diff --git a/README.md b/README.md
index a5fce2e53d82e3cff75a4f61000c616c62cbec69..a5317e05f1e48cc3fb03b304fa684bb502b95431 100644
--- a/README.md
+++ b/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):
diff --git a/docs/_source/customise.rst b/docs/_source/customise.rst
index a30488b5e16dec4e5ff24aea7f35a0e286e32897..558ebd0ab530d815e37ecff802211fbe7932156f 100644
--- a/docs/_source/customise.rst
+++ b/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):
diff --git a/docs/_source/installation.rst b/docs/_source/installation.rst
index c87e64b217b4207185cfc662fdf00d2f7e891cc5..7da1f25c28c42d302c31d050d06340e0c0f95e11 100644
--- a/docs/_source/installation.rst
+++ b/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
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~