(Optional) In the S3 Endpoint field, specify an S3 endpoint if you want to override the URL created by S3 to access your bucket.In the Region Name field, specify the AWS region name.In the File Filter Regex field, specify a regular expression to filter bucket objects.Specify the name of the S3 bucket, and if relevant, the bucket prefix to specify an internal folder or container.In the Storage Title field, type a name for the storage to appear in the Label Studio UI.In the dialog box that appears, select Amazon S3 as the storage type.For a specific project, open Settings > Cloud Storage.Task_with_predictions_and_annotations_01.json ]Īfter you configure access to your S3 bucket, do the following to set up Amazon S3 as a data source connection: Example with tasks in separate JSON filesĮxample with tasks, annotations and predictions in separate JSON files Otherwise, Label Studio will not load your data properly. If you plan to load JSON tasks from the Source Storage ( Treat every bucket object as a source file = No), you must place only one task as the dict per one JSON file. This is only possible for simple labeling tasks that involve a single media source (such as an image, text, etc.).* One Task - One JSON File When enabled, Label Studio automatically lists files from the storage bucket and constructs tasks. This approach is particularly helpful when dealing with complex tasks that involve multiple media sources. When disabled, tasks in JSON format can be loaded directly from storage buckets into Label Studio. Label Studio Source Storages feature an option called “Treat every bucket object as a source file.” This option enables two different methods of loading tasks into Label Studio. Treat every bucket object as a source file This way, user’s browsers are able to load media. URI resolving - when the Label Studio backend requests Cloud Storage to resolve URI links (e.g., s3://bucket/1.jpg) into HTTPS ( ).Sync - when Label Studio scans your storage and imports tasks from it.Source storage functionality can be divided into two parts: The browser then makes a GET request to retrieve the file body. The HEAD request is made at the beginning and allows the browser to determine the size of the audio, video, or other files. To load these URLs, the browser will require HEAD and GET permissions from your Cloud Storage. URLs will be returned to the frontend and loaded by the user’s browser. When your users access labeling, the backend will attempt to resolve URI (e.g., s3://) to URL ( links. If you disable this option in your storage settings, Label Studio backend will require GET permissions to read JSON files and convert them to Label Studio tasks.
Local cloud storage download#
If you enable the “Treat every bucket object as a source file” option, Label Studio backend will only need LIST permissions and won’t download any data from your buckets. For details, see Secure access to cloud storage. You can also secure access to cloud storage using cloud storage credentials. Instead, the data is accessed using a URL. Task data synced from cloud storage is not stored in Label Studio. See Label Studio API and locate the relevant storage connection type. You can also use the API to set up or sync storage connections. If you upload new data to a connected cloud storage bucket, sync the storage connection using the UI to add the new labeling tasks to Label Studio without restarting. Label Studio does not automatically sync data from source storage. You can connect multiple buckets, containers, databases, or directories as source or target storage for a project.
Each source and target storage setup is project-specific. You can add source storage connections to sync data from an external source to a Label Studio project, and add target storage connections to sync annotations from Label Studio to external storage. How external storage connections and sync work If something goes wrong, check the troubleshooting section. Set up the following cloud and other storage systems with Label Studio: Integrate popular cloud and external storage systems with Label Studio to collect new items uploaded to the buckets, containers, databases, or directories and return the annotation results so that you can use them in your machine learning pipelines.
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