Comment on page

Part 1 — Remote Developing with PyCharm [Docker SSH Server]

In this part, you will learn how to start developing using PyCharm and Docker.

Table of contents
1.​Create a working directory​
2.​Create SSH key​
3.​Docker Image​
4.​Connect to container over SSH​
5.​Connect to container in PyCharm​
Step 1 — Create a working directory
The first thing you need to do is to create a directory in which you can store docker and ssh related files.
As an example, we will create a directory named remote_dev inside our project and move into that directory with the command:
mkdir remote_dev && cd remote_dev
Step 2 — Create SSH key
On your client system – the one you’re using to connect to the server – you need to create a pair of key codes.
To generate a pair of SSH key codes, enter the command:
ssh-keygen -t rsa -b 4096 -f my_key
Files my_key and my_key.pub will be created in the working directory.
SSH Keys
Step 3 — Docker Image
Let's create all the files necessary for building the container.
1. Create Dockerfile
Let's create a simple image in which we will deploy the SSH server:
remote_dev/Dockerfile
ARG IMAGE
FROM $IMAGE
​
RUN apt-get update && apt-get install -y openssh-server
EXPOSE 22
​
RUN apt-get install -y sudo
RUN mkdir -p /run/sshd
​
ARG home=/root
RUN mkdir $home/.ssh
COPY my_key.pub $home/.ssh/authorized_keys
RUN chown root:root $home/.ssh/authorized_keys && \
    chmod 600 $home/.ssh/authorized_keys
​
COPY sshd_daemon.sh /sshd_daemon.sh
RUN chmod 755 /sshd_daemon.sh
CMD ["/sshd_daemon.sh"]
ENTRYPOINT ["sh", "-c", "/sshd_daemon.sh"]
Add a script to start the server:
remote_dev/sshd_daemon.sh
#!/bin/bash -l
​
echo $PATH
/usr/sbin/sshd -D
2. Create docker-compose
Since we need a GPU inside the container, we will take Image with pre-installed CUDA as a basis and set runtime to nvidia.
For convenience, let's create a docker-compose file:
remote_dev/docker-compose.yml
version: "2.2"
services:
  remote_dev_service:
    shm_size: '8gb'
    runtime: nvidia
    build:
      context: .
      args:
        IMAGE: nvidia/cuda:11.1.1-devel-ubuntu18.04
    ports:
      - "1234:22"
    volumes:
      - "./data:/data"
3. Build container
Don't forget to install docker and nvidia-docker2​
curl https://get.docker.com | sh \
  && sudo systemctl --now enable docker
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
&& curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \
&& curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt update
sudo apt install -y nvidia-docker2
sudo systemctl restart docker
The basic syntax used to build an image using a docker-compose is:
docker-compose up --build -d
Build docker image
Once the image is successfully built, you can verify whether it is on the list of containers with the command:
docker ps | grep remote_dev_service
Step 4 — Connect to container over SSH
Add server with the ports specified in docker-compose.yml
~/.ssh/config (example)
Host docker_remote_container
    HostName ip_of_your_docker_container
    User root
    Port 1234
    IdentityFile path_to_ssh_secret_key
To connect to container by SSH, use command:
ssh docker_remote_container
Step 5 — Connect to container in PyCharm
1. Create new project
PyCharm create new project
PyCharm create new project
2. Add new interpreter
Open Preferences -> Python Interpreter
Show all
Plus button
PyCharm create new interpreter
PyCharm create new interpreter
3. Configure interpreter
PyCharm add python interpreter
PyCharm add python interpreter
4. Run simple code
PyCharm run code

Chapter 4 Additionals

Custom Configuration

Last modified 1yr ago