173 linesLIBRARIES + = Glog gflags protobuf boost_system boost_filesystem m hdf5_serial_hl hdf5_serial　　Perform the compilation　　Make–j4Make Test -j4Make Runtest -j4　　Compilation succeeds when passed results are returnedCompilation of 3.Matconvnet(i) Open matlab　　cd/usr/local/matlab/r2015b/bin/sudo./matlab(ii) Locate the Matconvnet directory and perform the compilationcd/usr/local/matlab/r2015b/

Introduction to mxnet Deep Learning LibraryAbstract: Mxnet is a deep learning library that supports languages such as C + +, Python, R, Scala, Julia, Matlab, and JavaScript; Support command and symbol programming; Can run on CPU,GPU, clusters, servers, desktops or mobile dev

that the accuracy rate of YOLO in detecting small targets is about 8~10% than R-CNN, and the accuracy rate is higher than r-cnn in the detection of large targets. The accuracy of Fast-r-cnn+yolo is the highest, and the accuracy rate is 2.3% higher than that of FAST-R-CNN.5.4 SummaryYolo is a convolutional neural network that supports end-to-end training and testing, and can detect and recognize multiple targets in images under the premise of guaranteeing certain accuracy.6.SSD: SingleShot multi

Runtest-j4At this point Caffe the main program is compiled.The following compiles Pycaffe to executeMake PycaffeMake DistributeAfter execution, modify the BASHRC file to addPythonpath=${home}/caffe/distribute/python: $PYTHONPATHLd_library_path=${home}/caffe/build/lib: $LD _library_pathAllows Python to find Caffe dependencies.Enter Python,import Caffe, if successful then all OK, otherwise check the path from the beginning, and even need to recompile python.Ps:Problems can always google,bless!!!

path=/usr/local/cuda-8.0/bin/: $PATHExport ld_library_path= "/usr/local/cuda-8.0/lib64:/usr/local/cuda-8.0/extras/cupti/lib64"Installation CUDNN is relatively simple, after extracting the corresponding files copied to the corresponding Cuda directory can besudo cp cudnn.h/usr/local/cuda/include/#复制头文件sudo cp lib*/usr/local/cuda/lib64/#复制动态链接库sudo rm-rf libcudnn.so libcudnn.so.6 #删除原有动态文件sudo ln-s libcudnn.so.6.0.21 libcudnn.so.6 #生成软链接sudo ln-s libcudnn.so.6 libcudnn.so #生成软链接Installing Minicon

SLAM On the basis of the above articles, there is a complete lsd-slam algorithm. The homepage of the algorithm is as follows Https://github.com/tum-vision/lsd_slam Http://vision.in.tum.de/research/vslam/lsdslam?redirect=1 Installation under RosBo Master's programming environment is Ubuntu14.04+ros Indigo, in order to facilitate the record, the use of a virtual machine environment, may be a bit card. For the basic knowledge of ROS, please learn it yourself and don't repeat it here. Insta