Installation
Download the latest version by running:
pip install deeploy
Connecting to Deeploy
In order to initialize the Python client you need a personal key pair (account level access) or Deployment API token (deployment level access), the domain of your Deeploy installation and a workspace id. This ID can be found in the Workspace settings or the details of Deployments in that Workspace.
Use the following code to initialise the Deeploy Client
:
from deeploy import Client
client = Client(
# if using the Deeploy cloud version 'host' is 'app.deeploy.ml'
host="example.deeploy.ml",
workspace_id="b6d8c781-2526-4e03-9b43-4c1a62d064db",
access_key="DPAexample",
secret_key="Sexample",
)
Model and explainer Frameworks
Deeploy makes ML deployments easy by providing pre-build model and explainer images. For a complete list with supported versions see here.
Next to prebuild images Deeploy also supports custom model and explainer images. For more information about custom Docker Deployments we recommend to start here. You can find an example in the create Deployment section of the documentation.
Up next
The Python client currently includes the following functionalities:
- Create and update Deployments
- Inference Deployments
- Retrieve prediction logs
- Evaluate predictions
- Submit actuals for predictions
- Generate metadata.json
- Generate reference.json
For more information about authentication check this section