A manifest is a group of cloud and/or local datasets needed for an analysis. Only using cloud datasets guarantees that every service (with the correct permissions) that is sent the manifest will be able to access the datasets. There can be speed and cost benefits from using local datasets when dealing with large amounts of data, but the manifest can only include them if you can guarantee that all relevant services can access them.

You can instantiate a manifest like this:

manifest = Manifest(
    keys={"my_dataset_0: 0, "my_dataset_1": 1, "my_dataset_2": 2},

Manifests of local datasets

You can include local datasets in your manifest if you can guarantee all services that need them can access them. A use case for this is, for example, a supercomputer cluster running several octue services locally that process and transfer large amounts of data. It is much faster to store and access the required datasets locally than upload them to the cloud and then download them again for each service (as would happen with cloud datasets).


If you want to ask a child a question that includes a manifest containing one or more local datasets, you must include the allow_local_files parameter. For example, if you have an analysis object with a child called “wind_speed”:

input_manifest = Manifest(
    keys={"my_dataset_0: 0, "my_dataset_1": 1},


Storing manifests in the cloud

You can store a manifest as a JSON file in the cloud and retrieve it later:


downloaded_manifest = Manifest.from_cloud(cloud_path="gs://my-bucket/path/to/my_manifest.json")