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Installing Octave on Docker for NVIDIA GPUs using Ubuntu 18.04 LTS:
If you’re working with large datasets or running complex simulations in Octave that require GPU acceleration, it can be challenging to set up your environment properly. Fortunately, there is a way to use Docker containers to simplify the process and ensure consistent results across different systems. Here are some steps to follow:
1. First, make sure you have Docker installed on your system by running `sudo apt-get update` followed by `sudo apt-get install docker.io`. This will download and install the latest version of Docker from the official repository.
2. Next, create a new directory for your Octave project and navigate into it using the `cd` command:
# Create a new directory for the Octave project
mkdir octave_project
# Navigate into the newly created directory
cd octave_project/
# Install Docker on your system
sudo apt-get update
sudo apt-get install docker.io
# Create a new directory for the Octave project and navigate into it
mkdir octave_project && cd octave_project/
3. Pull the official NVIDIA GPU-optimized Docker image from Docker Hub by running this command in your terminal window:
# Pull the official NVIDIA GPU-optimized Docker image from Docker Hub
# by running this command in your terminal window:
docker pull nvidia/cuda:10.2-base-ubuntu18.04
This will download and install the latest version of CUDA (version 10.2) on top of an Ubuntu 18.04 LTS base image. Note that you can choose a different version or operating system depending on your needs.
4. Once the download is complete, create a new file called `Dockerfile` inside your project directory and add the following lines to it:
# This script is used to create a Docker image with CUDA (version 10.2) on top of an Ubuntu 18.04 LTS base image.
# Note that you can choose a different version or operating system depending on your needs.
# Specify the base image to be used for the Docker image
FROM nvidia/cuda:10.2-base-ubuntu18.04
# Set environment variables for CUDA_VISIBLE_DEVICES and LD_LIBRARY_PATH
# This allows the container to access the CUDA libraries and devices
ENV CUDA_VISIBLE_DEVICES all
ENV LD_LIBRARY_PATH /usr/local/cuda-10.2/lib64:$LD_LIBRARY_PATH
# Copy your Octave scripts and data files into the container
# This ensures that the necessary files are available inside the container
COPY . /opt/octave
# Set working directory inside the container to be /opt/octave
# This sets the default directory for any commands to be executed inside the container
WORKDIR /opt/octave
# Run a command inside the container when it starts up (in this case, Octave)
# This specifies the default command to be executed when the container is launched
CMD ["bash"]
5. Save and close the `Dockerfile` file.
6. Build your Docker image by running:
# This code builds a Docker image named "octave_gpu"
# The -t flag is used to tag the image with the specified name
# The . at the end indicates that the build should use the current directory as the build context
docker build -t octave_gpu .
7. Once the build is complete, start a new container from this image using the following command:
# This script starts a new container from an image called "octave_gpu" and opens a bash terminal within the container.
# The "--gpus all" flag allows the container to access all available GPUs on the host machine.
# The "-it" flag enables interactive mode, allowing the user to interact with the container's terminal.
docker run --gpus all -it octave_gpu /bin/bash
8. Inside the container, you can now use Octave with GPU acceleration by running `octave` or any other Octave script that requires it.