Jupyter Environments

Jupyter Environments define the available libraries (via the Docker Image), the mounted contents as the defaulit CPU/GPU and memory resources.

cat <<EOF | kubectl apply -f -
apiVersion: datalayer.io/v1
kind: JupyterEnvironment
metadata:
  name: python-simple-env
  namespace: datalayer-jupyter
  labels:
    project.datalayer.io/name: poc
spec:
  title: Python Simple Environment
  description: A Python environment for simple coding.
  language: python
  owner: datalayer
  visibility: public
  example: https://raw.githubusercontent.com/datalayer/examples/e8390ce9eb4aee36721eb6e7cdc31ef8455ff359/python-simple/python-simple-example.ipynb
  dockerImage: ${DATALAYER_DOCKER_REGISTRY}/datalayer/jupyter-python:0.0.8
  kernel:
    givenNameTemplate: A Python kernel for simple coding.
  resourcesRanges:
    default:
      requests:
        cpu: "250m"
        memory: "64Mi"
      limits:
        cpu: "500m"
        memory: "512Mi"
EOF

kubectl get jupyter-environments -A
kubectl get jupyter-environments -o yaml -A

cat <<EOF | kubectl apply -f -
apiVersion: datalayer.io/v1
kind: JupyterEnvironment
metadata:
  name: xarray-env
  namespace: datalayer-jupyter
  labels:
    project.datalayer.io/name: dev
spec:
  title: XArray Environment
  description: |
    A environment for analysis with Xarray. **[xarray](https://github.com/pydata/xarray)** (pronounced "ex-array", formerly known as **xray**) is an open source project and Python package that makes working with labelled multi-dimensional arrays simple, efficient, and fun!

    **Typical use cases**

    EO (Earth observation) analysis.

    **Mounted contents**

    Analysis-ready, cloud-optimized (ARCO) from Sentinel-2 mission:Lland monitoring constellation of two satellites that provide high resolution optical imagery and provide continuity for the current SPOT and Landsat missions. The mission provides a global coverage of the Earth's land surface every 5 days, making the data of great use in on-going studies. L1C data are available from June 2015 globally. L2A data are available from November 2016 over Europe region and globally since January 2017.

    ![](https://www.esa.int/var/esa/storage/images/esa_multimedia/images/2022/09/copernicus_sentinel_family/24451531-1-eng-GB/Copernicus_Sentinel_family_pillars.jpg)

    **Hardware recommandation for large scale analysis**

    - [x] GPU (CUDA > 11)
    - [x] GPU Memory: Minimum 8BG

  language: python
  owner: datalayer
  visibility: public
  example: https://raw.githubusercontent.com/datalayer/examples/e8390ce9eb4aee36721eb6e7cdc31ef8455ff359/python-simple/python-simple-example.ipynb
  dockerImage: 9ol9b008.c1.bhs5.container-registry.ovh.net/datalayer/jupyter-python:0.0.8
  kernel:
    givenNameTemplate: A kernel for analysis with XArray.
  resourcesRanges:
    default:
      requests:
        cpu: "250m"
        memory: "64Mi"
      limits:
        cpu: "500m"
        memory: "512Mi"
EOF