neuralhydrology · kratzert · Feb 13, 2022 · Feb 12, 2022 · Feb 12, 2022 · Feb 12, 2022
@@ -0,0 +1,34 @@
+name: "build and publish to PyPI"
+on: 
+  create
+
+jobs:
+  build-and-publish:
+    if: ${{ startsWith(github.ref, 'refs/tags') }}
+    name: Build and publish Python distributions to PyPI
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@master
+    - name: Set up Python 3.8
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.8'
+    - name: Install pypa/build
+      run: >-
+        python -m
+        pip install
+        build
+        --user
+    - name: Build a binary wheel and a source tarball
+      run: >-
+        python -m
+        build
+        --sdist
+        --wheel
+        --outdir dist/
+    - name: Publish distribution to PyPI
+      if: github.repository == 'neuralhydrology/neuralhydrology'
+      uses: pypa/gh-action-pypi-publish@master
+      with:
+        user: __token__
+        password: ${{ secrets.PYPI_API_TOKEN }}
@@ -19,5 +19,5 @@ this code in our day-to-day research and will continue to integrate our new rese
 
 # Contact
 
-If you have any questions regarding the usage of this repository, feature requests or comments, please open an issue.
-You can also reach out to Frederik Kratzert (kratzert(at)google.com) by email.
+For questions or comments regarding the usage of this repository, please use the [discussion section](https://github.com/neuralhydrology/neuralhydrology/discussions) on Github. For bug reports and feature requests, please open an [issue](https://github.com/neuralhydrology/neuralhydrology/issues) on GitHub.
+In special cases, you can also reach out to us by email: neuralhydrology(at)googlegroups.com
@@ -290,6 +290,10 @@ Training settings
    ``NSE``, ``RMSE``, ``GMMLoss``, ``CMALLoss``, and ``UMALLoss``. New 
    losses can be added :py:mod:`here <neuralhydrology.training.loss>`.
 
+- ``allow_subsequent_nan_losses``: Define a number of training steps for
+   which a loss value of ``NaN`` is ignored and no error is raised but 
+   instead the training loop proceeds to the next iteration step.
+
 -  ``target_loss_weights``: A list of float values specifying the 
    per-target loss weight, when training on multiple targets at once. 
    Can be combined with any loss. By default, the weight of each target

@@ -23,12 +23,17 @@ Installation
 ------------
 There are two ways how you can install NeuralHydrology: Editable or non-editable.
 If all you want to do is run experiments with existing datasets and existing models, you can use the non-editable
-installation:
+installation. To install the latest release from PyPI:
 
 .. code-block::
 
-    pip install git+https://github.com/neuralhydrology/neuralhydrology.git
+    pip install neuralhydrology
+
+To install the package directly from the current master branch of this repository, including any changes that are not yet part of a release, run:
 
+.. code-block::
+
+    pip install git+https://github.com/neuralhydrology/neuralhydrology.git
 
 If you want to try implementing your own models or datasets, you'll need an editable installation.
 For this, start by downloading or cloning the repository to your local machine.

@@ -1 +1 @@
-__version__ = "1.2.1"
+__version__ = "1.2.2"
@@ -47,11 +47,11 @@ def __init__(self, cfg: Config):
             self._get_embedding_net(cfg.dynamics_embedding, dynamics_input_size, 'dynamics')
 
         if cfg.statics_embedding is None:
-            self.statics_embedding_p_dropout = 0.0  # if net has no statics dropout we tread is as zero
+            self.statics_embedding_p_dropout = 0.0  # if net has no statics dropout we treat is as zero
         else:
             self.statics_embedding_p_dropout = cfg.statics_embedding['dropout']
         if cfg.dynamics_embedding is None:
-            self.dynamics_embedding_p_dropout = 0.0  # if net has no dynamics dropout we tread is as zero
+            self.dynamics_embedding_p_dropout = 0.0  # if net has no dynamics dropout we treat is as zero
         else:
             self.dynamics_embedding_p_dropout = cfg.dynamics_embedding['dropout']
 

@@ -9,6 +9,7 @@
 from neuralhydrology.modelzoo.inputlayer import InputLayer
 from neuralhydrology.modelzoo.head import get_head
 from neuralhydrology.modelzoo.basemodel import BaseModel
+from neuralhydrology.utils.config import Config
 
 LOGGER = logging.getLogger(__name__)
 
@@ -41,7 +42,7 @@ class Transformer(BaseModel):
     # specify submodules of the model that can later be used for finetuning. Names must match class attributes
     module_parts = ['embedding_net', 'encoder', 'head']
 
-    def __init__(self, cfg: Dict):
+    def __init__(self, cfg: Config):
         super(Transformer, self).__init__(cfg=cfg)
 
         # embedding net before transformer
@@ -61,7 +62,7 @@ def __init__(self, cfg: Dict):
         elif self._positional_encoding_type.lower() == 'sum':
             encoder_dim = self.embedding_net.output_size
         else:
-            raise RuntimeError(f"Unrecognized positional encoding type: {self.positional_encoding_type}")
+            raise RuntimeError(f"Unrecognized positional encoding type: {self._positional_encoding_type}")
         self.positional_encoder = _PositionalEncoding(embedding_dim=self.embedding_net.output_size,
                                                       dropout=cfg.transformer_positional_dropout,
                                                       position_type=cfg.transformer_positional_encoding_type,
@@ -106,7 +107,7 @@ def forward(self, data: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
         Returns
         -------
         Dict[str, torch.Tensor]
-            Model outputs and intermediate states as a dictionary. 
+            Model outputs and intermediate states as a dictionary.
                 - `y_hat`: model predictions of shape [batch size, sequence length, number of target variables].
         """
         # pass dynamic and static inputs through embedding layers, then concatenate them
@@ -173,7 +174,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         ----------
         x : torch.Tensor
             Dimension is ``[sequence length, batch size, embedding output dimension]``.
-            Data that is to be the input to a transformer encoder after including positional encoding. 
+            Data that is to be the input to a transformer encoder after including positional encoding.
             Typically this will be output from an embedding layer.
 
         Returns
@@ -182,7 +183,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
             Dimension is ``[sequence length, batch size, encoder input dimension]``.
             The encoder input dimension is either equal to the embedding output dimension (if ``position_type == sum``)
             or twice the embedding output dimension (if ``position_type == concatenate``).
-            Encoder input augmented with positional encoding. 
+            Encoder input augmented with positional encoding.
 
         """
         if self._concatenate:

@@ -28,7 +28,7 @@ def get_optimizer(model: torch.nn.Module, cfg: Config) -> torch.optim.Optimizer:
     torch.optim.Optimizer
         Optimizer object that can be used for model training.
     """
-    if cfg.optimizer == "Adam":
+    if cfg.optimizer.lower() == "adam":
         optimizer = torch.optim.Adam(model.parameters(), lr=cfg.learning_rate[0])
     else:
         raise NotImplementedError(f"{cfg.optimizer} not implemented or not linked in `get_optimizer()`")

@@ -567,15 +567,15 @@ def transformer_dim_feedforward(self) -> int:
         return self._get_value_verbose("transformer_dim_feedforward")
 
     @property
-    def transformer_positional_dropout(self) -> int:
+    def transformer_positional_dropout(self) -> float:
         return self._get_value_verbose("transformer_positional_dropout")
 
     @property
     def transformer_dropout(self) -> float:
         return self._get_value_verbose("transformer_dropout")
 
     @property
-    def transformer_nheads(self) -> float:
+    def transformer_nheads(self) -> int:
         return self._get_value_verbose("transformer_nheads")
 
     @seed.setter

@@ -11,8 +11,6 @@
 with open("neuralhydrology/__about__.py", "r") as fp:
     exec(fp.read(), about)
 
-# TODO: Add Classifier, License and update Authors etc.
-
 setup(name='neuralhydrology',
       version=about["__version__"],
       packages=[
@@ -25,7 +23,8 @@
           'Source': 'https://github.com/neuralhydrology/neuralhydrology',
           'Research Blog': 'https://neuralhydrology.github.io/'
       },
-      author='Frederik Kratzert <f.kratzert@gmail.com>, Daniel Klotz, Martin Gauch',
+      author='Frederik Kratzert, Daniel Klotz, Martin Gauch',
+      author_email='neuralhydrology@googlegroups.com',
       description='Library for training deep learning models with environmental focus',
       long_description=long_description,
       long_description_content_type='text/markdown',
@@ -35,7 +34,7 @@
               'nh-results-ensemble=neuralhydrology.utils.nh_results_ensemble:_main'
           ]
       },
-      python_requires='>=3.6',
+      python_requires='>=3.7',
       install_requires=[
           'matplotlib',
           'numba',