Merge branch 'lukas_issue375_tech_update-python-version-in-setup-venv' into 'develop'

Resolve "update python version in setup venv"

See merge request !411

HPC_setup/setup_venv_hdfml.sh

@@ -22,7 +22,7 @@ python3 -m venv ${cur}../venv_hdfml
 source ${cur}/../venv_hdfml/bin/activate
 
 # export path for side-packages 
-export PYTHONPATH=${cur}/../venv_hdfml/lib/python3.6/site-packages:${PYTHONPATH}
+export PYTHONPATH=${cur}/../venv_hdfml/lib/python3.8/site-packages:${PYTHONPATH}
 
 echo "##### START INSTALLING requirements_HDFML_additionals.txt #####"
 pip install -r ${cur}/requirements_HDFML_additionals.txt

HPC_setup/setup_venv_juwels.sh

@@ -22,7 +22,7 @@ python3 -m venv ${cur}/../venv_juwels
 source ${cur}/../venv_juwels/bin/activate
 
 # export path for side-packages 
-export PYTHONPATH=${cur}/../venv_juwels/lib/python3.6/site-packages:${PYTHONPATH}
+export PYTHONPATH=${cur}/../venv_juwels/lib/python3.8/site-packages:${PYTHONPATH}
 
 
 echo "##### START INSTALLING requirements_JUWELS_additionals.txt #####"

README.md

@@ -18,11 +18,12 @@ If the installation is still not working, we recommend skipping the geographical
 workaround [here](#workaround-to-skip-geographical-plot). For special instructions to install MLAir on the Juelich 
 HPC systems, see [here](#special-instructions-for-installation-on-jülich-hpc-systems).
 
-* Make sure to have the **python3.6** version installed.
+* Make sure to have the **python3.6** version installed (We are already using python3.8, but will refer to python3.6 
+  here as this was used for long time and is therefore tested well.)
 * (geo) A **c++ compiler** is required for the installation of the program **cartopy**
 * (geo) Install **proj** and **GEOS** on your machine using the console.
 * Install the **python3.6 develop** libraries.
-* Install all **requirements** from [`requirements.txt`](https://gitlab.version.fz-juelich.de/toar/mlair/-/blob/master/requirements.txt)
+* Install all **requirements** from [`requirements.txt`](https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair/-/blob/master/requirements.txt)
   preferably in a virtual environment. You can use `pip install -r requirements.txt` to install all requirements at 
   once. Note, we recently updated the version of Cartopy and there seems to be an ongoing 
   [issue](https://github.com/SciTools/cartopy/issues/1552) when installing **numpy** and **Cartopy** at the same time. 
@@ -31,15 +32,11 @@ HPC systems, see [here](#special-instructions-for-installation-on-jülich-hpc-sy
  `pip install numpy==<version_from_reqs>` followed be the default installation of requirements. For the latter, you can
   also use `grep numpy requirements.txt | xargs pip install`.
 * Installation of **MLAir**:
-    * Either clone MLAir from the [gitlab repository](https://gitlab.version.fz-juelich.de/toar/mlair.git) 
+    * Either clone MLAir from the [gitlab repository](https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair.git) 
       and use it without installation (beside the requirements) 
-    * or download the distribution file ([current version](https://gitlab.version.fz-juelich.de/toar/mlair/-/blob/master/dist/mlair-1.5.0-py3-none-any.whl)) 
+    * or download the distribution file ([current version](https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair/-/blob/master/dist/mlair-1.5.0-py3-none-any.whl)) 
       and install it via `pip install <dist_file>.whl`. In this case, you can simply import MLAir in any python script 
       inside your virtual environment using `import mlair`.
-* (tf) Currently, TensorFlow-1.13 is mentioned in the requirements. We already tested the TensorFlow-1.15 version and couldn't
-  find any compatibility errors. Please note, that tf-1.13 and 1.15 have two distinct branches each, the default branch 
-  for CPU support, and the "-gpu" branch for GPU support. If the GPU version is installed, MLAir will make use of the GPU
-  device.
 
 ## openSUSE Leap 15.1
 
@@ -316,8 +313,8 @@ class MyCustomisedModel(AbstractModelClass):
   `self._output_shape` and storing the model as `self.model`.
 
 ```python
-import keras
-from keras.layers import PReLU, Input, Conv2D, Flatten, Dropout, Dense
+import tensorflow.keras as keras
+from tensorflow.keras.layers import PReLU, Input, Conv2D, Flatten, Dropout, Dense
 
 class MyCustomisedModel(AbstractModelClass):
 
@@ -338,7 +335,7 @@ class MyCustomisedModel(AbstractModelClass):
 * Additionally, set your custom compile options including the loss definition.
 
 ```python
-from keras.losses import mean_squared_error as mse
+from tensorflow.keras.losses import mean_squared_error as mse
 
 class MyCustomisedModel(AbstractModelClass):
 

docs/_source/customise.rst

@@ -61,7 +61,7 @@ How to create a customised model?
 .. code-block:: python
 
     from mlair import AbstractModelClass
-    import keras
+    import tensorflow.keras as keras
 
     class MyCustomisedModel(AbstractModelClass):
 

docs/_source/installation.rst

@@ -15,7 +15,8 @@ HPC systems, see section :ref:`Installation on Jülich HPC systems`.
 Pre-requirements
 ~~~~~~~~~~~~~~~~
 
-* Make sure to have the **python3.6** version installed.
+* Make sure to have the **python3.6** version installed (We are already using python3.8, but will refer to python3.6
+  here as this was used for long time and is therefore tested well.)
 * (geo) A **c++ compiler** is required for the installation of the program **cartopy**
 * (geo) Install **proj** and **GEOS** on your machine using the console.
 * Install the **python3.6 develop** libraries.
@@ -23,16 +24,12 @@ Pre-requirements
 Installation of MLAir
 ~~~~~~~~~~~~~~~~~~~~~
 
-* Install all requirements from `requirements.txt <https://gitlab.version.fz-juelich.de/toar/machinelearningtools/-/blob/master/requirements.txt>`_
+* Install all requirements from `requirements.txt <https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair/-/blob/master/requirements.txt>`_
   preferably in a virtual environment
-* Either clone MLAir from the `gitlab repository <https://gitlab.version.fz-juelich.de/toar/machinelearningtools.git>`_
-* or download the distribution file (`current version <https://gitlab.version.fz-juelich.de/toar/mlair/-/blob/master/dist/mlair-1.5.0-py3-none-any.whl>`_)
+* Either clone MLAir from the `gitlab repository <https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair.git>`_
+* or download the distribution file (`current version <https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair/-/blob/master/dist/mlair-1.5.0-py3-none-any.whl>`_)
   and install it via :py:`pip install <dist_file>.whl`. In this case, you can simply
   import MLAir in any python script inside your virtual environment using :py:`import mlair`.
-* (tf) Currently, TensorFlow-1.13 is mentioned in the requirements. We already tested the TensorFlow-1.15 version and couldn't
-  find any compatibility errors. Please note, that tf-1.13 and 1.15 have two distinct branches each, the default branch
-  for CPU support, and the "-gpu" branch for GPU support. If the GPU version is installed, MLAir will make use of the GPU
-  device.
 
 Special Instructions for Installation
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

setup.py

@@ -16,11 +16,11 @@ setuptools.setup(
     description="A framework to enable easy time series predictions with machine learning.",
     long_description=long_description,
     long_description_content_type="text/markdown",
-    url="https://gitlab.version.fz-juelich.de/toar/machinelearningtools",
+    url="https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair",
     packages=setuptools.find_packages(),
     classifiers=[
         "Programming Language :: Python :: 3",
-        "License :: OSI Approved :: MIT License",  #  to be adjusted
+        "License :: OSI Approved :: MIT License",
         "Operating System :: OS Independent",
     ],
     python_requires='>=3.5',