Small edits (#174)

docs/guides/clearml-task/clearml_task_tutorial.md

@@ -53,7 +53,7 @@ For this example, use a local version of [this script](https://github.com/allegr
 1. Go to the root folder of the cloned repository 
 1. Run the following command:
 
-``` bash
+```bash
 clearml-task --project keras --name local_test --script webinar-0620/keras_mnist.py --requirements webinar-0620/requirements.txt --args epochs=1 --queue default
 ```  
 

docs/guides/datasets/data_man_python.md

@@ -36,8 +36,11 @@ This script downloads the data and `dataset_path` contains the path to the downl
 ```python
 from clearml import Dataset
 
-dataset = Dataset.create(dataset_name="cifar_dataset", dataset_project="dataset examples" )
- ```
+dataset = Dataset.create(
+    dataset_name="cifar_dataset", 
+    dataset_project="dataset examples" 
+)
+```
 
 This creates a data processing task called `cifar_dataset` in the `dataset examples` project, which
 can be viewed in the WebApp.

docs/guides/ide/remote_jupyter_tutorial.md

@@ -19,17 +19,19 @@ where a `clearml-agent` will run and spin an instance of the remote session.
 
 ### Step 1: Launch `clearml-session`
 
-Execute the `clearml-session` command with the following command line options:
+Execute the following command:
 
 ```bash
 clearml-session --docker nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04 --packages "clearml" "tensorflow>=2.2" "keras" --queue default
 ```
-   
-* Enter a docker image `--docker nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04` 
+
+This sets the following arguments:
+
+* `--docker nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04` - Docker image
     
-* Enter required python packages `--packages "clearml" "tensorflow>=2.2" "keras"`
+* `--packages "clearml" "tensorflow>=2.2" "keras"` - Required Python packages
       
-* Specify the resource queue `--queue default`. 
+* `--queue default` - Selected queue to launch the session from 
 
 :::note
 Enter a project name using `--project <name>`. If no project is input, the default project