Small edits (#455)

docs/apps/clearml_param_search.md

@@ -80,7 +80,7 @@ The following are the parameter type options and their corresponding fields:
     - `"values": List[Any]`- A list of valid parameter values to sample from
 
 For example: to specify a parameter search over uniform ranges of layer_1 and layer_2 sizes between 128 and 512 
-(in jumps of 128)  with varying batch sizes of 96, 128, and 160, use the following command:
+(in jumps of 128) with varying batch sizes of 96, 128, and 160, use the following command:
 
 <div className="wb-normal">
 

docs/clearml_agent.md

@@ -483,7 +483,7 @@ Self-hosted [ClearML Server](deploying_clearml/clearml_server.md) comes by defau
 By default, the server is open and does not require username and password, but it can be [password-protected](deploying_clearml/clearml_server_security.md#user-access-security).
 In case it is password-protected, the services agent will need to be configured with server credentials (associated with a user).
 
-To do that, set these environment variables on  the ClearML Server machine with the appropriate credentials:
+To do that, set these environment variables on the ClearML Server machine with the appropriate credentials:
 ```
 CLEARML_API_ACCESS_KEY
 CLEARML_API_SECRET_KEY
@@ -499,7 +499,7 @@ Build a Docker container that when launched executes a specific experiment, or a
   ```bash
   clearml-agent build --id <task-id> --docker --target <new-docker-name> --entry-point reuse_task
   ```
-- Build a Docker container that at launch will clone a Task  specified by Task ID, and will execute the newly cloned Task.
+- Build a Docker container that at launch will clone a Task specified by Task ID, and will execute the newly cloned Task.
   ```bash
   clearml-agent build --id <task-id> --docker --target <new-docker-name> --entry-point clone_task
   ```

docs/clearml_serving/clearml_serving_tutorial.md

@@ -37,7 +37,7 @@ clearml-serving --id <service_id> model add --engine sklearn --endpoint "test_mo
 ```
 
 :::info Service ID
-Make sure that you have executed `clearml-servings`'s
+Make sure that you have executed `clearml-serving`'s
 [initial setup](clearml_serving.md#initial-setup), in which you create a Serving Service. 
 The Serving Service's ID is required to register a model, and to execute `clearml-serving`'s `metrics` and `config` commands 
 :::
@@ -92,7 +92,7 @@ or with the `clearml-serving` CLI.
    ```
     
   You now have a new Model named `manual sklearn model` in the `serving examples` project. The CLI output prints 
-  the UID of the new model, which you will use it to register a new endpoint. 
+  the UID of the new model, which you will use to register a new endpoint. 
 
   In the [ClearML web UI](../webapp/webapp_overview.md), the new model is listed under the **Models** tab of its project. 
   You can also download the model file itself directly from the web UI. 
@@ -105,7 +105,7 @@ or with the `clearml-serving` CLI.
 :::info Model Storage
 You can also provide a different storage destination for the model, such as S3/GS/Azure, by passing
 `--destination="s3://bucket/folder"`, `gs://bucket/folder`, `azure://bucket/folder`. There is no need to provide a unique 
-path tp the destination argument, the location of the model will be a unique path based on the serving service ID and the 
+path to the destination argument, the location of the model will be a unique path based on the serving service ID and the 
 model name
 :::
 
@@ -116,7 +116,7 @@ model name
 The ClearML Serving Service supports automatic model deployment and upgrades, which is connected with the model 
 repository and API. When the model auto-deploy is configured, new model versions will be automatically deployed when you 
 `publish` or `tag` a new model in the ClearML model repository. This automation interface allows for simpler CI/CD model 
-deployment process, as a single API automatically deploy (or remove) a model from the Serving Service.
+deployment process, as a single API automatically deploys (or removes) a model from the Serving Service.
 
 #### Automatic Model Deployment Example
 
@@ -142,7 +142,7 @@ deployment process, as a single API automatically deploy (or remove) a model fro
 
 ### Canary Endpoint Setup
 
-Canary endpoint deployment add a new endpoint where the actual request is sent to a preconfigured set of endpoints with 
+Canary endpoint deployment adds a new endpoint where the actual request is sent to a preconfigured set of endpoints with 
 pre-provided distribution. For example, let's create a new endpoint "test_model_sklearn_canary", you can provide a list 
 of endpoints and probabilities (weights).
 
@@ -195,13 +195,13 @@ Example:
 ClearML serving instances send serving statistics (count/latency) automatically to Prometheus and Grafana can be used 
 to visualize and create live dashboards. 
 
-The default docker-compose installation is preconfigured with Prometheus and Grafana, do notice that by default data/ate 
+The default docker-compose installation is preconfigured with Prometheus and Grafana. Notice that by default data/ate 
 of both containers is *not* persistent. To add persistence, we recommend adding a volume mount.
 
 You can also add many custom metrics on the input/predictions of your models. Once a model endpoint is registered, 
 adding custom metric can be done using the CLI.
 
-For example, assume the mock scikit-learn model is deployed on endpoint `test_model_sklearn`,  you can log the requests 
+For example, assume the mock scikit-learn model is deployed on endpoint `test_model_sklearn`, you can log the requests 
 inputs and outputs (see examples/sklearn/preprocess.py example):
 
 ```bash

docs/community.md

@@ -55,7 +55,7 @@ help maintainers reproduce the problem:
 * **Describe the exact steps necessary to reproduce the problem** in as much detail as possible. Please do not just summarize what you did. Make sure to explain how you did it.
 * **Provide the specific environment setup.** Include the ``pip freeze`` output, specific environment variables, Python version, and other relevant information.
 * **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 
+* **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 [gist](https://gist.github.com) (and provide a link to that gist).

docs/deploying_clearml/clearml_server_gcp.md

@@ -73,7 +73,7 @@ The minimum requirements for ClearML Server are:
 
 ## Restarting
 
-**To restart  ClearML Server Docker deployment:**
+**To restart ClearML Server Docker deployment:**
 
 * Stop and then restart the Docker containers by executing the following commands:
 

docs/fundamentals/artifacts.md

@@ -72,6 +72,6 @@ the models associated with a project are listed.
 ## SDK Interface
 
 See [the Models SDK interface](../clearml_sdk/model_sdk.md) for an overview for using the most basic Pythonic methods of the model 
-classes. See a detailed list of all available methods  in the [Model](../references/sdk/model_model.md), [OutputModel](../references/sdk/model_outputmodel.md), and [InputModel](../references/sdk/model_inputmodel.md) 
+classes. See a detailed list of all available methods in the [Model](../references/sdk/model_model.md), [OutputModel](../references/sdk/model_outputmodel.md), and [InputModel](../references/sdk/model_inputmodel.md) 
 reference pages. 
 

docs/getting_started/ds/ds_first_steps.md

@@ -32,7 +32,7 @@ pip install clearml
    Please create new clearml credentials through the settings page in your `clearml-server` web app, 
    or create a free account at https://app.clear.ml/settings/webapp-configuration
     
-   In the settings > workspace  page, press "Create new credentials", then press "Copy to clipboard".
+   In the settings > workspace page, press "Create new credentials", then press "Copy to clipboard".
    Paste copied configuration here: 
    ```
       

docs/getting_started/ds/ds_second_steps.md

@@ -40,7 +40,7 @@ Check [this](../../fundamentals/hyperparameters.md) out for all Hyperparameter l
 
 ## Log Artifacts
 
-ClearML allows you to easily store the output products of an experiment -  Model snapshot / weights file, a preprocessing of your data, feature representation of data and more!
+ClearML allows you to easily store the output products of an experiment - Model snapshot / weights file, a preprocessing of your data, feature representation of data and more!
 
 Essentially, artifacts are files (or python objects) uploaded from a script and are stored alongside the Task.
 These Artifacts can be easily accessed by the web UI or programmatically.
@@ -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.
+It's possible to 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.
 

docs/getting_started/video_tutorials/pipelines_from_tasks.md

@@ -45,7 +45,7 @@ The structure of your pipeline will be derived from looking at this `parents` ar
 
 Now we do the same for the final step. However, remember the empty hyperparameters we saw before? We still have to overwrite these. We can use the `parameter_override` argument to do just that.
 
-For example, we can tell the first step to use the global pipeline parameter raw data url like so. But we can also reference output artifacts from a previous step by using its name and we can of course also just overwrite a parameter with a normal value. Finally, we can even pass along the unique task ID of a previous step, so you can get the task object based on that ID and access anything and everything within that task.
+For example, we can tell the first step to use the global pipeline parameter raw data url like so. But we can also reference output artifacts from a previous step by using its name, and we can of course also just overwrite a parameter with a normal value. Finally, we can even pass along the unique task ID of a previous step, so you can get the task object based on that ID and access anything and everything within that task.
 
 And that’s it! We now have our first pipeline!
 

docs/guides/docker/extra_docker_shell_script.md

@@ -29,7 +29,7 @@ it is commented out, make sure to uncomment the line. We will use the example sc
 1. Search for and go to `docker_force_pull` in the document, and make sure that it is set to `true`, so that your docker 
    image will be updated. 
    
-1. Run the `clearml-agent` in docker mode: `clearml-agent daemon --docker --queue default`. The agent  will use the default 
+1. Run the `clearml-agent` in docker mode: `clearml-agent daemon --docker --queue default`. The agent will use the default 
    Cuda/Nvidia Docker Image. 
 
 1. Enqueue any ClearML Task to the `default` queue, which the Agent is now listening to. The Agent pulls the Task, and then reproduces it, 

docs/guides/frameworks/pytorch_ignite/integration_pytorch_ignite.md

@@ -6,7 +6,7 @@ The [cifar_ignite.py](https://github.com/allegroai/clearml/blob/master/examples/
 script integrates ClearML into code that uses [PyTorch Ignite](https://github.com/pytorch/ignite). 
 
 The example script does the following:
-* Trains a  neural network on the CIFAR10 dataset for image classification.
+* Trains a neural network on the CIFAR10 dataset for image classification.
 * Creates a [ClearML Task](../../../fundamentals/task.md) named `image classification CIFAR10`, which is associated with 
   the `examples` project.
 * Calls the [`Task.connect`](../../../references/sdk/task.md#connect) method to track experiment configuration.

docs/guides/pipeline/pipeline_controller.md

@@ -68,7 +68,7 @@ The sections below describe in more detail what happens in the controller task a
    Custom configuration values specific to this step execution are defined through the `parameter_override` parameter, 
    where the first step’s artifact is fed into the second step.
 
-   Special pre-execution and post-execution logic is added for this step  through the use of `pre_execute_callback` 
+   Special pre-execution and post-execution logic is added for this step through the use of `pre_execute_callback` 
    and  `post_execute_callback` respectively. 
 
    ```python

docs/guides/pipeline/pipeline_functions.md

@@ -35,7 +35,7 @@ logged as required packages for the pipeline execution step.
    ```
     
 1. Set the default execution queue to be used. All the pipeline steps will be enqueued for execution in this queue 
-   (unless overridden by the `execution_queue` parameter of  the `add_function_step` method).
+   (unless overridden by the `execution_queue` parameter of the `add_function_step` method).
     
   ```python
   pipe.set_default_execution_queue('default')

docs/guides/services/cleanup_service.md

@@ -18,8 +18,8 @@ to your needs, and enqueue it for execution directly from the ClearML UI.
 Configure the task execution by modifying the `args` dictionary:
 * `delete_threshold_days` - Tasks older than this number of days will be deleted. The default value is 30 days.
 * `cleanup_period_in_days` - Repeat the cleanup service at this interval, in days. The default value is 1.0 (run once a day).
-* `force_delete` - If `False` (default), delete only Draft tasks. If `True`,  allows deletion of  tasks in any status. 
-* `run_as_service` - If `True` (default),  the task will be enqueued for remote execution (default queue: "services"). Otherwise, the script will execute locally. 
+* `force_delete` - If `False` (default), delete only Draft tasks. If `True`, allows deletion of tasks in any status. 
+* `run_as_service` - If `True` (default), the task will be enqueued for remote execution (default queue: "services"). Otherwise, the script will execute locally. 
 
 :::note Remote Execution
 If `run_as_service` is set to `True`, make sure a `clearml-agent` is assigned to the `services` queue.
@@ -48,7 +48,7 @@ This is followed by details from the cleanup.
 an `APIClient` object that establishes a session with the ClearML Server, and accomplishes the cleanup by calling:
 * [`Tasks.get_all`](../../references/api/tasks.md#post-tasksget_all) to get a list of Tasks to delete, providing the following parameters:
     * `system_tags` - Get only Tasks tagged as `archived`.
-    * `status_changed` - Get Tasks whose last status change is older than then delete threshold (in seconds).
+    * `status_changed` - Get Tasks whose last status change is older than the delete threshold (in seconds).
 * [`Task.delete`](../../references/sdk/task.md#delete) - Delete a Task.  
 
 ## Configuration

docs/hyperdatasets/dataset.md

@@ -124,7 +124,7 @@ Dataset.delete(
 ```
 
 This supports deleting sources located in AWS S3, GCP, and Azure Storage (not local storage). The `delete_sources` 
-parameter is ignored if `delete_all_versions` is `False`.  You can view the deletion process’ progress by passing 
+parameter is ignored if `delete_all_versions` is `False`. You can view the deletion process’ progress by passing 
 `show_progress=True` (`tqdm` required).  
 
 ### Tagging Datasets
@@ -147,7 +147,7 @@ MyDataset.remove_tags(["dogs"])
 Dataset versioning refers to the group of ClearML Enterprise SDK and WebApp (UI) features for creating, 
 modifying, and deleting Dataset versions.
 
-ClearML Enterprise supports simple and advanced Dataset versioning paradigms.  A **simple version structure** consists of 
+ClearML Enterprise supports simple and advanced Dataset versioning paradigms. A **simple version structure** consists of 
 a single evolving version, with historic static snapshots. Continuously push your changes to your single dataset version, 
 and take a snapshot to record the content of your dataset at a specific point in time.
 

docs/hyperdatasets/masks.md

@@ -26,7 +26,7 @@ a SingleFrame:
   * Metadata and data for the labeled area of an image
   
   
-See [Example 1](#example-1), which shows `masks` in `sources`, `mask` in `rois`,  and the key-value pairs used to relate 
+See [Example 1](#example-1), which shows `masks` in `sources`, `mask` in `rois`, and the key-value pairs used to relate 
 a mask to its source in a frame.
 
 

docs/hyperdatasets/sources.md

@@ -188,7 +188,7 @@ This example demonstrates `sources` for video, `masks`, and `preview`.
 
 This frame shows the `masks` section in `sources`, and the top-level `rois` array.
 
-In `sources`,  the `masks` subsection contains the sources for the two masks associated with the raw data. 
+In `sources`, the `masks` subsection contains the sources for the two masks associated with the raw data. 
 
 The raw mask data is located in:
 

docs/hyperdatasets/webapp/webapp_datasets_versioning.md

@@ -47,7 +47,7 @@ The version information is presented in the following tabs:
 * [Info](#info) 
    
 ## Frames
-The **Frames**  tab displays the contents of the selected dataset version.
+The **Frames** tab displays the contents of the selected dataset version.
 
 View the version's frames as thumbnail previews or in a table. Use the view toggle to switch between thumbnail 
 view <img src="/docs/latest/icons/ico-grid-view.svg" alt="thumbnail view" className="icon size-md space-sm" /> and 
@@ -71,7 +71,7 @@ To view the details of a specific frame, click on its preview, which will open t
 ### Simple Frame Filtering
 Simple frame filtering returns frames containing at least one annotation with a specified label.
 
-**To apply a simple frame filter,**  select a label from the **LABEL FILTER** list.
+**To apply a simple frame filter,** select a label from the **LABEL FILTER** list.
 
 <details className="cml-expansion-panel screenshot">
 <summary className="cml-expansion-panel-summary">Simple filter example</summary>

docs/hyperdatasets/webapp/webapp_dataviews.md

@@ -2,11 +2,11 @@
 title: The Dataviews Table
 ---
 
-[Dataviews](../dataviews.md) appear in the same Project as the experiment that stored the Dataview in the ClearML Enterprise platform, 
-as well as the **DATAVIEWS** tab in the **All Projects** page.
-
 The **Dataviews table** is a [customizable](#customizing-the-dataviews-table) list of Dataviews associated with a project.
-Use it to view, create, and edit Dataviews in the info panel. 
+Use it to view and create Dataviews, and access their info panels. 
+
+The table lists independent Dataview objects. To see Dataviews logged by a task, go
+to the specific task's **DATAVIEWS** tab (see [Experiment Dataviews](webapp_exp_track_visual.md)).
 
 View the Dataviews table in table view <img src="/docs/latest/icons/ico-table-view.svg" alt="Table view" className="icon size-md space-sm" /> 
 or in details view <img src="/docs/latest/icons/ico-split-view.svg" alt="Details view" className="icon size-md space-sm" />,
@@ -84,7 +84,7 @@ The same information can be found in the bottom menu, in a tooltip that appears
 
 ## Creating a Dataview 
 
-Create a new Dataview by clicking the **+ NEW DATAVIEW** button at the top right of the table, which open a 
+Create a new Dataview by clicking the **+ NEW DATAVIEW** button at the top right of the table, which opens a 
 **NEW DATAVIEW** window. 
 
 ![New Dataview window](../../img/webapp_dataview_new.png)

docs/pipelines/pipelines.md

@@ -58,7 +58,7 @@ when creating a pipeline step.
 
 ### Pipeline Step Caching
 The Pipeline controller also offers step caching, meaning, reusing outputs of previously executed pipeline steps, in the 
-case of  exact same step code, and the same step input values. By default, pipeline steps are not cached. Enable caching
+case of exact same step code, and the same step input values. By default, pipeline steps are not cached. Enable caching
 when creating a pipeline step.
 
 When a step is cached, the step code is hashed, alongside the step’s parameters (as passed in runtime), into a single 

docs/pipelines/pipelines_sdk_function_decorators.md

@@ -38,7 +38,7 @@ def main(pickle_url, mock_parameter='mock'):
   the following format: `{'section_name':['param_name']]}`. For example, the pipeline in the code above will store the 
   `pickle_url` parameter in the `General` section and `mock_parameter` in the `Mock` section. By default, arguments will 
   be stored in the `Args` section.  
-* `pool_frequency` - The pooling frequency (in minutes) for monitoring experiments / states.
+* `pool_frequency` - The polling frequency (in minutes) for monitoring experiments / states.
 * `add_pipeline_tags` - If `True`, add `pipe: <pipeline_task_id>` tag to all steps (Tasks) created by this pipeline 
   (this is useful to create better visibility in projects with multiple pipelines, and for easy selection) (default: 
   `False`).
@@ -111,11 +111,11 @@ def step_one(pickle_data_url: str, extra: int = 43):
   Example, assuming we have two functions, `parse_data()` and `load_data()`: `[parse_data, load_data]`
 * `parents` – Optional list of parent steps in the pipeline. The current step in the pipeline will be sent for execution only after all the parent steps have been executed successfully.
 
-Additionally, you can enable automatic logging of a step’s metrics / artifacts / models  to the pipeline task using the 
+Additionally, you can enable automatic logging of a step’s metrics / artifacts / models to the pipeline task using the 
 following arguments:
 * `monitor_metrics` (Optional) - Automatically log the step's reported metrics also on the pipeline Task. The expected 
   format is one of the following:
-  * List of pairs metric (title, series) to log: [(step_metric_title, step_metric_series), ].  Example: `[('test', 'accuracy'), ]`
+  * List of pairs metric (title, series) to log: [(step_metric_title, step_metric_series), ]. Example: `[('test', 'accuracy'), ]`
   * List of tuple pairs, to specify a different target metric to use on the pipeline Task: [((step_metric_title, step_metric_series), (target_metric_title, target_metric_series)), ].
   Example: `[[('test', 'accuracy'), ('model', 'accuracy')], ]`
 * `monitor_artifacts` (Optional) - Automatically log the step's artifacts on the pipeline Task. 

docs/pipelines/pipelines_sdk_tasks.md

@@ -187,7 +187,7 @@ def step_completed_callback(
 
 #### Models, Artifacts, and Metrics 
 
-You can enable automatic logging of a step’s metrics /artifacts / models  to the pipeline task using the following arguments:
+You can enable automatic logging of a step’s metrics /artifacts / models to the pipeline task using the following arguments:
 
 * `monitor_metrics` (Optional) - Automatically log the step's reported metrics also on the pipeline Task. The expected 
   format is one of the following:

docs/release_notes/ver_1_0.md

@@ -10,7 +10,7 @@ This release is not backwards compatible
 
 **Breaking Changes**
 * `preprocess` and `postprocess` class functions get 3 arguments
-* Add support for per-request state storage, passing information between the pre/post processing functions
+* Add support for per-request state storage, passing information between the pre/post-processing functions
 
 **Features & Bug Fixes**
 

docs/release_notes/ver_1_1.md

@@ -239,7 +239,7 @@ This release is not backwards compatible - see notes below on upgrading
 - Add support for uploading artifacts with a list of files using `Task.upload_artifcats(name, [Path(), Path()])`
 - Add missing *clearml-task* parameters `--docker_args`, `--docker_bash_setup_script` and `--output-uri`
 - Change `CreateAndPopulate` will auto list packages imported but not installed locally
-- Add `clearml.task.populate.create_task_from_function()` to create a Task from a function,  wrapping function input arguments into hyper-parameter section as kwargs and storing function results as named artifacts
+- Add `clearml.task.populate.create_task_from_function()` to create a Task from a function, wrapping function input arguments into hyper-parameter section as kwargs and storing function results as named artifacts
 - Add support for Task serialization (e.g. for pickle)
 - Add `Task.get_configuration_object_as_dict()`
 - Add `docker_image` argument to `Task.set_base_docker()` (deprecate `docker_cmd`)
@@ -367,7 +367,7 @@ ClearML k8s glue default pod label was changed to `CLEARML=agent` (instead of `T
 **Bug Fixes**
 
 - Fix experiment details UI failure opening hyperparameter sections beginning with `#` [ClearML Server GitHub issue #79](https://github.com/allegroai/clearml-server/issues/79)
-- Fix performance issues with UI comparison of  large experiments [Slack Channel](https://clearml.slack.com/archives/CTK20V944/p1621698235159800)
+- Fix performance issues with UI comparison of large experiments [Slack Channel](https://clearml.slack.com/archives/CTK20V944/p1621698235159800)
 - Fix filtering on hyperparameters [ClearML GitHub issue #385](https://github.com/allegroai/clearml/issues/385) [Slack Channel](https://clearml.slack.com/archives/CTK20V944/p1626600582284700)
 - Fix profile page user options toggle control area of effect
 - Fix browser resizing affecting plot zoom

docs/release_notes/ver_1_6.md

@@ -89,7 +89,7 @@ title: Version 1.6
 * Fix listed models in UI pipeline run info panel doesn't link to model 
 * Fix "Load more" button disappears from UI experiment page 	
 * Fix breadcrumb link to parent project does not navigate to the parent's project page
-* Fix spaces deleted while typing query in UI  search bars
+* Fix spaces deleted while typing query in UI search bars
 * Fix UI plots not loading in experiments 
 * Fix UI experiment debug sample full screen failing to display multiple metrics
 * Fix using search in UI tables removes custom columns

docs/webapp/applications/apps_aws_autoscaler.md

@@ -148,7 +148,7 @@ configuration [here](#aws-iam-restricted-access-policy).
 1. Complete creating the policy
 1. Attach the created policy to an IAM user/group whose credentials will be used in the autoscaler app (you can create a 
    new IAM user/group for this purpose)
-1. Obtain a set of AWS IAM credentials for the user/group to which  you have attached the created policy in the previous step  
+1. Obtain a set of AWS IAM credentials for the user/group to which you have attached the created policy in the previous step  
 
 
 ### AWS IAM Restricted Access Policy

docs/webapp/applications/apps_gcp_autoscaler.md

@@ -6,7 +6,7 @@ title: GCP Autoscaler
 The ClearML GCP Autoscaler App is available under the ClearML Pro plan
 :::
 
-The GCP Autoscaler Application optimizes GCP VM instance usage according to a user defined instance  budget: Define your 
+The GCP Autoscaler Application optimizes GCP VM instance usage according to a user defined instance budget: Define your 
 budget by specifying the type and amount of available compute resources.
 
 Each resource type is associated with a ClearML [queue](../../fundamentals/agents_and_queues.md#what-is-a-queue) whose 

docs/webapp/applications/apps_overview.md

@@ -16,7 +16,7 @@ ClearML provides the following applications:
 * [**GPU Compute**](apps_gpu_compute.md) - Launch cloud machines on demand and optimize their usage according to a 
   defined budget--no previous setup necessary
 * [**AWS Autoscaler**](apps_aws_autoscaler.md) - Optimize AWS EC2 instance usage according to a defined instance budget
-* [**GCP Autoscaler**](apps_gcp_autoscaler.md) - Optimize  GCP instance usage according to a defined instance budget
+* [**GCP Autoscaler**](apps_gcp_autoscaler.md) - Optimize GCP instance usage according to a defined instance budget
 * [**Hyperparameter Optimization**](apps_hpo.md) - Find the parameter values that yield the best performing models
 * **Nvidia Clara** - Train models using Nvidia’s Clara framework
 * [**Project Dashboard**](apps_dashboard.md) - High-level project monitoring with Slack alerts

docs/webapp/webapp_exp_table.md

@@ -132,7 +132,7 @@ The following table describes the actions that can be done from the experiments
 that allow each operation.  
 
 Access these actions with the context menu in any of the following ways:
-* In the experiments table, right-click an experiment  or hover over an experiment and click <img src="/docs/latest/icons/ico-dots-v-menu.svg" alt="Dot menu" className="icon size-md space-sm" />
+* In the experiments table, right-click an experiment or hover over an experiment and click <img src="/docs/latest/icons/ico-dots-v-menu.svg" alt="Dot menu" className="icon size-md space-sm" />
 * In an experiment info panel, click the menu button <img src="/docs/latest/icons/ico-bars-menu.svg" alt="Bar menu" className="icon size-md space-sm" />
 
 | Action | Description | States Valid for the Action | State Transition |

docs/webapp/webapp_projects_page.md

@@ -6,7 +6,7 @@ Use the Projects Page for project navigation and management.
 
 Your projects are displayed like folders: click a folder to access its contents. The Projects Page shows the top-level 
 projects in your workspace. Projects that contain nested subprojects are identified by an extra nested project tab. 
-An exception is the **All Experiments** folder, which shows all projects’ and subprojects’  contents in a single, flat
+An exception is the **All Experiments** folder, which shows all projects’ and subprojects’ contents in a single, flat
 list.
 
 ![Projects page](../img/webapp_project_page.png)

docs/webapp/webapp_workers_queues.md

@@ -24,7 +24,7 @@ The worker table shows the currently available workers and their current executi
 
 
 Clicking on a worker will open the worker’s details panel and replace the graph with that worker’s resource utilization 
-information.  The resource metric being monitored can be selected through the menu at the graph’s top left corner:
+information. The resource metric being monitored can be selected through the menu at the graph’s top left corner:
 * CPU and GPU Usage
 * Memory Usage
 * Video Memory Usage
@@ -37,7 +37,7 @@ The worker’s details panel includes the following two tabs:
     * Current Experiment - The experiment currently being executed by the worker
     * Experiment Runtime - How long the currently executing experiment has been running
     * Experiment iteration - The last reported training iteration for the experiment
-* **QUEUES** -  information about the queues that the worker is assigned to:
+* **QUEUES** -  Information about the queues that the worker is assigned to:
     * Queue - The name of the Queue
     * Next experiment - The next experiment available in this queue
     * In Queue - The number of experiments currently enqueued