From 70d31bfeed86fc803ccc31445b00fae48aa8b78d Mon Sep 17 00:00:00 2001
From: pollfly <75068813+pollfly@users.noreply.github.com>
Date: Mon, 27 Nov 2023 15:14:21 +0200
Subject: [PATCH] Small edits (#721)
---
.../data_management_examples/data_man_python.md | 6 +++---
docs/hyperdatasets/webapp/webapp_datasets_frames.md | 2 +-
docs/integrations/pytorch_lightning.md | 5 ++---
3 files changed, 6 insertions(+), 7 deletions(-)
diff --git a/docs/clearml_data/data_management_examples/data_man_python.md b/docs/clearml_data/data_management_examples/data_man_python.md
index ea12b5de..3e0f8cf4 100644
--- a/docs/clearml_data/data_management_examples/data_man_python.md
+++ b/docs/clearml_data/data_management_examples/data_man_python.md
@@ -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")
diff --git a/docs/hyperdatasets/webapp/webapp_datasets_frames.md b/docs/hyperdatasets/webapp/webapp_datasets_frames.md
index 1b5100fd..084c7413 100644
--- a/docs/hyperdatasets/webapp/webapp_datasets_frames.md
+++ b/docs/hyperdatasets/webapp/webapp_datasets_frames.md
@@ -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. |
|
|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.|
|
|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.|
-|
|Delete annotation|Click the annotation or bounded area in the frame and then click the button to delete the annotation.|
+||Delete annotation|Click the annotation or bounded area in the frame and then click the button to delete the annotation.|
|
|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.|
diff --git a/docs/integrations/pytorch_lightning.md b/docs/integrations/pytorch_lightning.md
index 5143e3cf..9fecd65e 100644
--- a/docs/integrations/pytorch_lightning.md
+++ b/docs/integrations/pytorch_lightning.md
@@ -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.