# ONNX Model Conversion

Machine Learning frameworks have specific formats for storing
neural network models. Qualcomm® Neural Processing SDK supports these various models by
converting them to a framework neutral **deep learningcontainer (DLC)** format. The DLC file is used by the Qualcomm® Neural Processing SDK
runtime for execution of the neural network. Qualcomm® Neural Processing SDK includes a
tool, “snpe-onnx-to-dlc”, for converting models serialized in
the ONNX format to DLC.

Converting Models from ONNX to DLC

The [snpe-onnx-to-dlc](https://docs.qualcomm.com/doc/80-63442-2/topic/tools.html#snpe-onnx-to-dlc)
tool converts a serialized ONNX model to an equivalent DLC
representation.

With the ONNX alexnet model obtained by following the
instructions in
[https://github.com/onnx/models/blob/main/validated/vision/classification/alexnet/README.md](https://github.com/onnx/models/blob/main/validated/vision/classification/alexnet/README.md),
the following command will produce a DLC representation of
alexnet:

snpe-onnx-to-dlc --input_network models/bvlc_alexnet/bvlc_alexnet/model.onnx
                     --output_path bvlc_alexnet.dlc
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\* When using converter tools in Windows PowerShell, make sure a virtual environment
with the required packages is activated and execute the converter script via **python**,
as shown in the following example.

(venv-3.10) > python snpe-onnx-to-dlc <options>

Note:

- Information about the ops, versions, and parameters Qualcomm® Neural Processing SDK
supports can be found at [Supported ONNX
Ops](https://docs.qualcomm.com/doc/80-63442-2/topic/supported_onnx_ops.html).
- Neither snpe-onnx-to-dlc nor the Qualcomm® Neural Processing SDK runtime support symbolic
tensor shape variables. See [Network
Resizing](https://docs.qualcomm.com/doc/80-63442-2/topic/network_resize.html) for information on resizing
Qualcomm® Neural Processing SDK networks at initialization.
- In general, Qualcomm® Neural Processing SDK determines the data types for tensors and
operations based upon the needs of the runtime and builder
parameters. Data types specified by the ONNX model will usually
be ignored.
- If the model contains ONNX functions, converter always does inlining of function nodes.

Last Published: Oct 02, 2025

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