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If my mxnet model takes 2 data inputs (both are float arrays), how should I make it work at this point?
I defined my model-shapes.json like [{"shape": [1, 12], "name": "data0"}, {"shape": [1, 12], "name": "data1"}]
And one example input looks like:
data = {'data0':[1.0, 2904.0, 1452.0, 464.0, 3022.0, 2948.0, 2548.0, 2.0, 0.0, 0.0, 0.0, 0.0], 'data1':[1.0, 2204.0, 1552.0, 494.0, 3032.0, 298.0, 2568.0, 2.0, 0.0, 0.0, 0.0, 0.0]}
But I got errors on the server side:
Traceback (most recent call last):
File "/usr/local/lib/python3.5/dist-packages/sagemaker_containers/_functions.py", line 84, in wrapper
return fn(*args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/sagemaker_mxnet_container/serving.py", line 229, in default_input_fn
[data_shape] = self._model.data_shapes
ValueError: too many values to unpack (expected 1)
The text was updated successfully, but these errors were encountered:
We don't natively support multiple inputs in our default hosting functions, but you can override the defaults by providing your own transform_fn in your entry point code.
same question here. I trained my own model which requires two inputs and deployed it as endpoint. But when I called predict(), the error was "Expects arg[1] to be int32 but float is provided".
https://github.com/aws/sagemaker-mxnet-container/blob/ee9098c8c2de6a635dcd9f4b0819dc5340061cde/src/sagemaker_mxnet_container/serving.py#L228
If my mxnet model takes 2 data inputs (both are float arrays), how should I make it work at this point?
I defined my model-shapes.json like
[{"shape": [1, 12], "name": "data0"}, {"shape": [1, 12], "name": "data1"}]
And one example input looks like:
data = {'data0':[1.0, 2904.0, 1452.0, 464.0, 3022.0, 2948.0, 2548.0, 2.0, 0.0, 0.0, 0.0, 0.0], 'data1':[1.0, 2204.0, 1552.0, 494.0, 3032.0, 298.0, 2568.0, 2.0, 0.0, 0.0, 0.0, 0.0]}
But I got errors on the server side:
The text was updated successfully, but these errors were encountered: