import os os.environ["CUDA_VISIBLE_DEVICES]='0,1,3' 1. 2. 来指定使用的显卡。 假设现在我们使用上面的三张显卡,运行时显卡会重新按照0-N进行编号,有: [38664] rank = 1, world_size = 3, n = 1, device_ids = [1] [76032] rank = 0, world_size = 3, n = 1, device_ids = [0] ...
importos os.environ['CUDA_VISIBLE_DEVICES']='-1' 经过测试 GPU比纯cpu在运行restnet18 快很多倍。 conda安装库时报错: conda install keras Collecting package metadata (current_repodata.json): done Solving environment: failed with initial frozen solve. Retrying with flexible solve. Solving environment:...
为了防止忘记上面调了那些参数,可以把命令写成shell脚本,即创建一个xxx.sh文件,把上面的这条命令放进去,然后赋予文件权限后用 ./xxx.sh 即可 还可以在y前面加上指令指定使用哪块GPU,比如 CUDA_VISIBLE_DEVICES=0 python train.py --dropout=0.6 --lr=0.005 > log_001.txt & 就是使用0号GPU 注意:对于Tensor...
os.environ["CUDA_VISIBLE_DEVICES"]="-1" Theano GPU 修改C:\Users\username\.keras\keras.jason { "backend":"theano", "image_dim_ordering":"th", "epsilon":1e-07, "floatx":"float32" } 修改C:\Users\username\.theanorc.txt [cuda] root=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA...
# os.environ["CUDA_VISIBLE_DEVICES"]="-1"# 这一行注释掉就是使用gpu,不注释就是使用cpu mnist=tf.keras.datasets.mnist(x_train,y_train),(x_test,y_test)=mnist.load_data()x_train,x_test=x_train/255.0,x_test/255.0model=tf.keras.models.Sequential([tf.keras.layers.Flatten(input_shape=(...
gpus = tf.config.experimental.list_physical_devices('GPU') tf.config.experimental.set_memory_growth(gpus[0], True) # tensorflow2.4 设置gpu--0 os.environ['CUDA_VISIBLE_DEVICES'] = '0' # 避免 Not creating XLA devices, tf_xla_enable_xla_devices not set ...
2019-11-29 11:21 −# 1: torch.cuda.set_device(1) # 2: device = torch.device("cuda:1") # 3:(官方推荐)import os os.environ["CUDA_VISIBLE_DEVICES"] = '1' (同时调用两块GPU的话) os.envir... you-wh 1 6516 Anaconda配置Pytorch ...
2019-11-29 11:21 −# 1: torch.cuda.set_device(1) # 2: device = torch.device("cuda:1") # 3:(官方推荐)import os os.environ["CUDA_VISIBLE_DEVICES"] = '1' (同时调用两块GPU的话) os.envi... you-wh 1 6515 linux安装keras+tensorflow-gpu步骤 ...
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\bin C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\libnvvp 到此为止可以检测有没有装好驱动和CUDA,任何一个没有装好,tensorflow和pytorch的cuda版本都会报错。可以通过 win+R-->cmd 中运行 ...
复制cudnn\lib\x64\cudnn.lib 到 C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\lib\x64\ 使用conda安装gpu版本 conda install tensorflow-gpu==1.11 1. 测试 import tensorflow as tf tf.test.is_gpu_available() 1. 2. 输出True则安装成功 ...