Skip to main content

modelbit.get_inference(deployment=, ...)

An easy way to call the REST API for your deployment.

Parameters​

  • deployment: str The name of the deployment to receive the inference request.
  • data: Any The data to send to the deployment. Can be formatted for single or batch inferences.
  • branch: Optional[str] The branch the deployment is on. If unspecified the current branch is used, which is main by default.
  • version: Optional[Union[str,int]] The version of the deployment to call. Can be latest, a numeric version, or an alias. If unspecific, latest is used.
  • region: Optional[str] The region of your Modelbit workspace. If unspecified, app is used. Customers in other regions (e.g. us-east-1) must specify the region.
  • workspace: Optional[str] The name of your Modelbit workspace. If unspecified the value in the MB_WORKSPACE_NAME envvar will be used. If no workspace name can be found then error will be raised.
  • api_key: Optional[str] The API key to send along with the request. If unspecified the value in the MB_API_KEY envvar will be used. If no workspace name can be found then error will be raised. Required if your workspace uses API keys to authenticate inference requests.
  • batch_size: Optional[int] If passing in a large DataFrame or a large batch, the get_inference call will subdivide the request into multiple requests. Setting this parameter changes the size of the subdivisions. The default is 10_000.

Returns​

Dict[str, Any] - The results of calling the REST API. Successful calls have data key with the results and unsuccessful calls have an error key with the error message.

Examples​

Most of these examples assume the envvars MB_WORKSPACE_NAME and MB_API_KEY (if needed) have already been set.

Get one inference​

import modelbit

modelbit.get_inference(deployment="example_model", data=10)

Get a batch of inferences​

modelbit.get_inference(deployment="example_model", data=[[1, 10], [2, 11]])

Specify the branch and version​

modelbit.get_inference(deployment="example_model", data=10, branch="my_branch", version=5)

Specify the workspace and region​

modelbit.get_inference(deployment="example_model", data=10, workspace="my_workspace", region="ap-south-1")

Set a timeout on your inference​

modelbit.get_inference(deployment="example_model", data=10, timeout_seconds=20)

Using a dataframe​

If your model is using DataFrame mode, you can send the dataframe in data=:

modelbit.get_inference(deployment="example_model", data=my_dataframe)

Splitting a dataframe into smaller chunks​

If your model is using DataFrame mode with a very large dataframe it will automatically get split into into several batches. You can change the batch size with batch_size=

modelbit.get_inference(deployment="example_model", data=my_dataframe, batch_size=500)

See also​