gpu

来源： https://stackoverflow.com/questions/63682454/predict-runs-only-on-cpu-even-though-gpu-is-available

来源： https://stackoverflow.com/questions/63682454/predict-runs-only-on-cpu-even-though-gpu-is-available

问题 I'm trying to get gpu temperature using nodeJS. I found one package on npm called "systeminformation" but I cant get gpu temperature from it. If there is no package/module for it I would like to know a way how to do it from NodeJS. 回答1: There are not Node.js packages with C/C++ submodules for checking GPU temperature, but you can use CLI for that. Pros and cons: 👍 Easy 👍 You need to know only the CLI command for your OS 👎 performance can be slow 👎 maybe you need run your app with sudo For

问题 I'm trying to get gpu temperature using nodeJS. I found one package on npm called "systeminformation" but I cant get gpu temperature from it. If there is no package/module for it I would like to know a way how to do it from NodeJS. 回答1: There are not Node.js packages with C/C++ submodules for checking GPU temperature, but you can use CLI for that. Pros and cons: 👍 Easy 👍 You need to know only the CLI command for your OS 👎 performance can be slow 👎 maybe you need run your app with sudo For

问题 I have installed TensorFlow 1.7 on an Ubuntu 16.04 with Cuda 9.0 and CuDNN 7.0.5 and vanilla Python 2.7 and although they samples for both CUDA and CuDNN run fine, and TensorFlow sees the GPU (so some TensorFlow examples run), those that use CuDNN (like most CNN examples) do not. They fail with these Informational messages: 2018-04-10 16:14:17.013026: I tensorflow/stream_executor/plugin_registry.cc:243] Selecting default DNN plugin, cuDNN 25428 2018-04-10 16:14:17.013100: E tensorflow/stream

问题 I have installed TensorFlow 1.7 on an Ubuntu 16.04 with Cuda 9.0 and CuDNN 7.0.5 and vanilla Python 2.7 and although they samples for both CUDA and CuDNN run fine, and TensorFlow sees the GPU (so some TensorFlow examples run), those that use CuDNN (like most CNN examples) do not. They fail with these Informational messages: 2018-04-10 16:14:17.013026: I tensorflow/stream_executor/plugin_registry.cc:243] Selecting default DNN plugin, cuDNN 25428 2018-04-10 16:14:17.013100: E tensorflow/stream

问题 I have installed TensorFlow 1.7 on an Ubuntu 16.04 with Cuda 9.0 and CuDNN 7.0.5 and vanilla Python 2.7 and although they samples for both CUDA and CuDNN run fine, and TensorFlow sees the GPU (so some TensorFlow examples run), those that use CuDNN (like most CNN examples) do not. They fail with these Informational messages: 2018-04-10 16:14:17.013026: I tensorflow/stream_executor/plugin_registry.cc:243] Selecting default DNN plugin, cuDNN 25428 2018-04-10 16:14:17.013100: E tensorflow/stream

问题 I have installed TensorFlow 1.7 on an Ubuntu 16.04 with Cuda 9.0 and CuDNN 7.0.5 and vanilla Python 2.7 and although they samples for both CUDA and CuDNN run fine, and TensorFlow sees the GPU (so some TensorFlow examples run), those that use CuDNN (like most CNN examples) do not. They fail with these Informational messages: 2018-04-10 16:14:17.013026: I tensorflow/stream_executor/plugin_registry.cc:243] Selecting default DNN plugin, cuDNN 25428 2018-04-10 16:14:17.013100: E tensorflow/stream

问题 For a vector quantization (k-means) program I like to know the amount of available memory on the present GPU (if there is one). This is needed to choose an optimal batch size in order to have as few batches as possible to run over the complete data set. I have written the following test program: import tensorflow as tf import numpy as np from kmeanstf import KMeansTF print("GPU Available: ", tf.test.is_gpu_available()) nn=1000 dd=250000 print("{:,d} bytes".format(nn*dd*4)) dic = {} for x in

问题 I am working to install xgboost on Ubuntu 20.04. I want to force CMake to use a specific CUDA installation (11.0) instead of the default one (10.1). However, the compiler repeatedly throws the error as follows: bill@magicMaker:~/xgboost/build$ cmake .. -DUSE_CUDA=ON -DR_LIB=ON . . . The CUDA compiler "/usr/bin/nvcc" is not able to compile a simple test program. It fails with the following output: Change Dir: /home/bill/xgboost/build/CMakeFiles/CMakeTmp Some of the attempted fixes included: