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2
docs/apps/clearml_param_search.md
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@ -40,7 +40,7 @@ of the optimization results in table and graph forms.
|`--pool-period-min`|The time between two consecutive polls (minutes).|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
|`--total-max-jobs`|The total maximum jobs for the optimization process. The default value is `None` for unlimited.|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
|`--min-iteration-per-job`|The minimum iterations (of the objective metric) per single job.|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
|`--max-iteration-per-job`|The maximum iterations (of the objective metric) per single job. When maximum iterations is exceeded, the job is aborted.|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
|`--max-iteration-per-job`|The maximum iterations (of the objective metric) per single job. When iteration maximum is exceeded, the job is aborted.|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
|`--save-top-k-tasks-only`| Keep only the top \<k\> performing tasks, and archive the rest of the experiments. Input `-1` to keep all tasks. Default: `10`.|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
|`--time-limit-per-job`|Maximum execution time per single job in minutes. When time limit is exceeded, the job is aborted. Default: no time limit.|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|

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docs/apps/clearml_session.md
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@ -45,7 +45,7 @@ to automatically sync local configurations with a remote session.
## How it Works
ClearML allows to leverage a resource (e.g. GPU or CPU machine) by utilizing the [ClearML Agent](../clearml_agent.md).
ClearML allows you to leverage a resource (e.g. GPU or CPU machine) by utilizing the [ClearML Agent](../clearml_agent.md).
A ClearML Agent runs on a target machine, and ClearML Session instructs it to execute the Jupyter / VS Code
server to develop remotely.
After entering a `clearml-session` command with all specifications:

8
docs/clearml_sdk/task_sdk.md
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@ -248,7 +248,7 @@ task_filter={
See [`Task.get_tasks`](../references/sdk/task.md#taskget_tasks) for all `task_filter` options.
### Tag Filters
The tags field supports advanced queries through combining tag names and operators into a list.
The `tags` field supports advanced queries through combining tag names and operators into a list.
The supported operators are:
* `not`
@ -438,7 +438,7 @@ To ensure task reproducibility, ClearML controls the deterministic behaviors of
packages by setting a fixed initial seed.
ClearML uses `1337` as the default initial seed. To set a different value for your task, use the [`Task.set_random_seed`](../references/sdk/task.md#taskset_random_seed)
class method and provide the new seed value, before initializing the task.
class method and provide the new seed value, **before initializing the task**.
You can disable the deterministic behavior entirely by passing `Task.set_random_seed(None)`.
@ -488,7 +488,7 @@ See more details in the [Artifacts Reporting example](../guides/reporting/artifa
### Using Artifacts
A task's artifacts are accessed through the task’s *artifact* property which lists the artifacts’ locations.
The artifacts can subsequently be retrieved from their respective locations by using:
The artifacts can subsequently be retrieved from their respective locations by using:
* `get_local_copy()`- Downloads the artifact and caches it for later use, returning the path to the cached copy.
* `get()` - Returns a Python object constructed from the downloaded artifact file.
@ -557,7 +557,7 @@ local_weights_path = last_snapshot.get_local_copy()
```
Notice that if one of the frameworks loads an existing weights file, the running task will automatically update its
"Input Model", pointing directly to the original training task's model. This makes it easy to get the full lineage of
"Input Model", pointing directly to the original training task's model. This makes it easy to get the full lineage of
every trained and used model in our system!
Models loaded by the ML framework appear under the "Input Models" section, under the Artifacts tab in the ClearML UI.

2
docs/deploying_clearml/clearml_config_for_clearml_server.md
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@ -106,7 +106,7 @@ The host URLs for the ClearML Server are required:
* ClearML Server API server
* ClearML Server file server
These may be localhost, the domain, or a sub-domain of the domain.
These may be localhost, the domain, or a subdomain of the domain.
**To add ClearML settings to an existing ClearML configuration file:**

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docs/deploying_clearml/clearml_server.md
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@ -34,7 +34,7 @@ ClearML Server also comes with a [services agent](../clearml_agent.md#services-m
## Deployment
The ClearML Server can be deployed in any of the formats listed below. Once deployed, configure the server for web login
authentication, sub-domains, and load balancers, and use any of its many configuration settings.
authentication, subdomains, and load balancers, and use any of its many configuration settings.
**To deploy your own ClearML Server:**
@ -46,7 +46,7 @@ authentication, sub-domains, and load balancers, and use any of its many configu
[Windows 10](clearml_server_win.md)
* [Kubernetes using Helm](clearml_server_kubernetes_helm.md)
1. Optionally, [configure ClearML Server](clearml_server_config.md) for additional features, including sub-domains and load balancers,
1. Optionally, [configure ClearML Server](clearml_server_config.md) for additional features, including subdomains and load balancers,
web login authentication, and the non-responsive task watchdog.
1. [Configure ClearML for ClearML Server](clearml_config_for_clearml_server.md)

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docs/deploying_clearml/clearml_server_config.md
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@ -8,7 +8,7 @@ This documentation page applies to deploying your own open source ClearML Server
This page describes the ClearML Server [deployment](#clearml-server-deployment-configuration) and [feature](#clearml-server-feature-configurations) configurations. Namely, it contains instructions on how to configure ClearML Server for:
* [Sub-domains and load balancers](#sub-domains-and-load-balancers) - An AWS load balancing example
* [Subdomains and load balancers](#subdomains-and-load-balancers) - An AWS load balancing example
* [Opening Elasticsearch, MongoDB, and Redis for External Access](#opening-elasticsearch-mongodb-and-redis-for-external-access)
* [Web login authentication](#web-login-authentication) - Create and manage users and passwords
* [Using hashed passwords](#using-hashed-passwords) - Option to use hashed passwords instead of plain-text passwords
@ -23,7 +23,7 @@ Using the latest version of ClearML Server is recommended.
## ClearML Server Deployment Configuration
ClearML Server supports two deployment configurations: single IP (domain) and sub-domains.
ClearML Server supports two deployment configurations: single IP (domain) and subdomains.
### Single IP (Domain) Configuration
@ -33,23 +33,23 @@ Single IP (domain) with the following open ports:
* API service on port `8008`
* File storage service on port `8081`
### Sub-domain Configuration
### Subdomain Configuration
Sub-domain configuration with default http/s ports (`80` or `443`):
Subdomain configuration with default http/s ports (`80` or `443`):
* Web application on sub-domain: `app.*.*`
* API service on sub-domain: `api.*.*`
* File storage service on sub-domain: `files.*.*`
* Web application on subdomain: `app.*.*`
* API service on subdomain: `api.*.*`
* File storage service on subdomain: `files.*.*`
When [configuring sub-domains](#sub-domains-and-load-balancers) for ClearML Server, they will map to the ClearML Server's
When [configuring subdomains](#subdomains-and-load-balancers) for ClearML Server, they will map to the ClearML Server's
internally configured ports for the Dockers. As a result, ClearML Server Dockers remain accessible if, for example,
some type of port forwarding is implemented.
:::important
``app``, ``api``, and ``files`` as the sub-domain labels must be used.
``app``, ``api``, and ``files`` as the subdomain labels must be used.
:::
For example, a domain is called `mydomain.com`, and a sub-domain named `clearml.mydomain.com` is created, use the following:
For example, a domain is called `mydomain.com`, and a subdomain named `clearml.mydomain.com` is created, use the following:
* `app.clearml.mydomain.com` (web server)
* `api.clearml.mydomain.com` (API server)
@ -156,7 +156,7 @@ the default secret for the system's apiserver component can be overridden by set
### Sub-domains and Load Balancers
### Subdomains and Load Balancers
The following example, which is based on AWS load balancing, demonstrates the configuration:

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docs/faq.md
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@ -85,7 +85,7 @@ title: FAQ
**ClearML Server Configuration**
* [How do I configure ClearML Server for sub-domains and load balancers?](#sub-domains)
* [How do I configure ClearML Server for subdomains and load balancers?](#subdomains)
* [Can I add web login authentication to ClearML Server?](#web-auth)
* [Can I modify a non-responsive task settings?](#watchdog)
@ -868,9 +868,9 @@ If you are using SELinux, run the following command (see this [discussion](https
## ClearML Server Configuration
**How do I configure ClearML Server for sub-domains and load balancers?** <a id="sub-domains"></a>
**How do I configure ClearML Server for subdomains and load balancers?** <a id="subdomains"></a>
For detailed instructions, see [Configuring Sub-domains and load balancers](deploying_clearml/clearml_server_config.md#sub-domains-and-load-balancers)
For detailed instructions, see [Configuring Subdomains and load balancers](deploying_clearml/clearml_server_config.md#subdomains-and-load-balancers)
on the "Configuring Your Own ClearML Server" page.
<br/>

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docs/fundamentals/hyperparameters.md
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@ -106,7 +106,7 @@ Configurations can be viewed in web UI experiment pages, in the **CONFIGURATION*
The configuration panel is split into three sections according to type:
- **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 based on how the parameters were logged (General / Args / TF_Define / Environment).

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docs/fundamentals/logger.md
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@ -16,7 +16,7 @@ A Logger object is used to do the following:
ClearML supports four types of reports:
- Text - Mostly captured automatically from stdout and stderr but can be logged manually.
- Scalars - Time series data. X-axis is always a sequential number, usually iterations but can be epochs or others.
- Plots - General graphs and diagrams, such as histograms, confusion matrices line plots, and custom plotly charts.
- Plots - General graphs and diagrams, such as histograms, confusion matrices, line plots, and custom plotly charts.
- Debug Samples - Images, audio, and videos. Can be reported per iteration.
![image](../img/fundamentals_logger_results.png)

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docs/fundamentals/projects.md
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@ -52,7 +52,7 @@ To view all projects in the system, use the `Task` class method `get_projects`:
project_list = Task.get_projects()
```
This returns a list of project sorted by last update time.
This returns a list of projects sorted by last update time.
### More Actions

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docs/fundamentals/task.md
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@ -97,7 +97,7 @@ Available task types are:
* *optimizer* - A specific type of controller for optimization tasks (e.g. [hyperparameter optimization](hpo.md))
* *service* - Long lasting or recurring service (e.g. server cleanup, auto ingress, sync services etc)
* *monitor* - A specific type of service for monitoring
* *application* - A task implementing custom applicative logic, like [auto-scaler](../guides/services/aws_autoscaler.md)
* *application* - A task implementing custom applicative logic, like [auto-scaler](../guides/services/aws_autoscaler.md)
or [clearml-session](../apps/clearml_session.md)
* *data_processing* - Any data ingress / preprocessing (see [ClearML Data](../clearml_data/clearml_data.md))
* *qc* - Quality Control (e.g. evaluating model performance vs. blind dataset)