Merge branch 'allegroai:main' into main

docs/clearml_agent/clearml_agent_deployment.md

@@ -39,28 +39,33 @@ clearml-agent daemon <arguments> --stop
 
 ### Allocating Resources
 To specify GPUs associated with the agent, add the `--gpus` flag.
+
+:::info Docker Mode
+Make sure to include the `--docker` flag, as GPU management through the agent is only supported in [Docker Mode](clearml_agent_execution_env.md#docker-mode).
+:::
+
 To execute multiple agents on the same machine (usually assigning GPU for the different agents), run:
 ```bash
-clearml-agent daemon --detached --queue default --gpus 0
-clearml-agent daemon --detached --queue default --gpus 1
+clearml-agent daemon --gpus 0 --queue default --docker
+clearml-agent daemon --gpus 1 --queue default --docker
 ```
 To allocate more than one GPU, provide a list of allocated GPUs
 ```bash
-clearml-agent daemon --gpus 0,1 --queue dual_gpu
+clearml-agent daemon --gpus 0,1 --queue dual_gpu --docker
 ```
 
 ### Queue Prioritization
 A single agent can listen to multiple queues. The priority is set by their order.
 
 ```bash
-clearml-agent daemon --detached --queue high_q low_q --gpus 0
+clearml-agent daemon --queue high_q low_q
 ```
 This ensures the agent first tries to pull a Task from the `high_q` queue, and only if it is empty, the agent will try to pull 
 from the `low_q` queue.
 
 To make sure an agent pulls from all queues equally, add the `--order-fairness` flag.
 ```bash
-clearml-agent daemon --detached --queue group_a group_b --order-fairness  --gpus 0
+clearml-agent daemon --queue group_a group_b --order-fairness
 ```
 It will make sure the agent will pull from the `group_a` queue, then from `group_b`, then back to `group_a`, etc. This ensures 
 that `group_a` or `group_b` will not be able to starve one another of resources.

docs/getting_started/main.md

@@ -4,10 +4,11 @@ title: What is ClearML?
 slug: /
 ---
 
-ClearML is an open source platform that automates and simplifies developing and managing machine learning solutions
-for thousands of data science teams all over the world.
-It is designed as an end-to-end MLOps and LLMOps suite allowing you to focus on developing your ML code and automation,
-while ClearML ensures your work is reproducible and scalable.
+ClearML is an open-source, end-to-end AI Platform designed to streamline AI adoption and the entire development lifecycle. 
+It supports every phase of AI development, from research to production, allowing users to 
+leverage any model, dataset, or architecture at scale. ClearML integrates seamlessly with existing tools, 
+frameworks, and infrastructures, offering unmatched flexibility and control for AI builders and DevOps teams building, 
+training, and deploying models at every scale on any AI infrastructure.
 
 
 ## Getting Started
@@ -95,7 +96,7 @@ Want a more in depth introduction to ClearML? Choose where you want to get start
                 <img src="/docs/latest/icons/ico-devops-engineer.svg" alt="DevOps Engineer logo" />
             </i>
             <h4>DevOps Engineers</h4>
-            <p>Learn learn how to deploy and configure a ClearML Server</p>
+            <p>Learn how to deploy and configure a ClearML Server</p>
             <span class="btn-link">
                 <a href="./deploying_clearml/clearml_server#deployment">START HERE</a>
             </span>

docs/getting_started/mlops/mlops_first_steps.md

@@ -53,18 +53,21 @@ required python packages, and execute and monitor the process.
    (or even multiple queues), but only a single agent will pull a Task to be executed.
 
 :::tip Agent Deployment Modes
-ClearML Agents can be deployed in Virtual Environment Mode or Docker Mode. In [virtual environment mode](../../clearml_agent/clearml_agent_execution_env.md), 
-the agent creates a new venv to execute an experiment. In [Docker mode](../../clearml_agent/clearml_agent_execution_env.md#docker-mode), 
-the agent executes an experiment inside a Docker container. For more information, see [Running Modes](../../fundamentals/agents_and_queues.md#running-modes).  
+ClearML Agents can be deployed in:
+* [Virtual environment mode](../../clearml_agent/clearml_agent_execution_env.md): Agent creates a new venv to execute an experiment. 
+* [Docker mode](../../clearml_agent/clearml_agent_execution_env.md#docker-mode): Agent executes an experiment inside a 
+Docker container. 
+
+For more information, see [Running Modes](../../fundamentals/agents_and_queues.md#running-modes).  
 :::
 
 ## Clone an Experiment
-Experiments already in the system can be reproduced for validation, or used as a baseline for further experimentation. 
+Experiments can be reproduced (cloned) for validation or as a baseline for further experimentation. 
 Cloning a task duplicates the task's configuration, but not its outputs.
 
 **To clone an experiment in the ClearML WebApp:** 
-1. Click on any project card to open its [experiments table](../../webapp/webapp_exp_table.md)
-1. Right-click one of the experiments on the table 
+1. Click on any project card to open its [experiments table](../../webapp/webapp_exp_table.md).
+1. Right-click one of the experiments on the table .
 1. Click **Clone** in the context menu, which will open a **CLONE EXPERIMENT** window.
 1. Click **CLONE** in the window. 
 
@@ -169,7 +172,7 @@ Logging models into the model repository is the easiest way to integrate the dev
 Any model stored by a supported framework (Keras / TensorFlow / PyTorch / Joblib etc.) will be automatically logged into ClearML.
 
 ClearML also supports methods to explicitly log models. Models can be automatically stored on a preferred storage medium 
-(s3 bucket, google storage, etc.). 
+(S3 bucket, Google storage, etc.). 
 
 #### Log Metrics
 Log as many metrics as you want from your processes using the [Logger](../../fundamentals/logger.md) module. This 

docs/getting_started/mlops/mlops_second_steps.md

@@ -62,7 +62,7 @@ dataset.tags = []
 new_dataset.tags = ['latest']
 ```
 
-The new dataset inherits the contents of the datasets specified in `Dataset.create`'s `parents` argument.
+The new dataset inherits the contents of the datasets specified in `Dataset.create`'s `parent_datasets` argument.
 This not only helps trace back dataset changes with full genealogy, but also makes the storage more efficient,
 since it only stores the changed and/or added files from the parent versions.
 When you access the Dataset, it automatically merges the files from all parent versions 

docs/guides/reporting/explicit_reporting.md

@@ -85,17 +85,17 @@ For example, if the Task ID is `9ed78536b91a44fbb3cc7a006128c1b0`, then the dire
 In addition to ClearML automagical logging, the `clearml` Python
 package contains methods for explicit reporting of plots, log text, media, and tables. These methods include:
 
-* [Logger.report_histogram](../../references/sdk/logger.md#report_histogram)
-* [Logger.report_confusion_matrix](../../references/sdk/logger.md#report_confusion_matrix)
-* [Logger.report_scatter2d](../../references/sdk/logger.md#report_scatter2d)
-* [Logger.report_scatter3d](../../references/sdk/logger.md#report_scatter3d)
-* [Logger.report_surface](../../references/sdk/logger.md#report_surface) 
+* [`Logger.report_histogram`](../../references/sdk/logger.md#report_histogram)
+* [`Logger.report_confusion_matrix`](../../references/sdk/logger.md#report_confusion_matrix)
+* [`Logger.report_scatter2d`](../../references/sdk/logger.md#report_scatter2d)
+* [`Logger.report_scatter3d`](../../references/sdk/logger.md#report_scatter3d)
+* [`Logger.report_surface`](../../references/sdk/logger.md#report_surface) 
   (surface diagrams)
-* [Logger.report_image](../../references/sdk/logger.md#report_image) - Report an image and upload its contents.
-* [Logger.report_table](../../references/sdk/logger.md#report_table) - Report a table as a Pandas DataFrame, CSV file, 
+* [`Logger.report_image`](../../references/sdk/logger.md#report_image) - Report an image and upload its contents.
+* [`Logger.report_table`](../../references/sdk/logger.md#report_table) - Report a table as a Pandas DataFrame, CSV file, 
   or URL for a CSV file.
-* [Logger.report_media](../../references/sdk/logger.md#report_media) - Report media including images, audio, and video.
-* [Logger.get_default_upload_destination](../../references/sdk/logger.md#get_default_upload_destination) - Retrieve the destination that is set for uploaded media.
+* [`Logger.report_media`](../../references/sdk/logger.md#report_media) - Report media including images, audio, and video.
+* [`Logger.get_default_upload_destination`](../../references/sdk/logger.md#get_default_upload_destination) - Retrieve the destination that is set for uploaded media.
 
 ### Get a Logger
 

docs/release_notes/ver_1_7.md

@@ -129,5 +129,5 @@ an empty string
 * Fix `report_histogram` - does not show "horizontal" orientation ([ClearML GitHub issue 699](https://github.com/allegroai/clearml/issues/699))
 * Fix table reporting - when using `logger.report_table(title, series, iteration, etc)`, the `series` arg does not appear in UI ([ClearML GitHub issue 684](https://github.com/allegroai/clearml/issues/684))
 * Fix artifacts (and models) use task's original name and not new name
-* Fix very long filenames from s3 can't be downloaded (with `get_local_copy()`)
+* Fix very long filenames from S3 can't be downloaded (with `get_local_copy()`)
 * Fix overwrite of existing output models on pipeline task with `monitor_models` ([ClearML GitHub issue #757](https://github.com/allegroai/clearml/issues/757))