Once a model is deployed, you can call it using the model class:

import slai

slai.login(client_id="CLIENT_ID", client_secret="CLIENT_SECRET")
my_model = slai.model("username/model/model_version")

result = my_model(input_1=100, input_2=200)
print(result)

​
The Model Object

constructor(model route: str) - takes in a model route, which is essentially a path to the deployed model, formatted as follows:

model = slai.model("<username>/<model_name>/<model_branch>")

the model_branch part of the route is optional; if left out the deployment will route to the initial branch of the model.

model(inputs) - call the deployed model, returning inference results. Any input data defined in your model handler class can be passed as keyword arguments to the model, for example:

>> model = slai.model("<username>/<model_name>/<model_branch>")
>> result = model(input_1=100.0, input_2=2000.0)
>> result
{
    "result": [0.3434, 0.4343]
}

info() - returns the input data required by the model, this is defined by your model handler class. For example:

>> model = slai.model("<username>/<model_name>/<model_branch>")
>> inputs = model.info()
>> inputs
{
    "input_1": "float"
    "input_2": "tensor"
}