nvidia

I have followed all the instructions from https://datanoord.com/2016/02/01/setup-a-deep-learning-environment-on-windows-theano-keras-with-gpu-enabled/ but can't seem to get it work. I have added C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC\bin to my PATH variable Every time I run the code from the Theano website to test whether a CPU or GPU is used, it gives me a fatal error of "nvcc fatal : Cannot find compiler 'cl.exe' in PATH" Here is the code I use to test: from theano import function, config, shared, sandbox import theano.tensor as T import numpy import time vlen = 10 * 30 * 768

I only have one GPU (Titan X Pascal, 12 GB VRAM) and I would like to train multiple models, in parallel, on the same GPU. I tried encapsulated my model in a single python program (called model.py), and I included code in model.py to restrict VRAM usage (based on this example ). I was able to run up to 3 instances of model.py concurrently on my GPU (with each instance taking a little less than 33% of my VRAM). Mysteriously, when I tried with 4 models I received an error: 2017-09-10 13:27:43.714908: E tensorflow/stream_executor/cuda/cuda_dnn.cc:371] coul d not create cudnn handle: CUDNN_STATUS

如何查看Nvidia的GPU运行状态 　　在使用nvidia的GPU进行运算的时候，通常会有需要了解GPU运行状态需求。在下面的文章中，将会介绍我在实际使用中到的方法。 　　 1.使用" nvidia-smi "查看GPU状态信息 　　通常安装好nvidia驱动程序后会自动安装好nvidia-smi，使用nvidia-smi是最简单直接的查看GPU状态信息的方法。 直接运行"nvidia-smi"，就可以查看当前的运行状态。可以查看到GPU的各个核心的工作状态、温度、内存信息、及使用GPU的进程信息等。 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 367.27 Driver Version: 367.27 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |======

This is on windows 10 computer with no monitor attached to the Nvidia card. I've included output from nvida-smi showing > 5.04G was available. Here is the tensorflow code asking it to allocate just slightly more than I had seen previously: (I want this to be as close as possible to memory fraction=1.0) config = tf.ConfigProto() #config.gpu_options.allow_growth=True config.gpu_options.per_process_gpu_memory_fraction=0.84 config.log_device_placement=True sess = tf.Session(config=config) Just before running the above line in a jupyter notebook I ran nvida-smi: +-----------------------------------

I have an OpenGL library written in c++ that is used from a C# application using C++/CLI adapters. My problem is that if the application is used on laptops with Nvidia Optimus technology the application will not use the hardware acceleration and fail. I have tried to use the info found in Nvidias document http://developer.download.nvidia.com/devzone/devcenter/gamegraphics/files/OptimusRenderingPolicies.pdf about linking libs to my C++-dll and exporting NvOptimusEnablement from my OpenGL-library but that fails. I guess I have to do something with the .exe not with the .dlls linked to the .exe

Downloading the CUDA toolkit DMG works, but the installer fails with a cryptic "package manifest parsing error" error after selecting packages. Running the installer from the command line using the binary inside fails in a similar manner. The log file at /var/log/cuda_installer.log basically says the same: Apr 28 18:16:10 CUDAMacOSXInstaller[58493] : Awoken from nib! Apr 28 18:16:10 CUDAMacOSXInstaller[58493] : Switched to local mode. Apr 28 18:16:24 CUDAMacOSXInstaller[58493] : Package manifest parsing error! Apr 28 18:16:24 CUDAMacOSXInstaller[58493] : Package manifest parsing error! Apr 28

I starting to implement custum video decoder that utilize cuda HW decoder to generate YUV frame for next to encode it. How can I fill "CUVIDPICPARAMS" struc ??? Is it possible? My algorithm are: For get video stream packet I'm use ffmpeg-dev libs avcodec, avformat... My steps: 1) Open input file: avformat_open_input(&ff_formatContext,in_filename,nullptr,nullptr); 2) Get video stream property's: avformat_find_stream_info(ff_formatContext,nullptr); 3) Get video stream: ff_video_stream=ff_formatContext->streams[i]; 4) Get CUDA device and init it: cuDeviceGet(&cu_device,0); CUcontext cu_vid_ctx; 5