Small edits (#435)

docs/apps/clearml_param_search.md

@@ -2,7 +2,7 @@
 title: ClearML Param Search
 ---
 
-Use the `clearml-param-search` CLI tool to launch ClearML's automated hyperparameter optimization. This process finds 
+Use the `clearml-param-search` CLI tool to launch ClearML's automated hyperparameter optimization (HPO). This process finds 
 the optimal values for your experiments' hyperparameters that yield the best performing models. 
 
 ## How Does `clearml-param-search` Work?

docs/clearml_data/clearml_data_cli.md

@@ -68,7 +68,7 @@ clearml-data add [-h] [--id ID] [--dataset-folder DATASET_FOLDER]
 |`--id` | Dataset's ID. Default: previously created / accessed dataset| <img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" /> |
 |`--files`| Files / folders to add. Items will be uploaded to the dataset’s designated storage.  | <img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" /> |
 |`--wildcard`|  Add specific set of files, denoted by these wildcards. For example: `~/data/*.jpg ~/data/json`. Multiple wildcards can be passed. | <img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" /> |
-|`--links`| Files / folders link to add. Supports s3, gs, azure links. Example: `s3://bucket/data` `azure://bucket/folder`. Items remain in their original location. | <img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" /> |
+|`--links`| Files / folders link to add. Supports S3, GS, Azure links. Example: `s3://bucket/data` `azure://bucket/folder`. Items remain in their original location. | <img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" /> |
 |`--dataset-folder` | Dataset base folder to add the files to in the dataset. Default: dataset root| <img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" /> |
 |`--non-recursive` | Disable recursive scan of files | <img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" /> |
 |`--verbose` | Verbose reporting | <img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|

docs/clearml_sdk/clearml_sdk.md

@@ -52,7 +52,7 @@ See the [Logger SDK reference page](../references/sdk/logger.md).
 
 ### Hyperparameter Optimization 
 
-ClearML's `optimization` module includes classes that support hyperparameter optimization:
+ClearML's `optimization` module includes classes that support hyperparameter optimization (HPO):
 * [HyperParameterOptimizer](../references/sdk/automation_controller_pipelinecontroller.md) - Hyperparameter search 
   controller class 
 * Optimization search strategy classes including [Optuna](../references/sdk/hpo_optuna_optuna_optimizeroptuna.md), [HpBandSter](../references/sdk/hpo_hpbandster_bandster_optimizerbohb.md),

docs/fundamentals/hpo.md

@@ -6,7 +6,7 @@ title: Hyperparameter Optimization
 Hyperparameters are variables that directly control the behaviors of training algorithms, and have a significant effect on 
 the performance of the resulting machine learning models. Finding the hyperparameter values that yield the best 
 performing models can be complicated. Manually adjusting hyperparameters over the course of many training trials can be 
-slow and tedious. Luckily, you can automate and boost hyperparameter optimization with ClearML's 
+slow and tedious. Luckily, you can automate and boost hyperparameter optimization (HPO) with ClearML's 
 [**`HyperParameterOptimizer`**](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class.
 
 ## ClearML's Hyperparameter Optimization

docs/getting_started/ds/ds_second_steps.md

@@ -178,7 +178,7 @@ or check these pages out:
 - Scale you work and deploy [ClearML Agents](../../clearml_agent.md)
 - Develop on remote machines with [ClearML Session](../../apps/clearml_session.md)
 - Structure your work and put it into [Pipelines](../../pipelines/pipelines.md)
-- Improve your experiments with [HyperParameter Optimization](../../fundamentals/hpo.md)
+- Improve your experiments with [Hyperparameter Optimization](../../fundamentals/hpo.md)
 - Check out ClearML's integrations to [external libraries](../../integrations/libraries.md).
 
 ## YouTube Playlist

docs/getting_started/mlops/mlops_second_steps.md

@@ -16,7 +16,7 @@ The sections below describe the following scenarios:
 ## Building Tasks
 ### Dataset Creation
 
-Let's assume we have some code that extracts data from a production Database into a local folder.
+Let's assume we have some code that extracts data from a production database into a local folder.
 Our goal is to create an immutable copy of the data to be used by further steps:
 
 ```bash
@@ -24,7 +24,7 @@ clearml-data create --project data --name dataset
 clearml-data sync --folder ./from_production 
 ```
 
-We could also add a Tag `latest` to the Dataset, marking it as the latest version.
+We could also add a tag `latest` to the Dataset, marking it as the latest version.
 
 ### Preprocessing Data
 The second step is to preprocess the date. First we need to access it, then we want to modify it,

docs/getting_started/video_tutorials/hyperparameter_optimization.md

@@ -20,7 +20,7 @@ keywords: [mlops, components, hyperparameter optimization, hyperparameter]
 <details className="cml-expansion-panel info">
 <summary className="cml-expansion-panel-summary">Read the transcript</summary>
 <div className="cml-expansion-panel-content">
-Hello and welcome to ClearML. In this video we’ll take a look at one cool way of using the agent other than rerunning a task remotely: hyperparameter optimization.
+Hello and welcome to ClearML. In this video we’ll take a look at one cool way of using the agent other than rerunning a task remotely: hyperparameter optimization (HPO).
 
 By now, we know that ClearML can easily capture our hyperparameters and scalars as part of the experiment tracking. We also know we can clone any task and change its hyperparameters, so they’ll be injected into the original code at runtime. In the last video, we learnt how to make a remote machine execute this task automatically by using the agent. 
 

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

@@ -4,7 +4,7 @@ title: Image Hyperparameter Optimization - Jupyter Notebook
 
 [hyperparameter_search.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/image/hyperparameter_search.ipynb) 
 demonstrates using ClearML's [HyperParameterOptimizer](../../../../../references/sdk/hpo_optimization_hyperparameteroptimizer.md)
-class to perform automated hyperparameter optimization. 
+class to perform automated hyperparameter optimization (HPO). 
 
 The code creates a HyperParameterOptimizer object, which is a search controller. The search controller uses the
 [Optuna](../../../../../references/sdk/hpo_optuna_optuna_optimizeroptuna.md) search strategy optimizer. 

docs/guides/optimization/hyper-parameter-optimization/examples_hyperparam_opt.md

@@ -3,7 +3,7 @@ title: Hyperparameter Optimization
 ---
 
 The [hyper_parameter_optimizer.py](https://github.com/allegroai/clearml/blob/master/examples/optimization/hyper-parameter-optimization/hyper_parameter_optimizer.py) 
-example script demonstrates hyperparameter optimization, which is automated by using ClearML 
+example script demonstrates hyperparameter optimization (HPO), which is automated by using ClearML. 
 
 ## Set the Search Strategy for Optimization
 

docs/integrations/storage.md

@@ -22,9 +22,9 @@ The ClearML configuration file uses [HOCON](https://github.com/lightbend/config/
 
 ### Configuring AWS S3
 
-Modify these parts of the clearml.conf file and add the key, secret, and region of the s3 bucket.
+Modify these parts of the clearml.conf file and add the key, secret, and region of the S3 bucket.
 
-It's possible to also give access to specific s3 buckets in the `aws.s3.credentials` section. The default configuration 
+It's possible to also give access to specific S3 buckets in the `aws.s3.credentials` section. The default configuration 
 provided in the `aws.s3` section is applied to any bucket without a bucket-specific configuration. 
 
 You can also enable using a credentials chain to let Boto3