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pollfly
2023-06-15 11:22:50 +03:00
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commit fdffc9c271
29 changed files with 62 additions and 62 deletions
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docs/clearml_data/clearml_data_sdk.md
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@@ -156,7 +156,7 @@ dataset = Dataset.create(dataset_name="my dataset", dataset_project="example pro
dataset.add_files(path="path/to/folder_or_file")
```
There is an option to add a set of files based on wildcard matching of a single string or a list of strings, using the
You can add a set of files based on wildcard matching of a single string or a list of strings, using the
`wildcard` parameter. Specify whether to match the wildcard files recursively using the `recursive` parameter.
For example:
@@ -207,7 +207,7 @@ To remove files from a current dataset, use the [`Dataset.remove_files`](../refe
Input the path to the folder or file to be removed in the `dataset_path` parameter. The path is relative to the dataset.
To remove links, specify their URL (e.g. `s3://bucket/file`).
There is also an option to input a wildcard into `dataset_path` in order to remove a set of files matching the wildcard.
You can also input a wildcard into `dataset_path` in order to remove a set of files matching the wildcard.
Set the `recursive` parameter to `True` in order to match all wildcard files recursively
For example:
@@ -220,7 +220,7 @@ dataset.remove_files(dataset_path="*.csv", recursive=True)
To upload the dataset files to network storage, use the [`Dataset.upload`](../references/sdk/dataset.md#upload) method.
Use the `output_url` parameter to specify storage target, such as S3 / GS / Azure (e.g. `s3://bucket/data`, `gs://bucket/data`, `azure://bucket/data` , `/mnt/share/data`).
Use the `output_url` parameter to specify storage target, such as S3 / GS / Azure (e.g. `s3://bucket/data`, `gs://bucket/data`, `azure://bucket/data`, `/mnt/share/data`).
By default, the dataset uploads to ClearML's file server. This target storage overrides the `output_uri` value of the
[`Dataset.create`](#creating-datasets) method.