# Model deployment

Source: [https://docs.qualcomm.com/doc/80-70014-15B/topic/snpe-run-model.html](https://docs.qualcomm.com/doc/80-70014-15B/topic/snpe-run-model.html)

A model DLC (quantized or non-quantized) can be deployed via a SNPE enabled app (an
            application written using SNPE C/C++ APIs). SNPE offers APIs to load a DLC, select a
            runtime to execute the model, and perform inference, etc.

SNPE provides a prebuilt [snpe-net-run](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-2/tools.html#snpe-net-run) tool (application written using C
            APIs) that can load an arbitrary model DLC and execute it on provided inputs.

- **Model file** – DLC model file generated by the SNPE converter tool or
                    `snpe-dlc-graph-prepare` tool (if running on HTP).
- **Input list** – Text file, like the input\_list.txt file used during
                quantization, except input raw files in this list are used for inference. For
                simplicity in this example, the same input list used for quantization is used for
                inference.
- **Runtime** – User must select a specific runtime to execute the model on target.
                Available runtime options are CPU, GPU, and DSP (HTP).

Note: See
            `snpe-net-run --help` for more details.

## Run SNPE DLC on x86 host
                machine

Converted SNPE DLC could be run using
                    `snpe-net-run` tool which takes a DLC and input\_list as
                arguments. The following command generates output files to
                    /output\_x86/. Execution of SNPE DLC on x86 is purely for
                debugging purposes.

Note: The default runtime in SNPE is CPU. When executing a
                model using `snpe-net-run`, there is no need to specify the CPU
                runtime.

    ${SNPE_ROOT}/bin/x86_64-linux-clang/snpe-net-run --container ~/models/inception_v3.dlc --input_list ~/models/input_list.txt --output_dir ~/models/output_x86Copy to clipboard

The
                above command loads model DLC and executes it on x86 CPU. Outputs from model
                execution will be written to the output\_x86 directory.

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)

## Preparing SNPE model to run on target

To run the model on target,
                    `snpe-net-run` **** requires the model DLC, SNPE runtime
                libraries, and input list to run inferences to generate outputs.

Note:  Before
                running on target, please ensure that SNPE SDK binaries and libraries are pushed to
                the target.

For RB3Gen2, use the below artifacts from
                    ${SNPE\_ROOT}/lib/aarch64-oe-linux-gcc11.2 and
                    ${SNPE\_ROOT}/bin/aarch64-oe-linux-gcc11.2

| **File** | **Source location** |
| --- | --- |
| snpe-net-run | ${SNPE\_ROOT}/bin/aarch64-oe-linux-gcc11.2 |
| libSNPE.so | ${SNPE\_ROOT}/lib/aarch64-oe-linux-gcc11.2/ |
| libSnpeHtpPrepare.so | ${SNPE\_ROOT}/lib/aarch64-oe-linux-gcc11.2 |
| libSnpeHtpV68Stub.so | ${SNPE\_ROOT}/lib/aarch64-oe-linux-gcc11.2 |
| libSnpeHtpV68Skel.so | ${SNPE\_ROOT}/lib/hexagon-v68/unsigned |
| Inception\_v3\_quantized\_with\_htp\_cache.dlc | ~/models |
| Inception\_v3.dlc | ~/models |
| input\_list.txt | ~/models |
| Inception V3 sample input images | /tmp/RandomInputsForInceptionV3 |

 The following `scp` commands needs to be executed on the host
            machine to copy SNPE SDK libraries and binaries to the
                device.

    scp ${SNPE_ROOT}/bin/aarch64-oe-linux-gcc11.2/snpe-net-run root@[ip-addr]:/opt/ 
    scp ${SNPE_ROOT}/lib/aarch64-oe-linux-gcc11.2/libSNPE.so root@[ip-addr]:/opt/ 
    scp ${SNPE_ROOT}/lib/aarch64-oe-linux-gcc11.2/libSnpeHtpV68Stub.so root@[ip- addr]:/opt/
    scp ${SNPE_ROOT}/lib/aarch64-oe-linux-gcc11.2/libSnpeHtpPrepare.so root@[ip- addr]:/opt/
    scp ${SNPE_ROOT}/lib/hexagon-v68/unsigned/libSnpeHtpV68Skel.so root@[ip- addr]:/opt/
                    
    scp ~/models/inception_v3.dlc root@[ip- addr]:/opt/
    scp ~/models/inception_v3_quantized_with_htp_cache.dlc root@[ip- addr]:/opt/
    scp ~/models/input_list.txt root@[ip- addr]:/opt/
    scp /tmp/RandomInputsForInceptionV3 root@[ip- addr]:/tmp/
                    
    ssh root@[ip-addr]
                    
    cd /optCopy to clipboard

The above steps prepared the device for model execution. The
                following sections provide details on running the model on the available runtimes.

## Run SNPE model on Arm CPU

Setup environment variables before
                executing the model to ensure SNPE binaries and libraries are accessible to run the
                model.

Note: The default runtime in SNPE is CPU. When executing a model using
                    `snpe-net-run`, there is no need to specify the CPU
                runtime.

    export LD_LIBRARY_PATH=/opt:$LD_LIBRARY_PATH 
    export PATH=/opt/:$PATH 
    
    snpe-net-run --container inception_v3.dlc --input_list input_list.txt --output_dir output_cpu Copy to clipboard

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)

## Run SNPE model on GPU

To run a model on the GPU runtime, use inception\_v3.dlc and
                save outputs in output\_gpu.

Note: To
                run on GPU, specify the runtime via the `--use_gpu` command line
                argument.

    export LD_LIBRARY_PATH=/opt:$LD_LIBRARY_PATH
    export PATH=/opt:$PATH
    
    snpe-net-run --container inception_v3.dlc --input_list input_list.txt --output_dir output_gpu --use_gpuCopy to clipboard

## Run SNPE model on HTP

To run a model on the HTP backend,
                    use inception\_v3\_quantized\_with\_htp\_cache.dlc model DLC and
                save outputs in output\_htp.

Note: To run on HTP, specify the
                runtime via the `--use_dsp` command line argument.

    export LD_LIBRARY_PATH=/opt:$LD_LIBRARY_PATH 
    export PATH=/opt:$PATH
    export ADSP_LIBRARY_PATH="/opt;/usr/lib/rfsa/adsp;/dsp" 
    
    snpe-net-run --container inception_v3_quantized_with_htp_cache.dlc --input_list input_list.txt --output_dir output_htp --use_dspCopy to clipboard

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)

## Validate output

For each input raw file fed to
                    `snpe-net-run` (via the input\_list file), an output folder is
                generated that contains output tensor saved as raw file(s) whose size will match the
                model’s output layer as shown in the image below. 
Note: The [Netron](https://netron.app/) tool
                    was used to visualize the model.

![](data:image/jpeg;base64,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)
In the example of inception\_v3,
                the output raw file is a binary file that contains probabilities for 1000
                classification classes.

We use a Python script to read the file as a NumPy
                array to perform postprocessing, validation of the output. The example below checks
                whether the HTP prediction is the same as the CPU prediction.

Copy the output
                files from the target device to the host machine so the outputs can be validated.

    ssh root@[ip-addr]
    cd /opt/
    
    scp -r /opt/output_cpu user@host-ip:<path>
    scp -r /opt/output_htp user@host-ip:<path>Copy to clipboard

Note: Please use
                    `<path>` from the above commands in the following
                    (compare.py) python script.

Once outputs from
                on-device inference are copied to the host machine, prepare a script to load the
                output tensors saved within output\_htp and
                    output\_cpu to compare. In this example, both
                    output\_htp and output\_cpu were copied
                to the ~/models directory.

The following is an example
                comparing output from one of the example inputs used. In this example, outputs are
                saved to the /opt directory on the host machine. Outputs from
                    `snpe-net-run` can be loaded into NumPy ndarrays using the
                    `numpy.fromfile(…)` API.

Note: By default,
                    `snpe-net-run` saves the output tensors to NumPy files in
                    **float32** format.

    # python postprocessing script (compare.py)
     
    import numpy as np
     
    htp_output_file_path = "<path>/output_htp/Result_1/875.raw" 
    cpu_output_file_path = "<path>/output_cpu/Result_1/875.raw"
     
    htp_output = np.fromfile(htp_output_file_path, dtype=np.float32)
    htp_output = htp_output.reshape(1,1000)
     
    cpu_output = np.fromfile(cpu_output_file_path, dtype=np.float32)
    cpu_output = cpu_output.reshape(1,1000)
    
    # np.argmax gives the cls_id with highest probability from tensor.
     
    cls_id_htp = np.argmax(htp_output)
    cls_id_cpu = np.argmax(cpu_output)
    
    # Let's compare CPU output vs HTP output
     
    print("Cpu prediction {} \n Htp Prediction {}".format(cls_id_cpu, cls_id_htp))Copy to clipboard

**Output**

![](data:image/png;base64,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)

## Model deployment using SNPE APIs

SNPE SDK provides C/C++ APIs to
                create/develop applications that run a model on chosen hardware (CPU, GPU, or HTP)
                with acceleration. A sample application that demonstrates SNPE C/C++ APIs to execute
                the model is provided [here](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-2/cplus_plus_tutorial.html).

## Model deployment using Qualcomm® Intelligent Multimedia SDK (IM SDK)

To improve the developer experience when building entire
                use case pipelines (stream from camera, preprocess images, perform inference, etc.),
                SNPE has been integrated into Qualcomm IM SDK as a plugin `qtimlsnpe`. The plugin has been developed
                on top of SNPE C APIs and provides SNPE capabilities (load and execute models).
                With the `qtimlsnpe` plugin, developers can plug in their converted model DLC into Qualcomm IM SDK
                pipeline to realize the use case.

See [Develop your own application](https://docs.qualcomm.com/doc/80-70014-15B/topic/develop-own-app.html)
                    for instructions on how to deploy SNPE DLC using Qualcomm IM SDK.

Last Published: Jan 21, 2026

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