Small edits (#174)

docs/clearml_agent.md

@@ -384,15 +384,15 @@ A single agent can listen to multiple queues. The priority is set by their order
 ```bash
 clearml-agent daemon --detached --queue high_q low_q --gpus 0
 ```
-This ensures the agent first tries to pull a Task from the “hiqh_q” queue, and only if it is empty, the agent will try to pull 
+This ensures the agent first tries to pull a Task from the `hiqh_q` queue, and only if it is empty, the agent will try to pull 
-from the “low_q” queue.
+from the `low_q` queue.
 
 To make sure an agent pulls from all queues equally, add the `--order-fairness` flag.
 ```bash
 clearml-agent daemon --detached --queue group_a group_b --order-fairness  --gpus 0
 ```
-It will make sure the agent will pull from the “group_a” queue, then from “group_b”, then back to “group_a”, etc. This ensures 
+It will make sure the agent will pull from the `group_a` queue, then from `group_b`, then back to `group_a`, etc. This ensures 
-that “group A” or ”group_b” will not be able to starve one another of resources.
+that `group A` or `group_b` will not be able to starve one another of resources.
 
 ### Explicit Task Execution
 
@@ -713,8 +713,8 @@ Currently, these runtime properties can only be set using an ClearML REST API ca
 endpoint, as follows: 
 
 * The body of the request must contain the `worker-id`, and the runtime property to add.
-* An expiry date is optional. Use the format `”expiry”:<time>`. For example,  `”expiry”:86400` will set an expiry of 24 hours.
+* An expiry date is optional. Use the format `"expiry":<time>`. For example,  `"expiry":86400` will set an expiry of 24 hours.
-* To delete the property, set the expiry date to zero, `'expiry:0'`.
+* To delete the property, set the expiry date to zero, `"expiry":0`.
 
 For example, to force a worker on for 24 hours:
 
@@ -736,10 +736,12 @@ 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 trains.backend_api.session.client import APIClient
+    from clearml.backend_api.session.client import APIClient
     client = APIClient()
-    client.queues.update(queue=”<queue_id>”, tags=["force_workers:on"]
+    client.queues.update(queue="<queue_id>", tags=["force_workers:on"]
 
 Or, force workers on for a queue using the REST API:
 
+```bash
 curl --user <key>:<secret> --header "Content-Type: application/json" --data '{"queue":"<queue_id>","tags":["force_workers:on"]}' http://<api-server-hostname-or-ip>:8008/queues.update
+```

docs/clearml_data/clearml_data_cli.md

@@ -13,7 +13,7 @@ The following page provides a reference to `clearml-data`'s CLI commands.
 
 ### Creating a Dataset
 ```bash
-clearml-data create --project <project_name> --name <dataset_name> --parents <existing_dataset_id>`
+clearml-data create --project <project_name> --name <dataset_name> --parents <existing_dataset_id>
 ```
 Creates a new dataset. <br/>
 
@@ -132,7 +132,7 @@ Once a dataset is finalized, it can no longer be modified.
 
 ### Syncing Local Storage
 ```
-clearml-data sync [--id <dataset_id] --folder <folder_location>  [--parents '<parent_id>']`
+clearml-data sync [--id <dataset_id] --folder <folder_location>  [--parents '<parent_id>']
 ```
 This option syncs a folder's content with ClearML. It is useful in case a user has a single point of truth (i.e. a folder) which
 updates from time to time.

docs/clearml_data/data_management_examples/data_man_python.md

@@ -36,7 +36,10 @@ 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

docs/faq.md

@@ -1096,10 +1096,10 @@ For example, to get the metrics for an experiment and to print metrics as a hist
 1. Send a request for a metrics histogram for experiment (task) ID `11` using the `events` service `ScalarMetricsIterHistogramRequest` method and print the histogram.
 
     ```python
-    # Import Session from the trains backend_api
+    # Import Session from the clearml backend_api
-    from trains.backend_api import Session
+    from clearml.backend_api import Session
     # Import the services for tasks, events, and projects
-    from trains.backend_api.services import tasks, events, projects
+    from clearml.backend_api.services import tasks, events, projects
         
     # Create an authenticated session
     session = Session()

docs/getting_started/ds/ds_second_steps.md

@@ -99,7 +99,11 @@ we need to pass a storage location for the model files to be uploaded to.
 
 For example, upload all snapshots to an S3 bucket:
 ```python
-task = Task.init(project_name='examples', task_name='storing model', output_uri='s3://my_models/')
+task = Task.init(
+    project_name='examples', 
+    task_name='storing model', 
+    output_uri='s3://my_models/'
+)
 ```
 
 Now, whenever the framework (TF/Keras/PyTorch etc.) stores a snapshot, the model file is automatically uploaded to the bucket to a specific folder for the experiment.

docs/getting_started/mlops/mlops_second_steps.md

@@ -43,7 +43,7 @@ dataset_folder = dataset.get_mutable_local_copy(
     overwrite=True
 )
 # change some files in the `./work_dataset` folder
-...
+
 # create a new version of the dataset with the pickle file
 new_dataset = Dataset.create(
     dataset_project='data', dataset_name='dataset_v2', 

docs/guides/datasets/data_man_python.md

@@ -36,7 +36,10 @@ 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

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:
 
-* Enter required python packages `--packages "clearml" "tensorflow>=2.2" "keras"`
+* `--docker nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04` - Docker image
     
-* Specify the resource queue `--queue default`. 
+* `--packages "clearml" "tensorflow>=2.2" "keras"` - Required Python packages
+      
+* `--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