hostexperience.blogg.se - Pycharm docker

#Pycharm docker how to#
#Pycharm docker install#
#Pycharm docker update#
#Pycharm docker windows 10#

To build the Docker image just navigate with your shell of choice to the path containing the Dockerfile and run: The last section is about the project setup, we are just installing the dependencies and copying all the files inside the image work directory and choosing the command to launch when docker run is called without a specified command. Swapping out Miniconda with a vanilla installation of Python in the Dockerfile is left to the reader as an exercise (do not panic, just use the same commands you would use on a new Ubuntu box). We are leveraging none of Miniconda capabilities so it is kind of overkill. I decided to use Miniconda instead of just using Python because it's my go-to platform for most of my projects. In the middle section of the Dockerfile there is a Miniconda3 installation.

#Pycharm docker install#

Now just need to install what's needed for Python development and setup our project. Nvidia/cuda:10.2-devel is a development image with the CUDA 10.2 toolkit already installed. This Dockerfile builds on top of the nvidia/cuda:10.2-devel image made available in DockerHub directly by NVIDIA. In layman's terms a Dockerfile describes a procedure to generate a Docker image that is than used to create Docker containers. # Project setup WORKDIR /code COPY requirements.txt. opt/conda/etc/profile.d/conda.sh" > ~/.bashrc & \ echo "conda activate base" > ~/.bashrc & \įind /opt/conda/ -follow -type f -name '*.a' -delete & \įind /opt/conda/ -follow -type f -name '*.js.map' -delete & \ Ln -s /opt/conda/etc/profile.d/conda.sh /etc/profile.d/conda.sh & \ echo ". bin/bash ~/miniconda.sh -b -p /opt/conda & \

#Pycharm docker update#

Here follows our Dockerfile and a brief explanationįROM nvidia/cuda:10.2-devel # Miniconda install copy-pasted from Miniconda's own Dockerfile reachable # at: ENV PATH /opt/conda/bin: $PATH RUN apt-get update -fix-missing & \Īpt-get install -y wget bzip2 ca-certificates libglib2.0-0 libxext6 libsm6 libxrender1 git mercurial subversion & \ Obviously your personal project will most likely be more complex, you may use a different method for dependency management and you might also use a docker-compose.yaml file but for the sake of getting my point through that is pointless complexity.įor an article more focused on Docker and Dockerfiles I recommend the Docker Beginner's Guide.

A single run.py file that contains some code to run.

A requirements.txt file that contains the dependencies of the project.

A Dockerfile to generate the container.

I'm just using 3 files in this toy project: The article also provides instructions to use their packages on Ubuntu.

#Pycharm docker how to#

In this article you can find how to setup CUDA and cuDNN very easily on their platform. If you are still setting up your Linux machine and you are not willing to research much about it I usually recommend Pop!_OS.

I will assume that you already installed CUDA on your machine.

I will assume that you already installed Docker on your machine.

Using Docker as a remote Python interpreter with P圜harm is possible only with the Professional edition.

#Pycharm docker windows 10#

At the time of writing I was unable to use CUDA inside of Docker in Windows 10 Home (even with the Insider build) so this tutorial has been implemented with Linux in mind even though there's basically nothing that is platform specific.

This tutorial is aimed to show you how to setup a basic Docker-based Python development environment with CUDA support in P圜harm or Visual Studio Code.

Setting up a development environment is not easy if you are inexperienced, especially if a lot of technologies you wish to learn are involved.