Small edits (#691)

docs/faq.md

@@ -642,7 +642,6 @@ logger.report_scatter2d(
     xaxis="title x", 
     yaxis="title y"
 )
-
 ```
 
 ## GIT and Storage

docs/guides/frameworks/catboost/catboost.md

@@ -18,8 +18,7 @@ ClearML automatically captures scalars logged by CatBoost. These scalars can be
 ![Experiment scalars](../../../img/examples_catboost_scalars.png)
 
 ## Hyperparameters
-ClearML automatically logs command line options defined with argparse. They appear in **CONFIGURATIONS > HYPER 
-PARAMETERS > Args**.
+ClearML automatically logs command line options defined with argparse. They appear in **CONFIGURATIONS > HYPERPARAMETERS > Args**.
 
 ![Experiment hyperparameters](../../../img/examples_catboost_configurations.png)
 

docs/guides/frameworks/lightgbm/lightgbm_example.md

@@ -13,7 +13,7 @@ The example script does the following:
 
 ## Scalars
 
-The scalars logged in the experiment can be visualized in a plot, which appears in the ClearML web UI, in the **experiment's page > SCALARS**.
+The scalars logged in the experiment can be visualized in a plot, which appears in the ClearML web UI, in the experiment's **SCALARS** tab.
 
 ![LightGBM scalars](../../../img/examples_lightgbm_scalars.png)
 

docs/guides/frameworks/pytorch/pytorch_distributed_example.md

@@ -28,7 +28,8 @@ on `Task.current_task` (the main Task). The dictionary contains the `dist.rank`
 
 ```python
 Task.current_task().upload_artifact(
-    'temp {:02d}'.format(dist.get_rank()), artifact_object={'worker_rank': dist.get_rank()})
+    'temp {:02d}'.format(dist.get_rank()), artifact_object={'worker_rank': dist.get_rank()}
+)
 ```
 
 All of these artifacts appear in the main Task, **ARTIFACTS** **>** **OTHER**.
@@ -43,7 +44,8 @@ same title (`loss`), but a different series name (containing the subprocess' `ra
 
 ```python
 Task.current_task().get_logger().report_scalar(
-    'loss', 'worker {:02d}'.format(dist.get_rank()), value=loss.item(), iteration=i)
+    'loss', 'worker {:02d}'.format(dist.get_rank()), value=loss.item(), iteration=i
+)
 ```
 
 The single scalar plot for loss appears in **SCALARS**.

docs/guides/frameworks/pytorch_ignite/pytorch_ignite_mnist.md

@@ -70,25 +70,29 @@ clearml_logger.attach(
 * Log metrics for training:
     
 ```python
-clearml_logger.attach(train_evaluator,
+clearml_logger.attach(
+    train_evaluator,
     log_handler=OutputHandler(
         tag="training",
         metric_names=["nll", "accuracy"],
         global_step_transform=global_step_from_engine(trainer)
     ),
-    event_name=Events.EPOCH_COMPLETED)
+    event_name=Events.EPOCH_COMPLETED
+)
 ```
 
 * Log metrics for validation:
                     
 ```python
-clearml_logger.attach(evaluator,
+clearml_logger.attach(
+    evaluator,
     log_handler=OutputHandler(
         tag="validation",
         metric_names=["nll", "accuracy"],
         global_step_transform=global_step_from_engine(trainer)
     ),
-    event_name=Events.EPOCH_COMPLETED)
+    event_name=Events.EPOCH_COMPLETED
+)
 ```
 
 To log optimizer parameters, use the `attach_opt_params_handler` method:

docs/guides/frameworks/tensorflow/integration_keras_tuner.md

@@ -29,7 +29,8 @@ tuner = kt.Hyperband(
     logger=ClearMLTunerLogger(),
     objective='val_accuracy',
     max_epochs=10,
-    hyperband_iterations=6)
+    hyperband_iterations=6
+)
 ```
 
 When the script runs, it logs: 

docs/guides/optimization/hyper-parameter-optimization/examples_hyperparam_opt.md

@@ -23,7 +23,7 @@ The following search strategies can be used:
   documentation.
      
 * Random uniform sampling of hyperparameter strategy - [automation.RandomSearch](../../../references/sdk/hpo_optimization_randomsearch.md)
-* Full grid sampling strategy of every hyperparameter combination - Grid search [automation.GridSearch](../../../references/sdk/hpo_optimization_gridsearch.md).
+* Full grid sampling strategy of every hyperparameter combination - [automation.GridSearch](../../../references/sdk/hpo_optimization_gridsearch.md).
 * Custom - Use a custom class and inherit from the ClearML automation base strategy class, automation.optimization.SearchStrategy.
 
 The search strategy class that is chosen will be passed to the [automation.HyperParameterOptimizer](../../../references/sdk/hpo_optimization_hyperparameteroptimizer.md) 
@@ -73,8 +73,8 @@ can be [reproduced](../../../webapp/webapp_exp_reproducing.md) and [tuned](../..
 
 Set the Task type to `optimizer`, and create a new experiment (and Task object) each time the optimizer runs (`reuse_last_task_id=False`). 
 
-When the code runs, it creates an experiment named **Automatic Hyper-Parameter Optimization** that is associated with 
-the project **Hyper-Parameter Optimization**, which can be seen in the **ClearML Web UI**. 
+When the code runs, it creates an experiment named **Automatic Hyper-Parameter Optimization** in 
+the **Hyper-Parameter Optimization** project, which can be seen in the **ClearML Web UI**. 
  
  ```python
 # Connecting CLEARML
@@ -174,7 +174,6 @@ Specify the remaining parameters, including the time limit per Task (minutes), p
     max_iteration_per_job=30,
     
 )  # done creating HyperParameterOptimizer
-
 ```
 
 ## Running as a Service

docs/guides/reporting/html_reporting.md

@@ -56,7 +56,6 @@ Logger.current_logger().report_media(
     iteration=iteration,
     local_path="bar_pandas_groupby_nested.html",
 )
-
 ```
 
 ### Bokeh Graph HTML

docs/guides/reporting/hyper_parameters.md

@@ -74,7 +74,6 @@ parameters['new_param'] = 'this is new'
   
 # changing the value of a parameter (new value will be stored instead of previous one)
 parameters['float'] = '9.9'
-
 ```
 
 Parameters from dictionaries connected to Tasks appear in **HYPERPARAMETERS** **>** **General**.

docs/hyperdatasets/single_frames.md

@@ -319,7 +319,6 @@ frame.meta['road_hazard'] = 'yes'
 # update the SingeFrame
 frames.append(frame)
 myDatasetVersion.update_frames(frames)                
-
 ```