deep learning gpu benchmarks

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!!!

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

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

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