小编Jac*_*Liu的帖子

我开始学习如何使用TensorFlow进行机器学习.并且发现docker非常方便将TensorFlow部署到我的机器上.但是,我找到的示例不适用于我的目标设置.这是

在ubuntu16.04操作系统下,使用nvidia-docker一起托管jupyter和tensorboard服务(可以是两个容器或一个容器有两个服务).从jupyter创建的文件应该对主机操作系统可见.

• Ubuntu 16.04
• Dokcer
• NVIDIA-泊坞窗
  • Jupyter
  • Tensorboard

Jupyter容器

nvidia-docker run \
    --name jupyter \
    -d \
    -v $(pwd)/notebooks:/root/notebooks \
    -v $(pwd)/logs:/root/logs \
    -e "PASSWORD=*****" \
    -p 8888:8888 \
    tensorflow/tensorflow:latest-gpu 

Tensorboard容器

nvidia-docker run \
    --name tensorboard \
    -d \
    -v $(pwd)/logs:/root/logs \
    -p 6006:6006 \
    tensorflow/tensorflow:latest-gpu \
    tensorboard --logdir /root/logs

我试图将logs文件夹挂载到两个容器,让Tensorboard访问jupyter的结果.但是山似乎确实有效.当我在带有notebooks文件夹的jupyter容器中创建新文件时,主机文件夹$(pwd)/ notebooks什么都没有出现.

我还按照Nvidia Docker,Jupyter Notebook和Tensorflow GPU中的说明进行操作

nvidia-docker run -d -e PASSWORD='winrar' -p 8888:8888 -p 6006:6006 gcr.io/tensorflow/tensorflow:latest-gpu-py3

只有Jupyter工作,张量板无法从6006端口到达.

我正在尝试从文件中对FlowNet2-C模型加载进行推断.但是,我遇到了一些数据类型问题.我该如何解决？

• 源代码
• FlowNet2-C预训练模型

$ python main.py

Initializing Datasets
  [0.000s] Loading checkpoint '/notebooks/data/model/FlowNet2-C_checkpoint.pth.tar'
  [1.293s] Loaded checkpoint '/notebooks/data/model/FlowNet2-C_checkpoint.pth.tar' (at epoch 0)
(1L, 6L, 384L, 512L)
<class 'torch.autograd.variable.Variable'>
  [1.642s] Operation failed


Traceback (most recent call last):
  File "main.py", line 102, in <module>
    main()
  File "main.py", line 98, in main
    summary(input_size, model)
  File "main.py", line 61, in summary
    model(x)
  File "/usr/local/lib/python2.7/dist-packages/torch/nn/modules/module.py", line 357, in __call__
    result = self.forward(*input, **kwargs)
  File "/notebooks/data/vinet/FlowNetC.py", line 75, in forward
    out_conv1a = self.conv1(x1)
  File "/usr/local/lib/python2.7/dist-packages/torch/nn/modules/module.py", line 357, in __call__ …