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6
docs/clearml_data/data_management_examples/data_man_python.md
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@ -84,6 +84,9 @@ Now that a new dataset is registered, you can consume it!
The [data_ingestion.py](https://github.com/allegroai/clearml/blob/master/examples/datasets/data_ingestion.py) script
demonstrates data ingestion using the dataset created in the first script.
The following script gets the dataset and uses [`Dataset.get_local_copy`](../../references/sdk/dataset.md#get_local_copy)
to return a path to the cached, read-only local dataset.
```python
dataset_name = "cifar_dataset"
dataset_project = "dataset_examples"
@ -94,9 +97,6 @@ dataset_path = Dataset.get(
).get_local_copy()
```
The preceding script gets the dataset and uses the [`Dataset.get_local_copy`](../../references/sdk/dataset.md#get_local_copy)
method to return a path to the cached, read-only local dataset.
If you need a modifiable copy of the dataset, use the following code:
```python
Dataset.get(dataset_name, dataset_project).get_mutable_local_copy("path/to/download")

2
docs/hyperdatasets/webapp/webapp_datasets_frames.md
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@ -180,7 +180,7 @@ or the arrow keys on the keyboard). Closing the frame editor will prompt you to
|| Resize annotation| Select an annotation, then click on a bounded area’s vertex and drag it. |
|<img src="/docs/latest/icons/ico-metadata.svg" alt="edit metadata" className="icon size-md space-sm" />|Edit metadata|Hover over an annotation in the list and click the icon to open the edit window. Input the metadata dictionary in JSON format. This metadata is specific to the selected annotation, not the entire frame.|
|<img src="/docs/latest/icons/ico-lock-open.svg" alt="Lock annotation" className="icon size-md space-sm" />|Lock / Unlock annotation |Click the button on a specific annotation to make it uneditable. You can also click the button on top of the annotations list to lock all annotations in the frame.|
|<img src="/docs/latest/icons/ico-trash.svg" alt="Trash" className="icon size-sm space-sm" />|Delete annotation|Click the annotation or bounded area in the frame and then click the button to delete the annotation.|
|<img src="/docs/latest/icons/ico-trash.svg" alt="Trash" className="icon size-md space-sm" />|Delete annotation|Click the annotation or bounded area in the frame and then click the button to delete the annotation.|
|<img src="/docs/latest/icons/ico-show.svg" alt="Eye Show All" className="icon size-md space-sm" />|Show/hide all annotations |Click the button to view the frame without annotations. When annotations are hidden, they can’t be modified. |
||Delete label |In the relevant annotation, click **x** on the label you want to remove.|

5
docs/integrations/pytorch_lightning.md
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@ -43,7 +43,8 @@ To control a task's framework logging, use the `auto_connect_frameworks` paramet
Completely disable all automatic logging by setting the parameter to `False`. For finer grained control of logged
frameworks, input a dictionary, with framework-boolean pairs.
For example:
For example, the following code will log PyTorch models, but will not log any information reported to TensorBoard.
:
```python
auto_connect_frameworks={
@ -54,8 +55,6 @@ auto_connect_frameworks={
}
```
The preceding code will log PyTorch models, but will not log any information reported to TensorBoard.
You can also input wildcards as dictionary values, so ClearML will log a model created by a framework only if its local
path matches at least one wildcard.