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				@ -246,7 +246,7 @@ dataset.get_logger().report_table(
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    title="Raw Dataset Metadata", series="Raw Dataset Metadata", csv="path/to/csv"
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)
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# Attach a historgram to the table
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# Attach a histogram to the table
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dataset.get_logger().report_histogram(
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    title="Class distribution",
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    series="Class distribution",
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@ -261,7 +261,7 @@ dataset.get_logger().report_histogram(
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To upload the dataset files to network storage, use [`Dataset.upload()`](../references/sdk/dataset.md#upload). 
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Use the `output_url` parameter to specify storage target, such as S3 / GS / Azure. For example:
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Use the `output_url` parameter to specify a storage target, such as S3 / GS / Azure. For example:
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* A shared folder: `/mnt/share/folder`
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* S3: `s3://bucket/folder`
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* Non-AWS S3-like services (such as MinIO): `s3://host_addr:port/bucket`. **Note that port specification is required**. 
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@ -30,7 +30,7 @@ the needed files.
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    ```
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1. Add a folder. File addition is recursive, so it's enough to point at the folder 
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to captures all files and subfolders:
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to capture all files and subfolders:
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   ```bash
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   clearml-data add --files data_samples
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@ -58,7 +58,7 @@ ClearML provides flexibility for explicitly connecting input models and experime
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## WebApp Interface
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In the ClearML's web UI, model information can be located through a project's Model Table or through the model's creating 
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In the ClearML web UI, model information can be located through a project's Model Table or through the model's creating 
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task.
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Models associated with a task appear in the task's **ARTIFACTS** tab. To see further model details, including design, 
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@ -48,7 +48,7 @@ flags.DEFINE_string('echo5', '5', 'Text to echo.', module_name='test')
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```    
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TensorFlow Definitions appear in **HYPEPARAMETERS** **>** **TF_DEFINE**.
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TensorFlow Definitions appear in **HYPERPARAMETERS** **>** **TF_DEFINE**.
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@ -380,7 +380,7 @@ list_of_frames = myDataView.to_list()
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##### Frame Query for Time Interval
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This example demonstrates a frame query filtering for frames containing the meta key `updated` with any value between 
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`08:000` and `09:00` on October 20th, 2024: 
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`08:00` and `09:00` on October 20th, 2024: 
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```python
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# Add a frame query for frames with the meta key's value between 08:00:00 and 09:00:00 on 2024-10-20
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@ -138,7 +138,7 @@ output_model.update_weights(weights_filename='models/model.pth')
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```
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## Analyzing Models 
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While experimenting, you build up your model catalog. In the ClearML's web UI, model information can be located through 
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While experimenting, you build up your model catalog. In the ClearML web UI, model information can be located through 
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a project's Model Table or through the model's associated task.
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Models associated with a task appear in the task's **ARTIFACTS** tab. To see further model details, including design, 
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