[PYTHON] Preparing the execution environment of PyTorch with Docker November 2019


--Prepare the execution environment of Deep Learning by PyTorch with Docker container --Docker version 19.03 or later, if you have the GPU driver of the host OS and nvidia-container-runtime, the others will be confined in the container ――In most cases, you can reproduce the environment with a single command.


Roughly the following preparations are required.

--docker version 19.03 or later -See Official Documentation (this link is for Ubuntu)

Create a Dockerfile

CUDA and CuDNN are stored in the container, and the GPU Driver on the host side is used. In this Dockerfile, Python 3.7.1 and the Python package are installed based on the image containing CUDA: 10.1 and CuDNN: 7 in ʻUbuntu: 18.04`.

FROM nvidia/cuda:10.1-cudnn7-devel-ubuntu18.04
ENV HOME /root
ENV TZ=Asia/Tokyo
ADD requirements.txt /code
RUN export DEBIAN_FRONTEND=noninteractive && \
    apt-get -y update && \
    apt-get -y upgrade && \
    apt-get -y install tzdata
RUN apt-get -y install git make build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev && \
     apt-get -y install wget curl llvm libncurses5-dev libncursesw5-dev xz-utils tk-dev libffi-dev liblzma-dev && \
    git clone https://github.com/pyenv/pyenv.git $PYENV_ROOT && \
    $PYENV_ROOT/plugins/python-build/install.sh && \
    /usr/local/bin/python-build -v $PYTHON_VERSION $PYTHON_ROOT && \
    rm -rf $PYENV_ROOT && \
    pip install -r requirements.txt

An example of the contents of requirements.txt



Use container environment

First, build the container. Execute the following command in the directory where the Dockerfile is created.

docker build . -t torch3.7

Enter the container and check if the GPU can be recognized. If the following display appears with the nvidia-smi command, it is ok.

$ docker run -it --gpus all torch3.7 bash
$ nvidia-smi
 Tue Nov 19 15:01:12 2019       
 | NVIDIA-SMI 430.50       Driver Version: 430.50       CUDA Version: 10.1     |
 | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
 | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
 |   0  GeForce GTX 166...  Off  | 00000000:1F:00.0 Off |                  N/A |
 | 45%   36C    P0     1W / 120W |      0MiB /  5944MiB |      0%      Default |
 | Processes:                                                       GPU Memory |
 |  GPU       PID   Type   Process name                             Usage      |
 |  No running processes found                                                 |

Finally, confirm that PyTorch's torch.Tensor can be loaded in the memory on the GPU. I was able to use torch.Tensor on the GPU.

$ python
>>> import torch
>>> torch.cuda.is_available()
>>> x = torch.Tensor([0, 1])
>>> x
tensor([0., 1.])
>>> x.cuda()
tensor([0., 1.], device='cuda:0')

By the way ...

It seems that volume is often used synchronously between the host and the container. In that case, change the following command as appropriate and use it.

docker run -it -v /path/to/your_project_directory:/code --gpus all torch3.7 bash

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