Edit docstrings (#1084)

clearml/automation/optimization.py

@@ -610,7 +610,7 @@ class SearchStrategy(object):
         :param int top_k: The number of Tasks (experiments) to return.
         :param all_metrics: Default False, only return the objective metric on the metrics dictionary.
             If True, return all scalar metrics of the experiment
-        :param all_hyper_parameters: Default False. If True, return all the hyper-parameters from all the sections.
+        :param all_hyper_parameters: Default False. If True, return all the hyperparameters from all the sections.
         :param only_completed: return only completed Tasks. Default False.
 
         :return: A list of dictionaries ({task_id: '', hyper_parameters: {}, metrics: {}}), ordered by performance,
@@ -791,7 +791,7 @@ class SearchStrategy(object):
     def _validate_base_task(self):
         # type: () -> ()
         """
-        Check the base task exists and contains the requested Objective metric and hyper parameters.
+        Check the base task exists and contains the requested Objective metric and hyperparameters.
         """
         # check if the task exists
         try:
@@ -929,7 +929,7 @@ class SearchStrategy(object):
 
 class GridSearch(SearchStrategy):
     """
-    Grid search strategy controller. Full grid sampling of every hyper-parameter combination.
+    Grid search strategy controller. Full grid sampling of every hyperparameter combination.
     """
 
     def __init__(
@@ -1001,7 +1001,7 @@ class GridSearch(SearchStrategy):
 
 class RandomSearch(SearchStrategy):
     """
-    Random search strategy controller. Random uniform sampling of hyper-parameters.
+    Random search strategy controller. Random uniform sampling of hyperparameters.
     """
 
     # Number of already chosen random samples before assuming we covered the entire hyper-parameter space
@@ -1105,7 +1105,7 @@ class HyperParameterOptimizer(object):
     ):
         # type: (...) -> ()
         """
-        Create a new hyper-parameter controller. The newly created object will launch and monitor the new experiments.
+        Create a new hyperparameter controller. The newly created object will launch and monitor the new experiments.
 
         :param str base_task_id: The Task ID to be used as template experiment to optimize.
         :param list hyper_parameters: The list of Parameter objects to optimize over.
@@ -1120,7 +1120,7 @@ class HyperParameterOptimizer(object):
           - ``min_global`` - Minimize the min value of *all* reported values for the specific title/series scalar.
           - ``max_global`` - Maximize the max value of *all* reported values for the specific title/series scalar.
 
-        :param class.SearchStrategy optimizer_class: The SearchStrategy optimizer to use for the hyper-parameter search
+        :param class.SearchStrategy optimizer_class: The SearchStrategy optimizer to use for the hyperparameter search
         :param int max_number_of_concurrent_tasks: The maximum number of concurrent Tasks (experiments) running at the
             same time.
         :param str execution_queue: The execution queue to use for launching Tasks (experiments).
@@ -1516,7 +1516,7 @@ class HyperParameterOptimizer(object):
         :param int top_k: The number of Tasks (experiments) to return.
         :param all_metrics: Default False, only return the objective metric on the metrics dictionary.
             If True, return all scalar metrics of the experiment
-        :param all_hyper_parameters: Default False. If True, return all the hyper-parameters from all the sections.
+        :param all_hyper_parameters: Default False. If True, return all the hyperparameters from all the sections.
         :param only_completed: return only completed Tasks. Default False.
 
         :return: A list of dictionaries ({task_id: '', hyper_parameters: {}, metrics: {}}), ordered by performance,

clearml/automation/parameters.py

@@ -15,7 +15,7 @@ class RandomSeed(object):
     def set_random_seed(seed=1337):
         # type: (int) -> ()
         """
-        Set global seed for all hyper-parameter strategy random number sampling.
+        Set global seed for all hyperparameter strategy random number sampling.
 
         :param int seed: The random seed.
         """
@@ -26,7 +26,7 @@ class RandomSeed(object):
     def get_random_seed():
         # type: () -> int
         """
-        Get the global seed for all hyper-parameter strategy random number sampling.
+        Get the global seed for all hyperparameter strategy random number sampling.
 
         :return: The random seed.
         """
@@ -35,14 +35,14 @@ class RandomSeed(object):
 
 class Parameter(RandomSeed):
     """
-    The base hyper-parameter optimization object.
+    The base hyperparameter optimization object.
     """
     _class_type_serialize_name = 'type'
 
     def __init__(self, name):
         # type: (Optional[str]) -> ()
         """
-        Create a new Parameter for hyper-parameter optimization
+        Create a new Parameter for hyperparameter optimization
 
         :param str name: The new Parameter name. This is the parameter name that will be passed to a Task.
         """
@@ -125,7 +125,7 @@ class UniformParameterRange(Parameter):
         """
         Create a parameter to be sampled by the SearchStrategy
 
-        :param str name: The parameter name. Match the Task hyper-parameter name.
+        :param str name: The parameter name. Match the Task hyperparameter name.
         :param float min_value: The minimum sample to use for uniform random sampling.
         :param float max_value: The maximum sample to use for uniform random sampling.
         :param float step_size: If not ``None``, set step size (quantization) for value sampling.
@@ -172,7 +172,7 @@ class UniformParameterRange(Parameter):
 
 class LogUniformParameterRange(UniformParameterRange):
     """
-    Logarithmic uniform randomly sampled hyper-parameter object.
+    Logarithmic uniform randomly sampled hyperparameter object.
     """
 
     def __init__(
@@ -188,7 +188,7 @@ class LogUniformParameterRange(UniformParameterRange):
         """
         Create a parameter to be sampled by the SearchStrategy
 
-        :param str name: The parameter name. Match the Task hyper-parameter name.
+        :param str name: The parameter name. Match the Task hyperparameter name.
         :param float min_value: The minimum exponent sample to use for uniform random sampling.
         :param float max_value: The maximum exponent sample to use for uniform random sampling.
         :param float base: The base used to raise the sampled exponent.
@@ -228,7 +228,7 @@ class UniformIntegerParameterRange(Parameter):
         """
         Create a parameter to be sampled by the SearchStrategy.
 
-        :param str name: The parameter name. Match the task hyper-parameter name.
+        :param str name: The parameter name. Match the task hyperparameter name.
         :param int min_value: The minimum sample to use for uniform random sampling.
         :param int max_value: The maximum sample to use for uniform random sampling.
         :param int step_size: The default step size is ``1``.
@@ -272,7 +272,7 @@ class UniformIntegerParameterRange(Parameter):
 
 class DiscreteParameterRange(Parameter):
     """
-    Discrete randomly sampled hyper-parameter object.
+    Discrete randomly sampled hyperparameter object.
     """
 
     def __init__(self, name, values=()):
@@ -280,7 +280,7 @@ class DiscreteParameterRange(Parameter):
         """
         Uniformly sample values form a list of discrete options.
 
-        :param str name: The parameter name. Match the task hyper-parameter name.
+        :param str name: The parameter name. Match the task hyperparameter name.
         :param list values: The list/tuple of valid parameter values to sample from.
         """
         super(DiscreteParameterRange, self).__init__(name=name)

clearml/model.py

@@ -2160,7 +2160,7 @@ class OutputModel(BaseModel):
         # type: (str) -> None
         """
         Set the URI of the storage destination for uploaded model weight files.
-        Supported storage destinations include S3, Google Cloud Storage), and file locations.
+        Supported storage destinations include S3, Google Cloud Storage, and file locations.
 
         Using this method, file uploads are separate and then a link to each is stored in the model object.
 

docs/logger.md

@@ -12,7 +12,7 @@ Using the **ClearML** [Logger](https://github.com/allegroai/clearml/blob/master/
   * [Surface diagrams](#surface-diagrams)
   * [Images](#images)
   
-* Track hyper-parameters and OS environment variables
+* Track hyperparameters and OS environment variables
   * Logging experiment parameter [dictionaries](#logging-experiment-parameter-dictionaries)
   * Specifying [environment variables](#specifying-environment-variables-to-track) to track
 
@@ -819,7 +819,7 @@ def report_surface(self, title, series, matrix, iteration, xlabels=None, ylabels
 ### Images
 
 Use to report an image and upload its contents to the bucket specified in the **ClearML** configuration file,
-or a [a default upload destination](#set-default-upload-destination), if you set a default. 
+or a [default upload destination](#set-default-upload-destination), if you set a default. 
 
 First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/clearml/blob/master/examples/manual_reporting.py)) with the following method.
 
@@ -925,13 +925,13 @@ def report_image(self, title, series, iteration, local_path=None, matrix=None, m
     </tbody>
 </table>
 
-## Hyper-parameters and Environment Variables
+## Hyperparameters and Environment Variables
 
 ### Logging Experiment Parameter Dictionaries
 
 In order for **ClearML** to log a dictionary of parameters, use the `Task.connect` method.
 
-For example, to log the hyper-parameters <code>learning_rate</code>, <code>batch_size</code>, <code>display_step</code>, <code>model_path</code>, <code>n_hidden_1</code>, and <code>n_hidden_2</code>:  
+For example, to log the hyperparameters <code>learning_rate</code>, <code>batch_size</code>, <code>display_step</code>, <code>model_path</code>, <code>n_hidden_1</code>, and <code>n_hidden_2</code>:  
 
 ```python
 # Create a dictionary of parameters

examples/frameworks/jsonargparse/pytorch_lightning_cli.py

@@ -3,7 +3,7 @@ try:
     from lightning.pytorch.demos.boring_classes import DemoModel, BoringDataModule
 except ImportError:
     import sys
-    print("Module 'lightning' not installed (only available for Python 3.8+")
+    print("Module 'lightning' not installed (only available for Python 3.8+)")
     sys.exit(0)
 from clearml import Task
 

examples/frameworks/pytorch-lightning/pytorch_lightning_example.py

@@ -65,7 +65,7 @@ if __name__ == '__main__':
     parser = LitClassifier.add_model_specific_args(parser)
     args = parser.parse_args()
 
-    Task.init(project_name="examples-internal", task_name="lightning checkpoint issue and argparser")
+    Task.init(project_name="examples", task_name="pytorch lightning MNIST")
 
     # ------------
     # data