deep learning gpu cluster

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/

Deep learning target detection (object detection) series (i) r-cnnDeep learning target detection (object detection) series (ii) spp-netDeep learning target detection (object detection) series (iii) Fast R-CNNDeep learning target detection (object detection) series (iv) Faste

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

Spark ML Model pipelines on distributed deep neural Nets This notebook describes what to build machine learning pipelines with Spark ML for distributed versions of Keras deep learn ING models. As data set we use the Otto Product Classification challenge from Kaggle. The reason we chose this data is, it is small and very structured. This is, we can focus on the t

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

matrix when you calculate Np.dot (A, A.T). The shape of A is (5, 1), and a. The shape of T is (1, 5).A.shape = (5,) This is an array of rank 1, not a row vector or a column vector. Many students appear to be difficult to debug bugs are from the rank of 1 arrays.In addition, if you do a lot of things in the code, you may not remember or are unsure of how a is, use assert (A.shape = = (5,1)) to check the dimensions of your matrix.If you get (5,) you can reshape it into (5, 1) or (1, 5), reshape i

installation was successful, import the NumPy with Python, as follows to complete the installation4. Installing TensorFlow1.> download the corresponding version of the TensorFlow, must be corresponding to the Python version, the latest is the support python3.6 version, for: https://pypi.org/project/tensorflow-gpu/#files, Because my Python version is 3.6, so download TENSORFLOW_GPU-1.8.0-CP36-CP36M-WIN_AMD64.WHL2.> Installation: command: Pip install T

addressed, as the presence of straggler will result in the decision of block at the intermediate stage being affected. The article compares the appearance of Straggler and the time of the extra choice decision, finds that the impact of Straggler is 20%-40%, so the article solves the problem by using the following method: When the K task in the M-upstream task finishes executing, the downstream task is started. The fact is to speed up the start time of the downstream stage by accelerating the st

Reprint Please specify source: http://www.cnblogs.com/Joanna-Yan/p/6956206.htmlMentioned earlier: Spring+springmvc+mybatis deep learning and Building (vii)--mybatis delay loading1. What is a query cacheMyBatis provides query caching to mitigate database stress and improve database performance.MyBatis provides a first-level cache and a level two cache.The first-level cache is a cache at the sqlsession level.

directly performed on the Mesos adopts fine-grained sharing. One advantage of this is that although some tasks do not execute fine-grained tasks at the same time, long tasks and short tasks can still share space. The framework determines which resources are required based on the task length. Long tasks generally require more resources. Then mesos allocates resources to the Framework (this policy can be specified by the user), but the framework determines which resources to receive, the accepte

experience in architecture design in large capacity, high concurrency and high reliability. He has served as a senior architect at Huawei and the sole product office. Wang Tao is a senior cloud computing engineer at the Hara-only product. After graduating from the Post graduate, he has been engaged in the development and architecture of cloud computing for nearly six years. He has worked in Huawei and Fusionsphere for the development of value-added services, as well as the architecture of

information query access is large, but require users every time can query the latest product information, At this point, if you use MyBatis's level two cache, you cannot realize that when a commodity changes, only the cache information of the product is refreshed and the information of other goods is not refreshed, because MyBatis's level two cache area is divided by mapper, and when an item information changes, the cached data of all commodity information is emptied. Solving such problems requ