Build a custom image

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Requirements

To use custom images to run a workspace, your custom images have to satisfy below requirements.

• Jupyterlab
  • VESSL runs Jupyterlab and expose port 8888. Jupyterlab should be pre-installed in the container image.
  • Jupyterlab daemon must be located in /usr/local/bin/jupyter.
• sshd
  • VESSL runs sshd and expose port 22 as NodePort. sshd package should be pre-installed in the container image.
• PVC mountable at /root
  • VESSL mounts a PVC at /root to keep state across Pod restarts.
• curl
  • VESSL verifies workspace status. curl package should be pre-installed in the container image.

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Building from VESSL’s pre-built images

VESSL offers pre-built images to run workspaces directly. You can use these images to build your own images. These images already have pre-installed Jupyterlab and sshd. The list of images is in the following table.

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Example

# Use CUDA 11.8.0, PyTorch 1.14.0a0 base image
FROM quay.io/vessl-ai/ngc-pytorch-kernel:22.12-py3-202301160809

# Install custom Python dependencies
RUN pip install transformers
...

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Building from community maintained images

You can make your own images from any community maintained Docker images. Make sure that your image meet our requirements.

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Example

FROM nvidia/cuda:11.2.2-devel-ubuntu20.04

RUN apt-get update
RUN DEBIAN_FRONTEND=noninteractive \
    apt-get install -y \
    software-properties-common curl openssh-server

# Install Python 3.9
# Note that base image has python3.8 (3.8.10) installed
RUN add-apt-repository ppa:deadsnakes/ppa
RUN apt-get install -y python3.9 python3.9-distutils
# Add symbolic links
RUN update-alternatives --install /usr/bin/python3 python3 $(which python3.9) 1
RUN update-alternatives --install /usr/bin/python python /usr/bin/python3 1

# Install pip
RUN curl https://bootstrap.pypa.io/get-pip.py | python
RUN pip install -U pip

# Install Jupyterlab
RUN pip install jupyterlab

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FAQ

• If you use conda for installing Jupyterlab, generally Jupyterlab daemon is located in /opt/conda/bin/jupyter. In this case, you should make a symbolic link in /usr/local/bin/jupyter.

  # In Dockerfile,
  RUN ln -s /opt/conda/bin/jupyter /usr/local/bin/jupyter