Small edits (#779)

docs/clearml_agent.md

@@ -757,11 +757,11 @@ Build a Docker container according to the execution environment of a specific ta
 clearml-agent build --id <task-id> --docker --target <new-docker-name>
 ```
 
-It's possible to add the Docker container as the base Docker image to a task (experiment), using one of the following methods:
+You can add the Docker container as the base Docker image to a task (experiment), using one of the following methods:
 
 - Using the **ClearML Web UI** - See [Base Docker image](webapp/webapp_exp_tuning.md#base-docker-image) on the "Tuning
   Experiments" page.
-- In the ClearML configuration file - Use the ClearML configuration file [agent.default_docker](configs/clearml_conf.md#agentdefault_docker)
+- In the ClearML configuration file - Use the ClearML configuration file [`agent.default_docker`](configs/clearml_conf.md#agentdefault_docker)
   options.
 
 Check out [this tutorial](guides/clearml_agent/exp_environment_containers.md) for building a Docker container 

docs/clearml_data/clearml_data.md

@@ -36,7 +36,7 @@ lineage and content information. See [dataset UI](../webapp/datasets/webapp_data
 
 ## Setup
 
-`clearml-data` comes built-in with the `clearml` python package! Just check out the [Getting Started](../getting_started/ds/ds_first_steps.md) 
+`clearml-data` comes built-in with the `clearml` python package! Check out the [Getting Started](../getting_started/ds/ds_first_steps.md) 
 guide for more info!
 
 ## Using ClearML Data

docs/clearml_data/clearml_data_cli.md

@@ -103,7 +103,7 @@ clearml-data remove [-h] [--id ID] [--files [FILES [FILES ...]]]
 
 ## upload
 
-Upload the local dataset changes to the server. By default, it's uploaded to the [ClearML Server](../deploying_clearml/clearml_server.md). It's possible to specify a different storage
+Upload the local dataset changes to the server. By default, it's uploaded to the [ClearML Server](../deploying_clearml/clearml_server.md). You can specify a different storage
 medium by entering an upload destination, such as `s3://bucket`, `gs://`, `azure://`, `/mnt/shared/`.
 
 ```bash

docs/clearml_data/data_management_examples/data_man_simple.md

@@ -29,7 +29,7 @@ the needed files.
     New dataset created id=24d05040f3e14fbfbed8edb1bf08a88c
     ```
 
-1. Now let's add a folder. File addition is recursive, so it's enough to point at the folder 
+1. Add a folder. File addition is recursive, so it's enough to point at the folder 
 to captures all files and sub-folders:
    
    ```bash

docs/clearml_sdk/task_sdk.md

@@ -171,7 +171,7 @@ In order to mitigate the clutter that a multitude of debugging tasks might creat
   the ClearML configuration reference)
 * The previous task execution did not have any artifacts / models
 
-It's possible to always create a new task by passing `reuse_last_task_id=False`.
+You can always create a new task by passing `reuse_last_task_id=False`.
 
 See full `Task.init` reference [here](../references/sdk/task.md#taskinit).
 
@@ -267,7 +267,7 @@ For example:
     a_task = Task.get_task(project_name='examples', task_name='artifacts')
     ```
 
-Once a task object is obtained, it's possible to query the state of the task, reported scalars, etc.
+Once a task object is obtained, you can query the state of the task, reported scalars, etc.
 The task's outputs, such as artifacts and models, can also be retrieved. 
 
 ## Querying / Searching Tasks
@@ -708,7 +708,7 @@ local_csv = preprocess_task.artifacts['data'].get_local_copy()
 See more details in the [Using Artifacts example](https://github.com/allegroai/clearml/blob/master/examples/reporting/using_artifacts_example.py).
 
 ## Models 
-The following is an overview of working with models through a `Task` object. It is also possible to work directly with model
+The following is an overview of working with models through a `Task` object. You can also work directly with model
 objects (see [Models (SDK)](model_sdk.md)).
 
 ### Logging Models Manually
@@ -737,7 +737,7 @@ The snapshots of manually uploaded models aren't automatically captured. To upda
 task.update_output_model(model_path='path/to/model')
 ```
 
-It's possible to modify the following parameters:
+You can modify the following parameters:
 * Model location
 * Model name
 * Model description

docs/cloud_autoscaling/autoscaling_overview.md

@@ -105,7 +105,7 @@ You can set up Kubernetes' cluster autoscaler to work with your cloud providers,
 your Kubernetes cluster as needed; increasing the amount of nodes when there aren't enough to execute pods and removing 
 underutilized nodes. See [charts](https://github.com/kubernetes/autoscaler/tree/master/charts) for specific cloud providers.
 
-:::note Enterprise features
+:::important Enterprise features
 The ClearML Enterprise plan supports K8S servicing multiple ClearML queues, as well as providing a pod template for each 
 queue for describing the resources for each pod to use. See [ClearML Helm Charts](https://github.com/allegroai/clearml-helm-charts/tree/main).  
 :::

docs/community.md

@@ -57,7 +57,7 @@ help maintainers reproduce the problem:
 * **Provide specific examples to demonstrate the steps.** Include links to files or GitHub projects, or copy / paste snippets which you use in those examples.
 * **If you are reporting any ClearML crash,** include a crash report with a stack trace from the operating system. Make 
   sure to add the crash report in the issue and place it in a [code block](https://docs.github.com/en/github/writing-on-github/working-with-advanced-formatting/creating-and-highlighting-code-blocks), 
-  a [file attachment](https://help.github.com/articles/file-attachments-on-issues-and-pull-requests), or just put it in 
+  a [file attachment](https://help.github.com/articles/file-attachments-on-issues-and-pull-requests), or put it in 
   a [gist](https://gist.github.com) (and provide a link to that gist).
 * **Describe the behavior you observed after following the steps** and the exact problem with that behavior.
 * **Explain which behavior you expected to see and why.**

docs/configs/clearml_conf.md

@@ -413,7 +413,7 @@ ___
 
 **`agent.match_rules`** (*[dict]*)
 
-:::note Enterprise Feature
+:::important Enterprise Feature
 This feature is available under the ClearML Enterprise plan
 :::
 
@@ -1437,7 +1437,7 @@ sdk {
 
 ## Configuration Vault
 
-:::note Enterprise Feature
+:::important Enterprise Feature
 This feature is available under the ClearML Enterprise plan
 :::
 

docs/deploying_clearml/clearml_server_config.md

@@ -382,7 +382,7 @@ options.
 
 ### Custom UI Context Menu Actions
 
-:::note Enterprise Feature
+:::important Enterprise Feature
 This feature is available under the ClearML Enterprise plan
 :::
 

docs/faq.md

@@ -204,10 +204,10 @@ See server upgrade instructions for any of the available formats:
 
 #### Can I log input and output models manually?   <a id="manually-log-models"></a>
 
-Yes! Use the [`InputModel.import_model`](references/sdk/model_inputmodel.md#inputmodelimport_model) 
-and [`Task.connect`](references/sdk/task.md#connect) methods to manually connect an input model. Use the 
-[`OutputModel.update_weights`](references/sdk/model_outputmodel.md#update_weights) 
-method to manually connect a model weights file.
+Yes! Use [`InputModel.import_model()`](references/sdk/model_inputmodel.md#inputmodelimport_model) 
+and [`Task.connect()`](references/sdk/task.md#connect) to connect an input model. Use
+[`OutputModel.update_weights()`](references/sdk/model_outputmodel.md#update_weights) 
+to connect a model weights file.
 
 ```python
 input_model = InputModel.import_model(link_to_initial_model_file)
@@ -801,8 +801,8 @@ To fix this issue, you could import the `time` package and add a `time.sleep(20)
 
 #### Can I use ClearML with scikit-learn?   <a id="use-scikit-learn"></a>
 
-Yes! `scikit-learn` is supported. Everything you do is logged. ClearML automatically logs models which are stored using `joblib`. 
-See the scikit-learn examples with [Matplotlib](guides/frameworks/scikit-learn/sklearn_matplotlib_example.md) and [Joblib](guides/frameworks/scikit-learn/sklearn_joblib_example.md).
+Yes! `scikit-learn` is supported. ClearML automatically logs models which are stored using `joblib`.
+For more information, see [scikit-learn](integrations/scikit_learn.md).
 
 ## ClearML Configuration
 

docs/fundamentals/hyperparameters.md

@@ -11,7 +11,7 @@ hyperparameters and results can be saved and compared, which is key to understan
 
 ClearML lets you easily try out different hyperparameter values without changing your original code. ClearML's [execution
 agent](../clearml_agent.md) will override the original values with any new ones you specify through the web UI (see 
-[Configuration](../webapp/webapp_exp_tuning.md#configuration) in the Tuning Experiments page). It's also possible to 
+[Configuration](../webapp/webapp_exp_tuning.md#configuration) in the Tuning Experiments page). You can also
 programmatically set experiment parameters.
 
 ## Tracking Hyperparameters 
@@ -50,7 +50,7 @@ parameter specifying parameters to log.
 log_os_environments: ["AWS_*", "CUDA_VERSION"]
 ```
 
-It's also possible to specify environment variables using the `CLEARML_LOG_ENVIRONMENT` variable.
+You can also specify environment variables using the `CLEARML_LOG_ENVIRONMENT` variable.
 
 :::note Overriding clearml.conf
 The `CLEARML_LOG_ENVIRONMENT` always overrides the `clearml.conf` file. 

docs/fundamentals/task.md

@@ -23,7 +23,7 @@ how to group tasks, though different models or objectives are usually grouped in
 Tasks can be accessed and utilized with code. [Access a task](../clearml_sdk/task_sdk.md#accessing-tasks) by 
 specifying project name and task name combination or by a unique ID. 
 
-It's possible to create copies of a task ([clone](../webapp/webapp_exp_reproducing.md)) then execute them with 
+You can create copies of a task ([clone](../webapp/webapp_exp_reproducing.md)) then execute them with 
 [ClearML Agent](../clearml_agent.md). When an agent executes a task, it uses the specified configuration to:
 
 * Install required Python packages
@@ -60,7 +60,7 @@ The captured [execution output](../webapp/webapp_exp_track_visual.md#experiment-
 * [Debug samples](../webapp/webapp_exp_track_visual.md#debug-samples)
 * [Models](artifacts.md) 
 
-To view a more in depth description of each task section, see [Tracking Experiments and Visualizing Results](../webapp/webapp_exp_track_visual.md).
+For a more in-depth description of each task section, see [Tracking Experiments and Visualizing Results](../webapp/webapp_exp_track_visual.md).
 
 ### Execution Configuration
 ClearML logs a task's hyperparameters specified as command line arguments, environment or code level variables. This 
@@ -115,7 +115,7 @@ they are attached to, and then retrieving the artifact with one of its following
 See more details in the [Using Artifacts example](https://github.com/allegroai/clearml/blob/master/examples/reporting/using_artifacts_example.py).
 
 ## Task Types
-Tasks have a *type* attribute, which denotes their purpose (e.g. training / testing / data processing). This helps to further 
+Tasks have a *type* attribute, which denotes their purpose. This helps to further 
 organize projects and ensure tasks are easy to [search and find](../clearml_sdk/task_sdk.md#querying--searching-tasks). 
 Available task types are: 
 * *training* (default) - Training a model 

docs/getting_started/ds/best_practices.md

@@ -27,9 +27,9 @@ The goal of this phase is to get a code, dataset, and environment set up, so you
 - [ClearML SDK](../../clearml_sdk/clearml_sdk.md) should be integrated into your code (check out [Getting Started](ds_first_steps.md)). 
   This helps visualizing the results and tracking progress.
 - [ClearML Agent](../../clearml_agent.md) helps moving your work to other machines without the hassle of rebuilding the environment every time, 
-  while also creating an easy queue interface that easily lets you just drop your experiments to be executed one by one
+  while also creating an easy queue interface that easily lets you drop your experiments to be executed one by one
   (great for ensuring that the GPUs are churning during the weekend).
-- [ClearML Session](../../apps/clearml_session.md) helps with developing on remote machines, just like you'd develop on your local laptop!
+- [ClearML Session](../../apps/clearml_session.md) helps with developing on remote machines, in the same way that you'd develop on your local laptop!
 
 ## Train Remotely
 
@@ -66,7 +66,7 @@ improving your results later on!
 
 ## Visibility Matters
 
-While it's possible to track experiments with one tool, and pipeline them with another, having 
+While you can track experiments with one tool, and pipeline them with another, having 
 everything under the same roof has its benefits! 
 
 Being able to track experiment progress and compare experiments, and, based on that, send experiments to execution on remote

docs/getting_started/ds/ds_second_steps.md

@@ -12,8 +12,8 @@ Every previously executed experiment is stored as a Task.
 A Task's project and name can be changed after the experiment has been executed.
 A Task is also automatically assigned an auto-generated unique identifier (UUID string) that cannot be changed and always locates the same Task in the system.
 
-It's possible to retrieve a Task object programmatically by querying the system based on either the Task ID,
-or project and name combination. It's also possible to query tasks based on their properties, like tags (see [Querying Tasks](../../clearml_sdk/task_sdk.md#querying--searching-tasks)).
+Retrieve a Task object programmatically by querying the system based on either the Task ID,
+or project and name combination. You can also query tasks based on their properties, like tags (see [Querying Tasks](../../clearml_sdk/task_sdk.md#querying--searching-tasks)).
 
 ```python
 prev_task = Task.get_task(task_id='123456deadbeef')
@@ -28,7 +28,7 @@ on model performance, saving and comparing these between experiments is sometime
 
 ClearML supports logging `argparse` module arguments out of the box, so once ClearML is integrated into the code, it automatically logs all parameters provided to the argument parser.
 
-It's also possible to log parameter dictionaries (very useful when parsing an external config file and storing as a dict object),
+You can also log parameter dictionaries (very useful when parsing an external config file and storing as a dict object),
 whole configuration files, or even custom objects or [Hydra](https://hydra.cc/docs/intro/) configurations!
 
 ```python
@@ -139,9 +139,9 @@ This feature lets you easily get a full genealogy of every trained and used mode
 Full metrics logging is the key to finding the best performing model!
 By default, everything that's reported to TensorBoard and Matplotlib is automatically captured and logged.
 
-Since not all metrics are tracked that way, it's also possible to manually report metrics using a [`Logger`](../../fundamentals/logger.md) object.
+Since not all metrics are tracked that way, you can also manually report metrics using a [`Logger`](../../fundamentals/logger.md) object.
 
-It's possible to log everything, from time series data to confusion matrices to HTML, Audio and Video, to custom plotly graphs! Everything goes!
+You can log everything, from time series data to confusion matrices to HTML, Audio and Video, to custom plotly graphs! Everything goes!
 
 ![image](../../img/report_plotly.png)
 
@@ -157,7 +157,7 @@ The experiment table is a powerful tool for creating dashboards and views of you
 
 ### Creating Leaderboards
 Customize the [experiments table](../../webapp/webapp_exp_table.md) to fit your own needs, adding desired views of parameters, metrics and tags.
-It's possible to filter and sort based on parameters and metrics, so creating custom views is simple and flexible.
+You can filter and sort based on parameters and metrics, so creating custom views is simple and flexible.
 
 Create a dashboard for a project, presenting the latest Models and their accuracy scores, for immediate insights.
 
@@ -166,7 +166,7 @@ This is helpful to monitor your projects' progress, and to share it across the o
 
 Any page is sharable by copying the URL from the address bar, allowing you to bookmark leaderboards or to send an exact view of a specific experiment or a comparison page.
 
-It's also possible to tag Tasks for visibility and filtering allowing you to add more information on the execution of the experiment.
+You can also tag Tasks for visibility and filtering allowing you to add more information on the execution of the experiment.
 Later you can search based on task name in the search bar, and filter experiments based on their tags, parameters, status, and more.
 
 ## What's Next?

docs/getting_started/mlops/mlops_first_steps.md

@@ -26,7 +26,7 @@ required python packages, and execute and monitor the process.
 
 ## Set up an Agent
 
-1. Let's install the agent!
+1. Install the agent:
 
     ```bash
     pip install clearml-agent
@@ -42,7 +42,7 @@ required python packages, and execute and monitor the process.
     If you've already created credentials, you can copy-paste the default agent section from [here](https://github.com/allegroai/clearml-agent/blob/master/docs/clearml.conf#L15) (this is optional. If the section is not provided the default values will be used)
     :::
 
-1. Start the agent's daemon and assign it to a [queue](../../fundamentals/agents_and_queues.md#what-is-a-queue). 
+1. Start the agent's daemon and assign it to a [queue](../../fundamentals/agents_and_queues.md#what-is-a-queue):
 
     ```bash
     clearml-agent daemon --queue default

docs/pipelines/pipelines_sdk_function_decorators.md

@@ -214,7 +214,7 @@ if __name__ == '__main__':
 ```
 
 :::tip RUN PIPELINE CONTROLLER LOCALLY
-It is possible to run the pipeline logic itself locally, while keeping the pipeline components execution remote
+You can run the pipeline logic locally, while keeping the pipeline components execution remote
 (enqueued and executed by the clearml-agent). Pass `pipeline_execution_queue=None` to the `@PipelineDecorator.pipeline` decorator.
 ```python
 @PipelineDecorator.pipeline(

docs/webapp/applications/apps_gcp_autoscaler.md

@@ -62,7 +62,7 @@ For more information about how autoscalers work, see [Autoscalers Overview](../.
   
 ![GCP autoscaler wizard](../../img/apps_gcp_autoscaler_wizard.png)
 
-:::note Enterprise Feature
+:::important Enterprise Feature
 You can utilize the [configuration vault](../../webapp/webapp_profile.md#configuration-vault) to configure GCP 
 credentials for the Autoscaler in the following format: 
 

docs/webapp/webapp_profile.md

@@ -498,7 +498,7 @@ The **USAGE & BILLING** section displays your ClearML workspace usage informatio
 ![Billing and Usage free](../img/settings_billing_usage_free.png)
 
 To add users to your workspace, click **INVITE USERS** in the **USERS** section. This will redirect you to the 
-**USER MANAGEMENT** page, where you can invite users (see details [here](#inviting-new-teammates))
+**USER MANAGEMENT** page, where you can invite users (see details [here](#inviting-new-teammates)).
 
 ### ClearML Pro