tensorflow gpu

2017.6.2 installation timeFirst install Anaconda3 or under Anaconda2 win+r cmd controller Conda create-n Anaconda3 python=3.5(The previous step will appear inside the file I cut to another place)Install Anaconda version 3 in Anaconda2/envs the prompt already exists I was deleted again under Envs Direct installation Anaconda3 Note To install 3.5 version do not 3.6 page below there is connected to install Anaconda3 4.2 Then copy and paste the two files you just made.And then call when it's activat

"Python 3.6 + tensorflow GPU 1.4.0 + CUDA 8.0 + CuDNN 6.0"There is no pycharm to install the Pycharm first.1, python:https://www.python.org/downloads/release/python-364/Pull to the bottom and select Windows x86-64 executable installer download.Note the Add Python 3.6 to path check box, and then select Install Now.2, TensorFlow

When using TensorFlow to train deep learning models, assuming that we did not specify a GPU to train before training, the default is to use the No. 0 GPU to train our model, and the other GPU's will be shown to be occupied. Sometimes we prefer to train our models by specifying a piece or a few gpus ourselves, rather than using this default method. The next step i

the profile file ( Note: If you are not using version 8.0, you need to modify the version number ):→~ Export cuda_home=/usr/local/cuda-8.0→~ Export Path=/usr/local/cuda-8.0/bin${path:+:${path}}→~ Export Ld_library_path=/usr/local/cuda-8.0/lib64${ld_library_path:+:${ld_library_path}}After modification:→~ Source/etc/profileVerify that the configuration is successful:→~ nvcc-vThe following message appears to be successful: 4. Installing the CUDNN Acceleration LibraryThis article uses the CUDA8.0,

To avoid trouble, install all the default pathsI installed the Cuda and CUDNN versionsTensorFlow version 1.7There is a small problem here, the direct import TensorFlow has an error, I Baidu the wrong some said to install a software, but I do not want to pretend, and then input import TensorFlow as TF no errorEffective tutorials for measurementsLook at this old brother's reading line and know how much artifi

other dependenciessudo apt-get install python-numpy swig python-dev python-wheel?? 8. Build GPU Support (this is a compile-time hint that the GCC version is too high to downgrade http://www.cnblogs.com/alan215m/p/5906139.html)bazel build -c opt --config=cuda //tensorflow/cc:tutorials_example_trainer? If an error occurs, add--verbose_failures to run the followingbazel build -c opt --config=cuda //

A server is loaded with multiple GPUs, and by default, when a deep learning training task is started, this task fills up almost all of the storage space for each GPU. This results in the fact that a server can only perform a single task, while the task may not require so many resources, which is tantamount to a waste of resources.The following solutions are available for this issue.First, directly set the visible GPUWrite a script that sets environmen

Small white one, please give more advice, thank you.Practice proves that WIN10 + tensorflow1.6 + cuda9.1 +cudnn8.0 + python3.6 installation is not suitable (perhaps aPerson reason)Because my computer is a new computer, Win10 +python3.5 (installed with Anaconda) + cudnn8.0 +cuda9.0 Use successSome of these environment variables are not added, some are automatically added, but need to cudnn compressed all the files to paste intoThe Cuda directory.The installation process encountered a lot of probl

This machine has installed Windows system, ready to install Ubuntu dual system for TensorFlow related work, need to separate the disk in Windows for Ubuntu use1. First download the Ubuntu17.04 version of ISO2. Download Win32diskimager as installation disk burning software3. Insert a USB flash drive to burn4. Insert the USB flash drive into the computer and reboot, select USB drive5. Choose to install Ubuntu system6. Installation Type Select other opti

I ran TensorFlow program on Ubuntu, halfway through the use of the Win+c key to the end of the program, but the GPU video memory is not released, has been in the occupied state.Using commandsNvidia-smiShown belowTwo GPU programs are in progress, in fact, gpu:0 has been stopped by the author, but the

1, install Cuda Toolkit and CUDNN (Baidu Cloud can download, version needs corresponding)2. Configure Environment variables:3, install CUDNN (need to copy some DLLs and Lib to configure)4, go to cmd, find the Anaconda3 pip path, with the following command to execute, you can uninstall the CPU version of TensorFlow, install the GPU version of the TensorFlowpip uninstall tensorflowpip install

Due to a lot of reasons I cuda9.0+cudnn7.0.5+tensorflow-gpu1.6 the environment of the machine into: cuda8.0+cudnn6.0+tensorflow-gpu1.6After the introduction of: Import TensorFlow Throws an exception when you: tensorflow:importerror:libcusolver.so.9.0 At first I was very puzzled, thought it was cuda did not uninstall clean, and re-uninstall + installation, but

PC configuration: GeForce GTX 1080Installing the GTX1080 DriveGo to the NVIDIA network, download the GTX1080 driver, start the search, and then download the required version. I downloaded the latest 384.130.can also be downloaded here.After the download is complete, save as a backup to refresh the new driver.Add Nvidia Source sudo add-apt-repository Ppa:graphics-drivers/ppa If the information is not considered, press ENTER directly.sudo apt-get update sudo apt-get install nvidia-384 sudo

The author runs TensorFlow program on Ubuntu, midway using the Win+c key to end the program, but the GPU's video memory is not released, has been in the occupied state.Using commandsWatch-n 1 Nvidia-smiShows the followingTwo GPU programs are in execution, in fact, gpu:0 has been stopped by the author, but the GPU is no

Viewing GPU conditions on the machine Command: Nvidia-smi Function: Shows the GPU on the machine Command: Nvidia-smi-l Function: Periodically update the GPU on the display machine Command: Watch-n 3 Nvidia-smi Function: Set refresh time (seconds) to show GPU usage The upper left side has a number of 0, 1, 2, 3, which

Recently tried to learn tensorflow, but because the problem of learning resources leads to a series of problems, in simple terms, to learn tensorflow, to directly view the guidance of the GitHub, rather than according to the blog, Baidu on the guidance, because the version of the change too fast, similar to the College of Geeks, Blog guidance and code has not run, according to the error step-by-step process

The questions are as follows: Invalidargumenterror (above for traceback): Cannot assign a device to node ' train/final/fc3/b/momentum ': Could not sat ISFY explicit device specification '/device:gpu:0 ' because no devices matching that specification are registered in this P rocess; Available devices:/job:localhost/replica:0/task:0/cpu:0 colocation Debug Info: colocation Group had the Following types and devices: Applymomentum:cpu mul:cpu sum:cpu abs:cpu const:cpu Assign : CPU identity:cpu var

-linux-x64-v5.1 Find the download path, CD in, find this file, enter the following actions: Tar xvzf cudnn-8.0-linux-x64-v5.1-prod.tgz sudo cp cuda/include/cudnn.h/usr/local/cuda/include sudo cp cuda/lib64/lib cudnn*/usr/local/cuda/lib64 Complete the above steps, CUDNN installation is complete. 9. Next, install the TensorFlow: First download the TENSORFLOW-GPU

algorithm parameters. CPU and GPU are the device layer, which is mainly responsible for the specific operation of neural network algorithm. Kernel is a concrete implementation of the algorithm operation in TensorFlow, such as convolution operation, activation operation and so on. Distributed master is used to build the child graph, the cut child graph is a plurality of slices, the different sub graph slice

23:58:34.771619:w tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn ' T compiled to usage SSE4.2 instructions, but these is available on your machine and could speed up CPU computations.2017-07 -05 23:58:34.771654:w tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wa