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AWS Template Configuration

The AWS template allows users to mount their Amazon Web Service S3 Bucket within JupyterLab.

JupyterLab DataMount uses rclone to mount AWS. Check out their documentation for more details.

Requirements

  • An Amazon S3 Storage Object. The setup process is not covered in this documentation, you can learn more about it here.
AWS Bucket

Configuration Parameters

To configure an AWS mount, the following parameters are required:

  • Bucket Name: AWS Bucket Name (myawsbucket4545 in the example above).
  • Region: Region to connect to (eu-north-1 in the example above).
  • Username: AWS Access Key ID.
  • Password: AWS Secret Access Key.
AWS

Advanced

RClone configuration
The following configuration is used to mount AWS with rclone:
aws.config:
[aws]
type = s3
provider = AWS
access_key_id = _username_
secret_access_key = _password_
region = _region_
<br>
Mounted via <br>
```bash
rclone mount --config aws.config aws:_bucketname_ _path_ --vfs-cache-max-size=10G --vfs-read-chunk-size=64M --vfs-cache-mode=writes --allow-other --uid=1000 --gid=100

JupyterHub pre_configuration
The following configuration can be used to preconfigure a AWS mount for users:
# pip install jupyterhub-datamountspawner
c.JupyterHub.spawner_class = 'datamountspawner.KubeSpawner'
c.KubeSpawner.init_mounts = [
  {
    "path": "_path_", # relative path. "aws" is a valid value
    "options": {
      "displayName": "AWS (external)",
      "template": "aws",
      "readonly": True, # default: False
      "config": {
        "remotepath": "_bucketname_",
        "type": "s3",
        "provider": "AWS",
        "access_key_id": "_id_",
        "secret_access_key": "_secret_",
        "region": "_region_"
      }
    }
  }
]
- Replace _path_, _bucketname_, _region_, _id_ and _secret_
- Users will not see the used configuration