Merge remote-tracking branch 'origin/develop' into develop

Author: RandomDefaultUser

docs/source/advanced_usage/predictions.rst

@@ -81,11 +81,13 @@ Gaussian representation of atomic positions. In this algorithm, most of the
 computational overhead of the total energy calculation is offloaded to the
 computation of this Gaussian representation. This calculation is realized via
 LAMMPS and can therefore be GPU accelerated (parallelized) in the same fashion
-as the bispectrum descriptor calculation. Simply activate this option via
+as the bispectrum descriptor calculation. If a GPU is activated (and LAMMPS
+is available), this option will be used by default. It can also manually be
+activated via
 
     .. code-block:: python
 
-        parameters.descriptors.use_atomic_density_energy_formula = True
+        parameters.use_atomic_density_formula = True
 
 The Gaussian representation algorithm is describe in
 the publication `Predicting electronic structures at any length scale with machine learning <doi.org/10.1038/s41524-023-01070-z>`_.

docs/source/advanced_usage/trainingmodel.rst

@@ -194,22 +194,64 @@ keyword, you can fine-tune the number of new snapshots being created.
 By default, the same number of snapshots as had been provided will be created
 (if possible).
 
-Using tensorboard
-******************
+Logging metrics during training
+*******************************
+
+Training progress in MALA can be visualized via tensorboard or wandb, as also shown
+in the file ``advanced/ex03_tensor_board``. Simply select a logger prior to training as
+
+      .. code-block:: python
+
+            parameters.running.logger = "tensorboard"
+            parameters.running.logging_dir = "mala_vis"
 
-Training routines in MALA can be visualized via tensorboard, as also shown
-in the file ``advanced/ex03_tensor_board``. Simply enable tensorboard
-visualization prior to training via
+or
 
       .. code-block:: python
 
-            # 0: No visualizatuon, 1: loss and learning rate, 2: like 1,
-            # but additionally weights and biases are saved
-            parameters.running.logging = 1
+            import wandb
+            wandb.init(
+                  project="mala_training",
+                  entity="your_wandb_entity"
+            )
+            parameters.running.logger = "wandb"
             parameters.running.logging_dir = "mala_vis"
 
 where ``logging_dir`` specifies some directory in which to save the
-MALA logging data. Afterwards, you can run the training without any
+MALA logging data. You can also select which metrics to record via
+
+      .. code-block:: python
+
+            parameters.validation_metrics = ["ldos", "dos", "density", "total_energy"]
+
+Full list of available metrics:
+      - "ldos": MSE of the LDOS.
+      - "band_energy": Band energy.
+      - "band_energy_actual_fe": Band energy computed with ground truth Fermi energy.
+      - "total_energy": Total energy.
+      - "total_energy_actual_fe": Total energy computed with ground truth Fermi energy.
+      - "fermi_energy": Fermi energy.
+      - "density": Electron density.
+      - "density_relative": Rlectron density (Mean Absolute Percentage Error).
+      - "dos": Density of states.
+      - "dos_relative": Density of states (Mean Absolute Percentage Error).
+
+To save time and resources you can specify the logging interval via
+
+      .. code-block:: python
+
+            parameters.running.validate_every_n_epochs = 10
+
+If you want to monitor the degree to which the model overfits to the training data,
+you can use the option
+
+      .. code-block:: python
+            
+            parameters.running.validate_on_training_data = True
+
+MALA will evaluate the validation metrics on the training set as well as the validation set.
+
+Afterwards, you can run the training without any
 other modifications. Once training is finished (or during training, in case
 you want to use tensorboard to monitor progress), you can launch tensorboard
 via
@@ -221,6 +263,7 @@ via
 The full path for ``path_to_log_directory`` can be accessed via
 ``trainer.full_logging_path``.
 
+If you're using wandb, you can monitor the training progress on the wandb website.
 
 Training in parallel
 ********************

docs/source/basic_usage/trainingmodel.rst

@@ -28,7 +28,7 @@ options to train a simple network with example data, namely
             parameters = mala.Parameters()
 
             parameters.data.input_rescaling_type = "feature-wise-standard"
-            parameters.data.output_rescaling_type = "normal"
+            parameters.data.output_rescaling_type = "minmax"
 
             parameters.network.layer_activations = ["ReLU"]
 
@@ -43,15 +43,18 @@ sub-objects dealing with the individual aspects of the workflow. In the first
 two lines, which data scaling MALA should employ. Scaling data greatly
 improves the performance of NN based ML models. Options are
 
-* ``None``: No normalization is applied.
+* ``None``: No scaling is applied.
 
-* ``standard``: Standardization (Scale to mean 0, standard deviation 1)
+* ``standard``: Standardization (Scale to mean 0, standard deviation 1) is
+  applied to the entire array.
 
-* ``normal``: Min-Max scaling (Scale to be in range 0...1)
+* ``minmax``: Min-Max scaling (Scale to be in range 0...1) is applied to the entire array.
 
-* ``feature-wise-standard``: Row Standardization (Scale to mean 0, standard deviation 1)
+* ``feature-wise-standard``: Standardization (Scale to mean 0, standard
+  deviation 1) is applied to each feature dimension individually.
 
-* ``feature-wise-normal``: Row Min-Max scaling (Scale to be in range 0...1)
+* ``feature-wise-minmax``: Min-Max scaling (Scale to be in range 0...1) is
+  applied to each feature dimension individually.
 
 Here, we specify that MALA should standardize the input (=descriptors)
 by feature (i.e., each entry of the vector separately on the grid) and

docs/source/conf.py

@@ -72,7 +72,6 @@
     "scipy",
     "oapackage",
     "matplotlib",
-    "horovod",
     "lammps",
     "total_energy",
     "pqkmeans",

examples/advanced/ex01_checkpoint_training.py

@@ -21,7 +21,7 @@ def initial_setup():
     parameters = mala.Parameters()
     parameters.data.data_splitting_type = "by_snapshot"
     parameters.data.input_rescaling_type = "feature-wise-standard"
-    parameters.data.output_rescaling_type = "normal"
+    parameters.data.output_rescaling_type = "minmax"
     parameters.network.layer_activations = ["ReLU"]
     parameters.running.max_number_epochs = 9
     parameters.running.mini_batch_size = 8

examples/advanced/ex03_tensor_board.py

@@ -13,7 +13,7 @@
 
 parameters = mala.Parameters()
 parameters.data.input_rescaling_type = "feature-wise-standard"
-parameters.data.output_rescaling_type = "normal"
+parameters.data.output_rescaling_type = "minmax"
 parameters.targets.ldos_gridsize = 11
 parameters.targets.ldos_gridspacing_ev = 2.5
 parameters.targets.ldos_gridoffset_ev = -5
@@ -32,11 +32,19 @@
 
 data_handler = mala.DataHandler(parameters)
 data_handler.add_snapshot(
-    "Be_snapshot0.in.npy", data_path, "Be_snapshot0.out.npy", data_path, "tr",
+    "Be_snapshot0.in.npy",
+    data_path,
+    "Be_snapshot0.out.npy",
+    data_path,
+    "tr",
     calculation_output_file=os.path.join(data_path, "Be_snapshot0.out"),
 )
 data_handler.add_snapshot(
-    "Be_snapshot1.in.npy", data_path, "Be_snapshot1.out.npy", data_path, "va",
+    "Be_snapshot1.in.npy",
+    data_path,
+    "Be_snapshot1.out.npy",
+    data_path,
+    "va",
     calculation_output_file=os.path.join(data_path, "Be_snapshot1.out"),
 )
 data_handler.prepare_data()

examples/advanced/ex05_checkpoint_hyperparameter_optimization.py

@@ -17,7 +17,7 @@
 def initial_setup():
     parameters = mala.Parameters()
     parameters.data.input_rescaling_type = "feature-wise-standard"
-    parameters.data.output_rescaling_type = "normal"
+    parameters.data.output_rescaling_type = "minmax"
     parameters.running.max_number_epochs = 10
     parameters.running.mini_batch_size = 40
     parameters.running.learning_rate = 0.00001

examples/advanced/ex06_distributed_hyperparameter_optimization.py

@@ -24,7 +24,7 @@
 parameters = mala.Parameters()
 # Specify the data scaling.
 parameters.data.input_rescaling_type = "feature-wise-standard"
-parameters.data.output_rescaling_type = "normal"
+parameters.data.output_rescaling_type = "minmax"
 parameters.running.max_number_epochs = 5
 parameters.running.mini_batch_size = 40
 parameters.running.learning_rate = 0.00001

examples/advanced/ex07_advanced_hyperparameter_optimization.py

@@ -17,7 +17,7 @@ def optimize_hyperparameters(hyper_optimizer):
 
     parameters = mala.Parameters()
     parameters.data.input_rescaling_type = "feature-wise-standard"
-    parameters.data.output_rescaling_type = "normal"
+    parameters.data.output_rescaling_type = "minmax"
     parameters.running.max_number_epochs = 10
     parameters.running.mini_batch_size = 40
     parameters.running.learning_rate = 0.00001

examples/advanced/ex10_convert_numpy_openpmd.py

@@ -29,7 +29,7 @@
     descriptor_save_path="./",
     target_save_path="./",
     additional_info_save_path="./",
-    naming_scheme="converted_from_numpy_*.bp5",
+    naming_scheme="converted_from_numpy_*.h5",
     descriptor_calculation_kwargs={"working_directory": "./"},
 )
 
@@ -40,11 +40,9 @@
 for snapshot in range(2):
     data_converter.add_snapshot(
         descriptor_input_type="openpmd",
-        descriptor_input_path="converted_from_numpy_{}.in.bp5".format(
-            snapshot
-        ),
+        descriptor_input_path="converted_from_numpy_{}.in.h5".format(snapshot),
         target_input_type="openpmd",
-        target_input_path="converted_from_numpy_{}.out.bp5".format(snapshot),
+        target_input_path="converted_from_numpy_{}.out.h5".format(snapshot),
         additional_info_input_type=None,
         additional_info_input_path=None,
         target_units=None,