Initial commit

Requirements.txt

+h5py==2.10.0
+absl-py==0.10.0
+argon2-cffi==20.1.0
+astor==0.8.1
+astunparse==1.6.3
+attrs==20.1.0
+backcall==0.2.0
+bleach==3.1.5
+cachetools==4.1.1
+certifi==2020.6.20
+cffi==1.14.2
+chardet==3.0.4
+colorama==0.4.3
+cycler==0.10.0
+decorator==4.4.2
+defusedxml==0.6.0
+entrypoints==0.3
+gast==0.3.3
+google-auth==1.20.1
+google-auth-oauthlib==0.4.1
+google-pasta==0.2.0
+grpcio==1.31.0
+h5py==2.10.0
+idna==2.10
+importlib-metadata==1.7.0
+ipykernel==5.3.4
+ipython==7.16.1
+ipython-genutils==0.2.0
+ipywidgets==7.5.1
+jedi==0.17.2
+Jinja2==2.11.2
+joblib==0.16.0
+jsonschema==3.2.0
+jupyter==1.0.0
+jupyter-client==6.1.6
+jupyter-console==6.1.0
+jupyter-core==4.6.3
+Keras==2.4.3
+Keras-Applications==1.0.8
+Keras-Preprocessing==1.1.2
+kiwisolver==1.2.0
+Markdown==3.2.2
+MarkupSafe==1.1.1
+matplotlib==3.3.1
+mistune==0.8.4
+nbconvert==5.6.1
+nbformat==5.0.7
+notebook==6.1.3
+numpy==1.18.5
+oauthlib==3.1.0
+opencv-python==4.4.0.42
+opt-einsum==3.3.0
+packaging==20.4
+pandas==1.1.1
+pandocfilters==1.4.2
+parso==0.7.1
+pickleshare==0.7.5
+Pillow==7.2.0
+prometheus-client==0.8.0
+prompt-toolkit==3.0.6
+protobuf==3.13.0
+pyasn1==0.4.8
+pyasn1-modules==0.2.8
+pycparser==2.20
+Pygments==2.6.1
+pyparsing==2.4.7
+pyrsistent==0.16.0
+python-dateutil==2.8.1
+pytz==2020.1
+pywin32==228
+pywinpty==0.5.7
+PyYAML==5.3.1
+pyzmq==19.0.2
+qtconsole==4.7.6
+QtPy==1.9.0
+requests==2.24.0
+requests-oauthlib==1.3.0
+rsa==4.6
+scikit-learn==0.21.3
+scipy==1.4.1
+Send2Trash==1.5.0
+six==1.15.0
+spectral==0.22.1
+tensorboard==2.3.0
+tensorboard-plugin-wit==1.7.0
+tensorflow==2.3.0
+tensorflow-estimator==2.3.0
+tensorflow-gpu==1.14.0
+termcolor==1.1.0
+terminado==0.8.3
+testpath==0.4.4
+threadpoolctl==2.1.0
+tornado==6.0.4
+traitlets==4.3.3
+urllib3==1.25.10
+wcwidth==0.2.5
+webencodings==0.5.1
+Werkzeug==1.0.1
+widgetsnbextension==3.5.1
+wrapt==1.12.1
+zipp==3.1.0
\ No newline at end of file

dev_file_GPU.txt

+metadata:
+  name: tress_jup
+attributes:
+  editorFree: 'true'
+components:
+  - endpoints:
+      - name: jupyter
+        port: 3100
+        attributes:
+          type: ide
+          discoverable: 'false'
+          path: /
+          protocol: http
+          public: 'true'
+    referenceContent: |
+      kind: List
+      items:
+        - apiVersion: v1
+          kind: Pod
+          metadata:
+            name: ws
+            labels:
+              name: ws
+          spec:
+           containers: 
+            - name: jupyter
+              image: 'kosted/maap-esa-jupyterlab:0.0.9'
+              imagePullPolicy: IfNotPresent
+              mountSources: true
+              resources:
+                limits:
+                  memory: 50000Mi
+              securityContext:
+                privileged: true
+           tolerations:
+            - key: "GPU"
+              operator: "Equal"
+              value: "true"
+              effect: "NoSchedule"
+           nodeSelector:
+             workerType: gpu
+    type: kubernetes
+    volumes:
+      - name: projects
+        containerPath: /projects
+    alias: maap-jupyterlab
+    env:
+      - value: WS_JUPYTER
+        name: MACHINE_NAME
+      - value: /projects
+        name: JUPYTER_NOTEBOOK_DIR
+      - value: 97262f0b-d3ca-4492-bcf8-9a0e12bdede8
+        name: CLIENT_ID
+      - value: VAL
+        name: MAAP_ENV_TYPE
+apiVersion: 1.0.0
\ No newline at end of file

readme.md

+
+# This repository recalls using Hyper3dnet for trees classification using the MAAP  and hyperspectral data
+
+## Description 
+
+At the MAAP stack, the GPU use is enabled.    
+This code conduct a supervised training on Hyperspectral data from PRISMA mission using the Hyper3Dnet model. 
+The model is implemented with Tensoflow package and it runs using GPU on the MAAP. 
+
+## The workspace 
+
+The Jupyter MAAP stack dev file sample in :  **dev_file_GPU.txt** allows using the GPU. 
+
+	This devfile contains a nodeSelector and a toleratino to be able to use the gpu node.
+
+
+## datasets and packages 
+
+First thing to do after creating GPU workspace is to dowload data that are stored in the S3. For this task you should : 
+
+*use the S3 script (in windows) : 
+
+    echo $MAAP_ENV_TYPE
+	export MAAP_ENV_TYPE=VAL
+	export CLIENT_ID=97262f0b-d3ca-4492-bcf8-9a0e12bdede8
+
+* Downloading request using maap-s3.py
+	Since data are stored in :  **maap-scientific-data/shared/imane**   
+	list first stored files in that repository by runing :
+	
+		maap-s3.py list /maap-scientific-data/shared/imane/
+	
+	Download the image file (.mat) : maap-s3.py download maap-scientific-data/shared/imane/*filename* *path_where_to_save_data* 
+	
+		ex : maap-s3.py download maap-scientific-data/shared/imane/france_region2_data.mat /projects/data/france_region2_data.mat
+
+	Download the ground truth file (.mat) : maap-s3.py download maap-scientific-data/shared/imane/*filename* *path_where_to_save_data*   
+	
+		ex : maap-s3.py download maap-scientific-data/shared/imane/france_region2_GT.mat /projects/data/france_region2_GT.mat
+
+ 
+
+*The second step would be installing the required *packages* for the code processing in your working environement weither it's the base env or a vir env
+	The required packages are listed in **requirements text file** 
+	Please run : 
+		
+		**pip install -r requirements.txt**
+	In your env run : ** conda install -c conda-forge psutil ** 
+
+## Execution 
+
+Followwing the jupyter notebook steps leads to Training the Hyper3Dnet Model with GPU use.
+A hint of the cells outputs are in the notebook : **Classiifcation_using_hyper3Dnet.ipynb**
+