Small edits (#671)

docs/apps/clearml_param_search.md

@@ -29,8 +29,8 @@ of the optimization results in table and graph forms.
 |`--task-id`|ID of a ClearML task whose hyperparameters will be optimized. Required unless `--script` is specified.|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
 |`--script`|Script to run the parameter search on. Required unless `--task-id` is specified.|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
 |`--queue`|Queue to enqueue the experiments on.|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
-|`--params-search`|Parameters space for optimization. See more information [here](#specifying-the-parameter-space). |<img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|
-|`--params-override`|Additional parameters of the base task to override for this parameter search. Use the following JSON format for each parameter: `{"name": "param_name", "value": <new_value>}`. Windows users, see JSON format note [here](#json_note).|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />| 
+|`--params-search`|Parameters space for optimization. See more information in [Specifying the Parameter Space](#specifying-the-parameter-space). |<img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|
+|`--params-override`|Additional parameters of the base task to override for this parameter search. Use the following JSON format for each parameter: `{"name": "param_name", "value": <new_value>}`. Windows users, see [JSON format note](#json_note).|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />| 
 |`--objective-metric-title`| Objective metric title to maximize/minimize (e.g. 'validation').|<img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|
 |`--objective-metric-series`| Objective metric series to maximize/minimize (e.g. 'loss').|<img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|
 |`--objective-metric-sign`| Optimization target, whether to maximize or minimize the value of the objective metric specified. Possible values: "min", "max", "min_global", "max_global". See more information [here](#optimization-objective). |<img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|

docs/clearml_agent.md

@@ -349,7 +349,7 @@ ClearML Agent supports executing tasks in multiple environments.
 ### PIP Mode 
 By default, ClearML Agent works in PIP Mode, in which it uses [pip](https://en.wikipedia.org/wiki/Pip_(package_manager)) 
 as the package manager. When ClearML runs, it will create a virtual environment 
-(or reuse an existing one, see [here](clearml_agent.md#virtual-environment-reuse)).
+(or [reuse an existing one](clearml_agent.md#virtual-environment-reuse)).
 Task dependencies (Python packages) will be installed in the virtual environment.
 
 ### Conda Mode 

docs/clearml_agent/clearml_agent_ref.md

@@ -15,9 +15,9 @@ The following page provides a reference to `clearml-agent`'s CLI commands:
 Use the `build` command to create worker environments without executing tasks. 
 
 You can build Docker containers according to the execution environments of specific tasks, which an agent can later
-use to execute other tasks. See tutorial [here](../guides/clearml_agent/exp_environment_containers.md).
+use to execute other tasks. See [tutorial](../guides/clearml_agent/exp_environment_containers.md).
 
-You can also create a Docker container that executes a specific task when launched. See tutorial [here](../guides/clearml_agent/executable_exp_containers.md). 
+You can also create a Docker container that executes a specific task when launched. See [tutorial](../guides/clearml_agent/executable_exp_containers.md). 
 
 ```bash
 clearml-agent build [-h] --id TASK_ID [--target TARGET]

docs/clearml_sdk/clearml_sdk.md

@@ -93,7 +93,7 @@ See an [overview](apiclient_sdk.md) for APIClient usage.
 Use the ClearmlJob to create and manage jobs based on existing tasks. The class supports changing a job's parameters, 
 configurations, and other execution details. 
 
-See reference page [here](../references/sdk/automation_job_clearmljob.md).
+See [reference page](../references/sdk/automation_job_clearmljob.md).
 
 ### AutoScaler
 The `AutoScaler` class facilitates implementing resource budgeting. See class methods [here](https://github.com/allegroai/clearml/blob/master/clearml/automation/auto_scaler.py).

docs/faq.md

@@ -1,4 +1,4 @@
---
+---
 title: FAQ
 ---
 

docs/guides/frameworks/pytorch/notebooks/image/image_classification_CIFAR10.md

@@ -32,8 +32,11 @@ By doubling clicking a thumbnail, you can view a spectrogram plot in the image v
 ClearML automatically logs TensorFlow Definitions. A parameter dictionary is logged by connecting it to the Task using 
 [`Task.connect()`](../../../../../references/sdk/task.md#connect).
 
-    configuration_dict = {'number_of_epochs': 3, 'batch_size': 4, 'dropout': 0.25, 'base_lr': 0.001}
-    configuration_dict = task.connect(configuration_dict)  # enabling configuration override by clearml
+```python
+configuration_dict = {'number_of_epochs': 3, 'batch_size': 4, 'dropout': 0.25, 'base_lr': 0.001}
+# enabling configuration override by clearml
+configuration_dict = task.connect(configuration_dict)
+```
 
 Parameter dictionaries appear in **CONFIGURATION** **>** **HYPERPARAMETERS** **>** **General**.
 

docs/guides/frameworks/pytorch/notebooks/table/download_and_preprocessing.md

@@ -28,7 +28,7 @@ For example, the raw data is read into a Pandas DataFrame named `train_set`, and
 ```python
 train_set = pd.read_csv(Path(path_to_ShelterAnimal) / 'train.csv')
 Logger.current_logger().report_table(
-    title='ClearMLet - raw',series='pandas DataFrame',iteration=0, table_plot=train_set.head()
+    title='ClearMLet - raw', series='pandas DataFrame', iteration=0, table_plot=train_set.head()
 )
 ```
 

docs/guides/frameworks/pytorch/pytorch_distributed_example.md

@@ -26,8 +26,10 @@ The script does the following:
 The example uploads a dictionary as an artifact in the main Task by calling [`Task.upload_artifact()`](../../../references/sdk/task.md#upload_artifact)
 on `Task.current_task` (the main Task). The dictionary contains the `dist.rank` of the subprocess, making each unique.
 
-    Task.current_task().upload_artifact(
-        'temp {:02d}'.format(dist.get_rank()), artifact_object={'worker_rank': dist.get_rank()})
+```python
+Task.current_task().upload_artifact(
+    'temp {:02d}'.format(dist.get_rank()), artifact_object={'worker_rank': dist.get_rank()})
+```
 
 All of these artifacts appear in the main Task, **ARTIFACTS** **>** **OTHER**.
 
@@ -39,8 +41,10 @@ Report loss to the main Task by calling [`Logger.report_scalar()`](../../../refe
 on `Task.current_task().get_logger`, which is the logger for the main Task. Since `Logger.report_scalar` is called with the 
 same title (`loss`), but a different series name (containing the subprocess' `rank`), all loss scalar series are logged together.
 
-    Task.current_task().get_logger().report_scalar(
-        'loss', 'worker {:02d}'.format(dist.get_rank()), value=loss.item(), iteration=i)
+```python
+Task.current_task().get_logger().report_scalar(
+    'loss', 'worker {:02d}'.format(dist.get_rank()), value=loss.item(), iteration=i)
+```
 
 The single scalar plot for loss appears in **SCALARS**.
 

docs/guides/reporting/image_reporting.md

@@ -1,5 +1,5 @@
 ---
-title: Images Reporting
+title: Image Reporting
 ---
 
 The [image_reporting.py](https://github.com/allegroai/clearml/blob/master/examples/reporting/image_reporting.py) example