Container Service for Kubernetes:Create and use notebooks

Introduction to notebooks

• Provide a machine learning experiment environment that can be integrated into a cloud platform. This environment allows you to develop machine learning models on your on-premises machine.

• Provide a tool to copy and process datasets. This allows you to submit processed data to persistent storage. You can also process datasets by using big data tools.

• Provide an environment for machine learning processes, such as model testing and data preprocessing. In the environment, you can write and debug the code of machine learning jobs, package the code into Docker images, and push the images to your cluster or remote Container Registry repositories.

Step 1: Create a notebook

1. Log on to AI Developer Console. For more information, see Log on to AI Developer Console.

2. In the left-side navigation pane of AI Developer Console, click Notebook.

3. On the Notebook page, click Create notebook.

4. In the Notebook message, Notebook configure, and Advanced Configuration sections, set the parameters and click Create notebook.

   

   • In the Notebook message section:

     1. Set Notebook Name, Notebook Image, and Namespace. The selected namespace must be the one that the cluster administrator created for AI development. You can select the default notebook image or a custom notebook image. Configure other parameters based on your requirement.

        Note

        • When you specify Notebook Image, select the notebook image that meets your requirement. You need to select Jupyter notebook images for Jupyter notebooks and select VS Code notebook images for VS Code notebooks. Otherwise, your notebooks cannot function as normal.

        • Custom notebook images are available only in the current cluster. You cannot share custom notebook images to other clusters or users.

     2. Turn on Workspace PVC. Select the persistent volume claim (PVC) associated to the workspace from the Target PVC drop-down list.

     3. Turn on the Token switch to inject a custom token into the notebook to enhance its security. Only Jupyter notebooks support this feature.

     4. Specify Notebook Type to select an image launch method. Different types of notebooks require different environments. For example, if Notebook Type is set to Jupyter, a Jupyter notebook is created.

   • In the Notebook configure section, set CPU (Cores), Memory (GB), and GPU (Card Numbers).

   • In the Advance Configuration section, you can configure Label, Annotation, NodeSelector, and Toleration for your notebook.

5. After the notebook is created, go to the Notebook page and click the created notebook to view the detailed information.

Step 2: Use a Jupyter notebook

Create a custom notebook image

Before you create a custom notebook image, you need to install the latest Arena component version in your Jupyter notebook or VS Code notebook. This allows you to run our demos in the notebook. For more information about how to install the Arena component, see Configure the Arena client.

FROM tensorflow/tensorflow:1.15.5-gpu
USER root
RUN pip install jupyter && \
    pip install ipywidgets && \
    jupyter nbextension enable --py widgetsnbextension && \
    pip install jupyterlab && jupyter serverextension enable --py jupyterlab
# Download and install Arena SDK for Python. 
RUN pip install arenasdk
# If you use other methods to install JupyterLab, you need to expose the Service through port 8888. 
EXPOSE 8888
USER jovyan

FROM ubuntu:18.04
USER root

RUN apt update && \
  	apt install -y curl

RUN cd /opt && \
  	wget https://aliacs-k8s-cn-hongkong.oss-cn-hongkong.aliyuncs.com/arena/install.sh && \
    /opt/install.sh

Save a notebook as an image

In the AI Developer Console of the cloud-native AI suite, the ack-ai-dev-console component in version 1.0.22 or later allows you to submit a notebook as an image and save it to the host. This allows you to save the development environment of the notebook.

To save a notebook as an image, perform the following steps:

1. Download the client binary tool for the ack-commit-agent component in Notebook. Click here to download the tool.

2. Run the following command in Notebook to make the client globally executable:

   chmod +x ack-commit-ctl
   mv ack-commit-ctl /usr/bin/

3. Use this tool to submit and save a notebook as an image. The following example shows how to submit an image.

   ack-commit-ctl commit image-commit-sample:v0.1.0-test
   INFO[0000] container id: xxx
   INFO[0002] Container save success, image: image-commit-sample:v0.1.0-test

   If the preceding output appears after the notebook is submitted, the notebook is saved as an image.

4. Run the following command to specify the username and password of the image repository and push the image to the image repository:

   ack-commit-ctl push image-commit-sample:v0.1.0-test --username=username --password=password
   INFO[0000] Start pushing the image:  image-commit-sample:v0.1.0-test 
   INFO[0000] Waiting...                                   
   INFO[0003] Image pushed successfully: image-commit-sample:v0.1.0-test

   If the preceding output appears, the image is pushed to the image repository.