How to make Custom Image

To use a Custom Image when creating Notebooks on the Deka GPU Portal Service, there are five stages, namely:

Create a Docker File

Docker is a text file that contains a series of instructions for Docker to build an image. Open File Explorer on your computer, press the New button, select Text Document.

Add Text Document

Enter the following syntax into the newly created Text Document file.

# Use an argument to allow the base image to be customizable 
FROM {BASE_IMAGE} 
# Switch to root to perform installations 
USER root  
# Define user and group ID, allowing customization 
ARG UID=10001 
ARG GID=10001 
RUN groupadd -g "${GID}" jovyan \ 
  && useradd jovyan --create-home --no-log-init -g jovyan -u "$UID" 
# Update system packages and install commonly used packages 
RUN apt-get update && apt-get install -y \ 
	python3-pip \ 
	libcurl4-openssl-dev \ 
	curl \ 
	zlib1g-dev \ 
	git \ 
	pkg-config \ 
	python3 \ 
	python3-dev \ 
	python3-wheel \ 
	g++ \ 
	libglib2.0-dev \ 
	libglib2.0-dev-bin \ 
	python-gi-dev \ 
	libtool\ 
	m4 \ 
	autoconf \ 
	automake \ 
	sudo \ 
	ssh \ 
	pbzip2 \ 
	pv \ 
	bzip2\ 
	unzip \ 
	build-essential \ 
	# Additional packages commonly used 
	python3-opencv \ 
	python3-matplotlib \ 
	python3-numpy \ 
	python3-scipy \ 
	python3-pandas \ 
	python3-sklearn \ 
	&& rm -rf /var/lib/apt/lists/* 
  
# Install JupyterLab 
RUN pip3 install jupyterlab 
  
# Set the working directory and copy the application code 
COPY . /app 
WORKDIR /app 
  
# Set environment variables for JupyterLab 
ENV NB_PREFIX / 
  
# Command to run JupyterLab with appropriate options 
CMD ["sh", "-c", "jupyter lab --notebook-dir=/home/jovyan --ip=0.0.0.0 --no-browser --allow-root --port=8888 --NotebookApp.token='' --NotebookApp.password='' --NotebookApp.allow_origin='*' --NotebookApp.base_url=${NB_PREFIX}"] 

If you have copied the syntax, the next step is to save the text document file with the name Dockerfile.

Dockerfile.txt

Access the NGC Catalog Container

NGC Catalog Container is a Docker container image that has been optimized and configured by NVIDIA to run various AI/ML and HPC applications and frameworks efficiently on NVIDIA GPUs. This container includes AI/ML Framework, Data Science Library, HPC Application. Open the NGC Catalog Container by pressing this link.

Open NGC Catalog

Select the Tags tab and then select the image tag to be copied. On this guide copy the tag 24.08-cuda11.8-py3.10 and press the copy icon.

Copy Tag

Paste the copied image tag into the {BASE_IMAGE} section of the Dockerfile.txt file

Paste Tag in Dockerfile.txt

Save the changes you have made and delete the .txt extension in the Dockerfile.txt file

Delete extention Dockerfile

Building a docker image

On your computer open Command Prompt or Powershell and run the syntax to build a docker image. If you already understand the syntax changes according to your needs, then run the syntax below with the adjustments above.

docker build -f Dockerfile -t username/repository_name:tag .

Just so you know, you need to adjust the syntax to make it work. The following is an explanation of the syntax adjustments that can be made.

• username is the name of the user or organization (repository namespace).

• repository is the name of the repository of the Docker image.

• tags are used to mark the image version. latest is usually used as a convention to indicate the latest version of an image.

Build Docker Image

Upload images to Docker Hub

To be able to proceed to the next stage, you need to make sure docker run is running to ensure there are no failures when running MLOps via Docker Desktop. If you already understand the syntax changes according to your needs, then run the syntax below with the adjustments above.

docker run -d --name rapid username/repository_name:tag

Just so you know, you need to adjust the syntax to make it work. The following is an explanation of the syntax adjustments that can be made.

• username is the name of the user or organization (repository namespace).

• repository is the name of the repository of the Docker image.

• tags are used to mark the image version. latest is usually used as a convention to indicate the latest version of an image.

Run Syntax

In the figure above, by running the syntax above, it can be concluded that the rapid container uses the Container ID, namely b74e775c37ca, and the rapid container is run from the azerap/rapids image with the latest tag currently running and the time that has passed since this container was run is about 1 hour ago. The log notes that ServerApp from Jupyter was successfully started on 2024-09-19 at 17:52:35. Jupyter Server runs on the URLs http://127.0.0.1:8888/lab and http://b74e775c37ca:8888/lab. This is the URL where you can access the JupyterLab interface running inside the container. 127.0.0.1 is the local IP address (localhost), and b74e775c37ca is the hostname of this container.

Back on your computer run Command Prompt or Powershell and upload the Docker image from the local machine to Docker Hub so that it can be accessed by other users or machines to pull and run as a container. Worth noting You need to adjust the syntax to make it work. If you already understand the syntax changes according to your needs, then run the syntax below with the adjustments above.

docker push username/repository_name:tag

Just so you know, you need to adjust the syntax to make it work. The following is an explanation of the syntax adjustments that can be made.

• username is the name of the user or organization (repository namespace).

• repository is the name of the repository of the Docker image.

• tags are used to mark the image version. latest is usually used as a convention to indicate the latest version of an image.

Push Image

The figure above shows the process of uploading the azerap/rapids:latest image to Docker Hub from your local computer. This process consists of uploading layers one by one, checking which layers are already in the registry, and displaying the size of the layer being uploaded. Once all layers are complete, the azerap/rapids:latest image is now available in Docker Hub and can be pulled from the registry by other machines.

Pull Image for MLOps

Pull image is the process of downloading a Docker image from Docker Hub to a local machine for use in MLOps on the Cloudeka Portal Service. In the previous stage, you have successfully uploaded the image to Docker Hub. If the image is available in Docker Hub, it will appear as shown in the image below.

Docker Hub

If the image has appeared and is public, then we can use it when creating new notebooks in the MLOps menu on the Deka GPU Portal Service. Enter the name of the repository that was successfully uploaded into the Custom Image column.

Use Custom Image

If the image from Docker Hub has been used on notebooks, it can be seen as in the image below.

Notebooks use Custom Image