EdgeClient.inferences.run
Provides a synchronous way to run an inference
EdgeClient.inferences.run(model_identifier: str, model_version: str, input_sources: List[InputSource], explain=False, tags=None)
This method provides a synchronous way to run an inference. This is simply a convenience function that is equivalent to the perform_inference
and block_until_complete
methods run sequentially.
Parameters
Parameter | Type | Description | Example |
---|---|---|---|
model_identifier | str | The model identifier. | 'ed542963de' |
model_version | str | The model version string in semantic version format. | '1.0.1' |
input_sources | List[InputSource] | A list of input sources of type InputSource | [InputSource(key="input.txt", text="Today is a great day.")] |
explain | bool | If the model supports explainability, flag this job to return an explanation of the predictions | True |
tags | Mapping[str, str] | An arbitrary set of key/value tags to associate with this inference. |
Returns
A Inference
object returned from Inference API
Examples
from modzy import EdgeClient
from modzy.edge import InputSource
image_bytes = open("image_path.jpg", "rb").read()
input_object = InputSource(
key="image", # input filename defined by model author
data=image_bytes,
)
client = EdgeClient('localhost', 55000)
client.connect()
inference = client.inferences.run("<model-id>", "<model-version>", input_object, explain=False, tags=None)
results = inference.result.outputs
client.close()
Updated 7 months ago