在Linux系统中,设置GPU可见性的主要方法是通过export命令来设置CUDA_VISIBLE_DEVICES环境变量。这个环境变量接受一个逗号分隔的设备ID列表,每个ID代表一个GPU设备。 3. 编写export命令来设置GPU可见性 你可以通过以下命令来设置GPU可见性: bash export CUDA_VISIBLE_DEVICES=0,1 在这个例子中,只有设备ID为0和1的GPU...
--batch-size 128 2>&1 | tail -2 >> $result_file
To Reproduce Steps to reproduce the behavior: $ export CUDA_VISIBLE_DEVICES=0,1,6,7 $ python ./deepy.py ./train.py ./configs/125M.yml ./configs/local_setup.yml [2023-03-08 12:00:27,863] [INFO] [launch.py:82:main] WORLD INFO DICT: {'local...
windows环境报错,已经尝试set CUDA_VISIBLE_DEVICES=0
# devices: 1Device 0Name: NVIDIA GeForce RTX 2060Preferred: TRUEPower Envelope: DISCRETEAttachment: UNKNOWN# attached displays: 0GPU accessible RAM: 6,442 MBVRAM: 6,442 MBDedicated System RAM: 0 MBShared System RAM: 0 MBAPI version: 3.0 (OpenCL 3.0 CUDA)...
-gpus all -e NVIDIA_DRIVER_CAPABILITIES=compute,utility -e NVIDIA_VISIBLE_DEVICES=all 表示赋予容器使用宿主机的GPU能力 --shm-size 创建共享内存 nvidia/cuda:10.2-cudnn7-devel-ubuntu18.04 表示使用的镜像和版本号 查看容器运行情况 docker ps -a ...
你可以尝试将NCCL_P2P_LEVEL设置为0，然后重新运行程序。如果问题仍然存在，建议检查你的CUDA和PyTorch...
os.environ["CUDA_VISIBLE_DEVICES"]="" importtensorflowastf fromtacotron.modelsimportcreate_model fromtacotron_hparamsimporthparams importshutil #with tf.device('/cpu:0'): inputs=tf.placeholder(tf.int32,[1,None],'inputs') input_lengths=tf.placeholder(tf.int32,[1],'input_lengths') ...
If you cannot interact, then the Save Dialog is probably open but not visible. If this is the case, it made have been moved to another location like the bottom of the screen or to a different monitor. You can test this by click the Enter or Esc key after c...
I created this model with TensorFlow 2.10.1 (Ubuntu Docker 20.04 (LTS)) : import os #os.environ["CUDA_VISIBLE_DEVICES"] = "-1" import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers from tensorflow.keras.applications import DenseNet121, MobileNetV3Small img_in...