Do you wish to build TensorFlow with CUDA support? [y/N]: YPlease specify the CUDA SDK version you want to use, e.g.7.0. [Leave empty to default to CUDA 10.0]:10.1Please specify the location where CUDA10.1toolkit is installed. Refer to README.mdformoredetails. [Default is /usr/local...
E tensorflow/stream_executor/cuda/cuda_dnn.cc:286] Loaded cudnn library: 5005 but source was compiled against 4007. If using a binary install, upgrade your cudnn library to match. If building from sources, make sure the library loaded matches the version you specified during compile configurat...
Please specify the CUDA SDK version you want to use. [Leave empty to default to CUDA 9.0]:9.1 Please specify the location where CUDA 9.1 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:/usr Please specify the cuDNN version you want to use. [L...
replacing <CudaInstaller> with the name of this run file: sudo <CudaInstaller>.run --silent...
编译Tensorflow 1.10 + CUDA9.2 + MKL【转】 本文转自:https://www.solarck.com/compile-tensorflow-gpu.html 我的电脑系统是基于 Archlinux 的 Manjaro,软件包更新的比较激进,很早就已经是CUDA 9.2 了,而目前 Tensorflow 的官方编译版本对CUDA 的支持还只停留在CUDA 9.0。由于还不太会用 mxnet 和 pytorch,这时...
最后我使用了最新的tf-nightly-gpu 2.6.0的版本+cuda11.1+最新版cudnn8.1 for cuda 11.1,方法和正文一样,用anaconda的好处就是虚拟环境可以直接重建。 以及如果出现function cupti_interface_->Subscribe( &subscriber_, (CUpti_CallbackFunc)ApiCallback, this)failed with error CUPTI could not be loaded or sy...
ERROR: Building with --config=cuda but TensorFlow is not configured to build with GPU support. Please re-run ./configure and enter 'Y' at the prompt to build with GPU support. ERROR: no such target '@local_config_cuda//crosstool:toolchain': target 'toolchain' not declared in package 'cr...
CUDA/cuDNN version: 10.1/7.6.5.32 GPU model and memory: NVIDIA TITAN Xp 12GB Describe the problem Building a TF2 for GPU on Windows craches with a (likely cmd-related) error: ( was unexpected at this time. This happens when building //tensorflow:tensorflow.dll, but in particular for any...
TensorFlow是一个流行的机器学习框架,可以用于训练和部署深度学习模型。使用多个GPU可以加快训练速度,提高模型的性能。下面是如何让TensorFlow使用多路GPU的步骤: 确保你的机器上有多个GPU,并且已经正确安装了CUDA和cuDNN。这些是GPU加速计算所必需的软件库。 在TensorFlow代码中,使用tf.distribute.Strategy来指定使用多个GPU...
首先,确保你的系统上安装了支持CUDA的GPU和相应的驱动程序,并且安装了TensorFlow 1.15或更高版本。要检查是否安装了正确版本的TensorFlow,可以运行以下命令: import tensorflow as tf print(tf.__version__) 如果输出显示版本号为1.15或更高,则说明TensorFlow已正确安装。接下来,我们需要检查系统上可用的GPU设备。运行以...