Pytorch execution environment with Docker


Build a Jupyter Notebook environment that can use Pytorch.

I wanted an environment where Pytorch can be used easily, and I often forget the contents and options of Dockerfile and yml file, so I wrote this article as a reminder.

Create a Docker image

Use jupyter/datascience-notebook as the base image.

This time, in addition to this, install pytorch. Check the pip command here ( I selected it as shown in the figure below. image.png

Next, create a container image from Dockerfile. Dockerfile looks like this

FROM jupyter/datascience-notebook

USER root

RUN pip install torch torchvision

USER jovyan
WORKDIR /home/jovyan

--Image build (Docker build)

docker build -t Favorite image name.

--Container creation (Docker run)

docker run -p 80:8888 -v ./:/home/jovyan/work image name

--Specify the port with the p option. --Specify a free port on the host side --- p "Container side": "Host side" --v Optional mount the host-side directory. --- v "Host side": "Container side" --Please put the source code in the "host side" directory.

After running docker run, you should see the jupyter notebook at http: // localhost: 8888.

However, as it is, it is troublesome to specify various options every time Docker run, so make it possible to execute parameters at once with docker-compose.

Run with docker-compose

Create docker-compose.yml like this,

version: "3"
      context: .
    user: root
      - "80:8888"
      - "./:/home/jovyan/work"
      GRANT_SUDO: "yes"
    command: --NotebookApp.token=""

Execute docker-compose up --build to create and start the container.

--You can create a container using Dockerfile with build. --This time it's context: . so I'm using Dockerfile in the current directory --Specify the ports with ports. --"Container side": "Host side" --Specify the directory to mount with volumes --"Host side": "Container side" --Specify the command when starting the container with command --Use to start jupyter notebook --If you add the option --NotebookApp.token =" " `, you will not be able to confirm the Token when accessing.

Now you should be able to use the jupyter notebook by accessing http: // localhost: 8888.

After that, let's implement various models with pytorch.

that's all.

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