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4
docs/apps/clearml_session.md
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@ -37,12 +37,10 @@ VS Code remote sessions use ports 8878 and 8898 respectively.
</div>
</details>
<!--![image](../img/clearml_session_jupyter.png)-->
## How it Works
ClearML allows to leverage a resource (e.g. GPU or CPU machine) by utilizing the [ClearML Agent](../clearml_agent).
A ClearML Agent will run on a target machine, and ClearML Session will instruct it to execute the Jupyter \ VS Code
A ClearML Agent will run on a target machine, and ClearML Session will instruct it to execute the Jupyter / VS Code
server to develop remotely.
After entering a `clearml-session` command with all specifications:

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docs/configs/env_vars.md
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@ -16,7 +16,7 @@ but can be overridden by command-line arguments.
|**CLEARML_TASK_NO_REUSE** | Control Task reuse|
|**CLEARML_CACHE_DIR** | Sets the location of the cache directory|
|**CLEARML_DOCKER_IMAGE** | Sets the default docker image to run from|
|**CLEARML_LOG_LEVEL** | debug \ warning \ error \ info | Sets the ClearML package's log verbosity|
|**CLEARML_LOG_LEVEL** | debug / warning / error / info - Sets the ClearML package's log verbosity|
|**CLEARML_SUPPRESS_UPDATE_MESSAGE** | Suppresses the message that notifies users of new ClearML package version|
### VCS
@ -38,7 +38,7 @@ Overrides Repository Auto-logging
|**CLEARML_FILES_HOST** | Sets the File Server URL
|**CLEARML_API_ACCESS_KEY** | Sets the Server's Public Access Key|
|**CLEARML_API_SECRET_KEY** | Sets the Server's Private Access Key|
|**CLEARML_API_HOST_VERIFY_CERT**| Enables / Disables server certificate verification (If behind a firewall)|
|**CLEARML_API_HOST_VERIFY_CERT**| Enables / Disables server certificate verification (if behind a firewall)|
|**CLEARML_OFFLINE_MODE** | Sets Offline mode|
|**CLEARML_NO_DEFAULT_SERVER** | Disables sending information to demo server when no HOST server is set|
@ -60,6 +60,6 @@ Overrides Repository Auto-logging
|**CLEARML_AGENT_EXTRA_DOCKER_ARGS**| Overrides extra docker args configuration |
|**CLEARML_AGENT_EXTRA_PYTHON_PATH**| Sets extra python path|
|**CLEARML_AGENT_INITIAL_CONNECT_RETRY_OVERRIDE**| Overrides initial server connection behavior (true by default), allows explicit number to specify number of connect retries) |
|**CLEARML_AGENT_K8S_HOST_MOUNT / CLEARML_AGENT_DOCKER_HOST_MOUNT**| Specifies Agent's mount point for Docker \ K8s|
|**CLEARML_AGENT_K8S_HOST_MOUNT / CLEARML_AGENT_DOCKER_HOST_MOUNT**| Specifies Agent's mount point for Docker / K8s|
|**CLEARML_AGENT_SKIP_PIP_VENV_INSTALL**| Skips Python virtual env installation on execute and provides a custom venv binary |
|**CLEARML_AGENT_VENV_CACHE_PATH**|Overrides venv cache folder configuration|

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docs/fundamentals/hyperparameters.md
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@ -8,9 +8,9 @@ ClearML logs hyperparameters used in experiments from multiple different sources
In ClearML, parameters are split into 3 sections:
- User Properties - Modifiable section that can be edited post execution.
- Hyperparameters - Individual parameters for configuration.
- Configuration Objects - Usually configuration files (Json \ YAML) or python objects.
- Configuration Objects - Usually configuration files (Json / YAML) or python objects.
These sections are further broken down into sub-sections (General \ Args \ TF_Define) for convenience.
These sections are further broken down into sub-sections (General / Args / TF_Define) for convenience.
![image](../img/hyperparameters_sections.png)

2
docs/fundamentals/logger.md
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@ -71,7 +71,7 @@ ClearML also supports reporting media (such as audio, video and images) for ever
This section is mostly used for debugging. It's recommended to use [artifacts](artifacts.md#artifacts) for storing script
outputs that would be used later on.
Only the last X results of each title \ series are saved to prevent overloading the server.
Only the last X results of each title / series are saved to prevent overloading the server.
See details in [Logger.report_media](../references/sdk/logger.md#report_media).
![image](../img/fundamentals_logger_reported_images.png)

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docs/fundamentals/task.md
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@ -226,7 +226,7 @@ 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.
The Task's outputs, such as artifacts and models, can also be retrieved.
### Querying \ Searching Tasks
### Querying / Searching Tasks
Searching and filtering Tasks can be done via the [web UI](../webapp/webapp_overview.md), but also programmatically.
Input search parameters into the `Task.get_tasks` method, which returns a list of Task objects that match the search.

2
docs/getting_started/ds/ds_second_steps.md
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@ -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.

2
docs/getting_started/mlops/mlops_second_steps.md
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@ -58,7 +58,7 @@ new_dataset.tags = ['latest']
We passed the `parents` argument when we created v2 of the Dataset, this inherits all the parent's version content.
This will not only help us in tracing back dataset changes with full genealogy, but will also make our storage more efficient,
as it will only store the files that were changed \ added from the parent versions.
as it will only store the files that were changed / added from the parent versions.
When we will later need access to the Dataset it will automatically merge the files from all parent versions
in a fully automatic and transparent process, as if they were always part of the requested Dataset.

2
docs/guides/pipeline/pipeline_controller.md
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@ -218,7 +218,7 @@ In the Step 3 Task ([step3_train_model.py](https://github.com/allegroai/clearml/
* Run the script.
python pipeline_controller.py
python pipeline_from_tasks.py
* Remotely execute the Task - If the Task `pipeline demo` in the project `examples` already exists in ClearML Server, clone it and enqueue it to execute.

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docs/guides/pipeline/pipeline_decorator.md
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@ -57,7 +57,7 @@ executing_pipeline(
By default, the pipeline controller and the pipeline steps are launched through ClearML [queues](../../fundamentals/agents_and_queues.md#what-is-a-queue).
Use the [`PipelineDecorator.set_default_execution_queue`](../../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorset_default_execution_queue)
method to specify the execution queue of all pipeline steps. The` execution_queue` parameter of the `PipelineDecorator.component`
method to specify the execution queue of all pipeline steps. The `execution_queue` parameter of the `@PipelineDecorator.component`
decorator overrides the default queue value for the specific step for which it was specified.
:::note Execution Modes
@ -95,7 +95,7 @@ Each function step’s arguments are stored in their respective task’s **CONFI
![Pipeline step configuration](../../img/pipeline_decorator_step_configuration.png)
Values that were listed in the `return_values`parameter of the `PipelineDecorator.component` decorator are stored as
Values that were listed in the `return_values`parameter of the `@PipelineDecorator.component` decorator are stored as
artifacts in the relevant step's task. These artifacts can be viewed in the step task’s **ARTIFACTS** tab.
![Pipeline step artifacts](../../img/pipeline_decorator_step_artifacts.png)

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docs/guides/reporting/explicit_reporting.md
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@ -19,7 +19,7 @@ example script from ClearML's GitHub repo:
## Before Starting
Make a copy of [`pytorch_mnist.py`](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/pytorch_mnist.py)
Make a copy of [pytorch_mnist.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/pytorch_mnist.py)
in order to add explicit reporting to it.
```bash

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docs/webapp/webapp_exp_tuning.md
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@ -134,7 +134,7 @@ Add, change, or delete hyperparameters, which are organized in the **ClearML Web
* Environment variables - Tracked if the `CLEARML_LOG_ENVIRONMENT` environment variable was set (see this [FAQ](../faq#track-env-vars)).
* Custom named parameter groups (see the `name` parameter in [Task.connect](../references/sdk/task.md#connectmutable-namenone)).
* Custom named parameter groups (see the `name` parameter in [Task.connect](../references/sdk/task.md#connect)).
**To add, change, or delete hyperparameters:**