Deploy an model trained using Sagemaker's built-in k-NN algorithm for AWS Panorama

Hi, I'm trying to use a k-Nearest Neighbour model for deployment to an Edge device (AWS Panorama). I understand I need to create an optimized model to suit the target device. As I understand it the built-in algorithms all include a SageMaker Neo 'compile_model' function which I am running over my trained model -

Build model..

knn = sagemaker.estimator.Estimator(container,
                                       role, 
                                       instance_count=1, 
                                       instance_type='ml.m5.2xlarge',
                                       output_path=output_location,
                                       sagemaker_session=sess,
                                       input_mode='Pipe'
                                   )
# Setup the hyperparameters
knn.set_hyperparameters(**hyperparams)

knn.fit(fit_input, job_name=job_name)

Build optimised model...

optimized_ic = knn.compile_model(
    target_instance_family="ml_c5",
    target_platform_os="LINUX",
    target_platform_arch="ARM64",
    input_shape={"data": [1,3,512,512]},
    output_path=s3_optimized_output_location,
    framework="mxnet",
    framework_version="1.8",
)

While the initial model builds fine I get an error running the optimised model / compile_model function:

Failed. Reason: ClientError: InputConfiguration: No valid Mxnet model file -symbol.json found. Please make sure the framework you select is correct.

I can find a few google references for attempts at something similar with KMeans algorithm, but is k-NN algorithm dramatically different? Do I just have the wrong settings? I understand all of the built-in algorithms use were originally mxnet trained? When I extract my model I only have three files:

• model_algo-1
• model_algo-1.json
• model_algo-1-labels.npy

I am struggling to work out where I am going wrong in porting this model!