Use notebooks

Supported notebook images

Notebook instances in KakaoCloud Kubeflow provide a web-based machine learning development environment.
These notebook instances run based on Docker images, and the following images are currently supported in KakaoCloud Kubeflow.

info

The image registry endpoint is bigdata-150.kr-central-2.kcr.dev/kc-kubeflow/(image-name).
For example, to pull the jupyter-scipy:v1.8.0.py38.1a image, use bigdata-150.kr-central-2.kcr.dev/kc-kubeflow/jupyter-scipy:v1.8.0.py38.1a.

Supported notebook images

Kubeflow 1.8 version

Image	Python version	Platform	Framework (version)	GPU support	HDE support
kc-kubeflow/jupyter-scipy:v1.8.0.py38.1a	python 3.8.10	JupyterLab	scipy(1.10.1)	X	X
kc-kubeflow/jupyter-scipy:v1.8.0.py311.1a	python 3.11.9	JupyterLab	scipy(1.11.3)	X	X
kc-kubeflow/jupyter-pytorch-full:v1.8.0.py38.1a	python 3.8.10	JupyterLab	pytorch(2.3.0)	X	X
kc-kubeflow/jupyter-pytorch-full:v1.8.0.py311.1a	python 3.11.9	JupyterLab	pytorch(2.3.0)	X	X
kc-kubeflow/jupyter-pytorch-cuda-full:v1.8.0.py38.1a	python 3.8.10	JupyterLab	pytorch(2.3.0)	O	X
kc-kubeflow/jupyter-pytorch-cuda-full:v1.8.0.py311.1a	python 3.11.9	JupyterLab	pytorch(2.3.0)	O	X
kc-kubeflow/jupyter-tensorflow-full:v1.8.0.py38.1a	python 3.8.10	JupyterLab	tensorflow(2.13.1)	X	X
kc-kubeflow/jupyter-tensorflow-full:v1.8.0.py311.1a	python 3.11.9	JupyterLab	tensorflow(2.15.1)	X	X
kc-kubeflow/jupyter-tensorflow-cuda-full:v1.8.0.py38.1a	python 3.8.10	JupyterLab	tensorflow(2.13.1)	O	X
kc-kubeflow/jupyter-tensorflow-cuda-full:v1.8.0.py311.1a	python 3.11.9	JupyterLab	tensorflow(2.15.1)	O	X
kc-kubeflow/jupyter-pyspark-pytorch:v1.8.0.py38.1a	python 3.8.10	JupyterLab	pytorch(2.3.0)	X	O
kc-kubeflow/jupyter-pyspark-pytorch:v1.8.0.py311.1a	python 3.11.9	JupyterLab	pytorch(2.3.0)	X	O
kc-kubeflow/jupyter-pyspark-pytorch-cuda:v1.8.0.py38.1a	python 3.8.10	JupyterLab	pytorch(2.3.0)	O	O
kc-kubeflow/jupyter-pyspark-pytorch-cuda:v1.8.0.py311.1a	python 3.11.9	JupyterLab	pytorch(2.3.0)	O	O
kc-kubeflow/jupyter-pyspark-tensorflow:v1.8.0.py38.1a	python 3.8.10	JupyterLab	tensorflow(2.13.1)	X	O
kc-kubeflow/jupyter-pyspark-tensorflow:v1.8.0.py311.1a	python 3.11.9	JupyterLab	tensorflow(2.15.1)	X	O
kc-kubeflow/jupyter-pyspark-tensorflow-cuda:v1.8.0.py38.1a	python 3.8.10	JupyterLab	tensorflow(2.13.1)	O	O
kc-kubeflow/jupyter-pyspark-tensorflow-cuda:v1.8.0.py311.1a	python 3.11.9	JupyterLab	tensorflow(2.15.1)	O	O
kc-kubeflow/codeserver-python:v1.8.0.py38.1a	python 3.8.10	CodeServer		X	X
kc-kubeflow/codeserver-python:v1.8.0.py311.1a	python 3.11.9	CodeServer		X	X

Kubeflow 1.6 version

Image	Python version	Platform	Framework (version)	GPU support	HDE support
kc-kubeflow/jupyter-pytorch-cuda-full:v1.0.3.py36	python 3.6.9	JupyterLab	pytorch(1.8.1)	O	X
kc-kubeflow/jupyter-pytorch-cuda-full:v1.0.3.py38	python 3.8.10	JupyterLab	pytorch(1.13.1)	O	X
kc-kubeflow/jupyter-tensorflow-full:v1.0.3.py36	python 3.6.9	JupyterLab	tensorflow(2.5.2)	X	X
kc-kubeflow/jupyter-tensorflow-full:v1.0.3.py38	python 3.8.10	JupyterLab	tensorflow(2.11.0)	X	X
kc-kubeflow/jupyter-tensorflow-cuda-full:v1.0.3.py36	python 3.6.9	JupyterLab	tensorflow(2.5.2)	O	X
kc-kubeflow/jupyter-tensorflow-cuda-full:v1.0.3.py38	python 3.8.10	JupyterLab	tensorflow(2.11.0)	O	X
kc-kubeflow/jupyter-pyspark-pytorch:v1.0.3.py36	python 3.6.9	JupyterLab	pytorch(1.8.1)	X	O
kc-kubeflow/jupyter-pyspark-pytorch:v1.0.3.py38	python 3.8.10	JupyterLab	pytorch(1.13.1)	X	O
kc-kubeflow/jupyter-pyspark-pytorch-cuda:v1.0.3.py36	python 3.6.9	JupyterLab	pytorch(1.8.1)	O	O
kc-kubeflow/jupyter-pyspark-pytorch-cuda:v1.0.3.py38	python 3.8.10	JupyterLab	pytorch(1.13.1)	O	O
kc-kubeflow/jupyter-pyspark-tensorflow:v1.0.3.py36	python 3.6.9	JupyterLab	tensorflow(2.5.2)	X	O
kc-kubeflow/jupyter-pyspark-tensorflow:v1.0.3.py38	python 3.8.10	JupyterLab	tensorflow(2.11.0)	X	O
kc-kubeflow/jupyter-pyspark-tensorflow-cuda:v1.0.3.py36	python 3.6.9	JupyterLab	tensorflow(2.5.2)	O	X
kc-kubeflow/jupyter-pyspark-tensorflow-cuda:v1.0.3.py38	python 3.8.10	JupyterLab	tensorflow(2.11.0)	O	X
kc-kubeflow/codeserver-python:v1.0.3.py36	python 3.6.9	CodeServer	pytorch(1.9.1)	O	O
kc-kubeflow/codeserver-python:v1.0.3.py38	python 3.8.10	CodeServer	pytorch(2.0.1)	O	O

Create notebook instance

To build a computing environment for running machine learning code and processing data, create a notebook instance with your desired specifications.

1. Access the Kubeflow dashboard.

   • For detailed instructions, refer to the Access Kubeflow dashboard guide.

2. Click the Notebooks tab on the left panel.

3. Click the [New Notebook] button at the top right.

   
   Accessing the Notebooks tab in Kubeflow dashboard

4. On the New Notebook screen, fill in the required information and click the [LAUNCH] button to create the notebook instance.

   • If you want to create a notebook using a specific image, refer to the guide below.

   Item	Field	Description
   Name	Name	Identifier used in the Kubeflow dashboard
   Platform type		Web development platform type Options: JupyterLab, VisualStudio Code, RStudio *VisualStudio Code and RStudio may not work properly without a connected domain.
   Custom notebook	Image	Select a supported KakaoCloud Kubeflow notebook image
   	Custom image	Enter a custom image by checking the box - Example: bigdata-150.kr-central-2.kcr.dev/kc-kubeflow/jupyter-pytorch-full:v1.0.1.py38
   	Image pull policy	Set the policy for pulling the image when starting the notebook
   CPU / RAM	Minimum CPU	Minimum CPU to allocate to the instance - Labeled as Requested CPUs in version 1.6
   	Minimum memory Gi	Minimum memory to allocate - Labeled as Requested memory in Gi in version 1.6
   	Maximum CPU	Maximum CPU to allocate - Labeled as CPU limit in version 1.6
   	Maximum memory Gi	Maximum memory to allocate - Labeled as Memory limit in Gi in version 1.6
   GPUs	Number of GPUs	Number of GPU resources to use
   	GPU vendor	Available GPU instance types
   Workspace volume		Volume to mount to the notebook’s home directory - Can create new or use an existing volume
   Data volume		Additional volumes to mount - Define mount paths and choose/create volumes
   Configurations		Register PodDefault resources in the current namespace
   Affinity / Tolerations	Affinity config	Specify the node on which to run the notebook
   	Tolerations group	Set tolerations - Toleration list is defined in the ConfigMap within KE
   Miscellaneous settings	Enable shared memory	Enable shared memory support (e.g., for Torch) - Mounts emptyDir to /dev/shm

caution

Notebook instances will not be created if Minimum CPU or Memory Gi exceeds the available resources of the selected node pool in Affinity Config.

Create CPU-based notebook

To create a notebook using a CPU-based image:

1. Access the Kubeflow dashboard and click the Notebooks tab, then click the [New Notebook] button.

2. On the New Notebook screen, enter the required info and click [LAUNCH].

   Item	Field	Description
   Name	Name	Identifier used in the Kubeflow dashboard
   Platform type		JupyterLab, VisualStudio Code, or RStudio
   Custom notebook	Image	Select an image matching the Kubeflow version
   	Custom image	Enter custom image if needed
   	Image pull policy	Set image pull policy
   CPU / RAM	Minimum CPU	2
   	Minimum memory Gi	8
   	Maximum CPU	-
   	Maximum memory Gi	-
   GPUs	Number of GPUs	None
   	GPU vendor	-
   Workspace volume		Mount volume to home directory
   Data volume		Register and configure additional mount volumes
   Configurations		Register PodDefault resource
   Affinity / Tolerations	Affinity config	Select CPU-type node
   	Tolerations group	None
   Miscellaneous settings	Enable shared memory	Mount emptyDir to /dev/shm

Create GPU-based notebook

To create a notebook using a GPU-based image:

1. Access the Kubeflow dashboard and click the Notebooks tab, then click the [New Notebook] button.

2. On the New Notebook screen, enter the required info and click [LAUNCH].

   Item	Field	Description
   Name	Name	Identifier used in the Kubeflow dashboard
   Platform type		JupyterLab, VisualStudio Code, or RStudio
   Custom notebook	Image	Select an image matching the Kubeflow version
   	Custom image	Enter custom image if needed
   	Image pull policy	Set image pull policy
   CPU / RAM	Minimum CPU	2
   	Minimum memory Gi	8
   	Maximum CPU	-
   	Maximum memory Gi	-
   GPUs	Number of GPUs	1
   	GPU vendor	NVIDIA MIG - 1g.10gb
   Workspace volume		Mount volume to home directory
   Data volume		Register and configure additional mount volumes
   Configurations		Register PodDefault resource
   Affinity / Tolerations	Affinity config	Select GPU-type node
   	Tolerations group	None
   Miscellaneous settings	Enable shared memory	Mount emptyDir to /dev/shm

Access notebook instance

1. Access the Kubeflow dashboard.

   • For details, see Access Kubeflow dashboard

2. Click the Notebooks tab.

3. In the Notebooks list, click [CONNECT] next to the desired instance.

   
   Connect to notebook

4. A new tab opens with the Jupyter Notebook interface.

   • You can write code and run ML models there.

   
   Jupyter Notebook interface

Stop notebook instance

Notebook instances can be stopped to save resources, improve security, reduce cost, and manage environments.

1. Access the Kubeflow dashboard.

   • For details, see Access Kubeflow dashboard

2. Click the Notebooks tab.

   
   Stop notebook

3. Click the ◼ (Stop) button for the notebook.

   • In the confirmation modal, click [STOP].

   
   Stop notebook confirmation

4. Check that the status of the notebook shows as stopped.

   
   Notebook stopped

Delete notebook instance

Notebook instances are deleted to reclaim resources, improve security, reduce cost, and maintain clean environments.

1. Access the Kubeflow dashboard.

   • For details, see Access Kubeflow dashboard

2. Click the Notebooks tab.

   
   Delete notebook

3. Click the trash icon for the notebook you want to delete.

   • In the popup, click [DELETE].

   
   Delete notebook confirmation

4. Verify that the notebook is no longer listed.

   
   Notebook deleted

caution

• Deleted notebook instance names cannot be reused.
• If reuse is needed, delete the {notebook-name}_volume PVC in the KakaoCloud Kubernetes Engine cluster before recreating.

info

• For detailed documentation, refer to the Kubeflow > Notebook official documentation.
• For more usage examples, visit the Machine Learning & AI tutorial category.