Guide to DLHubClient

The DLHubClient provides a Python wrapper around the web interface for DLHub. In this part of the guide, we describe how to use the client to publish, discover, and use servables.


DLHub uses GlobusAuth to control access to the web services and provide identities to each of our users. Before creating the DLHubClient, you must log in to Globus:

from dlhub_sdk.client import DLHubClient
client = DLHubClient()

The DLHubClient class stores your credentials in your home directory (~/.dlhub/credentials/DLHub_Client_tokens.json) so that you will only need to log in to Globus once when using the client or the DLHub CLI.

Call the logout function to remove access to DLHub from your system:

from dlhub_sdk.utils.auth import logout

logout removes the credentials from your hard disk and revokes their authorization to prevent further use.

Publishing Servables

The DLHubClient provides several routes for publishing servables to DLHub. The first is to request DLHub to publish from a GitHub repository:


There are also functions for publishing a model stored on the local system. In either case, you must first create a BaseServableModel describing your servable or load in an existing one from disk:

from dlhub_sdk.utils import unserialize_object
with open('dlhub.json') as fp:
    model = unserialize_object(json.load(fp))

Then, submit the model via HTTP by calling:


See the Publication Guide for details on how to describe a servable.

Discovering Servables

The client also provides tools for querying the library of servables available through DLHub.

The most general route for discovering models is to perform a free-text search of the model library:'"deep learning"')

You can also perform a query to match specific fields of the metadata records by setting the “advanced” flag on your query. For example, matching all Keras models related to materials science is accomplished by:'servable.type:"Keras Model" AND"chemistry"', advanced=True)

See Globus Search documentation for complete information about the query string syntax and the DLHub schemas for the available query terms.

The client also provides functions for common queries, such as:

Each of these tools returns metadata for only the most recent version of the servable by default, but can be configured to return all versions.

A way to perform advanced queries besides to craft your own query string is to use the “query helper” object that backs each of the pre-configured search functions. A new query helper is created by calling:


to return a blank query object, which you can then use to create a query with the advanced functions provided by the DLHubSearchHelper. For example, the advanced query shown above can be executed using:

client.query.match_term('servable.type', '"Keras Model"').match_domains('chemistry').search()

Running Servables

The command runs servables published through DLHub. To invoke the servable, you need to know the name of the servable and the username of the owner:, model_name, x)

By default, the data (x) is sent to DLHub after serializing it into JSON. You can also send the data as Python objects by changing the input type:, model_name, x, input_type='python')

The client will use pickle to send the input data to DLHub in this case, allowing for a broader range of data types to be used as inputs.

The DLHubClient.describe_servable and DLHubClient.describe_methods functions are especially useful when using an unfamiliar servable. The describe_servable method returns complete information about a servable, and the describe_method returns information about a certain method of the servable. Use these function to understand what the servable does and to learn how to use it.