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Optimize templates

The following scenarios are paired with a template to demonstrate how developers can consider optimizations as they define their infrastructure.

Consider resource optimization​

Scenario: A data scientist is working with large datasets and wants to optimize resource usage to prevent memory overload or slow performance.

Template:

services: 
  image: jupyter/scipy-notebook
  imagePullPolicy: Always
  resources:
    limits:
      cpu: 500m
      memory: 1024Mi
    requests:
      cpu: 500m
      memory: 1024Mi
  env:
    DATA_CHUNK_SIZE: "1000"

This template ensures efficient resource allocation and encourages chunk-based processing for large datasets.

Consider user behavior patterns​

Scenario: A shared Jupyter notebook server hosts users with varying needs—some performing light tasks, others running intensive computations.

Template:

services:
  image: jupyter/datascience-notebook
  resources:
    idle:
      cpu: 200m
      memory: 512Mi
    peak:
      cpu: 1000m
      memory: 2048Mi
  env:
    MODE: "{{ USER_MODE }}"

This template provides flexibility by defining both idle and peak resource allocations, accommodating diverse user behaviors.

Consider performance monitoring​

Scenario: Administrators need to monitor resource usage (CPU, memory) of running Jupyter notebooks to identify bottlenecks.

Template:

services:
  image: jupyter/base-notebook
  resources:
    idle:
      cpu: 300m
      memory: 1024Mi
  env:
    MONITORING_ENABLED: "true"
  command: >
    start-notebook.sh --NotebookApp.ResourceUsage.enabled=True

This template integrates monitoring tools (such as jupyter-resource-usage) for real-time tracking of notebook performance.

Consider cost management​

Scenario: A Cloud-based environment charges based on Compute usage, so precise resource allocation is critical to control costs.

Template:

services:
  image: jupyter/minimal-notebook
  resources:
    idle:
      cpu: 100m
      memory: 256Mi
    peak:
      cpu: 800m
      memory: 2048Mi

This template minimizes idle costs while allowing sufficient resources during peak usage, balancing performance and cost efficiency.

What next?​