Deploy the deep learning model to a data file
| Parameters: |
- path : string
Specifies the location to store the model files.
If the output_format is set to castable, then the location has to be on the server-side.
Otherwise, the location has to be on the client-side.
- output_format : string, optional
Specifies the format of the deployed model
Valid Values: astore, castable, or onnx
Default: astore
- model_weights : string, optional
Specifies the client-side path to the csv file of the
model weights table. Only effective when
output_format=’onnx’. If no csv file is specified when
deploying to ONNX, the weights will be fetched from the
CAS server. This may take a long time to complete if
the size of model weights is large.
- layers : string list, optional
Specifies the names of the layers to include in the output astore scoring results. This can be used to
extract the features for given layers.
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Notes
Currently, this function supports sashdat, astore, and onnx formats.
More information about ONNX can be found at: https://onnx.ai/
DLPy supports ONNX version >= 1.3.0, and Opset version 8.
For ONNX format, currently supported layers are convo, pool,
fc, batchnorm, residual, concat, reshape, and detection.
If dropout is specified in the model, train the model using
inverted dropout, which can be specified in Optimizer.
This will ensure the results are correct when running the
model during test phase.