tensorflow server

cuda/lib64/libcudnn*/usr/local/cuda/lib64/-DView CUDNN version information:Cat grep 2Output#define Cudnn_major 6#define cudnn_minor 0#define cudnn_patchlevel 21--#define cudnn_ VERSION (cudnn_major * + Cudnn_minor * + cudnn_patchlevel)"driver_types.h "detects if Cuda and CUDNN are installed successfully:Go to test Catalog// go to test Catalog:cd/usr/local/cuda-8.0/samples/1_utilities/devicequery// compilation environment Make-J4// run:./devicequeryresult = PASS indicates successf

Connection Server Windows-xshell xftp SSH Connect to a lab server via SSH Using SSH connection is no stranger. GitHub and OS classes are often used Currently using 192.168.7.169 Using Tools Xshell and Xftp Using Xshell to connect servers and operations, the Ubuntu 16.04 LTS operating system is installed on each node of the

A simple distributed service (local server) feature is provided in TensorFlow, and a simple distributed program can be run with the local server service. The local Server implements the same interface as the distributed service, so it is more convenient for development testing. Here's the simplest example. Import

TensorFlow Learning Notes 4: Distributed TensorFlow Brief Introduction The TensorFlow API provides cluster, server, and supervisor to support distributed training of models. The distributed training introduction about TensorFlow can refer to distributed

version has a great upgrade to achieve 64 GPU 58 times times the acceleration, here first the basic introduction of data parallelism and model parallelism: Data parallel each worker has a complete model, part of the data, The update of the parameter is passed to params Server; Model parallel to each worker on a part of the network model; How to write distributed code in TensorFlow, here I do not say, very

. "/job:worker/task:0" and "/job:ps/task:0" Represents an execution service in a worker. "Job:ps" represents a parameter server that is used to store and update model parameters. "Job:worker" is used to optimize model parameters, and concurrent parameters are sent to the parameter server. Distributed master and worker service only exist in distributed TensorFlow.

various business petabytes of data, at the beginning of the design to consider the needs of distributed computing, through the GRPC, Protobuf and other high-performance libraries to achieve the neural network model of distributed computing. The implementation of distributed TensorFlow application is not difficult, the construction of graph code and stand-alone version of the same, we implemented a distributed cancer_classifier.py example, the followi

Ubuntu1604 install tensorflow and tensorflow Operating System: ubuntu-16.04.2-desktop-amd64Tensorflow version: 1.0.0Python version: 2.7.12 Enable ssh: sudo apt install openssh-server sudo service ssh start Install pip: sudo apt-get install python-pip Install tensorflow: Github address: https://github.com/

") sess = tf. interactiveSession () sess. run (tf. initialize_all_variables () for I in range (2000): batch = mnist. train. next_batch (50) if I % 100 = 0: train_accuracy = accuracy. eval (feed_dict = {x: batch [0], y _: batch [1], keep_prob: 1.0}) print "step % d, training accuracy % g" % (I, train_accuracy) train_step.run (feed_dict = {x: batch [0], y _: batch [1], keep_prob: 0.5}) print "test accuracy % g" % accuracy. eval (feed_dict = {x: mnist. test. images [0: 500], y _: mnist. test. label

1. Download and install Anaconda1.1 downloadDownload the Linux version from Anaconda official website (https://www.continuum.io/downloads)https://repo.continuum.io/archive/(Recommended python3.5)1.2 InstallationCD ~/downloadssudo bash anaconda2-5.0.1-linux-x86_64.sh (download the corresponding version of Python2.7 here)Ask if you want to add the Anaconda bin to the user's environment variable and select yes!Installation is complete.2. Install tensorflow2.1 set up

Development environment: Mac OS 10.12.5Python 2.7.10GCC 4.2.1Mac default is no pip, install PIP.sudo easy_install pip1. Installing virtualenvsudo pip install virtualenv--upgradeCreate a working directory:sudo virtualenv--system-site-packages ~/tensorflowMake the directory, activate the sandboxCD ~/tensorflowSOURCE Bin/activateInstall TensorFlow in 2.virtualenvAfter entering the sandbox, execute the following command to install

binary file, which is downloaded to the corresponding directory. Command: Curl-Lo minikube https://storage.googleapis.com/minikube/releases/v0.14.0/minikube-darwin-amd64 chmod + x minikube sudo mv minikube/usr/local/bin/ The command line of the client kubectl and kubectl interacts with the cluster. Installation: Http://storage.googleapis.com/kubernetes-release/release/v1.5.1/bin/darwin/amd64/kubectl chmod + x kubectl sudo mv kubectl/usr/local/bin/ Minikube starts a Kubernetes cluster: Minik

Through a few routines, we gradually established a perceptual knowledge of tensorflow. This article will further from the internal principle of deep understanding, and then for reading source to lay a good foundation.1. Graph (graph)The TensorFlow calculation is abstracted as a forward graph that includes several nodes. As shown in the example:The corresponding TensorFl