Small changes (#113)

docs/getting_started/mlops/mlops_best_practices.md

@@ -30,8 +30,8 @@ Once we have a Task in ClearML, we can clone and edit its definitions in the UI,
 
 ## Manage Your Data
 Use [ClearML Data](../../clearml_data/clearml_data.md) to version your data, then link it to running experiments for easy reproduction.
-Make datasets machine agnostic (i.e. store original dataset in a shared storage location, e.g. shared-folder/S3/Gs/Azure)
-ClearML Data supports efficient Dataset storage and caching, differentiable & compressed
+Make datasets machine agnostic (i.e. store original dataset in a shared storage location, e.g. shared-folder/S3/Gs/Azure).
+ClearML Data supports efficient Dataset storage and caching, differentiable & compressed.
 
 ## Scale Your Work
 Use [ClearML Agent](../../clearml_agent.md) to scale work. Install the agent machines (Remote or local) and manage

docs/getting_started/mlops/mlops_first_steps.md

@@ -121,7 +121,12 @@ Log as many metrics from your processes! It improves visibility on their progres
 Use the Logger class from to report scalars and plots.
 ```python
 from clearml import Logger
-Logger.current_logger().report_scalar(graph='metric', series='variant', value=13.37, iteration=counter)
+Logger.current_logger().report_scalar(
+    graph='metric', 
+    series='variant', 
+    value=13.37, 
+    iteration=counter
+)
 ```
 
 You can later analyze reported scalars
@@ -139,7 +144,11 @@ You can also search and query Tasks in the system.
 Use the `Task.get_tasks` call to retrieve Tasks objects and filter based on the specific values of the Task - status, parameters, metrics and more!
 ```python
 from clearml import Task
-tasks = Task.get_tasks(project_name='examples', task_name='partial_name_match', task_filter={'status': 'in_progress'})
+tasks = Task.get_tasks(
+    project_name='examples', 
+    task_name='partial_name_match', 
+    task_filter={'status': 'in_progress'}
+)
 ```
 
 ## Manage Your Data