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docs/getting_started/mlops/mlops_first_steps.md
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@ -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

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docs/getting_started/mlops/mlops_second_steps.md
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@ -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

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docs/guides/reporting/explicit_reporting.md
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@ -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

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docs/release_notes/ver_1_7.md
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@ -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))