Small edits (#753)

docs/apps/clearml_task.md

@@ -80,7 +80,7 @@ errors in identifying the correct default branch.
 </div>
 
 ## Usage
-These commands demonstrate a few useful use cases for `clearml-task`
+These commands demonstrate a few useful use cases for `clearml-task`.
 
 ### Executing Code from a Remote Repository 
 

docs/clearml_agent.md

@@ -799,9 +799,11 @@ APIClient. The body of the call must contain the ``queue-id`` and the tags to ad
 
 For example, force workers on for a queue using the APIClient:
 
-    from clearml.backend_api.session.client import APIClient
-    client = APIClient()
-    client.queues.update(queue="<queue_id>", tags=["force_workers:on"]
+```python
+from clearml.backend_api.session.client import APIClient
+client = APIClient()
+client.queues.update(queue="<queue_id>", tags=["force_workers:on"])
+```
 
 Or, force workers on for a queue using the REST API:
 

docs/clearml_data/clearml_data_sdk.md

@@ -335,6 +335,7 @@ You can enable offline mode in one of the following ways:
 
   ```python
   from clearml import Dataset
+  
   # Use the set_offline class method before creating a Dataset
   Dataset.set_offline(offline_mode=True)
   # Create a dataset

docs/clearml_sdk/apiclient_sdk.md

@@ -25,6 +25,7 @@ in your workspace. The following code uses APIClient to retrieve a list of all p
 
 ```python
 from clearml.backend_api.session.client import APIClient
+
 # Create an instance of APIClient
 client = APIClient()
 project_list = client.projects.get_all(name="example*")

docs/clearml_sdk/task_sdk.md

@@ -560,6 +560,7 @@ the `offline_mode` argument to `True`
   
   ```python
   from clearml import Task
+  
   # Use the set_offline class method before initializing a Task
   Task.set_offline(offline_mode=True)
   # Initialize a Task
@@ -594,6 +595,7 @@ Upload the execution data that the Task captured offline to the ClearML Server u
 * [`Task.import_offline_session`](../references/sdk/task.md#taskimport_offline_session) class method
   ```python
   from clearml import Task
+  
   Task.import_offline_session(session_folder_zip="path/to/session/.clearml/cache/offline/b786845decb14eecadf2be24affc7418.zip")
   ```
 

docs/faq.md

@@ -1136,6 +1136,7 @@ in your workspace. The following code uses APIClient to retrieve a list of all p
 
 ```python
 from clearml.backend_api.session.client import APIClient
+
 # Create an instance of APIClient
 client = APIClient()
 project_list = client.projects.get_all(name="example*")

docs/getting_started/mlops/mlops_first_steps.md

@@ -97,6 +97,7 @@ All Tasks in the system can be accessed through their unique Task ID, or based o
 method. For example:
 ```python
 from clearml import Task
+
 executed_task = Task.get_task(task_id='aabbcc')
 ```
 
@@ -130,6 +131,7 @@ Users can programmatically change cloned experiments' parameters.
 For example:
 ```python
 from clearml import Task
+
 cloned_task = Task.clone(task_id='aabbcc')
 cloned_task.set_parameter(name='internal/magic', value=42)
 ```
@@ -141,6 +143,7 @@ objects and files to a task anywhere from code.
 ```python
 import numpy as np
 from clearml import Task
+
 Task.current_task().upload_artifact(name='a_file', artifact_object='local_file.bin')
 Task.current_task().upload_artifact(name='numpy', artifact_object=np.ones(4,4))
 ```
@@ -150,6 +153,7 @@ by accessing the Task that created them. These artifacts can be modified and upl
 
 ```python
 from clearml import Task
+
 executed_task = Task.get_task(task_id='aabbcc')
 # artifact as a file
 local_file = executed_task.artifacts['file'].get_local_copy()
@@ -173,6 +177,7 @@ improves the visibility of your processes’ progress.
 
 ```python
 from clearml import Logger
+
 Logger.current_logger().report_scalar(
    graph='metric',
    series='variant',
@@ -184,6 +189,7 @@ Logger.current_logger().report_scalar(
 You can also retrieve reported scalars for programmatic analysis:
 ```python
 from clearml import Task
+
 executed_task = Task.get_task(task_id='aabbcc')
 # get a summary of the min/max/last value of all reported scalars
 min_max_values = executed_task.get_last_scalar_metrics()
@@ -193,10 +199,11 @@ full_scalars = executed_task.get_reported_scalars()
 
 #### Query Experiments
 You can also search and query Tasks in the system. Use the [`Task.get_tasks`](../../references/sdk/task.md#taskget_tasks) 
-method to retrieve Task objects and filter based on the specific values of the Task - status, parameters, metrics and more!
+class method to retrieve Task 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',

docs/guides/clearml_agent/executable_exp_containers.md

@@ -35,7 +35,7 @@ script.
    This ID will be used in the following section.
 
 ## Building and Launching a Containerized Task
-1. Execute the following command to build the container. Input the ID of the task created above.  
+1. Execute the following command to build the container. Input the ID of the task created above:  
    ```console
    clearml-agent build --id <TASK_ID> --docker --target new-docker --entry-point clone_task
    ```

docs/guides/set_offline.md

@@ -14,6 +14,7 @@ the `offline_mode` argument to `True`
 
   ```python
   from clearml import Task
+  
   # Use the set_offline class method before initializing a Task
   Task.set_offline(offline_mode=True)
   # Initialize a Task 

docs/integrations/autokeras.md

@@ -13,6 +13,7 @@ All you have to do is simply add two lines of code to your AutoKeras script:
 
 ```python
 from clearml import Task
+
 task = Task.init(task_name="<task_name>", project_name="<project_name>")
 ```
 

docs/integrations/catboost.md

@@ -13,6 +13,7 @@ All you have to do is simply add two lines of code to your CatBoost script:
 
 ```python
 from clearml import Task
+
 task = Task.init(task_name="<task_name>", project_name="<project_name>")
 ```
 

docs/integrations/click.md

@@ -14,6 +14,7 @@ All you have to do is add two lines of code:
 
 ```python
 from clearml import Task
+
 task = Task.init(task_name="<task_name>", project_name="<project_name>")
 ```
 

docs/integrations/fastai.md

@@ -13,6 +13,7 @@ All you have to do is simply add two lines of code to your `fastai` script:
 
 ```python
 from clearml import Task
+
 task = Task.init(task_name="<task_name>", project_name="<project_name>")
 ```
 

docs/integrations/hydra.md

@@ -16,6 +16,7 @@ All you have to do is add two lines of code:
 
 ```python
 from clearml import Task
+
 task = Task.init(task_name="<task_name>", project_name="<project_name>")
 ```
 

docs/integrations/ignite.md

@@ -20,6 +20,7 @@ All you have to do is add two lines of code to your script:
 
 ```python
 from clearml import Task
+
 task = Task.init(task_name="<task_name>", project_name="<project_name>")
 ```
 

docs/integrations/keras.md

@@ -14,6 +14,7 @@ All you have to do is simply add two lines of code to your Keras script:
 
 ```python
 from clearml import Task
+
 task = Task.init(task_name="<task_name>", project_name="<project_name>")
 ```
 

docs/integrations/lightgbm.md

@@ -14,6 +14,7 @@ All you have to do is simply add two lines of code to your LightGBM script:
 
 ```python
 from clearml import Task
+
 task = Task.init(task_name="<task_name>", project_name="<project_name>")
 ```
 

docs/integrations/megengine.md

@@ -13,6 +13,7 @@ All you have to do is simply add two lines of code to your MegEngine script:
 
 ```python
 from clearml import Task
+
 task = Task.init(task_name="<task_name>", project_name="<project_name>")
 ```
 

docs/integrations/python_fire.md

@@ -9,6 +9,7 @@ All you have to do is add two lines of code:
 
 ```python
 from clearml import Task
+
 task = Task.init(task_name="<task_name>", project_name="<project_name>")
 ```
 

docs/integrations/pytorch.md

@@ -13,6 +13,7 @@ All you have to do is simply add two lines of code to your PyTorch script:
 
 ```python
 from clearml import Task
+
 task = Task.init(task_name="<task_name>", project_name="<project_name>")
 ```
 

docs/integrations/pytorch_lightning.md

@@ -15,6 +15,7 @@ All you have to do is simply add two lines of code to your PyTorch Lightning scr
 
 ```python
 from clearml import Task
+
 task = Task.init(task_name="<task_name>", project_name="<project_name>")
 ```
 

docs/integrations/scikit_learn.md

@@ -14,6 +14,7 @@ All you have to do is simply add two lines of code to your scikit-learn script:
 
 ```python
 from clearml import Task
+
 task = Task.init(task_name="<task_name>", project_name="<project_name>")
 ```
 

docs/integrations/seaborn.md

@@ -13,6 +13,7 @@ lines of code to your script:
 
 ```python
 from clearml import Task
+
 task = Task.init(task_name="<task_name>", project_name="<project_name>")
 ```
 

docs/integrations/tensorboard.md

@@ -12,6 +12,7 @@ lines of code to your script:
 
 ```python
 from clearml import Task
+
 task = Task.init(task_name="<task_name>", project_name="<project_name>")
 ```
 

docs/integrations/tensorboardx.md

@@ -13,6 +13,7 @@ to do is add two lines of code to your script:
 
 ```python
 from clearml import Task
+
 task = Task.init(task_name="<task_name>", project_name="<project_name>")
 ```
 

docs/integrations/tensorflow.md

@@ -14,6 +14,7 @@ All you have to do is simply add two lines of code to your TensorFlow script:
 
 ```python
 from clearml import Task
+
 task = Task.init(task_name="<task_name>", project_name="<project_name>")
 ```
 

docs/integrations/xgboost.md

@@ -14,6 +14,7 @@ All you have to do is simply add two lines of code to your XGBoost script:
 
 ```python
 from clearml import Task
+
 task = Task.init(task_name="<task_name>", project_name="<project_name>")
 ```
 

docs/model_registry.md

@@ -58,6 +58,7 @@ names that start with `final`.
 
 ```python
 from clearml import Task
+
 task = Task.init(
 	project_name="My Project",
 	task_name="My Task",

docs/pipelines/pipelines_sdk_tasks.md

@@ -8,6 +8,7 @@ Create the [PipelineController](../references/sdk/automation_controller_pipeline
 the pipeline's execution logic:
 ```python
 from clearml import PipelineController
+
 pipe = PipelineController(
   name="Pipeline Controller", project="Pipeline example", version="1.0.0"
 )