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4
docs/clearml_data/clearml_data_sdk.md
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@ -246,7 +246,7 @@ dataset.get_logger().report_table(
title="Raw Dataset Metadata", series="Raw Dataset Metadata", csv="path/to/csv" title="Raw Dataset Metadata", series="Raw Dataset Metadata", csv="path/to/csv"
) )
# Attach a historgram to the table # Attach a histogram to the table
dataset.get_logger().report_histogram( dataset.get_logger().report_histogram(
title="Class distribution", title="Class distribution",
series="Class distribution", series="Class distribution",
@ -261,7 +261,7 @@ dataset.get_logger().report_histogram(
To upload the dataset files to network storage, use [`Dataset.upload()`](../references/sdk/dataset.md#upload). To upload the dataset files to network storage, use [`Dataset.upload()`](../references/sdk/dataset.md#upload).
Use the `output_url` parameter to specify storage target, such as S3 / GS / Azure. For example: Use the `output_url` parameter to specify a storage target, such as S3 / GS / Azure. For example:
* A shared folder: `/mnt/share/folder` * A shared folder: `/mnt/share/folder`
* S3: `s3://bucket/folder` * S3: `s3://bucket/folder`
* Non-AWS S3-like services (such as MinIO): `s3://host_addr:port/bucket`. **Note that port specification is required**. * Non-AWS S3-like services (such as MinIO): `s3://host_addr:port/bucket`. **Note that port specification is required**.

2
docs/clearml_data/data_management_examples/data_man_simple.md
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@ -30,7 +30,7 @@ the needed files.
``` ```
1. Add a folder. File addition is recursive, so it's enough to point at the folder 1. Add a folder. File addition is recursive, so it's enough to point at the folder
to captures all files and subfolders: to capture all files and subfolders:
```bash ```bash
clearml-data add --files data_samples clearml-data add --files data_samples

2
docs/fundamentals/models.md
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@ -58,7 +58,7 @@ ClearML provides flexibility for explicitly connecting input models and experime
## WebApp Interface ## WebApp Interface
In the ClearML's web UI, model information can be located through a project's Model Table or through the model's creating In the ClearML web UI, model information can be located through a project's Model Table or through the model's creating
task. task.
Models associated with a task appear in the task's **ARTIFACTS** tab. To see further model details, including design, Models associated with a task appear in the task's **ARTIFACTS** tab. To see further model details, including design,

2
docs/guides/reporting/hyper_parameters.md
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@ -48,7 +48,7 @@ flags.DEFINE_string('echo5', '5', 'Text to echo.', module_name='test')
``` ```
TensorFlow Definitions appear in **HYPEPARAMETERS** **>** **TF_DEFINE**. TensorFlow Definitions appear in **HYPERPARAMETERS** **>** **TF_DEFINE**.
![image](../../img/examples_reporting_hyper_param_03.png) ![image](../../img/examples_reporting_hyper_param_03.png)

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docs/hyperdatasets/dataviews.md
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@ -380,7 +380,7 @@ list_of_frames = myDataView.to_list()
##### Frame Query for Time Interval ##### Frame Query for Time Interval
This example demonstrates a frame query filtering for frames containing the meta key `updated` with any value between This example demonstrates a frame query filtering for frames containing the meta key `updated` with any value between
`08:000` and `09:00` on October 20th, 2024: `08:00` and `09:00` on October 20th, 2024:
```python ```python
# Add a frame query for frames with the meta key's value between 08:00:00 and 09:00:00 on 2024-10-20 # 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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docs/model_registry.md
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@ -138,7 +138,7 @@ output_model.update_weights(weights_filename='models/model.pth')
``` ```
## Analyzing Models ## Analyzing Models
While experimenting, you build up your model catalog. In the ClearML's web UI, model information can be located through While experimenting, you build up your model catalog. In the ClearML web UI, model information can be located through
a project's Model Table or through the model's associated task. a project's Model Table or through the model's associated task.
Models associated with a task appear in the task's **ARTIFACTS** tab. To see further model details, including design, Models associated with a task appear in the task's **ARTIFACTS** tab. To see further model details, including design,

2
docs/release_notes/clearml_server/open_source/ver_0_10.md
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@ -15,7 +15,7 @@ title: Version 0.10
* Replace the Python web-server with NGINX. * Replace the Python web-server with NGINX.
* Improve sub-domain support ([GitHub trains-server Issue 9](https://github.com/clearml/clearml-server/issues/9)). * Improve subdomain support ([GitHub trains-server Issue 9](https://github.com/clearml/clearml-server/issues/9)).
* Extend configuration options * Extend configuration options

2
docs/release_notes/sdk/open_source/ver_0_10.md
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@ -68,7 +68,7 @@ title: Version 0.10
* Add scikit-learn support (load/store using joblib) ([GitHub Issue #20](https://github.com/clearml/clearml/issues/20)). * Add scikit-learn support (load/store using joblib) ([GitHub Issue #20](https://github.com/clearml/clearml/issues/20)).
* Add xgboost support ([GitHub Issue #10](https://github.com/clearml/clearml/issues/10)). * Add xgboost support ([GitHub Issue #10](https://github.com/clearml/clearml/issues/10)).
* Add loguru support ([GitHub Issue #29](https://github.com/clearml/clearml/issues/29)). * Add loguru support ([GitHub Issue #29](https://github.com/clearml/clearml/issues/29)).
* Add sub-domain support [trains.conf](https://github.com/clearml/clearml/blob/master/docs/trains.conf#L3) ([GitHub Issue #27](https://github.com/clearml/clearml/issues/27)). * Add subdomain support [trains.conf](https://github.com/clearml/clearml/blob/master/docs/trains.conf#L3) ([GitHub Issue #27](https://github.com/clearml/clearml/issues/27)).
* Fix sub-process support. * Fix sub-process support.
* Fix multiple TensorBoard writers ([GitHub Issue #26](https://github.com/clearml/clearml/issues/26)). * Fix multiple TensorBoard writers ([GitHub Issue #26](https://github.com/clearml/clearml/issues/26)).